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R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationWed, 13 Dec 2017 20:38:34 +0100
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2017/Dec/13/t1513194018fpa10t4jdfaf96u.htm/, Retrieved Wed, 15 May 2024 10:42:51 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=309403, Retrieved Wed, 15 May 2024 10:42:51 +0000
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Estimated Impact79
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [3 12] [2017-12-13 19:38:34] [624f75095443dc501dbf5befeca42dec] [Current]
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Dataseries X:
62,4	99,5
67,4	89,9
76,1	96
67,4	86,9
74,5	85,6
72,6	82,5
60,5	80,5
66,1	82,7
76,5	87,7
76,8	92,2
77	93,9
71	94,5
74,8	94,8
73,7	85
80,5	87,4
71,8	79,5
76,9	80,5
79,9	79,8
65,9	78,8
69,5	81,5
75,1	82,6
79,6	89,5
75,2	90,7
68	90,7
72,8	95,7
71,5	86,6
78,5	92,4
76,8	86,3
75,3	84,7
76,7	83,1
69,7	82,2
67,8	84,5
77,5	81,2
82,5	88,2
75,3	89,1
70,9	89,1
76	98
73,7	91,7
79,7	90,9
77,8	87,1
73,3	84,5
78,3	83,5
71,9	85,9
67	89
82	87,6
83,7	92,9
74,8	89,1
80	96,9
74,3	104,1
76,8	93
89	98
81,9	85,9
76,8	84,8
88,9	81,5
75,8	85,3
75,5	79,3
89,1	82,3
88	87,8
85,9	95
89,3	104,4
82,9	103,5
81,2	99,5
90,5	96,6
86,4	88,1
81,8	86,4
91,3	83,6
73,4	85,7
76,6	79,8
91	81,9
87	87,1
89,7	92
90,7	106,1
86,5	108,5
86,6	101,4
98,8	100,1
84,4	84,4
91,4	81,6
95,7	81,5
78,5	80,9
81,7	79,9
94,3	81,2
98,5	90,5
95,4	91,7
91,7	102,7
92,8	104,8
90,5	98,7
102,2	100,8
91,8	93,6
95	88,1
102	86,8
88,9	80,8
89,6	84,6
97,9	82
108,6	93,6
100,8	99,7
95,1	102,1
101	106,6
100,9	95,9
102,5	92,1
105,4	85,9
98,4	79,3
105,3	83,7
96,5	84,1
88,1	83,2
107,9	85
107	93,1
92,5	95,4
95,7	107,3
85,2	112,5
85,5	97,8
94,7	99,1
86,2	85,6
88,8	87,2
93,4	86
83,4	92,7
82,9	98,8
96,7	99,2
96,2	101,4
92,8	98,8
92,8	113,2
90	119,2
95,4	107,4
108,3	111,6
96,3	94,8
95	97,7
109	87,3
92	91,4
92,3	93,4
107	90,8
105,5	96,1
105,4	102,6
103,9	107,7
99,2	111,4
102,2	98,9
121,5	100,7
102,3	91
110	94,8
105,9	87,3
91,9	88,8
100	92,3
111,7	90,9
104,9	95,2
103,3	98,2
101,8	103,5
100,8	109,7
104,2	116,4
116,5	87,5
97,9	87,2
100,7	85,5
107	79
96,3	81,8
96	78,2
104,5	78,9
107,4	76,9
102,4	84,4
94,9	93,1
98,8	101,6
96,8	97,1
108,2	99,3
103,8	77,8
102,3	74,3
107,2	80,4
102	85,3
92,6	80,1
105,2	78,8
113	91,8
105,6	100
101,6	108,4
101,7	101,7
102,7	94,4
109	89,5
105,5	69,8
103,3	72,5
108,6	69,1
98,2	71,9
90	67
112,4	63,8
111,9	73,2
102,1	74,2
102,4	84,7
101,7	97,8
98,7	87,4
114	81,8
105,1	68,6
98,3	64,9
110	64,1
96,5	63,6
92,2	59,8
112	66,3
111,4	78,1
107,5	86,8
103,4	89
103,5	111,3
107,4	99,7
117,6	103,7
110,2	90,4
104,3	77,6
115,9	73,9
98,9	81,5
101,9	88,2
113,5	78
109,5	84,7
110	94,8
114,2	101,5
106,9	112,4
109,2	96,6
124,2	96,9
104,7	76,1
111,9	76,9
119	83,8
102,9	89,4
106,3	89,1




Summary of computational transaction
Raw Input view raw input (R code)
Raw Outputview raw output of R engine
Computing time11 seconds
R ServerBig Analytics Cloud Computing Center
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input view raw input (R code)  \tabularnewline
Raw Outputview raw output of R engine  \tabularnewline
Computing time11 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
R Framework error message & 
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
\tabularnewline \hline \end{tabular} %Source: https://freestatistics.org/blog/index.php?pk=309403&T=0

[TABLE]
[ROW]
Summary of computational transaction[/C][/ROW] [ROW]Raw Input[/C] view raw input (R code) [/C][/ROW] [ROW]Raw Output[/C]view raw output of R engine [/C][/ROW] [ROW]Computing time[/C]11 seconds[/C][/ROW] [ROW]R Server[/C]Big Analytics Cloud Computing Center[/C][/ROW] [ROW]R Framework error message[/C][C]
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=309403&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309403&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 Outputview raw output of R engine
Computing time11 seconds
R ServerBig Analytics Cloud Computing Center
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.







Multiple Linear Regression - Estimated Regression Equation
(1-Bs)(1-B)EnergySupply [t] = + 0.140871 + 1.29633`(1-Bs)(1-B)Totind`[t] + 0.27939`(1-Bs)(1-B)Totind(t-1)`[t] -0.0592663`(1-Bs)(1-B)Totind(t-2)`[t] -0.302437`(1-Bs)(1-B)Totind(t-3)`[t] + 0.833581`(1-Bs)(1-B)Totind(t-1s)`[t] -0.0974955`(1-Bs)(1-B)Totind(t-2s)`[t] + 0.319853`(1-Bs)(1-B)Totind(t-3s)`[t] -0.107948`(1-Bs)(1-B)Totind(t-4s)`[t] + 0.234491`(1-Bs)(1-B)Totind(t-5s)`[t] -0.107156`(1-Bs)(1-B)Totind(t-6s)`[t] -0.0679719`(1-Bs)(1-B)Totind(t-7s)`[t] -0.184292`(1-Bs)(1-B)Totind(t-8s)`[t] -0.204764`(1-Bs)(1-B)Totind(t-9s)`[t] -0.202809`(1-Bs)(1-B)Totind(t-10s)`[t] -0.870503`(1-Bs)(1-B)Totind(t-11s)`[t] -0.647966`(1-Bs)(1-B)Totind(t-12s)`[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
(1-Bs)(1-B)EnergySupply
[t] =  +  0.140871 +  1.29633`(1-Bs)(1-B)Totind`[t] +  0.27939`(1-Bs)(1-B)Totind(t-1)`[t] -0.0592663`(1-Bs)(1-B)Totind(t-2)`[t] -0.302437`(1-Bs)(1-B)Totind(t-3)`[t] +  0.833581`(1-Bs)(1-B)Totind(t-1s)`[t] -0.0974955`(1-Bs)(1-B)Totind(t-2s)`[t] +  0.319853`(1-Bs)(1-B)Totind(t-3s)`[t] -0.107948`(1-Bs)(1-B)Totind(t-4s)`[t] +  0.234491`(1-Bs)(1-B)Totind(t-5s)`[t] -0.107156`(1-Bs)(1-B)Totind(t-6s)`[t] -0.0679719`(1-Bs)(1-B)Totind(t-7s)`[t] -0.184292`(1-Bs)(1-B)Totind(t-8s)`[t] -0.204764`(1-Bs)(1-B)Totind(t-9s)`[t] -0.202809`(1-Bs)(1-B)Totind(t-10s)`[t] -0.870503`(1-Bs)(1-B)Totind(t-11s)`[t] -0.647966`(1-Bs)(1-B)Totind(t-12s)`[t]  + e[t] \tabularnewline
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309403&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C](1-Bs)(1-B)EnergySupply
[t] =  +  0.140871 +  1.29633`(1-Bs)(1-B)Totind`[t] +  0.27939`(1-Bs)(1-B)Totind(t-1)`[t] -0.0592663`(1-Bs)(1-B)Totind(t-2)`[t] -0.302437`(1-Bs)(1-B)Totind(t-3)`[t] +  0.833581`(1-Bs)(1-B)Totind(t-1s)`[t] -0.0974955`(1-Bs)(1-B)Totind(t-2s)`[t] +  0.319853`(1-Bs)(1-B)Totind(t-3s)`[t] -0.107948`(1-Bs)(1-B)Totind(t-4s)`[t] +  0.234491`(1-Bs)(1-B)Totind(t-5s)`[t] -0.107156`(1-Bs)(1-B)Totind(t-6s)`[t] -0.0679719`(1-Bs)(1-B)Totind(t-7s)`[t] -0.184292`(1-Bs)(1-B)Totind(t-8s)`[t] -0.204764`(1-Bs)(1-B)Totind(t-9s)`[t] -0.202809`(1-Bs)(1-B)Totind(t-10s)`[t] -0.870503`(1-Bs)(1-B)Totind(t-11s)`[t] -0.647966`(1-Bs)(1-B)Totind(t-12s)`[t]  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309403&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309403&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
(1-Bs)(1-B)EnergySupply [t] = + 0.140871 + 1.29633`(1-Bs)(1-B)Totind`[t] + 0.27939`(1-Bs)(1-B)Totind(t-1)`[t] -0.0592663`(1-Bs)(1-B)Totind(t-2)`[t] -0.302437`(1-Bs)(1-B)Totind(t-3)`[t] + 0.833581`(1-Bs)(1-B)Totind(t-1s)`[t] -0.0974955`(1-Bs)(1-B)Totind(t-2s)`[t] + 0.319853`(1-Bs)(1-B)Totind(t-3s)`[t] -0.107948`(1-Bs)(1-B)Totind(t-4s)`[t] + 0.234491`(1-Bs)(1-B)Totind(t-5s)`[t] -0.107156`(1-Bs)(1-B)Totind(t-6s)`[t] -0.0679719`(1-Bs)(1-B)Totind(t-7s)`[t] -0.184292`(1-Bs)(1-B)Totind(t-8s)`[t] -0.204764`(1-Bs)(1-B)Totind(t-9s)`[t] -0.202809`(1-Bs)(1-B)Totind(t-10s)`[t] -0.870503`(1-Bs)(1-B)Totind(t-11s)`[t] -0.647966`(1-Bs)(1-B)Totind(t-12s)`[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)+0.1409 0.8639+1.6310e-01 0.8714 0.4357
`(1-Bs)(1-B)Totind`+1.296 0.3247+3.9920e+00 0.0003192 0.0001596
`(1-Bs)(1-B)Totind(t-1)`+0.2794 0.3048+9.1650e-01 0.3657 0.1828
`(1-Bs)(1-B)Totind(t-2)`-0.05927 0.3092-1.9170e-01 0.8491 0.4245
`(1-Bs)(1-B)Totind(t-3)`-0.3024 0.2546-1.1880e+00 0.2428 0.1214
`(1-Bs)(1-B)Totind(t-1s)`+0.8336 0.3931+2.1210e+00 0.04112 0.02056
`(1-Bs)(1-B)Totind(t-2s)`-0.09749 0.4662-2.0910e-01 0.8356 0.4178
`(1-Bs)(1-B)Totind(t-3s)`+0.3199 0.5223+6.1240e-01 0.5442 0.2721
`(1-Bs)(1-B)Totind(t-4s)`-0.108 0.5172-2.0870e-01 0.8359 0.4179
`(1-Bs)(1-B)Totind(t-5s)`+0.2345 0.4969+4.7190e-01 0.6399 0.32
`(1-Bs)(1-B)Totind(t-6s)`-0.1072 0.4998-2.1440e-01 0.8315 0.4157
`(1-Bs)(1-B)Totind(t-7s)`-0.06797 0.5205-1.3060e-01 0.8968 0.4484
`(1-Bs)(1-B)Totind(t-8s)`-0.1843 0.4989-3.6940e-01 0.714 0.357
`(1-Bs)(1-B)Totind(t-9s)`-0.2048 0.5718-3.5810e-01 0.7224 0.3612
`(1-Bs)(1-B)Totind(t-10s)`-0.2028 0.4639-4.3720e-01 0.6647 0.3323
`(1-Bs)(1-B)Totind(t-11s)`-0.8705 0.4397-1.9800e+00 0.05566 0.02783
`(1-Bs)(1-B)Totind(t-12s)`-0.648 0.3554-1.8230e+00 0.07681 0.03841

\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) & +0.1409 &  0.8639 & +1.6310e-01 &  0.8714 &  0.4357 \tabularnewline
`(1-Bs)(1-B)Totind` & +1.296 &  0.3247 & +3.9920e+00 &  0.0003192 &  0.0001596 \tabularnewline
`(1-Bs)(1-B)Totind(t-1)` & +0.2794 &  0.3048 & +9.1650e-01 &  0.3657 &  0.1828 \tabularnewline
`(1-Bs)(1-B)Totind(t-2)` & -0.05927 &  0.3092 & -1.9170e-01 &  0.8491 &  0.4245 \tabularnewline
`(1-Bs)(1-B)Totind(t-3)` & -0.3024 &  0.2546 & -1.1880e+00 &  0.2428 &  0.1214 \tabularnewline
`(1-Bs)(1-B)Totind(t-1s)` & +0.8336 &  0.3931 & +2.1210e+00 &  0.04112 &  0.02056 \tabularnewline
`(1-Bs)(1-B)Totind(t-2s)` & -0.09749 &  0.4662 & -2.0910e-01 &  0.8356 &  0.4178 \tabularnewline
`(1-Bs)(1-B)Totind(t-3s)` & +0.3199 &  0.5223 & +6.1240e-01 &  0.5442 &  0.2721 \tabularnewline
`(1-Bs)(1-B)Totind(t-4s)` & -0.108 &  0.5172 & -2.0870e-01 &  0.8359 &  0.4179 \tabularnewline
`(1-Bs)(1-B)Totind(t-5s)` & +0.2345 &  0.4969 & +4.7190e-01 &  0.6399 &  0.32 \tabularnewline
`(1-Bs)(1-B)Totind(t-6s)` & -0.1072 &  0.4998 & -2.1440e-01 &  0.8315 &  0.4157 \tabularnewline
`(1-Bs)(1-B)Totind(t-7s)` & -0.06797 &  0.5205 & -1.3060e-01 &  0.8968 &  0.4484 \tabularnewline
`(1-Bs)(1-B)Totind(t-8s)` & -0.1843 &  0.4989 & -3.6940e-01 &  0.714 &  0.357 \tabularnewline
`(1-Bs)(1-B)Totind(t-9s)` & -0.2048 &  0.5718 & -3.5810e-01 &  0.7224 &  0.3612 \tabularnewline
`(1-Bs)(1-B)Totind(t-10s)` & -0.2028 &  0.4639 & -4.3720e-01 &  0.6647 &  0.3323 \tabularnewline
`(1-Bs)(1-B)Totind(t-11s)` & -0.8705 &  0.4397 & -1.9800e+00 &  0.05566 &  0.02783 \tabularnewline
`(1-Bs)(1-B)Totind(t-12s)` & -0.648 &  0.3554 & -1.8230e+00 &  0.07681 &  0.03841 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309403&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]+0.1409[/C][C] 0.8639[/C][C]+1.6310e-01[/C][C] 0.8714[/C][C] 0.4357[/C][/ROW]
[ROW][C]`(1-Bs)(1-B)Totind`[/C][C]+1.296[/C][C] 0.3247[/C][C]+3.9920e+00[/C][C] 0.0003192[/C][C] 0.0001596[/C][/ROW]
[ROW][C]`(1-Bs)(1-B)Totind(t-1)`[/C][C]+0.2794[/C][C] 0.3048[/C][C]+9.1650e-01[/C][C] 0.3657[/C][C] 0.1828[/C][/ROW]
[ROW][C]`(1-Bs)(1-B)Totind(t-2)`[/C][C]-0.05927[/C][C] 0.3092[/C][C]-1.9170e-01[/C][C] 0.8491[/C][C] 0.4245[/C][/ROW]
[ROW][C]`(1-Bs)(1-B)Totind(t-3)`[/C][C]-0.3024[/C][C] 0.2546[/C][C]-1.1880e+00[/C][C] 0.2428[/C][C] 0.1214[/C][/ROW]
[ROW][C]`(1-Bs)(1-B)Totind(t-1s)`[/C][C]+0.8336[/C][C] 0.3931[/C][C]+2.1210e+00[/C][C] 0.04112[/C][C] 0.02056[/C][/ROW]
[ROW][C]`(1-Bs)(1-B)Totind(t-2s)`[/C][C]-0.09749[/C][C] 0.4662[/C][C]-2.0910e-01[/C][C] 0.8356[/C][C] 0.4178[/C][/ROW]
[ROW][C]`(1-Bs)(1-B)Totind(t-3s)`[/C][C]+0.3199[/C][C] 0.5223[/C][C]+6.1240e-01[/C][C] 0.5442[/C][C] 0.2721[/C][/ROW]
[ROW][C]`(1-Bs)(1-B)Totind(t-4s)`[/C][C]-0.108[/C][C] 0.5172[/C][C]-2.0870e-01[/C][C] 0.8359[/C][C] 0.4179[/C][/ROW]
[ROW][C]`(1-Bs)(1-B)Totind(t-5s)`[/C][C]+0.2345[/C][C] 0.4969[/C][C]+4.7190e-01[/C][C] 0.6399[/C][C] 0.32[/C][/ROW]
[ROW][C]`(1-Bs)(1-B)Totind(t-6s)`[/C][C]-0.1072[/C][C] 0.4998[/C][C]-2.1440e-01[/C][C] 0.8315[/C][C] 0.4157[/C][/ROW]
[ROW][C]`(1-Bs)(1-B)Totind(t-7s)`[/C][C]-0.06797[/C][C] 0.5205[/C][C]-1.3060e-01[/C][C] 0.8968[/C][C] 0.4484[/C][/ROW]
[ROW][C]`(1-Bs)(1-B)Totind(t-8s)`[/C][C]-0.1843[/C][C] 0.4989[/C][C]-3.6940e-01[/C][C] 0.714[/C][C] 0.357[/C][/ROW]
[ROW][C]`(1-Bs)(1-B)Totind(t-9s)`[/C][C]-0.2048[/C][C] 0.5718[/C][C]-3.5810e-01[/C][C] 0.7224[/C][C] 0.3612[/C][/ROW]
[ROW][C]`(1-Bs)(1-B)Totind(t-10s)`[/C][C]-0.2028[/C][C] 0.4639[/C][C]-4.3720e-01[/C][C] 0.6647[/C][C] 0.3323[/C][/ROW]
[ROW][C]`(1-Bs)(1-B)Totind(t-11s)`[/C][C]-0.8705[/C][C] 0.4397[/C][C]-1.9800e+00[/C][C] 0.05566[/C][C] 0.02783[/C][/ROW]
[ROW][C]`(1-Bs)(1-B)Totind(t-12s)`[/C][C]-0.648[/C][C] 0.3554[/C][C]-1.8230e+00[/C][C] 0.07681[/C][C] 0.03841[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309403&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309403&T=2

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)+0.1409 0.8639+1.6310e-01 0.8714 0.4357
`(1-Bs)(1-B)Totind`+1.296 0.3247+3.9920e+00 0.0003192 0.0001596
`(1-Bs)(1-B)Totind(t-1)`+0.2794 0.3048+9.1650e-01 0.3657 0.1828
`(1-Bs)(1-B)Totind(t-2)`-0.05927 0.3092-1.9170e-01 0.8491 0.4245
`(1-Bs)(1-B)Totind(t-3)`-0.3024 0.2546-1.1880e+00 0.2428 0.1214
`(1-Bs)(1-B)Totind(t-1s)`+0.8336 0.3931+2.1210e+00 0.04112 0.02056
`(1-Bs)(1-B)Totind(t-2s)`-0.09749 0.4662-2.0910e-01 0.8356 0.4178
`(1-Bs)(1-B)Totind(t-3s)`+0.3199 0.5223+6.1240e-01 0.5442 0.2721
`(1-Bs)(1-B)Totind(t-4s)`-0.108 0.5172-2.0870e-01 0.8359 0.4179
`(1-Bs)(1-B)Totind(t-5s)`+0.2345 0.4969+4.7190e-01 0.6399 0.32
`(1-Bs)(1-B)Totind(t-6s)`-0.1072 0.4998-2.1440e-01 0.8315 0.4157
`(1-Bs)(1-B)Totind(t-7s)`-0.06797 0.5205-1.3060e-01 0.8968 0.4484
`(1-Bs)(1-B)Totind(t-8s)`-0.1843 0.4989-3.6940e-01 0.714 0.357
`(1-Bs)(1-B)Totind(t-9s)`-0.2048 0.5718-3.5810e-01 0.7224 0.3612
`(1-Bs)(1-B)Totind(t-10s)`-0.2028 0.4639-4.3720e-01 0.6647 0.3323
`(1-Bs)(1-B)Totind(t-11s)`-0.8705 0.4397-1.9800e+00 0.05566 0.02783
`(1-Bs)(1-B)Totind(t-12s)`-0.648 0.3554-1.8230e+00 0.07681 0.03841







Multiple Linear Regression - Regression Statistics
Multiple R 0.7513
R-squared 0.5644
Adjusted R-squared 0.3653
F-TEST (value) 2.835
F-TEST (DF numerator)16
F-TEST (DF denominator)35
p-value 0.004955
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation 6.077
Sum Squared Residuals 1293

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.7513 \tabularnewline
R-squared &  0.5644 \tabularnewline
Adjusted R-squared &  0.3653 \tabularnewline
F-TEST (value) &  2.835 \tabularnewline
F-TEST (DF numerator) & 16 \tabularnewline
F-TEST (DF denominator) & 35 \tabularnewline
p-value &  0.004955 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  6.077 \tabularnewline
Sum Squared Residuals &  1293 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309403&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.7513[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.5644[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.3653[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 2.835[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]16[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]35[/C][/ROW]
[ROW][C]p-value[/C][C] 0.004955[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C] 6.077[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 1293[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309403&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309403&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 0.7513
R-squared 0.5644
Adjusted R-squared 0.3653
F-TEST (value) 2.835
F-TEST (DF numerator)16
F-TEST (DF denominator)35
p-value 0.004955
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation 6.077
Sum Squared Residuals 1293







Menu of Residual Diagnostics
DescriptionLink
HistogramCompute
Central TendencyCompute
QQ PlotCompute
Kernel Density PlotCompute
Skewness/Kurtosis TestCompute
Skewness-Kurtosis PlotCompute
Harrell-Davis PlotCompute
Bootstrap Plot -- Central TendencyCompute
Blocked Bootstrap Plot -- Central TendencyCompute
(Partial) Autocorrelation PlotCompute
Spectral AnalysisCompute
Tukey lambda PPCC PlotCompute
Box-Cox Normality PlotCompute
Summary StatisticsCompute

\begin{tabular}{lllllllll}
\hline
Menu of Residual Diagnostics \tabularnewline
Description & Link \tabularnewline
Histogram & Compute \tabularnewline
Central Tendency & Compute \tabularnewline
QQ Plot & Compute \tabularnewline
Kernel Density Plot & Compute \tabularnewline
Skewness/Kurtosis Test & Compute \tabularnewline
Skewness-Kurtosis Plot & Compute \tabularnewline
Harrell-Davis Plot & Compute \tabularnewline
Bootstrap Plot -- Central Tendency & Compute \tabularnewline
Blocked Bootstrap Plot -- Central Tendency & Compute \tabularnewline
(Partial) Autocorrelation Plot & Compute \tabularnewline
Spectral Analysis & Compute \tabularnewline
Tukey lambda PPCC Plot & Compute \tabularnewline
Box-Cox Normality Plot & Compute \tabularnewline
Summary Statistics & Compute \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309403&T=4

[TABLE]
[ROW][C]Menu of Residual Diagnostics[/C][/ROW]
[ROW][C]Description[/C][C]Link[/C][/ROW]
[ROW][C]Histogram[/C][C]Compute[/C][/ROW]
[ROW][C]Central Tendency[/C][C]Compute[/C][/ROW]
[ROW][C]QQ Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Kernel Density Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Skewness/Kurtosis Test[/C][C]Compute[/C][/ROW]
[ROW][C]Skewness-Kurtosis Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Harrell-Davis Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Bootstrap Plot -- Central Tendency[/C][C]Compute[/C][/ROW]
[ROW][C]Blocked Bootstrap Plot -- Central Tendency[/C][C]Compute[/C][/ROW]
[ROW][C](Partial) Autocorrelation Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Spectral Analysis[/C][C]Compute[/C][/ROW]
[ROW][C]Tukey lambda PPCC Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Box-Cox Normality Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Summary Statistics[/C][C]Compute[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309403&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309403&T=4

As an alternative you can also use a QR Code:  

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

Menu of Residual Diagnostics
DescriptionLink
HistogramCompute
Central TendencyCompute
QQ PlotCompute
Kernel Density PlotCompute
Skewness/Kurtosis TestCompute
Skewness-Kurtosis PlotCompute
Harrell-Davis PlotCompute
Bootstrap Plot -- Central TendencyCompute
Blocked Bootstrap Plot -- Central TendencyCompute
(Partial) Autocorrelation PlotCompute
Spectral AnalysisCompute
Tukey lambda PPCC PlotCompute
Box-Cox Normality PlotCompute
Summary StatisticsCompute







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1-1.8-2.118 0.3179
2 12.6 6.798 5.802
3 2.1 0.9825 1.117
4-1.6-13.86 12.26
5-2 3.457-5.457
6 15 9.4 5.6
7 0.7 0.8898-0.1898
8-0.3-2.834 2.534
9-15.2-6.563-8.637
10-2.8-1.796-1.004
11-7.1-2.444-4.656
12 1.8 4.314-2.514
13 6.2 7.445-1.245
14-9.5-9.791 0.2905
15-2.1-4.607 2.507
16 0.3 3.597-3.297
17-1.9 6.875-8.775
18-3.6-1.733-1.867
19-7.2-3.097-4.103
20 2.1-3.78 5.88
21 19.8 11.86 7.941
22-3.1-2.984-0.1159
23-0.7-1.579 0.8791
24 6.5 1.386 5.114
25-6.4-9.918 3.518
26 2.6 5.539-2.939
27-3.3-0.61-2.69
28 1.1 1.351-0.2512
29 9.7 2.033 7.667
30 2.4-0.9325 3.333
31 7.7 0.3459 7.354
32-8.3-5.248-3.052
33 9.2 5.536 3.664
34-1.2 1.519-2.719
35 9.6 5.678 3.922
36-0.1-1.064 0.9641
37-9.1-2.536-6.564
38-2.9-1.471-1.429
39 8.1 2.676 5.424
40 10.5 6.858 3.642
41-16.7-10.73-5.967
42-5.1-2.366-2.734
43 1.4 1.254 0.1465
44 4.5 15.85-11.35
45-11.4-7.314-4.086
46-4.2 2.982-7.182
47-3.7-5.273 1.573
48-7.5-4.6-2.9
49 13.6 4.2 9.4
50 10.6 6.652 3.948
51-2-0.00065-1.999
52-7 0.07336-7.073

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & -1.8 & -2.118 &  0.3179 \tabularnewline
2 &  12.6 &  6.798 &  5.802 \tabularnewline
3 &  2.1 &  0.9825 &  1.117 \tabularnewline
4 & -1.6 & -13.86 &  12.26 \tabularnewline
5 & -2 &  3.457 & -5.457 \tabularnewline
6 &  15 &  9.4 &  5.6 \tabularnewline
7 &  0.7 &  0.8898 & -0.1898 \tabularnewline
8 & -0.3 & -2.834 &  2.534 \tabularnewline
9 & -15.2 & -6.563 & -8.637 \tabularnewline
10 & -2.8 & -1.796 & -1.004 \tabularnewline
11 & -7.1 & -2.444 & -4.656 \tabularnewline
12 &  1.8 &  4.314 & -2.514 \tabularnewline
13 &  6.2 &  7.445 & -1.245 \tabularnewline
14 & -9.5 & -9.791 &  0.2905 \tabularnewline
15 & -2.1 & -4.607 &  2.507 \tabularnewline
16 &  0.3 &  3.597 & -3.297 \tabularnewline
17 & -1.9 &  6.875 & -8.775 \tabularnewline
18 & -3.6 & -1.733 & -1.867 \tabularnewline
19 & -7.2 & -3.097 & -4.103 \tabularnewline
20 &  2.1 & -3.78 &  5.88 \tabularnewline
21 &  19.8 &  11.86 &  7.941 \tabularnewline
22 & -3.1 & -2.984 & -0.1159 \tabularnewline
23 & -0.7 & -1.579 &  0.8791 \tabularnewline
24 &  6.5 &  1.386 &  5.114 \tabularnewline
25 & -6.4 & -9.918 &  3.518 \tabularnewline
26 &  2.6 &  5.539 & -2.939 \tabularnewline
27 & -3.3 & -0.61 & -2.69 \tabularnewline
28 &  1.1 &  1.351 & -0.2512 \tabularnewline
29 &  9.7 &  2.033 &  7.667 \tabularnewline
30 &  2.4 & -0.9325 &  3.333 \tabularnewline
31 &  7.7 &  0.3459 &  7.354 \tabularnewline
32 & -8.3 & -5.248 & -3.052 \tabularnewline
33 &  9.2 &  5.536 &  3.664 \tabularnewline
34 & -1.2 &  1.519 & -2.719 \tabularnewline
35 &  9.6 &  5.678 &  3.922 \tabularnewline
36 & -0.1 & -1.064 &  0.9641 \tabularnewline
37 & -9.1 & -2.536 & -6.564 \tabularnewline
38 & -2.9 & -1.471 & -1.429 \tabularnewline
39 &  8.1 &  2.676 &  5.424 \tabularnewline
40 &  10.5 &  6.858 &  3.642 \tabularnewline
41 & -16.7 & -10.73 & -5.967 \tabularnewline
42 & -5.1 & -2.366 & -2.734 \tabularnewline
43 &  1.4 &  1.254 &  0.1465 \tabularnewline
44 &  4.5 &  15.85 & -11.35 \tabularnewline
45 & -11.4 & -7.314 & -4.086 \tabularnewline
46 & -4.2 &  2.982 & -7.182 \tabularnewline
47 & -3.7 & -5.273 &  1.573 \tabularnewline
48 & -7.5 & -4.6 & -2.9 \tabularnewline
49 &  13.6 &  4.2 &  9.4 \tabularnewline
50 &  10.6 &  6.652 &  3.948 \tabularnewline
51 & -2 & -0.00065 & -1.999 \tabularnewline
52 & -7 &  0.07336 & -7.073 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309403&T=5

[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]-1.8[/C][C]-2.118[/C][C] 0.3179[/C][/ROW]
[ROW][C]2[/C][C] 12.6[/C][C] 6.798[/C][C] 5.802[/C][/ROW]
[ROW][C]3[/C][C] 2.1[/C][C] 0.9825[/C][C] 1.117[/C][/ROW]
[ROW][C]4[/C][C]-1.6[/C][C]-13.86[/C][C] 12.26[/C][/ROW]
[ROW][C]5[/C][C]-2[/C][C] 3.457[/C][C]-5.457[/C][/ROW]
[ROW][C]6[/C][C] 15[/C][C] 9.4[/C][C] 5.6[/C][/ROW]
[ROW][C]7[/C][C] 0.7[/C][C] 0.8898[/C][C]-0.1898[/C][/ROW]
[ROW][C]8[/C][C]-0.3[/C][C]-2.834[/C][C] 2.534[/C][/ROW]
[ROW][C]9[/C][C]-15.2[/C][C]-6.563[/C][C]-8.637[/C][/ROW]
[ROW][C]10[/C][C]-2.8[/C][C]-1.796[/C][C]-1.004[/C][/ROW]
[ROW][C]11[/C][C]-7.1[/C][C]-2.444[/C][C]-4.656[/C][/ROW]
[ROW][C]12[/C][C] 1.8[/C][C] 4.314[/C][C]-2.514[/C][/ROW]
[ROW][C]13[/C][C] 6.2[/C][C] 7.445[/C][C]-1.245[/C][/ROW]
[ROW][C]14[/C][C]-9.5[/C][C]-9.791[/C][C] 0.2905[/C][/ROW]
[ROW][C]15[/C][C]-2.1[/C][C]-4.607[/C][C] 2.507[/C][/ROW]
[ROW][C]16[/C][C] 0.3[/C][C] 3.597[/C][C]-3.297[/C][/ROW]
[ROW][C]17[/C][C]-1.9[/C][C] 6.875[/C][C]-8.775[/C][/ROW]
[ROW][C]18[/C][C]-3.6[/C][C]-1.733[/C][C]-1.867[/C][/ROW]
[ROW][C]19[/C][C]-7.2[/C][C]-3.097[/C][C]-4.103[/C][/ROW]
[ROW][C]20[/C][C] 2.1[/C][C]-3.78[/C][C] 5.88[/C][/ROW]
[ROW][C]21[/C][C] 19.8[/C][C] 11.86[/C][C] 7.941[/C][/ROW]
[ROW][C]22[/C][C]-3.1[/C][C]-2.984[/C][C]-0.1159[/C][/ROW]
[ROW][C]23[/C][C]-0.7[/C][C]-1.579[/C][C] 0.8791[/C][/ROW]
[ROW][C]24[/C][C] 6.5[/C][C] 1.386[/C][C] 5.114[/C][/ROW]
[ROW][C]25[/C][C]-6.4[/C][C]-9.918[/C][C] 3.518[/C][/ROW]
[ROW][C]26[/C][C] 2.6[/C][C] 5.539[/C][C]-2.939[/C][/ROW]
[ROW][C]27[/C][C]-3.3[/C][C]-0.61[/C][C]-2.69[/C][/ROW]
[ROW][C]28[/C][C] 1.1[/C][C] 1.351[/C][C]-0.2512[/C][/ROW]
[ROW][C]29[/C][C] 9.7[/C][C] 2.033[/C][C] 7.667[/C][/ROW]
[ROW][C]30[/C][C] 2.4[/C][C]-0.9325[/C][C] 3.333[/C][/ROW]
[ROW][C]31[/C][C] 7.7[/C][C] 0.3459[/C][C] 7.354[/C][/ROW]
[ROW][C]32[/C][C]-8.3[/C][C]-5.248[/C][C]-3.052[/C][/ROW]
[ROW][C]33[/C][C] 9.2[/C][C] 5.536[/C][C] 3.664[/C][/ROW]
[ROW][C]34[/C][C]-1.2[/C][C] 1.519[/C][C]-2.719[/C][/ROW]
[ROW][C]35[/C][C] 9.6[/C][C] 5.678[/C][C] 3.922[/C][/ROW]
[ROW][C]36[/C][C]-0.1[/C][C]-1.064[/C][C] 0.9641[/C][/ROW]
[ROW][C]37[/C][C]-9.1[/C][C]-2.536[/C][C]-6.564[/C][/ROW]
[ROW][C]38[/C][C]-2.9[/C][C]-1.471[/C][C]-1.429[/C][/ROW]
[ROW][C]39[/C][C] 8.1[/C][C] 2.676[/C][C] 5.424[/C][/ROW]
[ROW][C]40[/C][C] 10.5[/C][C] 6.858[/C][C] 3.642[/C][/ROW]
[ROW][C]41[/C][C]-16.7[/C][C]-10.73[/C][C]-5.967[/C][/ROW]
[ROW][C]42[/C][C]-5.1[/C][C]-2.366[/C][C]-2.734[/C][/ROW]
[ROW][C]43[/C][C] 1.4[/C][C] 1.254[/C][C] 0.1465[/C][/ROW]
[ROW][C]44[/C][C] 4.5[/C][C] 15.85[/C][C]-11.35[/C][/ROW]
[ROW][C]45[/C][C]-11.4[/C][C]-7.314[/C][C]-4.086[/C][/ROW]
[ROW][C]46[/C][C]-4.2[/C][C] 2.982[/C][C]-7.182[/C][/ROW]
[ROW][C]47[/C][C]-3.7[/C][C]-5.273[/C][C] 1.573[/C][/ROW]
[ROW][C]48[/C][C]-7.5[/C][C]-4.6[/C][C]-2.9[/C][/ROW]
[ROW][C]49[/C][C] 13.6[/C][C] 4.2[/C][C] 9.4[/C][/ROW]
[ROW][C]50[/C][C] 10.6[/C][C] 6.652[/C][C] 3.948[/C][/ROW]
[ROW][C]51[/C][C]-2[/C][C]-0.00065[/C][C]-1.999[/C][/ROW]
[ROW][C]52[/C][C]-7[/C][C] 0.07336[/C][C]-7.073[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309403&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309403&T=5

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1-1.8-2.118 0.3179
2 12.6 6.798 5.802
3 2.1 0.9825 1.117
4-1.6-13.86 12.26
5-2 3.457-5.457
6 15 9.4 5.6
7 0.7 0.8898-0.1898
8-0.3-2.834 2.534
9-15.2-6.563-8.637
10-2.8-1.796-1.004
11-7.1-2.444-4.656
12 1.8 4.314-2.514
13 6.2 7.445-1.245
14-9.5-9.791 0.2905
15-2.1-4.607 2.507
16 0.3 3.597-3.297
17-1.9 6.875-8.775
18-3.6-1.733-1.867
19-7.2-3.097-4.103
20 2.1-3.78 5.88
21 19.8 11.86 7.941
22-3.1-2.984-0.1159
23-0.7-1.579 0.8791
24 6.5 1.386 5.114
25-6.4-9.918 3.518
26 2.6 5.539-2.939
27-3.3-0.61-2.69
28 1.1 1.351-0.2512
29 9.7 2.033 7.667
30 2.4-0.9325 3.333
31 7.7 0.3459 7.354
32-8.3-5.248-3.052
33 9.2 5.536 3.664
34-1.2 1.519-2.719
35 9.6 5.678 3.922
36-0.1-1.064 0.9641
37-9.1-2.536-6.564
38-2.9-1.471-1.429
39 8.1 2.676 5.424
40 10.5 6.858 3.642
41-16.7-10.73-5.967
42-5.1-2.366-2.734
43 1.4 1.254 0.1465
44 4.5 15.85-11.35
45-11.4-7.314-4.086
46-4.2 2.982-7.182
47-3.7-5.273 1.573
48-7.5-4.6-2.9
49 13.6 4.2 9.4
50 10.6 6.652 3.948
51-2-0.00065-1.999
52-7 0.07336-7.073







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
20 0.7223 0.5553 0.2777
21 0.8395 0.321 0.1605
22 0.7757 0.4485 0.2243
23 0.6913 0.6174 0.3087
24 0.7067 0.5867 0.2933
25 0.5959 0.8081 0.4041
26 0.6259 0.7482 0.3741
27 0.5801 0.8398 0.4199
28 0.4464 0.8928 0.5536
29 0.3467 0.6935 0.6533
30 0.5279 0.9443 0.4721
31 0.3868 0.7735 0.6132
32 0.2821 0.5642 0.7179

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
20 &  0.7223 &  0.5553 &  0.2777 \tabularnewline
21 &  0.8395 &  0.321 &  0.1605 \tabularnewline
22 &  0.7757 &  0.4485 &  0.2243 \tabularnewline
23 &  0.6913 &  0.6174 &  0.3087 \tabularnewline
24 &  0.7067 &  0.5867 &  0.2933 \tabularnewline
25 &  0.5959 &  0.8081 &  0.4041 \tabularnewline
26 &  0.6259 &  0.7482 &  0.3741 \tabularnewline
27 &  0.5801 &  0.8398 &  0.4199 \tabularnewline
28 &  0.4464 &  0.8928 &  0.5536 \tabularnewline
29 &  0.3467 &  0.6935 &  0.6533 \tabularnewline
30 &  0.5279 &  0.9443 &  0.4721 \tabularnewline
31 &  0.3868 &  0.7735 &  0.6132 \tabularnewline
32 &  0.2821 &  0.5642 &  0.7179 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309403&T=6

[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] 0.7223[/C][C] 0.5553[/C][C] 0.2777[/C][/ROW]
[ROW][C]21[/C][C] 0.8395[/C][C] 0.321[/C][C] 0.1605[/C][/ROW]
[ROW][C]22[/C][C] 0.7757[/C][C] 0.4485[/C][C] 0.2243[/C][/ROW]
[ROW][C]23[/C][C] 0.6913[/C][C] 0.6174[/C][C] 0.3087[/C][/ROW]
[ROW][C]24[/C][C] 0.7067[/C][C] 0.5867[/C][C] 0.2933[/C][/ROW]
[ROW][C]25[/C][C] 0.5959[/C][C] 0.8081[/C][C] 0.4041[/C][/ROW]
[ROW][C]26[/C][C] 0.6259[/C][C] 0.7482[/C][C] 0.3741[/C][/ROW]
[ROW][C]27[/C][C] 0.5801[/C][C] 0.8398[/C][C] 0.4199[/C][/ROW]
[ROW][C]28[/C][C] 0.4464[/C][C] 0.8928[/C][C] 0.5536[/C][/ROW]
[ROW][C]29[/C][C] 0.3467[/C][C] 0.6935[/C][C] 0.6533[/C][/ROW]
[ROW][C]30[/C][C] 0.5279[/C][C] 0.9443[/C][C] 0.4721[/C][/ROW]
[ROW][C]31[/C][C] 0.3868[/C][C] 0.7735[/C][C] 0.6132[/C][/ROW]
[ROW][C]32[/C][C] 0.2821[/C][C] 0.5642[/C][C] 0.7179[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309403&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309403&T=6

As an alternative you can also use a QR Code:  

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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
20 0.7223 0.5553 0.2777
21 0.8395 0.321 0.1605
22 0.7757 0.4485 0.2243
23 0.6913 0.6174 0.3087
24 0.7067 0.5867 0.2933
25 0.5959 0.8081 0.4041
26 0.6259 0.7482 0.3741
27 0.5801 0.8398 0.4199
28 0.4464 0.8928 0.5536
29 0.3467 0.6935 0.6533
30 0.5279 0.9443 0.4721
31 0.3868 0.7735 0.6132
32 0.2821 0.5642 0.7179







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level0 0OK
5% type I error level00OK
10% type I error level00OK

\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 & 0 & 0 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309403&T=7

[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]0[/C][C]0[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309403&T=7

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309403&T=7

As an alternative you can also use a QR Code:  

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level0 0OK
5% type I error level00OK
10% type I error level00OK







Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted
	RESET test
data:  mylm
RESET = 5.4261, df1 = 2, df2 = 33, p-value = 0.009175
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors
	RESET test
data:  mylm
RESET = 2.7244, df1 = 32, df2 = 3, p-value = 0.2228
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 2.5231, df1 = 2, df2 = 33, p-value = 0.09558

\begin{tabular}{lllllllll}
\hline
Ramsey RESET F-Test for powers (2 and 3) of fitted values \tabularnewline
> reset_test_fitted
	RESET test
data:  mylm
RESET = 5.4261, df1 = 2, df2 = 33, p-value = 0.009175
\tabularnewline Ramsey RESET F-Test for powers (2 and 3) of regressors \tabularnewline
> reset_test_regressors
	RESET test
data:  mylm
RESET = 2.7244, df1 = 32, df2 = 3, p-value = 0.2228
\tabularnewline Ramsey RESET F-Test for powers (2 and 3) of principal components \tabularnewline
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 2.5231, df1 = 2, df2 = 33, p-value = 0.09558
\tabularnewline \hline \end{tabular} %Source: https://freestatistics.org/blog/index.php?pk=309403&T=8

[TABLE]
[ROW][C]Ramsey RESET F-Test for powers (2 and 3) of fitted values[/C][/ROW]
[ROW][C]
> reset_test_fitted
	RESET test
data:  mylm
RESET = 5.4261, df1 = 2, df2 = 33, p-value = 0.009175
[/C][/ROW] [ROW][C]Ramsey RESET F-Test for powers (2 and 3) of regressors[/C][/ROW] [ROW][C]
> reset_test_regressors
	RESET test
data:  mylm
RESET = 2.7244, df1 = 32, df2 = 3, p-value = 0.2228
[/C][/ROW] [ROW][C]Ramsey RESET F-Test for powers (2 and 3) of principal components[/C][/ROW] [ROW][C]
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 2.5231, df1 = 2, df2 = 33, p-value = 0.09558
[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=309403&T=8

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309403&T=8

As an alternative you can also use a QR Code:  

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

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted
	RESET test
data:  mylm
RESET = 5.4261, df1 = 2, df2 = 33, p-value = 0.009175
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors
	RESET test
data:  mylm
RESET = 2.7244, df1 = 32, df2 = 3, p-value = 0.2228
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 2.5231, df1 = 2, df2 = 33, p-value = 0.09558







Variance Inflation Factors (Multicollinearity)
> vif
       `(1-Bs)(1-B)Totind`   `(1-Bs)(1-B)Totind(t-1)` 
                  4.182536                   4.185984 
  `(1-Bs)(1-B)Totind(t-2)`   `(1-Bs)(1-B)Totind(t-3)` 
                  4.308399                   2.936814 
 `(1-Bs)(1-B)Totind(t-1s)`  `(1-Bs)(1-B)Totind(t-2s)` 
                  6.258467                  11.044385 
 `(1-Bs)(1-B)Totind(t-3s)`  `(1-Bs)(1-B)Totind(t-4s)` 
                 12.858956                  14.799516 
 `(1-Bs)(1-B)Totind(t-5s)`  `(1-Bs)(1-B)Totind(t-6s)` 
                 17.075875                  18.139760 
 `(1-Bs)(1-B)Totind(t-7s)`  `(1-Bs)(1-B)Totind(t-8s)` 
                 17.771454                  14.772243 
 `(1-Bs)(1-B)Totind(t-9s)` `(1-Bs)(1-B)Totind(t-10s)` 
                 12.359671                   6.801661 
`(1-Bs)(1-B)Totind(t-11s)` `(1-Bs)(1-B)Totind(t-12s)` 
                  5.797113                   3.192240 

\begin{tabular}{lllllllll}
\hline
Variance Inflation Factors (Multicollinearity) \tabularnewline
> vif
       `(1-Bs)(1-B)Totind`   `(1-Bs)(1-B)Totind(t-1)` 
                  4.182536                   4.185984 
  `(1-Bs)(1-B)Totind(t-2)`   `(1-Bs)(1-B)Totind(t-3)` 
                  4.308399                   2.936814 
 `(1-Bs)(1-B)Totind(t-1s)`  `(1-Bs)(1-B)Totind(t-2s)` 
                  6.258467                  11.044385 
 `(1-Bs)(1-B)Totind(t-3s)`  `(1-Bs)(1-B)Totind(t-4s)` 
                 12.858956                  14.799516 
 `(1-Bs)(1-B)Totind(t-5s)`  `(1-Bs)(1-B)Totind(t-6s)` 
                 17.075875                  18.139760 
 `(1-Bs)(1-B)Totind(t-7s)`  `(1-Bs)(1-B)Totind(t-8s)` 
                 17.771454                  14.772243 
 `(1-Bs)(1-B)Totind(t-9s)` `(1-Bs)(1-B)Totind(t-10s)` 
                 12.359671                   6.801661 
`(1-Bs)(1-B)Totind(t-11s)` `(1-Bs)(1-B)Totind(t-12s)` 
                  5.797113                   3.192240 
\tabularnewline \hline \end{tabular} %Source: https://freestatistics.org/blog/index.php?pk=309403&T=9

[TABLE]
[ROW][C]Variance Inflation Factors (Multicollinearity)[/C][/ROW]
[ROW][C]
> vif
       `(1-Bs)(1-B)Totind`   `(1-Bs)(1-B)Totind(t-1)` 
                  4.182536                   4.185984 
  `(1-Bs)(1-B)Totind(t-2)`   `(1-Bs)(1-B)Totind(t-3)` 
                  4.308399                   2.936814 
 `(1-Bs)(1-B)Totind(t-1s)`  `(1-Bs)(1-B)Totind(t-2s)` 
                  6.258467                  11.044385 
 `(1-Bs)(1-B)Totind(t-3s)`  `(1-Bs)(1-B)Totind(t-4s)` 
                 12.858956                  14.799516 
 `(1-Bs)(1-B)Totind(t-5s)`  `(1-Bs)(1-B)Totind(t-6s)` 
                 17.075875                  18.139760 
 `(1-Bs)(1-B)Totind(t-7s)`  `(1-Bs)(1-B)Totind(t-8s)` 
                 17.771454                  14.772243 
 `(1-Bs)(1-B)Totind(t-9s)` `(1-Bs)(1-B)Totind(t-10s)` 
                 12.359671                   6.801661 
`(1-Bs)(1-B)Totind(t-11s)` `(1-Bs)(1-B)Totind(t-12s)` 
                  5.797113                   3.192240 
[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=309403&T=9

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309403&T=9

As an alternative you can also use a QR Code:  

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

Variance Inflation Factors (Multicollinearity)
> vif
       `(1-Bs)(1-B)Totind`   `(1-Bs)(1-B)Totind(t-1)` 
                  4.182536                   4.185984 
  `(1-Bs)(1-B)Totind(t-2)`   `(1-Bs)(1-B)Totind(t-3)` 
                  4.308399                   2.936814 
 `(1-Bs)(1-B)Totind(t-1s)`  `(1-Bs)(1-B)Totind(t-2s)` 
                  6.258467                  11.044385 
 `(1-Bs)(1-B)Totind(t-3s)`  `(1-Bs)(1-B)Totind(t-4s)` 
                 12.858956                  14.799516 
 `(1-Bs)(1-B)Totind(t-5s)`  `(1-Bs)(1-B)Totind(t-6s)` 
                 17.075875                  18.139760 
 `(1-Bs)(1-B)Totind(t-7s)`  `(1-Bs)(1-B)Totind(t-8s)` 
                 17.771454                  14.772243 
 `(1-Bs)(1-B)Totind(t-9s)` `(1-Bs)(1-B)Totind(t-10s)` 
                 12.359671                   6.801661 
`(1-Bs)(1-B)Totind(t-11s)` `(1-Bs)(1-B)Totind(t-12s)` 
                  5.797113                   3.192240 



Parameters (Session):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = First and Seasonal Differences (s) ; par4 = 4 ; par5 = 1 ; par6 = 12 ;
Parameters (R input):
par1 = 2 ; par2 = Do not include Seasonal Dummies ; par3 = First and Seasonal Differences (s) ; par4 = 3 ; par5 = 12 ; par6 = 12 ;
R code (references can be found in the software module):
par6 <- '12'
par5 <- '1'
par4 <- '3'
par3 <- 'First and Seasonal Differences (s)'
par2 <- 'Do not include Seasonal Dummies'
par1 <- '2'
library(lattice)
library(lmtest)
library(car)
library(MASS)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
mywarning <- ''
par6 <- as.numeric(par6)
if(is.na(par6)) {
par6 <- 12
mywarning = 'Warning: you did not specify the seasonality. The seasonal period was set to s = 12.'
}
par1 <- as.numeric(par1)
if(is.na(par1)) {
par1 <- 1
mywarning = 'Warning: you did not specify the column number of the endogenous series! The first column was selected by default.'
}
if (par4=='') par4 <- 0
par4 <- as.numeric(par4)
if (!is.numeric(par4)) par4 <- 0
if (par5=='') par5 <- 0
par5 <- as.numeric(par5)
if (!is.numeric(par5)) par5 <- 0
x <- na.omit(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'){
(n <- n -1)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par3 == 'Seasonal Differences (s)'){
(n <- n - par6)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-Bs)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+par6,j] - x[i,j]
}
}
x <- x2
}
if (par3 == 'First and Seasonal Differences (s)'){
(n <- n -1)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
(n <- n - par6)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-Bs)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+par6,j] - x[i,j]
}
}
x <- x2
}
if(par4 > 0) {
x2 <- array(0, dim=c(n-par4,par4), dimnames=list(1:(n-par4), paste(colnames(x)[par1],'(t-',1:par4,')',sep='')))
for (i in 1:(n-par4)) {
for (j in 1:par4) {
x2[i,j] <- x[i+par4-j,par1]
}
}
x <- cbind(x[(par4+1):n,], x2)
n <- n - par4
}
if(par5 > 0) {
x2 <- array(0, dim=c(n-par5*par6,par5), dimnames=list(1:(n-par5*par6), paste(colnames(x)[par1],'(t-',1:par5,'s)',sep='')))
for (i in 1:(n-par5*par6)) {
for (j in 1:par5) {
x2[i,j] <- x[i+par5*par6-j*par6,par1]
}
}
x <- cbind(x[(par5*par6+1):n,], x2)
n <- n - par5*par6
}
if (par2 == 'Include Seasonal Dummies'){
x2 <- array(0, dim=c(n,par6-1), dimnames=list(1:n, paste('M', seq(1:(par6-1)), sep ='')))
for (i in 1:(par6-1)){
x2[seq(i,n,par6),i] <- 1
}
x <- cbind(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[n,]))
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
print(x)
(k <- length(x[n,]))
head(x)
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$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
sresid <- studres(mylm)
hist(sresid, freq=FALSE, main='Distribution of Studentized Residuals')
xfit<-seq(min(sresid),max(sresid),length=40)
yfit<-dnorm(xfit)
lines(xfit, yfit)
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqPlot(mylm, main='QQ Plot')
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
print(z)
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$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.row.start(a)
a<-table.element(a, mywarning)
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,'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$coefficients)[i],header=TRUE)
a<-table.element(a,formatC(signif(mysum$coefficients[i,1],5),format='g',flag='+'))
a<-table.element(a,formatC(signif(mysum$coefficients[i,2],5),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$coefficients[i,3],4),format='e',flag='+'))
a<-table.element(a,formatC(signif(mysum$coefficients[i,4],4),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$coefficients[i,4]/2,4),format='g',flag=' '))
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,formatC(signif(sqrt(mysum$r.squared),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a,formatC(signif(mysum$r.squared,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a,formatC(signif(mysum$adj.r.squared,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a,formatC(signif(mysum$fstatistic[1],6),format='g',flag=' '))
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,formatC(signif(1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a,formatC(signif(mysum$sigma,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a,formatC(signif(sum(myerror*myerror),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
myr <- as.numeric(mysum$resid)
myr
a <-table.start()
a <- table.row.start(a)
a <- table.element(a,'Menu of Residual Diagnostics',2,TRUE)
a <- table.row.end(a)
a <- table.row.start(a)
a <- table.element(a,'Description',1,TRUE)
a <- table.element(a,'Link',1,TRUE)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Histogram',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_histogram.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Central Tendency',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_centraltendency.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'QQ Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_fitdistrnorm.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Kernel Density Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_density.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Skewness/Kurtosis Test',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_skewness_kurtosis.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Skewness-Kurtosis Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_skewness_kurtosis_plot.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Harrell-Davis Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_harrell_davis.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Bootstrap Plot -- Central Tendency',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_bootstrapplot1.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Blocked Bootstrap Plot -- Central Tendency',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_bootstrapplot.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'(Partial) Autocorrelation Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_autocorrelation.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Spectral Analysis',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_spectrum.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Tukey lambda PPCC Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_tukeylambda.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Box-Cox Normality Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_boxcoxnorm.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <- table.element(a,'Summary Statistics',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_summary1.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable7.tab')
if(n < 200) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,formatC(signif(x[i],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(x[i]-mysum$resid[i],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$resid[i],6),format='g',flag=' '))
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,formatC(signif(gqarr[mypoint-kp3+1,1],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,2],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,3],6),format='g',flag=' '))
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,formatC(signif(numsignificant1/numgqtests,6),format='g',flag=' '))
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')
}
}
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Ramsey RESET F-Test for powers (2 and 3) of fitted values',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
reset_test_fitted <- resettest(mylm,power=2:3,type='fitted')
a<-table.element(a,paste('
',RC.texteval('reset_test_fitted'),'
',sep=''))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Ramsey RESET F-Test for powers (2 and 3) of regressors',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
reset_test_regressors <- resettest(mylm,power=2:3,type='regressor')
a<-table.element(a,paste('
',RC.texteval('reset_test_regressors'),'
',sep=''))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Ramsey RESET F-Test for powers (2 and 3) of principal components',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
reset_test_principal_components <- resettest(mylm,power=2:3,type='princomp')
a<-table.element(a,paste('
',RC.texteval('reset_test_principal_components'),'
',sep=''))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable8.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Variance Inflation Factors (Multicollinearity)',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
vif <- vif(mylm)
a<-table.element(a,paste('
',RC.texteval('vif'),'
',sep=''))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable9.tab')