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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationThu, 17 Dec 2015 12:55:19 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2015/Dec/17/t1450357280ftuzbwr1b3qucrl.htm/, Retrieved Thu, 16 May 2024 18:30:09 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=286786, Retrieved Thu, 16 May 2024 18:30:09 +0000
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IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact100

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Paper 3] [2015-12-06 20:58:13] [7c4d8ff25a79c0ca04f65cc37f1af957]
- R P     [Multiple Regression] [Statistiek 3] [2015-12-17 12:55:19] [fb7ef44ef6cdfac67cf9078e3093d323] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
-5	-6	50	19	-29
-1	-3	53	20	-29
-2	-4	50	21	-29
-5	-7	50	20	-27
-4	-7	51	21	-29
-6	-7	53	19	-24
-2	-3	49	22	-29
-2	0	54	20	-21
-2	-5	57	18	-20
-2	-3	58	16	-26
2	3	56	17	-19
1	2	60	18	-22
-8	-7	55	19	-22
-1	-1	54	18	-15
1	0	52	20	-16
-1	-3	55	21	-22
2	4	56	18	-21
2	2	54	19	-11
1	3	53	19	-10
-1	0	59	19	-6
-2	-10	62	21	-8
-2	-10	63	19	-15
-1	-9	64	19	-16
-8	-22	75	17	-24
-4	-16	77	16	-27
-6	-18	79	16	-33
-3	-14	77	17	-29
-3	-12	82	16	-34
-7	-17	83	15	-37
-9	-23	81	16	-31
-11	-28	78	16	-33
-13	-31	79	16	-25
-11	-21	79	18	-27
-9	-19	73	19	-21
-17	-22	72	16	-32
-22	-22	67	16	-31
-25	-25	67	16	-32
-20	-16	50	18	-30
-24	-22	45	16	-34
-24	-21	39	15	-35
-22	-10	39	15	-37
-19	-7	37	16	-32
-18	-5	30	18	-28
-17	-4	24	16	-26
-11	7	27	19	-24
-11	6	19	19	-27
-12	3	19	18	-26
-10	10	25	17	-27
-15	0	16	19	-27
-15	-2	20	22	-24
-15	-1	25	19	-28
-13	2	34	19	-23
-8	8	39	16	-23
-13	-6	40	18	-29
-9	-4	38	20	-25
-7	4	42	17	-24
-4	7	46	17	-20
-4	3	48	17	-22
-2	3	51	20	-24
0	8	55	21	-27
-2	3	52	19	-25
-3	-3	55	18	-26
1	4	58	20	-24
-2	-5	72	17	-26
-1	-1	70	15	-22
1	5	70	17	-20
-3	0	63	18	-26
-4	-6	66	20	-22
-9	-13	65	19	-29
-9	-15	55	20	-30
-7	-8	57	22	-26
-14	-20	60	20	-30
-12	-10	63	21	-33
-16	-22	65	19	-33
-20	-25	61	22	-31
-12	-10	65	19	-36
-12	-8	63	21	-43
-10	-9	59	19	-40
-10	-5	56	21	-38
-13	-7	54	18	-41
-16	-11	56	18	-38
-14	-11	54	20	-40
-17	-16	58	19	-41
-24	-28	59	19	-45
-25	-27	60	17	-54
-23	-23	57	18	-47
-17	-10	54	17	-44
-24	-22	52	18	-47
-20	-15	50	19	-47
-19	-14	51	17	-45
-18	-12	47	19	-42
-16	-10	51	19	-42
-12	1	46	17	-39
-7	9	44	19	-35
-6	7	39	21	-29
-6	9	43	20	-37
-5	7	46	19	-35
-4	12	43	21	-32
-4	10	34	20	-33
-8	7	36	18	-37
-9	4	34	18	-36
-6	5	38	16	-34
-7	5	32	18	-38
-10	-1	38	19	-33
-11	-5	30	18	-41
-11	-6	17	18	-39
-12	-9	14	17	-40
-14	-15	18	18	-42
-12	-10	18	19	-45
-9	-5	13	18	-39
-5	2	9	19	-44
-6	-1	12	19	-44
-6	0	19	20	-43
-3	4	20	21	-39
-2	8	25	17	-38
-6	-1	26	20	-43
-6	-4	29	21	-46
-10	-10	28	18	-42
-8	-6	30	19	-45
-4	-2	38	20	-46

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 5 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=286786&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]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=286786&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=286786&T=0

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


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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net






Multiple Linear Regression - Estimated Regression Equation
consumentenvertrouwen[t] = -9.14325 + 0.510627vooruitz_economie[t] -0.0414924cons_prijzen_12m[t] + 0.358832fin_sit_gezinnen[t] + 0.0944583gunstig_sparen[t] + 0.0430886`consumentenvertrouwen(t-1)`[t] + 0.066497`consumentenvertrouwen(t-2)`[t] + 0.0800992`consumentenvertrouwen(t-3)`[t] + 0.0404485`consumentenvertrouwen(t-4)`[t] + 0.0500868`consumentenvertrouwen(t-1s)`[t] + 0.0211835`consumentenvertrouwen(t-2s)`[t] -0.158327`consumentenvertrouwen(t-3s)`[t] -0.077604`consumentenvertrouwen(t-4s)`[t] + 0.075934`consumentenvertrouwen(t-5s)`[t] -0.124849`consumentenvertrouwen(t-6s)`[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
consumentenvertrouwen[t] =  -9.14325 +  0.510627vooruitz_economie[t] -0.0414924cons_prijzen_12m[t] +  0.358832fin_sit_gezinnen[t] +  0.0944583gunstig_sparen[t] +  0.0430886`consumentenvertrouwen(t-1)`[t] +  0.066497`consumentenvertrouwen(t-2)`[t] +  0.0800992`consumentenvertrouwen(t-3)`[t] +  0.0404485`consumentenvertrouwen(t-4)`[t] +  0.0500868`consumentenvertrouwen(t-1s)`[t] +  0.0211835`consumentenvertrouwen(t-2s)`[t] -0.158327`consumentenvertrouwen(t-3s)`[t] -0.077604`consumentenvertrouwen(t-4s)`[t] +  0.075934`consumentenvertrouwen(t-5s)`[t] -0.124849`consumentenvertrouwen(t-6s)`[t]  + e[t] \tabularnewline
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=286786&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]consumentenvertrouwen[t] =  -9.14325 +  0.510627vooruitz_economie[t] -0.0414924cons_prijzen_12m[t] +  0.358832fin_sit_gezinnen[t] +  0.0944583gunstig_sparen[t] +  0.0430886`consumentenvertrouwen(t-1)`[t] +  0.066497`consumentenvertrouwen(t-2)`[t] +  0.0800992`consumentenvertrouwen(t-3)`[t] +  0.0404485`consumentenvertrouwen(t-4)`[t] +  0.0500868`consumentenvertrouwen(t-1s)`[t] +  0.0211835`consumentenvertrouwen(t-2s)`[t] -0.158327`consumentenvertrouwen(t-3s)`[t] -0.077604`consumentenvertrouwen(t-4s)`[t] +  0.075934`consumentenvertrouwen(t-5s)`[t] -0.124849`consumentenvertrouwen(t-6s)`[t]  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=286786&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=286786&T=1

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


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

Multiple Linear Regression - Estimated Regression Equation
consumentenvertrouwen[t] = -9.14325 + 0.510627vooruitz_economie[t] -0.0414924cons_prijzen_12m[t] + 0.358832fin_sit_gezinnen[t] + 0.0944583gunstig_sparen[t] + 0.0430886`consumentenvertrouwen(t-1)`[t] + 0.066497`consumentenvertrouwen(t-2)`[t] + 0.0800992`consumentenvertrouwen(t-3)`[t] + 0.0404485`consumentenvertrouwen(t-4)`[t] + 0.0500868`consumentenvertrouwen(t-1s)`[t] + 0.0211835`consumentenvertrouwen(t-2s)`[t] -0.158327`consumentenvertrouwen(t-3s)`[t] -0.077604`consumentenvertrouwen(t-4s)`[t] + 0.075934`consumentenvertrouwen(t-5s)`[t] -0.124849`consumentenvertrouwen(t-6s)`[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-9.143 4.508-2.0280e+00 0.0518 0.0259
vooruitz_economie+0.5106 0.04154+1.2290e+01 5.032e-13 2.516e-13
cons_prijzen_12m-0.04149 0.02745-1.5120e+00 0.1414 0.07072
fin_sit_gezinnen+0.3588 0.1711+2.0970e+00 0.04482 0.02241
gunstig_sparen+0.09446 0.08736+1.0810e+00 0.2885 0.1442
`consumentenvertrouwen(t-1)`+0.04309 0.08701+4.9520e-01 0.6242 0.3121
`consumentenvertrouwen(t-2)`+0.0665 0.08622+7.7130e-01 0.4468 0.2234
`consumentenvertrouwen(t-3)`+0.0801 0.07883+1.0160e+00 0.318 0.159
`consumentenvertrouwen(t-4)`+0.04045 0.06216+6.5070e-01 0.5204 0.2602
`consumentenvertrouwen(t-1s)`+0.05009 0.06508+7.6960e-01 0.4478 0.2239
`consumentenvertrouwen(t-2s)`+0.02118 0.06628+3.1960e-01 0.7516 0.3758
`consumentenvertrouwen(t-3s)`-0.1583 0.07244-2.1860e+00 0.03707 0.01854
`consumentenvertrouwen(t-4s)`-0.0776 0.07298-1.0630e+00 0.2964 0.1482
`consumentenvertrouwen(t-5s)`+0.07593 0.06085+1.2480e+00 0.2221 0.111
`consumentenvertrouwen(t-6s)`-0.1249 0.07344-1.7000e+00 0.09981 0.04991

\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) & -9.143 &  4.508 & -2.0280e+00 &  0.0518 &  0.0259 \tabularnewline
vooruitz_economie & +0.5106 &  0.04154 & +1.2290e+01 &  5.032e-13 &  2.516e-13 \tabularnewline
cons_prijzen_12m & -0.04149 &  0.02745 & -1.5120e+00 &  0.1414 &  0.07072 \tabularnewline
fin_sit_gezinnen & +0.3588 &  0.1711 & +2.0970e+00 &  0.04482 &  0.02241 \tabularnewline
gunstig_sparen & +0.09446 &  0.08736 & +1.0810e+00 &  0.2885 &  0.1442 \tabularnewline
`consumentenvertrouwen(t-1)` & +0.04309 &  0.08701 & +4.9520e-01 &  0.6242 &  0.3121 \tabularnewline
`consumentenvertrouwen(t-2)` & +0.0665 &  0.08622 & +7.7130e-01 &  0.4468 &  0.2234 \tabularnewline
`consumentenvertrouwen(t-3)` & +0.0801 &  0.07883 & +1.0160e+00 &  0.318 &  0.159 \tabularnewline
`consumentenvertrouwen(t-4)` & +0.04045 &  0.06216 & +6.5070e-01 &  0.5204 &  0.2602 \tabularnewline
`consumentenvertrouwen(t-1s)` & +0.05009 &  0.06508 & +7.6960e-01 &  0.4478 &  0.2239 \tabularnewline
`consumentenvertrouwen(t-2s)` & +0.02118 &  0.06628 & +3.1960e-01 &  0.7516 &  0.3758 \tabularnewline
`consumentenvertrouwen(t-3s)` & -0.1583 &  0.07244 & -2.1860e+00 &  0.03707 &  0.01854 \tabularnewline
`consumentenvertrouwen(t-4s)` & -0.0776 &  0.07298 & -1.0630e+00 &  0.2964 &  0.1482 \tabularnewline
`consumentenvertrouwen(t-5s)` & +0.07593 &  0.06085 & +1.2480e+00 &  0.2221 &  0.111 \tabularnewline
`consumentenvertrouwen(t-6s)` & -0.1249 &  0.07344 & -1.7000e+00 &  0.09981 &  0.04991 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=286786&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]-9.143[/C][C] 4.508[/C][C]-2.0280e+00[/C][C] 0.0518[/C][C] 0.0259[/C][/ROW]
[ROW][C]vooruitz_economie[/C][C]+0.5106[/C][C] 0.04154[/C][C]+1.2290e+01[/C][C] 5.032e-13[/C][C] 2.516e-13[/C][/ROW]
[ROW][C]cons_prijzen_12m[/C][C]-0.04149[/C][C] 0.02745[/C][C]-1.5120e+00[/C][C] 0.1414[/C][C] 0.07072[/C][/ROW]
[ROW][C]fin_sit_gezinnen[/C][C]+0.3588[/C][C] 0.1711[/C][C]+2.0970e+00[/C][C] 0.04482[/C][C] 0.02241[/C][/ROW]
[ROW][C]gunstig_sparen[/C][C]+0.09446[/C][C] 0.08736[/C][C]+1.0810e+00[/C][C] 0.2885[/C][C] 0.1442[/C][/ROW]
[ROW][C]`consumentenvertrouwen(t-1)`[/C][C]+0.04309[/C][C] 0.08701[/C][C]+4.9520e-01[/C][C] 0.6242[/C][C] 0.3121[/C][/ROW]
[ROW][C]`consumentenvertrouwen(t-2)`[/C][C]+0.0665[/C][C] 0.08622[/C][C]+7.7130e-01[/C][C] 0.4468[/C][C] 0.2234[/C][/ROW]
[ROW][C]`consumentenvertrouwen(t-3)`[/C][C]+0.0801[/C][C] 0.07883[/C][C]+1.0160e+00[/C][C] 0.318[/C][C] 0.159[/C][/ROW]
[ROW][C]`consumentenvertrouwen(t-4)`[/C][C]+0.04045[/C][C] 0.06216[/C][C]+6.5070e-01[/C][C] 0.5204[/C][C] 0.2602[/C][/ROW]
[ROW][C]`consumentenvertrouwen(t-1s)`[/C][C]+0.05009[/C][C] 0.06508[/C][C]+7.6960e-01[/C][C] 0.4478[/C][C] 0.2239[/C][/ROW]
[ROW][C]`consumentenvertrouwen(t-2s)`[/C][C]+0.02118[/C][C] 0.06628[/C][C]+3.1960e-01[/C][C] 0.7516[/C][C] 0.3758[/C][/ROW]
[ROW][C]`consumentenvertrouwen(t-3s)`[/C][C]-0.1583[/C][C] 0.07244[/C][C]-2.1860e+00[/C][C] 0.03707[/C][C] 0.01854[/C][/ROW]
[ROW][C]`consumentenvertrouwen(t-4s)`[/C][C]-0.0776[/C][C] 0.07298[/C][C]-1.0630e+00[/C][C] 0.2964[/C][C] 0.1482[/C][/ROW]
[ROW][C]`consumentenvertrouwen(t-5s)`[/C][C]+0.07593[/C][C] 0.06085[/C][C]+1.2480e+00[/C][C] 0.2221[/C][C] 0.111[/C][/ROW]
[ROW][C]`consumentenvertrouwen(t-6s)`[/C][C]-0.1249[/C][C] 0.07344[/C][C]-1.7000e+00[/C][C] 0.09981[/C][C] 0.04991[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=286786&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=286786&T=2

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


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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-9.143 4.508-2.0280e+00 0.0518 0.0259
vooruitz_economie+0.5106 0.04154+1.2290e+01 5.032e-13 2.516e-13
cons_prijzen_12m-0.04149 0.02745-1.5120e+00 0.1414 0.07072
fin_sit_gezinnen+0.3588 0.1711+2.0970e+00 0.04482 0.02241
gunstig_sparen+0.09446 0.08736+1.0810e+00 0.2885 0.1442
`consumentenvertrouwen(t-1)`+0.04309 0.08701+4.9520e-01 0.6242 0.3121
`consumentenvertrouwen(t-2)`+0.0665 0.08622+7.7130e-01 0.4468 0.2234
`consumentenvertrouwen(t-3)`+0.0801 0.07883+1.0160e+00 0.318 0.159
`consumentenvertrouwen(t-4)`+0.04045 0.06216+6.5070e-01 0.5204 0.2602
`consumentenvertrouwen(t-1s)`+0.05009 0.06508+7.6960e-01 0.4478 0.2239
`consumentenvertrouwen(t-2s)`+0.02118 0.06628+3.1960e-01 0.7516 0.3758
`consumentenvertrouwen(t-3s)`-0.1583 0.07244-2.1860e+00 0.03707 0.01854
`consumentenvertrouwen(t-4s)`-0.0776 0.07298-1.0630e+00 0.2964 0.1482
`consumentenvertrouwen(t-5s)`+0.07593 0.06085+1.2480e+00 0.2221 0.111
`consumentenvertrouwen(t-6s)`-0.1249 0.07344-1.7000e+00 0.09981 0.04991






Multiple Linear Regression - Regression Statistics
Multiple R 0.9886
R-squared 0.9773
Adjusted R-squared 0.9663
F-TEST (value) 89.16
F-TEST (DF numerator)14
F-TEST (DF denominator)29
p-value 0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation 1.126
Sum Squared Residuals 36.8

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.9886 \tabularnewline
R-squared &  0.9773 \tabularnewline
Adjusted R-squared &  0.9663 \tabularnewline
F-TEST (value) &  89.16 \tabularnewline
F-TEST (DF numerator) & 14 \tabularnewline
F-TEST (DF denominator) & 29 \tabularnewline
p-value &  0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  1.126 \tabularnewline
Sum Squared Residuals &  36.8 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=286786&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.9886[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.9773[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.9663[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 89.16[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]14[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]29[/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] 1.126[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 36.8[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=286786&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=286786&T=3

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


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

Multiple Linear Regression - Regression Statistics
Multiple R 0.9886
R-squared 0.9773
Adjusted R-squared 0.9663
F-TEST (value) 89.16
F-TEST (DF numerator)14
F-TEST (DF denominator)29
p-value 0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation 1.126
Sum Squared Residuals 36.8






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1-12-11.52-0.4756
2-10-12.33 2.329
3-10-9.385-0.6153
4-13-11.39-1.611
5-16-14.57-1.432
6-14-14.36 0.3594
7-17-17.39 0.3872
8-24-24.94 0.9355
9-25-23.99-1.013
10-23-23.1 0.1031
11-17-16.91-0.0946
12-24-22.58-1.424
13-20-19.95-0.05336
14-19-19.19 0.1884
15-18-17.83-0.1683
16-16-17.3 1.297
17-12-12.43 0.4329
18-7-6.491-0.5085
19-6-6.418 0.418
20-6-6.377 0.3768
21-5-7.094 2.094
22-4-2.287-1.713
23-4-4.334 0.3336
24-8-6.759-1.241
25-9-7.86-1.14
26-6-7.671 1.671
27-7-6.641-0.3586
28-10-8.843-1.157
29-11-10.76-0.2375
30-11-10.7-0.2969
31-12-12.47 0.4735
32-14-14.2 0.1969
33-12-11.9-0.1009
34-9-8.878-0.1216
35-5-4.24-0.7595
36-6-6.676 0.6763
37-6-5.372-0.6283
38-3-3.313 0.3134
39-2-2.148 0.1478
40-6-5.507-0.4931
41-6-6.577 0.577
42-10-10.57 0.5733
43-8-8.423 0.423
44-4-5.336 1.336

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & -12 & -11.52 & -0.4756 \tabularnewline
2 & -10 & -12.33 &  2.329 \tabularnewline
3 & -10 & -9.385 & -0.6153 \tabularnewline
4 & -13 & -11.39 & -1.611 \tabularnewline
5 & -16 & -14.57 & -1.432 \tabularnewline
6 & -14 & -14.36 &  0.3594 \tabularnewline
7 & -17 & -17.39 &  0.3872 \tabularnewline
8 & -24 & -24.94 &  0.9355 \tabularnewline
9 & -25 & -23.99 & -1.013 \tabularnewline
10 & -23 & -23.1 &  0.1031 \tabularnewline
11 & -17 & -16.91 & -0.0946 \tabularnewline
12 & -24 & -22.58 & -1.424 \tabularnewline
13 & -20 & -19.95 & -0.05336 \tabularnewline
14 & -19 & -19.19 &  0.1884 \tabularnewline
15 & -18 & -17.83 & -0.1683 \tabularnewline
16 & -16 & -17.3 &  1.297 \tabularnewline
17 & -12 & -12.43 &  0.4329 \tabularnewline
18 & -7 & -6.491 & -0.5085 \tabularnewline
19 & -6 & -6.418 &  0.418 \tabularnewline
20 & -6 & -6.377 &  0.3768 \tabularnewline
21 & -5 & -7.094 &  2.094 \tabularnewline
22 & -4 & -2.287 & -1.713 \tabularnewline
23 & -4 & -4.334 &  0.3336 \tabularnewline
24 & -8 & -6.759 & -1.241 \tabularnewline
25 & -9 & -7.86 & -1.14 \tabularnewline
26 & -6 & -7.671 &  1.671 \tabularnewline
27 & -7 & -6.641 & -0.3586 \tabularnewline
28 & -10 & -8.843 & -1.157 \tabularnewline
29 & -11 & -10.76 & -0.2375 \tabularnewline
30 & -11 & -10.7 & -0.2969 \tabularnewline
31 & -12 & -12.47 &  0.4735 \tabularnewline
32 & -14 & -14.2 &  0.1969 \tabularnewline
33 & -12 & -11.9 & -0.1009 \tabularnewline
34 & -9 & -8.878 & -0.1216 \tabularnewline
35 & -5 & -4.24 & -0.7595 \tabularnewline
36 & -6 & -6.676 &  0.6763 \tabularnewline
37 & -6 & -5.372 & -0.6283 \tabularnewline
38 & -3 & -3.313 &  0.3134 \tabularnewline
39 & -2 & -2.148 &  0.1478 \tabularnewline
40 & -6 & -5.507 & -0.4931 \tabularnewline
41 & -6 & -6.577 &  0.577 \tabularnewline
42 & -10 & -10.57 &  0.5733 \tabularnewline
43 & -8 & -8.423 &  0.423 \tabularnewline
44 & -4 & -5.336 &  1.336 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=286786&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[/C][C]-11.52[/C][C]-0.4756[/C][/ROW]
[ROW][C]2[/C][C]-10[/C][C]-12.33[/C][C] 2.329[/C][/ROW]
[ROW][C]3[/C][C]-10[/C][C]-9.385[/C][C]-0.6153[/C][/ROW]
[ROW][C]4[/C][C]-13[/C][C]-11.39[/C][C]-1.611[/C][/ROW]
[ROW][C]5[/C][C]-16[/C][C]-14.57[/C][C]-1.432[/C][/ROW]
[ROW][C]6[/C][C]-14[/C][C]-14.36[/C][C] 0.3594[/C][/ROW]
[ROW][C]7[/C][C]-17[/C][C]-17.39[/C][C] 0.3872[/C][/ROW]
[ROW][C]8[/C][C]-24[/C][C]-24.94[/C][C] 0.9355[/C][/ROW]
[ROW][C]9[/C][C]-25[/C][C]-23.99[/C][C]-1.013[/C][/ROW]
[ROW][C]10[/C][C]-23[/C][C]-23.1[/C][C] 0.1031[/C][/ROW]
[ROW][C]11[/C][C]-17[/C][C]-16.91[/C][C]-0.0946[/C][/ROW]
[ROW][C]12[/C][C]-24[/C][C]-22.58[/C][C]-1.424[/C][/ROW]
[ROW][C]13[/C][C]-20[/C][C]-19.95[/C][C]-0.05336[/C][/ROW]
[ROW][C]14[/C][C]-19[/C][C]-19.19[/C][C] 0.1884[/C][/ROW]
[ROW][C]15[/C][C]-18[/C][C]-17.83[/C][C]-0.1683[/C][/ROW]
[ROW][C]16[/C][C]-16[/C][C]-17.3[/C][C] 1.297[/C][/ROW]
[ROW][C]17[/C][C]-12[/C][C]-12.43[/C][C] 0.4329[/C][/ROW]
[ROW][C]18[/C][C]-7[/C][C]-6.491[/C][C]-0.5085[/C][/ROW]
[ROW][C]19[/C][C]-6[/C][C]-6.418[/C][C] 0.418[/C][/ROW]
[ROW][C]20[/C][C]-6[/C][C]-6.377[/C][C] 0.3768[/C][/ROW]
[ROW][C]21[/C][C]-5[/C][C]-7.094[/C][C] 2.094[/C][/ROW]
[ROW][C]22[/C][C]-4[/C][C]-2.287[/C][C]-1.713[/C][/ROW]
[ROW][C]23[/C][C]-4[/C][C]-4.334[/C][C] 0.3336[/C][/ROW]
[ROW][C]24[/C][C]-8[/C][C]-6.759[/C][C]-1.241[/C][/ROW]
[ROW][C]25[/C][C]-9[/C][C]-7.86[/C][C]-1.14[/C][/ROW]
[ROW][C]26[/C][C]-6[/C][C]-7.671[/C][C] 1.671[/C][/ROW]
[ROW][C]27[/C][C]-7[/C][C]-6.641[/C][C]-0.3586[/C][/ROW]
[ROW][C]28[/C][C]-10[/C][C]-8.843[/C][C]-1.157[/C][/ROW]
[ROW][C]29[/C][C]-11[/C][C]-10.76[/C][C]-0.2375[/C][/ROW]
[ROW][C]30[/C][C]-11[/C][C]-10.7[/C][C]-0.2969[/C][/ROW]
[ROW][C]31[/C][C]-12[/C][C]-12.47[/C][C] 0.4735[/C][/ROW]
[ROW][C]32[/C][C]-14[/C][C]-14.2[/C][C] 0.1969[/C][/ROW]
[ROW][C]33[/C][C]-12[/C][C]-11.9[/C][C]-0.1009[/C][/ROW]
[ROW][C]34[/C][C]-9[/C][C]-8.878[/C][C]-0.1216[/C][/ROW]
[ROW][C]35[/C][C]-5[/C][C]-4.24[/C][C]-0.7595[/C][/ROW]
[ROW][C]36[/C][C]-6[/C][C]-6.676[/C][C] 0.6763[/C][/ROW]
[ROW][C]37[/C][C]-6[/C][C]-5.372[/C][C]-0.6283[/C][/ROW]
[ROW][C]38[/C][C]-3[/C][C]-3.313[/C][C] 0.3134[/C][/ROW]
[ROW][C]39[/C][C]-2[/C][C]-2.148[/C][C] 0.1478[/C][/ROW]
[ROW][C]40[/C][C]-6[/C][C]-5.507[/C][C]-0.4931[/C][/ROW]
[ROW][C]41[/C][C]-6[/C][C]-6.577[/C][C] 0.577[/C][/ROW]
[ROW][C]42[/C][C]-10[/C][C]-10.57[/C][C] 0.5733[/C][/ROW]
[ROW][C]43[/C][C]-8[/C][C]-8.423[/C][C] 0.423[/C][/ROW]
[ROW][C]44[/C][C]-4[/C][C]-5.336[/C][C] 1.336[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=286786&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=286786&T=4

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


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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1-12-11.52-0.4756
2-10-12.33 2.329
3-10-9.385-0.6153
4-13-11.39-1.611
5-16-14.57-1.432
6-14-14.36 0.3594
7-17-17.39 0.3872
8-24-24.94 0.9355
9-25-23.99-1.013
10-23-23.1 0.1031
11-17-16.91-0.0946
12-24-22.58-1.424
13-20-19.95-0.05336
14-19-19.19 0.1884
15-18-17.83-0.1683
16-16-17.3 1.297
17-12-12.43 0.4329
18-7-6.491-0.5085
19-6-6.418 0.418
20-6-6.377 0.3768
21-5-7.094 2.094
22-4-2.287-1.713
23-4-4.334 0.3336
24-8-6.759-1.241
25-9-7.86-1.14
26-6-7.671 1.671
27-7-6.641-0.3586
28-10-8.843-1.157
29-11-10.76-0.2375
30-11-10.7-0.2969
31-12-12.47 0.4735
32-14-14.2 0.1969
33-12-11.9-0.1009
34-9-8.878-0.1216
35-5-4.24-0.7595
36-6-6.676 0.6763
37-6-5.372-0.6283
38-3-3.313 0.3134
39-2-2.148 0.1478
40-6-5.507-0.4931
41-6-6.577 0.577
42-10-10.57 0.5733
43-8-8.423 0.423
44-4-5.336 1.336






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
18 0.9096 0.1809 0.09044
19 0.8542 0.2915 0.1458
20 0.7737 0.4526 0.2263
21 0.8502 0.2995 0.1498
22 0.9308 0.1384 0.06918
23 0.9109 0.1781 0.08907
24 0.8193 0.3615 0.1807
25 0.7132 0.5736 0.2868
26 0.7306 0.5388 0.2694

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
18 &  0.9096 &  0.1809 &  0.09044 \tabularnewline
19 &  0.8542 &  0.2915 &  0.1458 \tabularnewline
20 &  0.7737 &  0.4526 &  0.2263 \tabularnewline
21 &  0.8502 &  0.2995 &  0.1498 \tabularnewline
22 &  0.9308 &  0.1384 &  0.06918 \tabularnewline
23 &  0.9109 &  0.1781 &  0.08907 \tabularnewline
24 &  0.8193 &  0.3615 &  0.1807 \tabularnewline
25 &  0.7132 &  0.5736 &  0.2868 \tabularnewline
26 &  0.7306 &  0.5388 &  0.2694 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=286786&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]18[/C][C] 0.9096[/C][C] 0.1809[/C][C] 0.09044[/C][/ROW]
[ROW][C]19[/C][C] 0.8542[/C][C] 0.2915[/C][C] 0.1458[/C][/ROW]
[ROW][C]20[/C][C] 0.7737[/C][C] 0.4526[/C][C] 0.2263[/C][/ROW]
[ROW][C]21[/C][C] 0.8502[/C][C] 0.2995[/C][C] 0.1498[/C][/ROW]
[ROW][C]22[/C][C] 0.9308[/C][C] 0.1384[/C][C] 0.06918[/C][/ROW]
[ROW][C]23[/C][C] 0.9109[/C][C] 0.1781[/C][C] 0.08907[/C][/ROW]
[ROW][C]24[/C][C] 0.8193[/C][C] 0.3615[/C][C] 0.1807[/C][/ROW]
[ROW][C]25[/C][C] 0.7132[/C][C] 0.5736[/C][C] 0.2868[/C][/ROW]
[ROW][C]26[/C][C] 0.7306[/C][C] 0.5388[/C][C] 0.2694[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=286786&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=286786&T=5

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


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
18 0.9096 0.1809 0.09044
19 0.8542 0.2915 0.1458
20 0.7737 0.4526 0.2263
21 0.8502 0.2995 0.1498
22 0.9308 0.1384 0.06918
23 0.9109 0.1781 0.08907
24 0.8193 0.3615 0.1807
25 0.7132 0.5736 0.2868
26 0.7306 0.5388 0.2694






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=286786&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]0[/C][C]0[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=286786&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=286786&T=6

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


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

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


Figures (Output of Computation)

Figure 1
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Figure 10
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Input Parameters & R Code

Parameters (Session):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ; par4 = 4 ; par5 = 2 ;
Parameters (R input):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ; par4 = 4 ; par5 = 2 ;
R code (references can be found in the software module):
par5 <- '6'
par4 <- '4'
par3 <- 'No Linear Trend'
par2 <- 'Do not include Seasonal Dummies'
par1 <- '1'
library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
mywarning <- ''
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 (par5=='') par5 <- 0
par5 <- as.numeric(par5)
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=12)'){
(n <- n - 12)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B12)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+12,j] - x[i,j]
}
}
x <- x2
}
if (par3 == 'First and Seasonal Differences (s=12)'){
(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 - 12)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B12)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+12,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*12,par5), dimnames=list(1:(n-par5*12), paste(colnames(x)[par1],'(t-',1:par5,'s)',sep='')))
for (i in 1:(n-par5*12)) {
for (j in 1:par5) {
x2[i,j] <- x[i+par5*12-j*12,par1]
}
}
x <- cbind(x[(par5*12+1):n,], x2)
n <- n - par5*12
}
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'
}
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$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, mywarning)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,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$sigma,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a,formatC(signif(sum(myerror*myerror),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
if(n < 200) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,formatC(signif(x[i],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(x[i]-mysum$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')
}
}