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R Software Module

rwasp_multipleregression.wasp

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Multiple Regression

Date of computation

Wed, 09 Dec 2015 09:39:49 +0000

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2015/Dec/09/t1449654504ycc7wf366rq6gs2.htm/, Retrieved Thu, 16 May 2024 15:06:00 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=285589, Retrieved Thu, 16 May 2024 15:06:00 +0000

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123

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-       [Multiple Regression] [Multiple regression ] [2015-12-09 09:39:49] [c17cb904c35dabaa75a7864f3bc4b0f8] [Current]

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6.5 2.3 80.8
6.8 1.9 83.7
6.8 0.6 94.2
6.5 0.6 86.2
6.2 -0.4 89
6.2 -1.1 94.7
6.6 -1.7 81.9
6.7 -0.8 80.2
6.5 -1.2 96.5
6.4 -1 95.6
6.5 -0.1 91.9
6.8 0.3 89.9
7.1 0.6 86.5
7.2 0.7 94.6
7.1 1.7 107.1
7 1.8 98.3
6.9 2.3 94.6
6.9 2.5 111.1
7.4 2.6 91.7
7.3 2.3 91.3
7 2.9 110.7
6.8 3 106.4
6.5 2.9 105.1
6.4 3.1 102.6
6.3 3.2 97.5
6 3.4 103.7
5.9 3.5 124.5
5.7 3.4 103.8
5.7 3.4 111.8
5.7 3.7 108.4
6.2 3.8 91.7
6.4 3.6 100.9
6.2 3.6 114.6
6.2 3.6 106.6
6.1 3.9 103.5
6.1 3.5 101.3
6.2 3.7 97.6
6.1 3.7 100.7
6.1 3.4 118.2
6.2 3.2 98.6
6.2 2.8 101.5
6.2 2.3 109.8
6.4 2.3 96.8
6.4 2.9 97.2
6.4 2.8 107
6.7 2.8 111.3
6.9 2.3 104.6
7.1 2.2 98.7
7.3 1.5 97
7.2 1.2 95.5
7.1 1.1 107.7
6.9 1 106.9
6.8 1.2 105.5
6.7 1.6 110
7.2 1.5 103.4
7.2 1 92.8
7.1 0.9 109
7.1 0.6 115.1
7 0.8 105.4
7.1 1 102.3
7.3 1.1 100.4
7.2 1 103.3
7.1 0.9 111.3
7 0.6 109.9
6.9 0.4 106.7
7 0.3 114.3
7.5 0.3 101.5
7.6 0 92.5
7.5 -0.1 119
7.3 0.1 117
7.3 -0.1 105.3
7.4 -0.4 105.5
7.7 -0.7 100.4
7.8 -0.4 98.6
7.7 -0.4 118.5
7.5 0.3 110.1
7.3 0.6 102.8
7.3 0.6 116.5
7.6 0.5 100.5
7.6 0.9 96.8

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	5 seconds
R Server	'Sir Ronald Aylmer Fisher' @ fisher.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 & 'Sir Ronald Aylmer Fisher' @ fisher.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=285589&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]'Sir Ronald Aylmer Fisher' @ fisher.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=285589&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=285589&T=0

As an alternative you can also use a QR Code:

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

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	5 seconds
R Server	'Sir Ronald Aylmer Fisher' @ fisher.wessa.net

Multiple Linear Regression - Estimated Regression Equation
werkloosheid[t] = + 3.90231 -0.0315544inflatie[t] -0.0032082industrie[t] + 1.03974`werkloosheid(t-1)`[t] -0.290611`werkloosheid(t-2)`[t] -0.364461`werkloosheid(t-3)`[t] + 0.114798`werkloosheid(t-4)`[t] + 0.0510378`werkloosheid(t-5)`[t] -0.0522447`werkloosheid(t-1s)`[t] -0.0855007`werkloosheid(t-2s)`[t] + 0.0439465M1[t] + 0.393746M2[t] + 0.00926968M3[t] -0.00843813M4[t] + 0.286618M5[t] + 0.163451M6[t] + 0.187576M7[t] + 0.286809M8[t] + 0.0311455M9[t] + 0.119348M10[t] + 0.0930027M11[t] + 0.0140002t + e[t]
Warning: you did not specify the column number of the endogenous series! The first column was selected by default.

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
werkloosheid[t] =  +  3.90231 -0.0315544inflatie[t] -0.0032082industrie[t] +  1.03974`werkloosheid(t-1)`[t] -0.290611`werkloosheid(t-2)`[t] -0.364461`werkloosheid(t-3)`[t] +  0.114798`werkloosheid(t-4)`[t] +  0.0510378`werkloosheid(t-5)`[t] -0.0522447`werkloosheid(t-1s)`[t] -0.0855007`werkloosheid(t-2s)`[t] +  0.0439465M1[t] +  0.393746M2[t] +  0.00926968M3[t] -0.00843813M4[t] +  0.286618M5[t] +  0.163451M6[t] +  0.187576M7[t] +  0.286809M8[t] +  0.0311455M9[t] +  0.119348M10[t] +  0.0930027M11[t] +  0.0140002t  + e[t] \tabularnewline
Warning: you did not specify the column number of the endogenous series! The first column was selected by default. \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=285589&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]werkloosheid[t] =  +  3.90231 -0.0315544inflatie[t] -0.0032082industrie[t] +  1.03974`werkloosheid(t-1)`[t] -0.290611`werkloosheid(t-2)`[t] -0.364461`werkloosheid(t-3)`[t] +  0.114798`werkloosheid(t-4)`[t] +  0.0510378`werkloosheid(t-5)`[t] -0.0522447`werkloosheid(t-1s)`[t] -0.0855007`werkloosheid(t-2s)`[t] +  0.0439465M1[t] +  0.393746M2[t] +  0.00926968M3[t] -0.00843813M4[t] +  0.286618M5[t] +  0.163451M6[t] +  0.187576M7[t] +  0.286809M8[t] +  0.0311455M9[t] +  0.119348M10[t] +  0.0930027M11[t] +  0.0140002t  + e[t][/C][/ROW]
[ROW][C]Warning: you did not specify the column number of the endogenous series! The first column was selected by default.[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=285589&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=285589&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
werkloosheid[t] = + 3.90231 -0.0315544inflatie[t] -0.0032082industrie[t] + 1.03974`werkloosheid(t-1)`[t] -0.290611`werkloosheid(t-2)`[t] -0.364461`werkloosheid(t-3)`[t] + 0.114798`werkloosheid(t-4)`[t] + 0.0510378`werkloosheid(t-5)`[t] -0.0522447`werkloosheid(t-1s)`[t] -0.0855007`werkloosheid(t-2s)`[t] + 0.0439465M1[t] + 0.393746M2[t] + 0.00926968M3[t] -0.00843813M4[t] + 0.286618M5[t] + 0.163451M6[t] + 0.187576M7[t] + 0.286809M8[t] + 0.0311455M9[t] + 0.119348M10[t] + 0.0930027M11[t] + 0.0140002t + e[t]
Warning: you did not specify the column number of the endogenous series! The first column was selected by default.

Multiple Linear Regression - Ordinary Least Squares
Variable	Parameter	S.D.	T-STATH0: parameter = 0	2-tail p-value	1-tail p-value
(Intercept)	+3.902	1.52	+2.5670e+00	0.0157	0.007849
inflatie	-0.03155	0.03372	-9.3580e-01	0.3571	0.1786
industrie	-0.003208	0.004435	-7.2340e-01	0.4752	0.2376
`werkloosheid(t-1)`	+1.04	0.1975	+5.2650e+00	1.215e-05	6.073e-06
`werkloosheid(t-2)`	-0.2906	0.2708	-1.0730e+00	0.2921	0.146
`werkloosheid(t-3)`	-0.3645	0.2588	-1.4080e+00	0.1696	0.08482
`werkloosheid(t-4)`	+0.1148	0.2666	+4.3060e-01	0.6699	0.335
`werkloosheid(t-5)`	+0.05104	0.1729	+2.9520e-01	0.7699	0.385
`werkloosheid(t-1s)`	-0.05224	0.03854	-1.3560e+00	0.1857	0.09284
`werkloosheid(t-2s)`	-0.0855	0.05397	-1.5840e+00	0.124	0.06198
M1	+0.04395	0.08475	+5.1850e-01	0.608	0.304
M2	+0.3937	0.07981	+4.9340e+00	3.05e-05	1.525e-05
M3	+0.00927	0.1095	+8.4700e-02	0.9331	0.4665
M4	-0.008438	0.1144	-7.3750e-02	0.9417	0.4709
M5	+0.2866	0.118	+2.4300e+00	0.02154	0.01077
M6	+0.1635	0.08515	+1.9190e+00	0.06482	0.03241
M7	+0.1876	0.0763	+2.4590e+00	0.02016	0.01008
M8	+0.2868	0.09335	+3.0720e+00	0.004586	0.002293
M9	+0.03115	0.09081	+3.4300e-01	0.7341	0.367
M10	+0.1193	0.1021	+1.1690e+00	0.2521	0.126
M11	+0.093	0.0813	+1.1440e+00	0.262	0.131
t	+0.014	0.007092	+1.9740e+00	0.05795	0.02898

\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) & +3.902 &  1.52 & +2.5670e+00 &  0.0157 &  0.007849 \tabularnewline
inflatie & -0.03155 &  0.03372 & -9.3580e-01 &  0.3571 &  0.1786 \tabularnewline
industrie & -0.003208 &  0.004435 & -7.2340e-01 &  0.4752 &  0.2376 \tabularnewline
`werkloosheid(t-1)` & +1.04 &  0.1975 & +5.2650e+00 &  1.215e-05 &  6.073e-06 \tabularnewline
`werkloosheid(t-2)` & -0.2906 &  0.2708 & -1.0730e+00 &  0.2921 &  0.146 \tabularnewline
`werkloosheid(t-3)` & -0.3645 &  0.2588 & -1.4080e+00 &  0.1696 &  0.08482 \tabularnewline
`werkloosheid(t-4)` & +0.1148 &  0.2666 & +4.3060e-01 &  0.6699 &  0.335 \tabularnewline
`werkloosheid(t-5)` & +0.05104 &  0.1729 & +2.9520e-01 &  0.7699 &  0.385 \tabularnewline
`werkloosheid(t-1s)` & -0.05224 &  0.03854 & -1.3560e+00 &  0.1857 &  0.09284 \tabularnewline
`werkloosheid(t-2s)` & -0.0855 &  0.05397 & -1.5840e+00 &  0.124 &  0.06198 \tabularnewline
M1 & +0.04395 &  0.08475 & +5.1850e-01 &  0.608 &  0.304 \tabularnewline
M2 & +0.3937 &  0.07981 & +4.9340e+00 &  3.05e-05 &  1.525e-05 \tabularnewline
M3 & +0.00927 &  0.1095 & +8.4700e-02 &  0.9331 &  0.4665 \tabularnewline
M4 & -0.008438 &  0.1144 & -7.3750e-02 &  0.9417 &  0.4709 \tabularnewline
M5 & +0.2866 &  0.118 & +2.4300e+00 &  0.02154 &  0.01077 \tabularnewline
M6 & +0.1635 &  0.08515 & +1.9190e+00 &  0.06482 &  0.03241 \tabularnewline
M7 & +0.1876 &  0.0763 & +2.4590e+00 &  0.02016 &  0.01008 \tabularnewline
M8 & +0.2868 &  0.09335 & +3.0720e+00 &  0.004586 &  0.002293 \tabularnewline
M9 & +0.03115 &  0.09081 & +3.4300e-01 &  0.7341 &  0.367 \tabularnewline
M10 & +0.1193 &  0.1021 & +1.1690e+00 &  0.2521 &  0.126 \tabularnewline
M11 & +0.093 &  0.0813 & +1.1440e+00 &  0.262 &  0.131 \tabularnewline
t & +0.014 &  0.007092 & +1.9740e+00 &  0.05795 &  0.02898 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=285589&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]+3.902[/C][C] 1.52[/C][C]+2.5670e+00[/C][C] 0.0157[/C][C] 0.007849[/C][/ROW]
[ROW][C]inflatie[/C][C]-0.03155[/C][C] 0.03372[/C][C]-9.3580e-01[/C][C] 0.3571[/C][C] 0.1786[/C][/ROW]
[ROW][C]industrie[/C][C]-0.003208[/C][C] 0.004435[/C][C]-7.2340e-01[/C][C] 0.4752[/C][C] 0.2376[/C][/ROW]
[ROW][C]`werkloosheid(t-1)`[/C][C]+1.04[/C][C] 0.1975[/C][C]+5.2650e+00[/C][C] 1.215e-05[/C][C] 6.073e-06[/C][/ROW]
[ROW][C]`werkloosheid(t-2)`[/C][C]-0.2906[/C][C] 0.2708[/C][C]-1.0730e+00[/C][C] 0.2921[/C][C] 0.146[/C][/ROW]
[ROW][C]`werkloosheid(t-3)`[/C][C]-0.3645[/C][C] 0.2588[/C][C]-1.4080e+00[/C][C] 0.1696[/C][C] 0.08482[/C][/ROW]
[ROW][C]`werkloosheid(t-4)`[/C][C]+0.1148[/C][C] 0.2666[/C][C]+4.3060e-01[/C][C] 0.6699[/C][C] 0.335[/C][/ROW]
[ROW][C]`werkloosheid(t-5)`[/C][C]+0.05104[/C][C] 0.1729[/C][C]+2.9520e-01[/C][C] 0.7699[/C][C] 0.385[/C][/ROW]
[ROW][C]`werkloosheid(t-1s)`[/C][C]-0.05224[/C][C] 0.03854[/C][C]-1.3560e+00[/C][C] 0.1857[/C][C] 0.09284[/C][/ROW]
[ROW][C]`werkloosheid(t-2s)`[/C][C]-0.0855[/C][C] 0.05397[/C][C]-1.5840e+00[/C][C] 0.124[/C][C] 0.06198[/C][/ROW]
[ROW][C]M1[/C][C]+0.04395[/C][C] 0.08475[/C][C]+5.1850e-01[/C][C] 0.608[/C][C] 0.304[/C][/ROW]
[ROW][C]M2[/C][C]+0.3937[/C][C] 0.07981[/C][C]+4.9340e+00[/C][C] 3.05e-05[/C][C] 1.525e-05[/C][/ROW]
[ROW][C]M3[/C][C]+0.00927[/C][C] 0.1095[/C][C]+8.4700e-02[/C][C] 0.9331[/C][C] 0.4665[/C][/ROW]
[ROW][C]M4[/C][C]-0.008438[/C][C] 0.1144[/C][C]-7.3750e-02[/C][C] 0.9417[/C][C] 0.4709[/C][/ROW]
[ROW][C]M5[/C][C]+0.2866[/C][C] 0.118[/C][C]+2.4300e+00[/C][C] 0.02154[/C][C] 0.01077[/C][/ROW]
[ROW][C]M6[/C][C]+0.1635[/C][C] 0.08515[/C][C]+1.9190e+00[/C][C] 0.06482[/C][C] 0.03241[/C][/ROW]
[ROW][C]M7[/C][C]+0.1876[/C][C] 0.0763[/C][C]+2.4590e+00[/C][C] 0.02016[/C][C] 0.01008[/C][/ROW]
[ROW][C]M8[/C][C]+0.2868[/C][C] 0.09335[/C][C]+3.0720e+00[/C][C] 0.004586[/C][C] 0.002293[/C][/ROW]
[ROW][C]M9[/C][C]+0.03115[/C][C] 0.09081[/C][C]+3.4300e-01[/C][C] 0.7341[/C][C] 0.367[/C][/ROW]
[ROW][C]M10[/C][C]+0.1193[/C][C] 0.1021[/C][C]+1.1690e+00[/C][C] 0.2521[/C][C] 0.126[/C][/ROW]
[ROW][C]M11[/C][C]+0.093[/C][C] 0.0813[/C][C]+1.1440e+00[/C][C] 0.262[/C][C] 0.131[/C][/ROW]
[ROW][C]t[/C][C]+0.014[/C][C] 0.007092[/C][C]+1.9740e+00[/C][C] 0.05795[/C][C] 0.02898[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=285589&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=285589&T=2

As an alternative you can also use a QR Code:

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

Multiple Linear Regression - Ordinary Least Squares
Variable	Parameter	S.D.	T-STATH0: parameter = 0	2-tail p-value	1-tail p-value
(Intercept)	+3.902	1.52	+2.5670e+00	0.0157	0.007849
inflatie	-0.03155	0.03372	-9.3580e-01	0.3571	0.1786
industrie	-0.003208	0.004435	-7.2340e-01	0.4752	0.2376
`werkloosheid(t-1)`	+1.04	0.1975	+5.2650e+00	1.215e-05	6.073e-06
`werkloosheid(t-2)`	-0.2906	0.2708	-1.0730e+00	0.2921	0.146
`werkloosheid(t-3)`	-0.3645	0.2588	-1.4080e+00	0.1696	0.08482
`werkloosheid(t-4)`	+0.1148	0.2666	+4.3060e-01	0.6699	0.335
`werkloosheid(t-5)`	+0.05104	0.1729	+2.9520e-01	0.7699	0.385
`werkloosheid(t-1s)`	-0.05224	0.03854	-1.3560e+00	0.1857	0.09284
`werkloosheid(t-2s)`	-0.0855	0.05397	-1.5840e+00	0.124	0.06198
M1	+0.04395	0.08475	+5.1850e-01	0.608	0.304
M2	+0.3937	0.07981	+4.9340e+00	3.05e-05	1.525e-05
M3	+0.00927	0.1095	+8.4700e-02	0.9331	0.4665
M4	-0.008438	0.1144	-7.3750e-02	0.9417	0.4709
M5	+0.2866	0.118	+2.4300e+00	0.02154	0.01077
M6	+0.1635	0.08515	+1.9190e+00	0.06482	0.03241
M7	+0.1876	0.0763	+2.4590e+00	0.02016	0.01008
M8	+0.2868	0.09335	+3.0720e+00	0.004586	0.002293
M9	+0.03115	0.09081	+3.4300e-01	0.7341	0.367
M10	+0.1193	0.1021	+1.1690e+00	0.2521	0.126
M11	+0.093	0.0813	+1.1440e+00	0.262	0.131
t	+0.014	0.007092	+1.9740e+00	0.05795	0.02898

Multiple Linear Regression - Regression Statistics
Multiple R	0.9916
R-squared	0.9833
Adjusted R-squared	0.9712
F-TEST (value)	81.25
F-TEST (DF numerator)	21
F-TEST (DF denominator)	29
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.09297
Sum Squared Residuals	0.2507

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.9916 \tabularnewline
R-squared &  0.9833 \tabularnewline
Adjusted R-squared &  0.9712 \tabularnewline
F-TEST (value) &  81.25 \tabularnewline
F-TEST (DF numerator) & 21 \tabularnewline
F-TEST (DF denominator) & 29 \tabularnewline
p-value &  0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  0.09297 \tabularnewline
Sum Squared Residuals &  0.2507 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=285589&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.9916[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.9833[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.9712[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 81.25[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]21[/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] 0.09297[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 0.2507[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=285589&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=285589&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	0.9916
R-squared	0.9833
Adjusted R-squared	0.9712
F-TEST (value)	81.25
F-TEST (DF numerator)	21
F-TEST (DF denominator)	29
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.09297
Sum Squared Residuals	0.2507

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	5.7	5.735	-0.0352
2	6.2	6.135	0.0648
3	6.4	6.23	0.17
4	6.2	6.268	-0.06755
5	6.2	6.173	0.02703
6	6.1	6.114	-0.01403
7	6.1	6.169	-0.06881
8	6.2	6.283	-0.08348
9	6.1	6.169	-0.06921
10	6.1	6.094	0.006006
11	6.2	6.157	0.04265
12	6.2	6.242	-0.04212
13	6.2	6.254	-0.05377
14	6.4	6.549	-0.1489
15	6.4	6.376	0.0243
16	6.4	6.327	0.07322
17	6.7	6.566	0.1337
18	6.9	6.86	0.03988
19	7.1	7.06	0.04015
20	7.3	7.244	0.05556
21	7.2	7.159	0.0407
22	7.1	7.037	0.06265
23	6.9	6.928	-0.02796
24	6.8	6.738	0.06213
25	6.7	6.758	-0.05808
26	7.2	7.074	0.1256
27	7.2	7.294	-0.09393
28	7.1	7.128	-0.02795
29	7.1	7.108	-0.008445
30	7	7.104	-0.1035
31	7.1	7.093	0.007155
32	7.3	7.312	-0.01158
33	7.2	7.288	-0.08777
34	7.1	7.163	-0.06266
35	7	7.025	-0.02474
36	6.9	6.957	-0.05714
37	7	6.959	0.04065
38	7.5	7.474	0.02611
39	7.6	7.652	-0.05242
40	7.5	7.478	0.02228
41	7.3	7.452	-0.1523
42	7.3	7.222	0.07769
43	7.4	7.378	0.0215
44	7.7	7.661	0.03949
45	7.8	7.684	0.1163
46	7.7	7.706	-0.005995
47	7.5	7.49	0.01006
48	7.3	7.263	0.03712
49	7.3	7.194	0.1064
50	7.6	7.668	-0.06766
51	7.6	7.648	-0.04796

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 &  5.7 &  5.735 & -0.0352 \tabularnewline
2 &  6.2 &  6.135 &  0.0648 \tabularnewline
3 &  6.4 &  6.23 &  0.17 \tabularnewline
4 &  6.2 &  6.268 & -0.06755 \tabularnewline
5 &  6.2 &  6.173 &  0.02703 \tabularnewline
6 &  6.1 &  6.114 & -0.01403 \tabularnewline
7 &  6.1 &  6.169 & -0.06881 \tabularnewline
8 &  6.2 &  6.283 & -0.08348 \tabularnewline
9 &  6.1 &  6.169 & -0.06921 \tabularnewline
10 &  6.1 &  6.094 &  0.006006 \tabularnewline
11 &  6.2 &  6.157 &  0.04265 \tabularnewline
12 &  6.2 &  6.242 & -0.04212 \tabularnewline
13 &  6.2 &  6.254 & -0.05377 \tabularnewline
14 &  6.4 &  6.549 & -0.1489 \tabularnewline
15 &  6.4 &  6.376 &  0.0243 \tabularnewline
16 &  6.4 &  6.327 &  0.07322 \tabularnewline
17 &  6.7 &  6.566 &  0.1337 \tabularnewline
18 &  6.9 &  6.86 &  0.03988 \tabularnewline
19 &  7.1 &  7.06 &  0.04015 \tabularnewline
20 &  7.3 &  7.244 &  0.05556 \tabularnewline
21 &  7.2 &  7.159 &  0.0407 \tabularnewline
22 &  7.1 &  7.037 &  0.06265 \tabularnewline
23 &  6.9 &  6.928 & -0.02796 \tabularnewline
24 &  6.8 &  6.738 &  0.06213 \tabularnewline
25 &  6.7 &  6.758 & -0.05808 \tabularnewline
26 &  7.2 &  7.074 &  0.1256 \tabularnewline
27 &  7.2 &  7.294 & -0.09393 \tabularnewline
28 &  7.1 &  7.128 & -0.02795 \tabularnewline
29 &  7.1 &  7.108 & -0.008445 \tabularnewline
30 &  7 &  7.104 & -0.1035 \tabularnewline
31 &  7.1 &  7.093 &  0.007155 \tabularnewline
32 &  7.3 &  7.312 & -0.01158 \tabularnewline
33 &  7.2 &  7.288 & -0.08777 \tabularnewline
34 &  7.1 &  7.163 & -0.06266 \tabularnewline
35 &  7 &  7.025 & -0.02474 \tabularnewline
36 &  6.9 &  6.957 & -0.05714 \tabularnewline
37 &  7 &  6.959 &  0.04065 \tabularnewline
38 &  7.5 &  7.474 &  0.02611 \tabularnewline
39 &  7.6 &  7.652 & -0.05242 \tabularnewline
40 &  7.5 &  7.478 &  0.02228 \tabularnewline
41 &  7.3 &  7.452 & -0.1523 \tabularnewline
42 &  7.3 &  7.222 &  0.07769 \tabularnewline
43 &  7.4 &  7.378 &  0.0215 \tabularnewline
44 &  7.7 &  7.661 &  0.03949 \tabularnewline
45 &  7.8 &  7.684 &  0.1163 \tabularnewline
46 &  7.7 &  7.706 & -0.005995 \tabularnewline
47 &  7.5 &  7.49 &  0.01006 \tabularnewline
48 &  7.3 &  7.263 &  0.03712 \tabularnewline
49 &  7.3 &  7.194 &  0.1064 \tabularnewline
50 &  7.6 &  7.668 & -0.06766 \tabularnewline
51 &  7.6 &  7.648 & -0.04796 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=285589&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] 5.7[/C][C] 5.735[/C][C]-0.0352[/C][/ROW]
[ROW][C]2[/C][C] 6.2[/C][C] 6.135[/C][C] 0.0648[/C][/ROW]
[ROW][C]3[/C][C] 6.4[/C][C] 6.23[/C][C] 0.17[/C][/ROW]
[ROW][C]4[/C][C] 6.2[/C][C] 6.268[/C][C]-0.06755[/C][/ROW]
[ROW][C]5[/C][C] 6.2[/C][C] 6.173[/C][C] 0.02703[/C][/ROW]
[ROW][C]6[/C][C] 6.1[/C][C] 6.114[/C][C]-0.01403[/C][/ROW]
[ROW][C]7[/C][C] 6.1[/C][C] 6.169[/C][C]-0.06881[/C][/ROW]
[ROW][C]8[/C][C] 6.2[/C][C] 6.283[/C][C]-0.08348[/C][/ROW]
[ROW][C]9[/C][C] 6.1[/C][C] 6.169[/C][C]-0.06921[/C][/ROW]
[ROW][C]10[/C][C] 6.1[/C][C] 6.094[/C][C] 0.006006[/C][/ROW]
[ROW][C]11[/C][C] 6.2[/C][C] 6.157[/C][C] 0.04265[/C][/ROW]
[ROW][C]12[/C][C] 6.2[/C][C] 6.242[/C][C]-0.04212[/C][/ROW]
[ROW][C]13[/C][C] 6.2[/C][C] 6.254[/C][C]-0.05377[/C][/ROW]
[ROW][C]14[/C][C] 6.4[/C][C] 6.549[/C][C]-0.1489[/C][/ROW]
[ROW][C]15[/C][C] 6.4[/C][C] 6.376[/C][C] 0.0243[/C][/ROW]
[ROW][C]16[/C][C] 6.4[/C][C] 6.327[/C][C] 0.07322[/C][/ROW]
[ROW][C]17[/C][C] 6.7[/C][C] 6.566[/C][C] 0.1337[/C][/ROW]
[ROW][C]18[/C][C] 6.9[/C][C] 6.86[/C][C] 0.03988[/C][/ROW]
[ROW][C]19[/C][C] 7.1[/C][C] 7.06[/C][C] 0.04015[/C][/ROW]
[ROW][C]20[/C][C] 7.3[/C][C] 7.244[/C][C] 0.05556[/C][/ROW]
[ROW][C]21[/C][C] 7.2[/C][C] 7.159[/C][C] 0.0407[/C][/ROW]
[ROW][C]22[/C][C] 7.1[/C][C] 7.037[/C][C] 0.06265[/C][/ROW]
[ROW][C]23[/C][C] 6.9[/C][C] 6.928[/C][C]-0.02796[/C][/ROW]
[ROW][C]24[/C][C] 6.8[/C][C] 6.738[/C][C] 0.06213[/C][/ROW]
[ROW][C]25[/C][C] 6.7[/C][C] 6.758[/C][C]-0.05808[/C][/ROW]
[ROW][C]26[/C][C] 7.2[/C][C] 7.074[/C][C] 0.1256[/C][/ROW]
[ROW][C]27[/C][C] 7.2[/C][C] 7.294[/C][C]-0.09393[/C][/ROW]
[ROW][C]28[/C][C] 7.1[/C][C] 7.128[/C][C]-0.02795[/C][/ROW]
[ROW][C]29[/C][C] 7.1[/C][C] 7.108[/C][C]-0.008445[/C][/ROW]
[ROW][C]30[/C][C] 7[/C][C] 7.104[/C][C]-0.1035[/C][/ROW]
[ROW][C]31[/C][C] 7.1[/C][C] 7.093[/C][C] 0.007155[/C][/ROW]
[ROW][C]32[/C][C] 7.3[/C][C] 7.312[/C][C]-0.01158[/C][/ROW]
[ROW][C]33[/C][C] 7.2[/C][C] 7.288[/C][C]-0.08777[/C][/ROW]
[ROW][C]34[/C][C] 7.1[/C][C] 7.163[/C][C]-0.06266[/C][/ROW]
[ROW][C]35[/C][C] 7[/C][C] 7.025[/C][C]-0.02474[/C][/ROW]
[ROW][C]36[/C][C] 6.9[/C][C] 6.957[/C][C]-0.05714[/C][/ROW]
[ROW][C]37[/C][C] 7[/C][C] 6.959[/C][C] 0.04065[/C][/ROW]
[ROW][C]38[/C][C] 7.5[/C][C] 7.474[/C][C] 0.02611[/C][/ROW]
[ROW][C]39[/C][C] 7.6[/C][C] 7.652[/C][C]-0.05242[/C][/ROW]
[ROW][C]40[/C][C] 7.5[/C][C] 7.478[/C][C] 0.02228[/C][/ROW]
[ROW][C]41[/C][C] 7.3[/C][C] 7.452[/C][C]-0.1523[/C][/ROW]
[ROW][C]42[/C][C] 7.3[/C][C] 7.222[/C][C] 0.07769[/C][/ROW]
[ROW][C]43[/C][C] 7.4[/C][C] 7.378[/C][C] 0.0215[/C][/ROW]
[ROW][C]44[/C][C] 7.7[/C][C] 7.661[/C][C] 0.03949[/C][/ROW]
[ROW][C]45[/C][C] 7.8[/C][C] 7.684[/C][C] 0.1163[/C][/ROW]
[ROW][C]46[/C][C] 7.7[/C][C] 7.706[/C][C]-0.005995[/C][/ROW]
[ROW][C]47[/C][C] 7.5[/C][C] 7.49[/C][C] 0.01006[/C][/ROW]
[ROW][C]48[/C][C] 7.3[/C][C] 7.263[/C][C] 0.03712[/C][/ROW]
[ROW][C]49[/C][C] 7.3[/C][C] 7.194[/C][C] 0.1064[/C][/ROW]
[ROW][C]50[/C][C] 7.6[/C][C] 7.668[/C][C]-0.06766[/C][/ROW]
[ROW][C]51[/C][C] 7.6[/C][C] 7.648[/C][C]-0.04796[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=285589&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=285589&T=4

As an alternative you can also use a QR Code:

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	5.7	5.735	-0.0352
2	6.2	6.135	0.0648
3	6.4	6.23	0.17
4	6.2	6.268	-0.06755
5	6.2	6.173	0.02703
6	6.1	6.114	-0.01403
7	6.1	6.169	-0.06881
8	6.2	6.283	-0.08348
9	6.1	6.169	-0.06921
10	6.1	6.094	0.006006
11	6.2	6.157	0.04265
12	6.2	6.242	-0.04212
13	6.2	6.254	-0.05377
14	6.4	6.549	-0.1489
15	6.4	6.376	0.0243
16	6.4	6.327	0.07322
17	6.7	6.566	0.1337
18	6.9	6.86	0.03988
19	7.1	7.06	0.04015
20	7.3	7.244	0.05556
21	7.2	7.159	0.0407
22	7.1	7.037	0.06265
23	6.9	6.928	-0.02796
24	6.8	6.738	0.06213
25	6.7	6.758	-0.05808
26	7.2	7.074	0.1256
27	7.2	7.294	-0.09393
28	7.1	7.128	-0.02795
29	7.1	7.108	-0.008445
30	7	7.104	-0.1035
31	7.1	7.093	0.007155
32	7.3	7.312	-0.01158
33	7.2	7.288	-0.08777
34	7.1	7.163	-0.06266
35	7	7.025	-0.02474
36	6.9	6.957	-0.05714
37	7	6.959	0.04065
38	7.5	7.474	0.02611
39	7.6	7.652	-0.05242
40	7.5	7.478	0.02228
41	7.3	7.452	-0.1523
42	7.3	7.222	0.07769
43	7.4	7.378	0.0215
44	7.7	7.661	0.03949
45	7.8	7.684	0.1163
46	7.7	7.706	-0.005995
47	7.5	7.49	0.01006
48	7.3	7.263	0.03712
49	7.3	7.194	0.1064
50	7.6	7.668	-0.06766
51	7.6	7.648	-0.04796

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
25	0.6114	0.7771	0.3886
26	0.4033	0.8066	0.5967

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
25 &  0.6114 &  0.7771 &  0.3886 \tabularnewline
26 &  0.4033 &  0.8066 &  0.5967 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=285589&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]25[/C][C] 0.6114[/C][C] 0.7771[/C][C] 0.3886[/C][/ROW]
[ROW][C]26[/C][C] 0.4033[/C][C] 0.8066[/C][C] 0.5967[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=285589&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=285589&T=5

As an alternative you can also use a QR Code:

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

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
25	0.6114	0.7771	0.3886
26	0.4033	0.8066	0.5967

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

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 &  0 & OK \tabularnewline
5% type I error level & 0 & 0 & OK \tabularnewline
10% type I error level & 0 & 0 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=285589&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=285589&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=285589&T=6

As an alternative you can also use a QR Code:

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

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

Figure 1

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Figure 4

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Figure 6

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Figure 7

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Figure 8

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Figure 9

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Figure 10

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Parameters (Session):

par2 = Include Monthly Dummies ; par3 = Linear Trend ; par4 = 5 ; par5 = 2 ;

Parameters (R input):

par1 = ; par2 = Include Monthly Dummies ; par3 = Linear Trend ; par4 = 5 ; par5 = 2 ;

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

par5 <- '4'
par4 <- '2'
par3 <- 'Linear Trend'
par2 <- 'Include Monthly Dummies'
par1 <- ''
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')
}
}

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code