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rwasp_multipleregression.wasp

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Multiple Regression

Date of computation

Mon, 23 Jan 2017 12:13:34 +0100

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2017/Jan/23/t1485170045fa3reu38fcqr7ej.htm/, Retrieved Wed, 15 May 2024 21:39:40 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=305014, Retrieved Wed, 15 May 2024 21:39:40 +0000

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-       [Multiple Regression] [] [2017-01-23 11:13:34] [d06ec19b175650a2a09ee5879d174acf] [Current]

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14 22 13 4 2 4 3 5 4
19 24 16 5 3 3 4 5 4
17 21 17 4 4 5 4 5 4
17 21 NA 3 4 3 3 4 4
15 24 NA 4 4 5 4 5 4
20 20 16 3 4 4 4 5 5
15 22 NA 3 4 4 3 3 4
19 20 NA 3 4 5 4 4 4
15 19 NA 4 5 4 4 5 5
15 23 17 4 5 5 4 5 5
19 21 17 4 4 2 4 5 4
NA 19 15 4 4 5 3 5 4
20 19 16 4 4 4 3 4 5
18 21 14 3 3 5 4 4 5
15 21 16 4 4 5 4 2 5
14 22 17 3 4 5 4 4 5
20 22 NA 3 4 5 4 4 5
NA 19 NA NA NA 5 NA 5 5
16 21 NA 5 5 4 3 4 4
16 21 NA 4 4 4 4 5 4
16 21 16 3 4 5 3 4 5
10 20 NA 4 4 4 4 5 5
19 22 16 4 4 5 4 4 5
19 22 NA 4 4 5 4 4 4
16 24 NA 4 4 5 4 4 5
15 21 NA 3 4 4 4 4 4
18 19 16 3 4 4 3 5 5
17 19 15 4 4 4 4 4 4
19 23 16 2 4 5 4 5 5
17 21 16 5 4 4 4 4 4
NA 21 13 4 3 5 4 4 4
19 19 15 4 5 5 4 5 5
20 21 17 5 4 5 4 4 5
5 19 NA 4 3 5 4 NA 5
19 21 13 2 3 5 4 5 4
16 21 17 4 5 2 4 4 4
15 23 NA 3 4 5 4 4 4
16 19 14 4 3 5 3 4 5
18 19 14 4 3 3 4 4 4
16 19 18 4 4 5 4 4 4
15 18 NA 5 4 4 4 4 4
17 22 17 4 5 5 4 5 5
NA 18 13 3 3 4 4 4 4
20 22 16 5 5 5 3 5 5
19 18 15 5 4 5 3 4 4
7 22 15 4 4 4 3 4 5
13 22 NA 4 4 4 4 4 4
16 19 15 3 5 5 3 3 4
16 22 13 4 4 4 4 5 4
NA 25 NA 2 3 4 2 NA 4
18 19 17 4 5 5 4 4 4
18 19 NA 5 5 2 4 5 4
16 19 NA 5 5 5 4 4 4
17 19 11 4 3 5 4 5 5
19 21 14 4 3 4 3 4 5
16 21 13 4 4 5 4 4 4
19 20 NA 3 4 4 3 3 4
13 19 17 3 4 4 4 4 3
16 19 16 4 4 4 3 5 4
13 22 NA 4 4 4 4 5 4
12 26 17 5 5 3 4 5 5
17 19 16 2 4 4 4 5 5
17 21 16 4 4 4 4 5 5
17 21 16 3 4 4 4 2 4
16 20 15 4 4 5 4 5 5
16 23 12 4 2 4 4 4 4
14 22 17 4 4 4 3 5 3
16 22 14 4 4 4 3 5 4
13 22 14 5 4 5 3 3 5
16 21 16 3 4 4 3 5 5
14 21 NA 3 4 4 3 4 5
20 22 NA 4 5 5 5 5 4
12 23 NA 4 4 3 4 NA 4
13 18 NA 4 4 4 4 4 4
18 24 NA 4 4 4 5 5 4
14 22 15 3 4 3 4 4 4
19 21 16 4 4 4 4 5 4
18 21 14 3 4 5 3 5 5
14 21 15 3 3 5 4 4 5
18 23 17 4 3 5 4 4 4
19 21 NA 4 4 5 4 4 5
15 23 10 3 3 3 4 4 4
14 21 NA 4 4 4 4 5 4
17 19 17 4 4 3 4 5 5
19 21 NA 4 4 4 4 5 5
13 21 20 5 4 4 4 4 4
19 21 17 5 4 3 5 4 5
18 23 18 4 4 5 4 5 5
20 23 NA 3 4 5 4 4 5
15 20 17 3 NA 4 4 4 4
15 20 14 4 2 3 3 4 4
15 19 NA 4 4 5 4 4 3
20 23 17 4 4 5 4 4 5
15 22 NA 4 4 4 4 5 4
19 19 17 4 5 4 4 5 3
18 23 NA 3 4 4 3 5 5
18 22 16 4 4 5 4 4 5
15 22 18 5 4 3 4 4 5
20 21 18 5 4 5 5 4 5
17 21 16 4 5 4 4 5 5
12 21 NA 3 4 5 4 4 5
18 21 NA 5 3 4 4 5 5
19 22 15 4 4 5 4 4 5
20 25 13 5 4 4 4 4 5
NA 21 NA 3 4 4 3 NA 4
17 23 NA 5 4 4 5 5 5
15 19 NA 4 4 5 3 NA 5
16 22 NA 4 4 3 3 4 3
18 20 NA 4 4 5 4 4 4
18 21 16 4 4 5 4 4 4
14 25 NA 3 4 5 4 5 3
15 21 NA 4 4 4 4 4 4
12 19 NA 4 4 4 3 4 5
17 23 12 3 3 4 3 5 5
14 22 NA 4 4 4 3 4 4
18 21 16 3 4 5 4 4 4
17 24 16 4 4 5 4 3 4
17 21 NA 5 4 5 1 5 5
20 19 16 5 4 5 4 5 5
16 18 14 4 4 4 4 4 3
14 19 15 4 4 5 3 4 4
15 20 14 3 4 4 3 4 5
18 19 NA 4 4 4 4 4 4
20 22 15 4 4 4 4 5 4
17 21 NA 4 5 3 4 4 4
17 22 15 3 4 4 4 4 4
17 24 16 4 4 4 3 4 4
17 28 NA 4 4 4 4 4 5
15 19 NA 3 4 3 3 4 4
17 18 NA 4 4 4 3 4 3
18 23 11 3 2 4 2 4 4
17 19 NA 4 4 4 3 5 4
20 23 18 5 4 4 3 5 4
15 19 NA 2 4 4 3 3 5
16 22 11 3 3 4 4 4 4
15 21 NA 4 4 4 3 4 4
18 19 18 5 5 4 4 5 4
11 22 NA NA NA 2 NA NA NA
15 21 15 4 5 5 4 4 4
18 23 19 5 5 5 5 5 4
20 22 17 4 5 5 4 5 5
19 19 NA 4 4 4 3 4 5
14 19 14 3 4 5 4 5 4
16 21 NA 4 4 5 4 4 4
15 22 13 4 4 2 4 4 4
17 21 17 4 4 3 4 5 5
18 20 14 4 4 4 4 5 5
20 23 19 5 4 5 3 5 4
17 22 14 4 3 5 4 4 4
18 23 NA 4 4 5 4 4 4
15 22 NA 3 3 2 3 4 4
16 21 16 4 5 5 4 4 3
11 20 16 4 4 4 3 4 4
15 18 15 4 4 4 4 4 5
18 18 12 3 4 5 3 5 5
17 20 NA 4 4 5 4 4 5
16 19 17 5 4 5 4 5 4
12 21 NA 4 4 5 4 3 4
19 24 NA 2 3 5 4 4 4
18 19 18 4 4 4 4 4 5
15 20 15 4 3 4 3 5 5
17 19 18 4 4 4 4 4 3
19 23 15 4 5 5 5 4 4
18 22 NA 5 4 3 4 4 4
19 21 NA 5 4 4 3 4 4
16 24 NA 3 3 1 4 5 5
16 21 16 4 4 4 4 4 5
16 21 NA 4 4 4 4 5 4
14 22 16 2 3 4 5 5 4

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	7 seconds
R Server	Big Analytics Cloud Computing Center

\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 time7 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=305014&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]

7 seconds[/C][/ROW] [ROW]

R Server[/C]

Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=305014&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=305014&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	7 seconds
R Server	Big Analytics Cloud Computing Center

Multiple Linear Regression - Estimated Regression Equation
ITHSUM[t] = + 0.423865 + 0.413604Bevr_Leeftijd[t] -0.140288TVDC[t] + 1.04256SKEOU1[t] -0.0923341SKEOU2[t] + 0.908761SKEOU3[t] + 0.687748SKEOU4[t] + 1.33907SKEOU5[t] + 0.375958SKEOU6[t] -0.120687`ITHSUM(t-1)`[t] -0.0495446`ITHSUM(t-1s)`[t] -0.109061`ITHSUM(t-2s)`[t] -0.180209`ITHSUM(t-3s)`[t] -0.000815886`ITHSUM(t-4s)`[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
ITHSUM[t] =  +  0.423865 +  0.413604Bevr_Leeftijd[t] -0.140288TVDC[t] +  1.04256SKEOU1[t] -0.0923341SKEOU2[t] +  0.908761SKEOU3[t] +  0.687748SKEOU4[t] +  1.33907SKEOU5[t] +  0.375958SKEOU6[t] -0.120687`ITHSUM(t-1)`[t] -0.0495446`ITHSUM(t-1s)`[t] -0.109061`ITHSUM(t-2s)`[t] -0.180209`ITHSUM(t-3s)`[t] -0.000815886`ITHSUM(t-4s)`[t]  + e[t] \tabularnewline
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=305014&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]ITHSUM[t] =  +  0.423865 +  0.413604Bevr_Leeftijd[t] -0.140288TVDC[t] +  1.04256SKEOU1[t] -0.0923341SKEOU2[t] +  0.908761SKEOU3[t] +  0.687748SKEOU4[t] +  1.33907SKEOU5[t] +  0.375958SKEOU6[t] -0.120687`ITHSUM(t-1)`[t] -0.0495446`ITHSUM(t-1s)`[t] -0.109061`ITHSUM(t-2s)`[t] -0.180209`ITHSUM(t-3s)`[t] -0.000815886`ITHSUM(t-4s)`[t]  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=305014&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=305014&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
ITHSUM[t] = + 0.423865 + 0.413604Bevr_Leeftijd[t] -0.140288TVDC[t] + 1.04256SKEOU1[t] -0.0923341SKEOU2[t] + 0.908761SKEOU3[t] + 0.687748SKEOU4[t] + 1.33907SKEOU5[t] + 0.375958SKEOU6[t] -0.120687`ITHSUM(t-1)`[t] -0.0495446`ITHSUM(t-1s)`[t] -0.109061`ITHSUM(t-2s)`[t] -0.180209`ITHSUM(t-3s)`[t] -0.000815886`ITHSUM(t-4s)`[t] + e[t]

Multiple Linear Regression - Ordinary Least Squares
Variable	Parameter	S.D.	T-STATH0: parameter = 0	2-tail p-value	1-tail p-value
(Intercept)	+0.4239	6.456	+6.5650e-02	0.948	0.474
Bevr_Leeftijd	+0.4136	0.1681	+2.4600e+00	0.01884	0.009419
TVDC	-0.1403	0.1759	-7.9760e-01	0.4303	0.2152
SKEOU1	+1.043	0.4969	+2.0980e+00	0.04296	0.02148
SKEOU2	-0.09233	0.5655	-1.6330e-01	0.8712	0.4356
SKEOU3	+0.9088	0.415	+2.1900e+00	0.0351	0.01755
SKEOU4	+0.6877	0.5631	+1.2210e+00	0.2299	0.1149
SKEOU5	+1.339	0.573	+2.3370e+00	0.02511	0.01256
SKEOU6	+0.376	0.4591	+8.1900e-01	0.4182	0.2091
`ITHSUM(t-1)`	-0.1207	0.1453	-8.3040e-01	0.4118	0.2059
`ITHSUM(t-1s)`	-0.04954	0.1351	-3.6660e-01	0.7161	0.358
`ITHSUM(t-2s)`	-0.1091	0.1224	-8.9100e-01	0.3788	0.1894
`ITHSUM(t-3s)`	-0.1802	0.1246	-1.4460e+00	0.1569	0.07846
`ITHSUM(t-4s)`	-0.0008159	0.1263	-6.4590e-03	0.9949	0.4974

\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) & +0.4239 &  6.456 & +6.5650e-02 &  0.948 &  0.474 \tabularnewline
Bevr_Leeftijd & +0.4136 &  0.1681 & +2.4600e+00 &  0.01884 &  0.009419 \tabularnewline
TVDC & -0.1403 &  0.1759 & -7.9760e-01 &  0.4303 &  0.2152 \tabularnewline
SKEOU1 & +1.043 &  0.4969 & +2.0980e+00 &  0.04296 &  0.02148 \tabularnewline
SKEOU2 & -0.09233 &  0.5655 & -1.6330e-01 &  0.8712 &  0.4356 \tabularnewline
SKEOU3 & +0.9088 &  0.415 & +2.1900e+00 &  0.0351 &  0.01755 \tabularnewline
SKEOU4 & +0.6877 &  0.5631 & +1.2210e+00 &  0.2299 &  0.1149 \tabularnewline
SKEOU5 & +1.339 &  0.573 & +2.3370e+00 &  0.02511 &  0.01256 \tabularnewline
SKEOU6 & +0.376 &  0.4591 & +8.1900e-01 &  0.4182 &  0.2091 \tabularnewline
`ITHSUM(t-1)` & -0.1207 &  0.1453 & -8.3040e-01 &  0.4118 &  0.2059 \tabularnewline
`ITHSUM(t-1s)` & -0.04954 &  0.1351 & -3.6660e-01 &  0.7161 &  0.358 \tabularnewline
`ITHSUM(t-2s)` & -0.1091 &  0.1224 & -8.9100e-01 &  0.3788 &  0.1894 \tabularnewline
`ITHSUM(t-3s)` & -0.1802 &  0.1246 & -1.4460e+00 &  0.1569 &  0.07846 \tabularnewline
`ITHSUM(t-4s)` & -0.0008159 &  0.1263 & -6.4590e-03 &  0.9949 &  0.4974 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=305014&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]+0.4239[/C][C] 6.456[/C][C]+6.5650e-02[/C][C] 0.948[/C][C] 0.474[/C][/ROW]
[ROW][C]Bevr_Leeftijd[/C][C]+0.4136[/C][C] 0.1681[/C][C]+2.4600e+00[/C][C] 0.01884[/C][C] 0.009419[/C][/ROW]
[ROW][C]TVDC[/C][C]-0.1403[/C][C] 0.1759[/C][C]-7.9760e-01[/C][C] 0.4303[/C][C] 0.2152[/C][/ROW]
[ROW][C]SKEOU1[/C][C]+1.043[/C][C] 0.4969[/C][C]+2.0980e+00[/C][C] 0.04296[/C][C] 0.02148[/C][/ROW]
[ROW][C]SKEOU2[/C][C]-0.09233[/C][C] 0.5655[/C][C]-1.6330e-01[/C][C] 0.8712[/C][C] 0.4356[/C][/ROW]
[ROW][C]SKEOU3[/C][C]+0.9088[/C][C] 0.415[/C][C]+2.1900e+00[/C][C] 0.0351[/C][C] 0.01755[/C][/ROW]
[ROW][C]SKEOU4[/C][C]+0.6877[/C][C] 0.5631[/C][C]+1.2210e+00[/C][C] 0.2299[/C][C] 0.1149[/C][/ROW]
[ROW][C]SKEOU5[/C][C]+1.339[/C][C] 0.573[/C][C]+2.3370e+00[/C][C] 0.02511[/C][C] 0.01256[/C][/ROW]
[ROW][C]SKEOU6[/C][C]+0.376[/C][C] 0.4591[/C][C]+8.1900e-01[/C][C] 0.4182[/C][C] 0.2091[/C][/ROW]
[ROW][C]`ITHSUM(t-1)`[/C][C]-0.1207[/C][C] 0.1453[/C][C]-8.3040e-01[/C][C] 0.4118[/C][C] 0.2059[/C][/ROW]
[ROW][C]`ITHSUM(t-1s)`[/C][C]-0.04954[/C][C] 0.1351[/C][C]-3.6660e-01[/C][C] 0.7161[/C][C] 0.358[/C][/ROW]
[ROW][C]`ITHSUM(t-2s)`[/C][C]-0.1091[/C][C] 0.1224[/C][C]-8.9100e-01[/C][C] 0.3788[/C][C] 0.1894[/C][/ROW]
[ROW][C]`ITHSUM(t-3s)`[/C][C]-0.1802[/C][C] 0.1246[/C][C]-1.4460e+00[/C][C] 0.1569[/C][C] 0.07846[/C][/ROW]
[ROW][C]`ITHSUM(t-4s)`[/C][C]-0.0008159[/C][C] 0.1263[/C][C]-6.4590e-03[/C][C] 0.9949[/C][C] 0.4974[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=305014&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=305014&T=2

As an alternative you can also use a QR Code:

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

Multiple Linear Regression - Ordinary Least Squares
Variable	Parameter	S.D.	T-STATH0: parameter = 0	2-tail p-value	1-tail p-value
(Intercept)	+0.4239	6.456	+6.5650e-02	0.948	0.474
Bevr_Leeftijd	+0.4136	0.1681	+2.4600e+00	0.01884	0.009419
TVDC	-0.1403	0.1759	-7.9760e-01	0.4303	0.2152
SKEOU1	+1.043	0.4969	+2.0980e+00	0.04296	0.02148
SKEOU2	-0.09233	0.5655	-1.6330e-01	0.8712	0.4356
SKEOU3	+0.9088	0.415	+2.1900e+00	0.0351	0.01755
SKEOU4	+0.6877	0.5631	+1.2210e+00	0.2299	0.1149
SKEOU5	+1.339	0.573	+2.3370e+00	0.02511	0.01256
SKEOU6	+0.376	0.4591	+8.1900e-01	0.4182	0.2091
`ITHSUM(t-1)`	-0.1207	0.1453	-8.3040e-01	0.4118	0.2059
`ITHSUM(t-1s)`	-0.04954	0.1351	-3.6660e-01	0.7161	0.358
`ITHSUM(t-2s)`	-0.1091	0.1224	-8.9100e-01	0.3788	0.1894
`ITHSUM(t-3s)`	-0.1802	0.1246	-1.4460e+00	0.1569	0.07846
`ITHSUM(t-4s)`	-0.0008159	0.1263	-6.4590e-03	0.9949	0.4974

Multiple Linear Regression - Regression Statistics
Multiple R	0.6616
R-squared	0.4377
Adjusted R-squared	0.2347
F-TEST (value)	2.156
F-TEST (DF numerator)	13
F-TEST (DF denominator)	36
p-value	0.03452
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.832
Sum Squared Residuals	120.8

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.6616 \tabularnewline
R-squared &  0.4377 \tabularnewline
Adjusted R-squared &  0.2347 \tabularnewline
F-TEST (value) &  2.156 \tabularnewline
F-TEST (DF numerator) & 13 \tabularnewline
F-TEST (DF denominator) & 36 \tabularnewline
p-value &  0.03452 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  1.832 \tabularnewline
Sum Squared Residuals &  120.8 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=305014&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.6616[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.4377[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.2347[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 2.156[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]13[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]36[/C][/ROW]
[ROW][C]p-value[/C][C] 0.03452[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C] 1.832[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 120.8[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=305014&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=305014&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	0.6616
R-squared	0.4377
Adjusted R-squared	0.2347
F-TEST (value)	2.156
F-TEST (DF numerator)	13
F-TEST (DF denominator)	36
p-value	0.03452
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.832
Sum Squared Residuals	120.8

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	18	17.92	0.08308
2	15	16.51	-1.506
3	17	16.32	0.6771
4	13	16.37	-3.368
5	19	17.12	1.876
6	18	18.7	-0.7023
7	15	14.58	0.4195
8	20	18.64	1.36
9	19	16.03	2.965
10	18	17.73	0.2655
11	15	17.1	-2.103
12	20	18.67	1.33
13	17	16.94	0.06274
14	19	19.82	-0.8248
15	20	19.63	0.3717
16	18	17.12	0.8809
17	17	17.48	-0.4768
18	18	16.01	1.989
19	17	16.95	0.04785
20	20	19.07	0.9328
21	16	15.34	0.6635
22	14	15.65	-1.655
23	15	15.85	-0.8523
24	20	18.4	1.603
25	17	15.13	1.875
26	17	16.77	0.2331
27	18	15.41	2.586
28	20	18.77	1.226
29	16	16.21	-0.211
30	18	17.41	0.5896
31	15	17.86	-2.86
32	18	20.33	-2.325
33	20	19.15	0.854
34	14	16.06	-2.063
35	15	15.61	-0.6067
36	17	17.06	-0.05875
37	18	17.49	0.5116
38	20	19.12	0.8828
39	17	17.04	-0.04389
40	16	16.99	-0.991
41	11	14.8	-3.797
42	15	15.75	-0.7482
43	18	17	0.9954
44	16	17.37	-1.372
45	18	15.08	2.922
46	15	17.11	-2.106
47	17	15.52	1.479
48	19	17.69	1.307
49	16	16.17	-0.1708
50	14	16.15	-2.147

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 &  18 &  17.92 &  0.08308 \tabularnewline
2 &  15 &  16.51 & -1.506 \tabularnewline
3 &  17 &  16.32 &  0.6771 \tabularnewline
4 &  13 &  16.37 & -3.368 \tabularnewline
5 &  19 &  17.12 &  1.876 \tabularnewline
6 &  18 &  18.7 & -0.7023 \tabularnewline
7 &  15 &  14.58 &  0.4195 \tabularnewline
8 &  20 &  18.64 &  1.36 \tabularnewline
9 &  19 &  16.03 &  2.965 \tabularnewline
10 &  18 &  17.73 &  0.2655 \tabularnewline
11 &  15 &  17.1 & -2.103 \tabularnewline
12 &  20 &  18.67 &  1.33 \tabularnewline
13 &  17 &  16.94 &  0.06274 \tabularnewline
14 &  19 &  19.82 & -0.8248 \tabularnewline
15 &  20 &  19.63 &  0.3717 \tabularnewline
16 &  18 &  17.12 &  0.8809 \tabularnewline
17 &  17 &  17.48 & -0.4768 \tabularnewline
18 &  18 &  16.01 &  1.989 \tabularnewline
19 &  17 &  16.95 &  0.04785 \tabularnewline
20 &  20 &  19.07 &  0.9328 \tabularnewline
21 &  16 &  15.34 &  0.6635 \tabularnewline
22 &  14 &  15.65 & -1.655 \tabularnewline
23 &  15 &  15.85 & -0.8523 \tabularnewline
24 &  20 &  18.4 &  1.603 \tabularnewline
25 &  17 &  15.13 &  1.875 \tabularnewline
26 &  17 &  16.77 &  0.2331 \tabularnewline
27 &  18 &  15.41 &  2.586 \tabularnewline
28 &  20 &  18.77 &  1.226 \tabularnewline
29 &  16 &  16.21 & -0.211 \tabularnewline
30 &  18 &  17.41 &  0.5896 \tabularnewline
31 &  15 &  17.86 & -2.86 \tabularnewline
32 &  18 &  20.33 & -2.325 \tabularnewline
33 &  20 &  19.15 &  0.854 \tabularnewline
34 &  14 &  16.06 & -2.063 \tabularnewline
35 &  15 &  15.61 & -0.6067 \tabularnewline
36 &  17 &  17.06 & -0.05875 \tabularnewline
37 &  18 &  17.49 &  0.5116 \tabularnewline
38 &  20 &  19.12 &  0.8828 \tabularnewline
39 &  17 &  17.04 & -0.04389 \tabularnewline
40 &  16 &  16.99 & -0.991 \tabularnewline
41 &  11 &  14.8 & -3.797 \tabularnewline
42 &  15 &  15.75 & -0.7482 \tabularnewline
43 &  18 &  17 &  0.9954 \tabularnewline
44 &  16 &  17.37 & -1.372 \tabularnewline
45 &  18 &  15.08 &  2.922 \tabularnewline
46 &  15 &  17.11 & -2.106 \tabularnewline
47 &  17 &  15.52 &  1.479 \tabularnewline
48 &  19 &  17.69 &  1.307 \tabularnewline
49 &  16 &  16.17 & -0.1708 \tabularnewline
50 &  14 &  16.15 & -2.147 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=305014&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] 18[/C][C] 17.92[/C][C] 0.08308[/C][/ROW]
[ROW][C]2[/C][C] 15[/C][C] 16.51[/C][C]-1.506[/C][/ROW]
[ROW][C]3[/C][C] 17[/C][C] 16.32[/C][C] 0.6771[/C][/ROW]
[ROW][C]4[/C][C] 13[/C][C] 16.37[/C][C]-3.368[/C][/ROW]
[ROW][C]5[/C][C] 19[/C][C] 17.12[/C][C] 1.876[/C][/ROW]
[ROW][C]6[/C][C] 18[/C][C] 18.7[/C][C]-0.7023[/C][/ROW]
[ROW][C]7[/C][C] 15[/C][C] 14.58[/C][C] 0.4195[/C][/ROW]
[ROW][C]8[/C][C] 20[/C][C] 18.64[/C][C] 1.36[/C][/ROW]
[ROW][C]9[/C][C] 19[/C][C] 16.03[/C][C] 2.965[/C][/ROW]
[ROW][C]10[/C][C] 18[/C][C] 17.73[/C][C] 0.2655[/C][/ROW]
[ROW][C]11[/C][C] 15[/C][C] 17.1[/C][C]-2.103[/C][/ROW]
[ROW][C]12[/C][C] 20[/C][C] 18.67[/C][C] 1.33[/C][/ROW]
[ROW][C]13[/C][C] 17[/C][C] 16.94[/C][C] 0.06274[/C][/ROW]
[ROW][C]14[/C][C] 19[/C][C] 19.82[/C][C]-0.8248[/C][/ROW]
[ROW][C]15[/C][C] 20[/C][C] 19.63[/C][C] 0.3717[/C][/ROW]
[ROW][C]16[/C][C] 18[/C][C] 17.12[/C][C] 0.8809[/C][/ROW]
[ROW][C]17[/C][C] 17[/C][C] 17.48[/C][C]-0.4768[/C][/ROW]
[ROW][C]18[/C][C] 18[/C][C] 16.01[/C][C] 1.989[/C][/ROW]
[ROW][C]19[/C][C] 17[/C][C] 16.95[/C][C] 0.04785[/C][/ROW]
[ROW][C]20[/C][C] 20[/C][C] 19.07[/C][C] 0.9328[/C][/ROW]
[ROW][C]21[/C][C] 16[/C][C] 15.34[/C][C] 0.6635[/C][/ROW]
[ROW][C]22[/C][C] 14[/C][C] 15.65[/C][C]-1.655[/C][/ROW]
[ROW][C]23[/C][C] 15[/C][C] 15.85[/C][C]-0.8523[/C][/ROW]
[ROW][C]24[/C][C] 20[/C][C] 18.4[/C][C] 1.603[/C][/ROW]
[ROW][C]25[/C][C] 17[/C][C] 15.13[/C][C] 1.875[/C][/ROW]
[ROW][C]26[/C][C] 17[/C][C] 16.77[/C][C] 0.2331[/C][/ROW]
[ROW][C]27[/C][C] 18[/C][C] 15.41[/C][C] 2.586[/C][/ROW]
[ROW][C]28[/C][C] 20[/C][C] 18.77[/C][C] 1.226[/C][/ROW]
[ROW][C]29[/C][C] 16[/C][C] 16.21[/C][C]-0.211[/C][/ROW]
[ROW][C]30[/C][C] 18[/C][C] 17.41[/C][C] 0.5896[/C][/ROW]
[ROW][C]31[/C][C] 15[/C][C] 17.86[/C][C]-2.86[/C][/ROW]
[ROW][C]32[/C][C] 18[/C][C] 20.33[/C][C]-2.325[/C][/ROW]
[ROW][C]33[/C][C] 20[/C][C] 19.15[/C][C] 0.854[/C][/ROW]
[ROW][C]34[/C][C] 14[/C][C] 16.06[/C][C]-2.063[/C][/ROW]
[ROW][C]35[/C][C] 15[/C][C] 15.61[/C][C]-0.6067[/C][/ROW]
[ROW][C]36[/C][C] 17[/C][C] 17.06[/C][C]-0.05875[/C][/ROW]
[ROW][C]37[/C][C] 18[/C][C] 17.49[/C][C] 0.5116[/C][/ROW]
[ROW][C]38[/C][C] 20[/C][C] 19.12[/C][C] 0.8828[/C][/ROW]
[ROW][C]39[/C][C] 17[/C][C] 17.04[/C][C]-0.04389[/C][/ROW]
[ROW][C]40[/C][C] 16[/C][C] 16.99[/C][C]-0.991[/C][/ROW]
[ROW][C]41[/C][C] 11[/C][C] 14.8[/C][C]-3.797[/C][/ROW]
[ROW][C]42[/C][C] 15[/C][C] 15.75[/C][C]-0.7482[/C][/ROW]
[ROW][C]43[/C][C] 18[/C][C] 17[/C][C] 0.9954[/C][/ROW]
[ROW][C]44[/C][C] 16[/C][C] 17.37[/C][C]-1.372[/C][/ROW]
[ROW][C]45[/C][C] 18[/C][C] 15.08[/C][C] 2.922[/C][/ROW]
[ROW][C]46[/C][C] 15[/C][C] 17.11[/C][C]-2.106[/C][/ROW]
[ROW][C]47[/C][C] 17[/C][C] 15.52[/C][C] 1.479[/C][/ROW]
[ROW][C]48[/C][C] 19[/C][C] 17.69[/C][C] 1.307[/C][/ROW]
[ROW][C]49[/C][C] 16[/C][C] 16.17[/C][C]-0.1708[/C][/ROW]
[ROW][C]50[/C][C] 14[/C][C] 16.15[/C][C]-2.147[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=305014&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=305014&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	18	17.92	0.08308
2	15	16.51	-1.506
3	17	16.32	0.6771
4	13	16.37	-3.368
5	19	17.12	1.876
6	18	18.7	-0.7023
7	15	14.58	0.4195
8	20	18.64	1.36
9	19	16.03	2.965
10	18	17.73	0.2655
11	15	17.1	-2.103
12	20	18.67	1.33
13	17	16.94	0.06274
14	19	19.82	-0.8248
15	20	19.63	0.3717
16	18	17.12	0.8809
17	17	17.48	-0.4768
18	18	16.01	1.989
19	17	16.95	0.04785
20	20	19.07	0.9328
21	16	15.34	0.6635
22	14	15.65	-1.655
23	15	15.85	-0.8523
24	20	18.4	1.603
25	17	15.13	1.875
26	17	16.77	0.2331
27	18	15.41	2.586
28	20	18.77	1.226
29	16	16.21	-0.211
30	18	17.41	0.5896
31	15	17.86	-2.86
32	18	20.33	-2.325
33	20	19.15	0.854
34	14	16.06	-2.063
35	15	15.61	-0.6067
36	17	17.06	-0.05875
37	18	17.49	0.5116
38	20	19.12	0.8828
39	17	17.04	-0.04389
40	16	16.99	-0.991
41	11	14.8	-3.797
42	15	15.75	-0.7482
43	18	17	0.9954
44	16	17.37	-1.372
45	18	15.08	2.922
46	15	17.11	-2.106
47	17	15.52	1.479
48	19	17.69	1.307
49	16	16.17	-0.1708
50	14	16.15	-2.147

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
17	0.6339	0.7323	0.3661
18	0.5142	0.9716	0.4858
19	0.3627	0.7253	0.6373
20	0.2315	0.463	0.7685
21	0.1364	0.2729	0.8636
22	0.2184	0.4367	0.7816
23	0.1427	0.2854	0.8573
24	0.1143	0.2286	0.8857
25	0.08881	0.1776	0.9112
26	0.06708	0.1342	0.9329
27	0.1822	0.3643	0.8178
28	0.194	0.388	0.806
29	0.1529	0.3059	0.8471
30	0.1005	0.2011	0.8995
31	0.1277	0.2554	0.8723
32	0.2782	0.5563	0.7218
33	0.8206	0.3587	0.1794

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
17 &  0.6339 &  0.7323 &  0.3661 \tabularnewline
18 &  0.5142 &  0.9716 &  0.4858 \tabularnewline
19 &  0.3627 &  0.7253 &  0.6373 \tabularnewline
20 &  0.2315 &  0.463 &  0.7685 \tabularnewline
21 &  0.1364 &  0.2729 &  0.8636 \tabularnewline
22 &  0.2184 &  0.4367 &  0.7816 \tabularnewline
23 &  0.1427 &  0.2854 &  0.8573 \tabularnewline
24 &  0.1143 &  0.2286 &  0.8857 \tabularnewline
25 &  0.08881 &  0.1776 &  0.9112 \tabularnewline
26 &  0.06708 &  0.1342 &  0.9329 \tabularnewline
27 &  0.1822 &  0.3643 &  0.8178 \tabularnewline
28 &  0.194 &  0.388 &  0.806 \tabularnewline
29 &  0.1529 &  0.3059 &  0.8471 \tabularnewline
30 &  0.1005 &  0.2011 &  0.8995 \tabularnewline
31 &  0.1277 &  0.2554 &  0.8723 \tabularnewline
32 &  0.2782 &  0.5563 &  0.7218 \tabularnewline
33 &  0.8206 &  0.3587 &  0.1794 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=305014&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]17[/C][C] 0.6339[/C][C] 0.7323[/C][C] 0.3661[/C][/ROW]
[ROW][C]18[/C][C] 0.5142[/C][C] 0.9716[/C][C] 0.4858[/C][/ROW]
[ROW][C]19[/C][C] 0.3627[/C][C] 0.7253[/C][C] 0.6373[/C][/ROW]
[ROW][C]20[/C][C] 0.2315[/C][C] 0.463[/C][C] 0.7685[/C][/ROW]
[ROW][C]21[/C][C] 0.1364[/C][C] 0.2729[/C][C] 0.8636[/C][/ROW]
[ROW][C]22[/C][C] 0.2184[/C][C] 0.4367[/C][C] 0.7816[/C][/ROW]
[ROW][C]23[/C][C] 0.1427[/C][C] 0.2854[/C][C] 0.8573[/C][/ROW]
[ROW][C]24[/C][C] 0.1143[/C][C] 0.2286[/C][C] 0.8857[/C][/ROW]
[ROW][C]25[/C][C] 0.08881[/C][C] 0.1776[/C][C] 0.9112[/C][/ROW]
[ROW][C]26[/C][C] 0.06708[/C][C] 0.1342[/C][C] 0.9329[/C][/ROW]
[ROW][C]27[/C][C] 0.1822[/C][C] 0.3643[/C][C] 0.8178[/C][/ROW]
[ROW][C]28[/C][C] 0.194[/C][C] 0.388[/C][C] 0.806[/C][/ROW]
[ROW][C]29[/C][C] 0.1529[/C][C] 0.3059[/C][C] 0.8471[/C][/ROW]
[ROW][C]30[/C][C] 0.1005[/C][C] 0.2011[/C][C] 0.8995[/C][/ROW]
[ROW][C]31[/C][C] 0.1277[/C][C] 0.2554[/C][C] 0.8723[/C][/ROW]
[ROW][C]32[/C][C] 0.2782[/C][C] 0.5563[/C][C] 0.7218[/C][/ROW]
[ROW][C]33[/C][C] 0.8206[/C][C] 0.3587[/C][C] 0.1794[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=305014&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=305014&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
17	0.6339	0.7323	0.3661
18	0.5142	0.9716	0.4858
19	0.3627	0.7253	0.6373
20	0.2315	0.463	0.7685
21	0.1364	0.2729	0.8636
22	0.2184	0.4367	0.7816
23	0.1427	0.2854	0.8573
24	0.1143	0.2286	0.8857
25	0.08881	0.1776	0.9112
26	0.06708	0.1342	0.9329
27	0.1822	0.3643	0.8178
28	0.194	0.388	0.806
29	0.1529	0.3059	0.8471
30	0.1005	0.2011	0.8995
31	0.1277	0.2554	0.8723
32	0.2782	0.5563	0.7218
33	0.8206	0.3587	0.1794

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=305014&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=305014&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=305014&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

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted RESET test data: mylm RESET = 1.301, df1 = 2, df2 = 34, p-value = 0.2855
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 0.72048, df1 = 26, df2 = 10, p-value = 0.7599
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 1.8615, df1 = 2, df2 = 34, p-value = 0.1709

\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 = 1.301, df1 = 2, df2 = 34, p-value = 0.2855
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of regressors \tabularnewline
> reset_test_regressors
	RESET test
data:  mylm
RESET = 0.72048, df1 = 26, df2 = 10, p-value = 0.7599
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of principal components \tabularnewline
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 1.8615, df1 = 2, df2 = 34, p-value = 0.1709
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=305014&T=7

[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 = 1.301, df1 = 2, df2 = 34, p-value = 0.2855
[/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 = 0.72048, df1 = 26, df2 = 10, p-value = 0.7599
[/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 = 1.8615, df1 = 2, df2 = 34, p-value = 0.1709
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=305014&T=7

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=305014&T=7

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 = 1.301, df1 = 2, df2 = 34, p-value = 0.2855
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 0.72048, df1 = 26, df2 = 10, p-value = 0.7599
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 1.8615, df1 = 2, df2 = 34, p-value = 0.1709

Variance Inflation Factors (Multicollinearity)

> vif
 Bevr_Leeftijd           TVDC         SKEOU1         SKEOU2         SKEOU3 
      1.270130       2.134805       1.765936       2.174885       1.417774 
        SKEOU4         SKEOU5         SKEOU6  `ITHSUM(t-1)` `ITHSUM(t-1s)` 
      1.702991       1.322884       1.207267       1.352349       1.115883 
`ITHSUM(t-2s)` `ITHSUM(t-3s)` `ITHSUM(t-4s)` 
      1.341068       1.367242       1.375490

\begin{tabular}{lllllllll}
\hline
Variance Inflation Factors (Multicollinearity) \tabularnewline
> vif
 Bevr_Leeftijd           TVDC         SKEOU1         SKEOU2         SKEOU3 
      1.270130       2.134805       1.765936       2.174885       1.417774 
        SKEOU4         SKEOU5         SKEOU6  `ITHSUM(t-1)` `ITHSUM(t-1s)` 
      1.702991       1.322884       1.207267       1.352349       1.115883 
`ITHSUM(t-2s)` `ITHSUM(t-3s)` `ITHSUM(t-4s)` 
      1.341068       1.367242       1.375490 
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=305014&T=8

[TABLE]
[ROW][C]Variance Inflation Factors (Multicollinearity)[/C][/ROW]
[ROW][C]> vif
 Bevr_Leeftijd           TVDC         SKEOU1         SKEOU2         SKEOU3 
      1.270130       2.134805       1.765936       2.174885       1.417774 
        SKEOU4         SKEOU5         SKEOU6  `ITHSUM(t-1)` `ITHSUM(t-1s)` 
      1.702991       1.322884       1.207267       1.352349       1.115883 
`ITHSUM(t-2s)` `ITHSUM(t-3s)` `ITHSUM(t-4s)` 
      1.341068       1.367242       1.375490 
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=305014&T=8

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=305014&T=8

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
 Bevr_Leeftijd           TVDC         SKEOU1         SKEOU2         SKEOU3 
      1.270130       2.134805       1.765936       2.174885       1.417774 
        SKEOU4         SKEOU5         SKEOU6  `ITHSUM(t-1)` `ITHSUM(t-1s)` 
      1.702991       1.322884       1.207267       1.352349       1.115883 
`ITHSUM(t-2s)` `ITHSUM(t-3s)` `ITHSUM(t-4s)` 
      1.341068       1.367242       1.375490

Figure 1

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Figure 3

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Figure 4

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Figure 5

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Figure 6

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Figure 7

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Figure 8

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Figure 9

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Figure 10

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Parameters (Session):

par1 = Default ; par2 = 1 ; par3 = 0 ; par4 = 1 ; par5 = 4 ; par6 = White Noise ; par7 = 0.95 ;

Parameters (R input):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = 0 ; par4 = 1 ; par5 = 4 ;

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

par5 <- '4'
par4 <- '1'
par3 <- '0'
par2 <- '1'
par1 <- '1'
library(lattice)
library(lmtest)
library(car)
library(MASS)
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'
}
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$resid)
myr
if(n < 200) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,formatC(signif(x[i],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(x[i]-mysum$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')

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code