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rwasp_multipleregression.wasp

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Multiple Regression

Date of computation

Tue, 13 Dec 2016 20:13:05 +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/2016/Dec/13/t1481656472q1jsds0sv8ag4u1.htm/, Retrieved Sun, 05 May 2024 04:12:13 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=299205, Retrieved Sun, 05 May 2024 04:12:13 +0000

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-       [Multiple Regression] [Multiple Regression] [2016-12-13 19:13:05] [2e2b863c9581eba851d0277c64dc678f] [Current]

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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	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 time5 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=299205&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]

5 seconds[/C][/ROW] [ROW]

R Server[/C]

Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=299205&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=299205&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	Big Analytics Cloud Computing Center

Multiple Linear Regression - Estimated Regression Equation
Geslacht[t] = + 3.90449 -0.201907Belang[t] -0.0349516`Geslacht(t-1s)`[t] + 0.204825`Geslacht(t-2s)`[t] -0.242727`Geslacht(t-3s)`[t] + 0.601888`Geslacht(t-4s)`[t] + 0.117862`Geslacht(t-5s)`[t] -0.0693141`Geslacht(t-6s)`[t] -0.123602`Geslacht(t-7s)`[t] -0.186596`Geslacht(t-8s)`[t] -0.898018`Geslacht(t-9s)`[t] -0.0771512`Geslacht(t-10s)`[t] + 0.496141`Geslacht(t-11s)`[t] -0.637831`Geslacht(t-12s)`[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Geslacht[t] =  +  3.90449 -0.201907Belang[t] -0.0349516`Geslacht(t-1s)`[t] +  0.204825`Geslacht(t-2s)`[t] -0.242727`Geslacht(t-3s)`[t] +  0.601888`Geslacht(t-4s)`[t] +  0.117862`Geslacht(t-5s)`[t] -0.0693141`Geslacht(t-6s)`[t] -0.123602`Geslacht(t-7s)`[t] -0.186596`Geslacht(t-8s)`[t] -0.898018`Geslacht(t-9s)`[t] -0.0771512`Geslacht(t-10s)`[t] +  0.496141`Geslacht(t-11s)`[t] -0.637831`Geslacht(t-12s)`[t]  + e[t] \tabularnewline
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=299205&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Geslacht[t] =  +  3.90449 -0.201907Belang[t] -0.0349516`Geslacht(t-1s)`[t] +  0.204825`Geslacht(t-2s)`[t] -0.242727`Geslacht(t-3s)`[t] +  0.601888`Geslacht(t-4s)`[t] +  0.117862`Geslacht(t-5s)`[t] -0.0693141`Geslacht(t-6s)`[t] -0.123602`Geslacht(t-7s)`[t] -0.186596`Geslacht(t-8s)`[t] -0.898018`Geslacht(t-9s)`[t] -0.0771512`Geslacht(t-10s)`[t] +  0.496141`Geslacht(t-11s)`[t] -0.637831`Geslacht(t-12s)`[t]  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=299205&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=299205&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
Geslacht[t] = + 3.90449 -0.201907Belang[t] -0.0349516`Geslacht(t-1s)`[t] + 0.204825`Geslacht(t-2s)`[t] -0.242727`Geslacht(t-3s)`[t] + 0.601888`Geslacht(t-4s)`[t] + 0.117862`Geslacht(t-5s)`[t] -0.0693141`Geslacht(t-6s)`[t] -0.123602`Geslacht(t-7s)`[t] -0.186596`Geslacht(t-8s)`[t] -0.898018`Geslacht(t-9s)`[t] -0.0771512`Geslacht(t-10s)`[t] + 0.496141`Geslacht(t-11s)`[t] -0.637831`Geslacht(t-12s)`[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)	+3.905	1.967	+1.9850e+00	0.07845	0.03922
Belang	-0.2019	0.1918	-1.0530e+00	0.3199	0.1599
`Geslacht(t-1s)`	-0.03495	0.2665	-1.3120e-01	0.8985	0.4493
`Geslacht(t-2s)`	+0.2048	0.2302	+8.8970e-01	0.3968	0.1984
`Geslacht(t-3s)`	-0.2427	0.3579	-6.7820e-01	0.5147	0.2574
`Geslacht(t-4s)`	+0.6019	0.2944	+2.0440e+00	0.07125	0.03562
`Geslacht(t-5s)`	+0.1179	0.2345	+5.0260e-01	0.6273	0.3136
`Geslacht(t-6s)`	-0.06931	0.2512	-2.7590e-01	0.7888	0.3944
`Geslacht(t-7s)`	-0.1236	0.2888	-4.2790e-01	0.6788	0.3394
`Geslacht(t-8s)`	-0.1866	0.252	-7.4050e-01	0.4779	0.2389
`Geslacht(t-9s)`	-0.898	0.368	-2.4400e+00	0.03735	0.01867
`Geslacht(t-10s)`	-0.07715	0.2393	-3.2240e-01	0.7545	0.3772
`Geslacht(t-11s)`	+0.4961	0.3376	+1.4700e+00	0.1757	0.08785
`Geslacht(t-12s)`	-0.6378	0.3111	-2.0500e+00	0.0706	0.0353

\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) & +3.905 &  1.967 & +1.9850e+00 &  0.07845 &  0.03922 \tabularnewline
Belang & -0.2019 &  0.1918 & -1.0530e+00 &  0.3199 &  0.1599 \tabularnewline
`Geslacht(t-1s)` & -0.03495 &  0.2665 & -1.3120e-01 &  0.8985 &  0.4493 \tabularnewline
`Geslacht(t-2s)` & +0.2048 &  0.2302 & +8.8970e-01 &  0.3968 &  0.1984 \tabularnewline
`Geslacht(t-3s)` & -0.2427 &  0.3579 & -6.7820e-01 &  0.5147 &  0.2574 \tabularnewline
`Geslacht(t-4s)` & +0.6019 &  0.2944 & +2.0440e+00 &  0.07125 &  0.03562 \tabularnewline
`Geslacht(t-5s)` & +0.1179 &  0.2345 & +5.0260e-01 &  0.6273 &  0.3136 \tabularnewline
`Geslacht(t-6s)` & -0.06931 &  0.2512 & -2.7590e-01 &  0.7888 &  0.3944 \tabularnewline
`Geslacht(t-7s)` & -0.1236 &  0.2888 & -4.2790e-01 &  0.6788 &  0.3394 \tabularnewline
`Geslacht(t-8s)` & -0.1866 &  0.252 & -7.4050e-01 &  0.4779 &  0.2389 \tabularnewline
`Geslacht(t-9s)` & -0.898 &  0.368 & -2.4400e+00 &  0.03735 &  0.01867 \tabularnewline
`Geslacht(t-10s)` & -0.07715 &  0.2393 & -3.2240e-01 &  0.7545 &  0.3772 \tabularnewline
`Geslacht(t-11s)` & +0.4961 &  0.3376 & +1.4700e+00 &  0.1757 &  0.08785 \tabularnewline
`Geslacht(t-12s)` & -0.6378 &  0.3111 & -2.0500e+00 &  0.0706 &  0.0353 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=299205&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]+3.905[/C][C] 1.967[/C][C]+1.9850e+00[/C][C] 0.07845[/C][C] 0.03922[/C][/ROW]
[ROW][C]Belang[/C][C]-0.2019[/C][C] 0.1918[/C][C]-1.0530e+00[/C][C] 0.3199[/C][C] 0.1599[/C][/ROW]
[ROW][C]`Geslacht(t-1s)`[/C][C]-0.03495[/C][C] 0.2665[/C][C]-1.3120e-01[/C][C] 0.8985[/C][C] 0.4493[/C][/ROW]
[ROW][C]`Geslacht(t-2s)`[/C][C]+0.2048[/C][C] 0.2302[/C][C]+8.8970e-01[/C][C] 0.3968[/C][C] 0.1984[/C][/ROW]
[ROW][C]`Geslacht(t-3s)`[/C][C]-0.2427[/C][C] 0.3579[/C][C]-6.7820e-01[/C][C] 0.5147[/C][C] 0.2574[/C][/ROW]
[ROW][C]`Geslacht(t-4s)`[/C][C]+0.6019[/C][C] 0.2944[/C][C]+2.0440e+00[/C][C] 0.07125[/C][C] 0.03562[/C][/ROW]
[ROW][C]`Geslacht(t-5s)`[/C][C]+0.1179[/C][C] 0.2345[/C][C]+5.0260e-01[/C][C] 0.6273[/C][C] 0.3136[/C][/ROW]
[ROW][C]`Geslacht(t-6s)`[/C][C]-0.06931[/C][C] 0.2512[/C][C]-2.7590e-01[/C][C] 0.7888[/C][C] 0.3944[/C][/ROW]
[ROW][C]`Geslacht(t-7s)`[/C][C]-0.1236[/C][C] 0.2888[/C][C]-4.2790e-01[/C][C] 0.6788[/C][C] 0.3394[/C][/ROW]
[ROW][C]`Geslacht(t-8s)`[/C][C]-0.1866[/C][C] 0.252[/C][C]-7.4050e-01[/C][C] 0.4779[/C][C] 0.2389[/C][/ROW]
[ROW][C]`Geslacht(t-9s)`[/C][C]-0.898[/C][C] 0.368[/C][C]-2.4400e+00[/C][C] 0.03735[/C][C] 0.01867[/C][/ROW]
[ROW][C]`Geslacht(t-10s)`[/C][C]-0.07715[/C][C] 0.2393[/C][C]-3.2240e-01[/C][C] 0.7545[/C][C] 0.3772[/C][/ROW]
[ROW][C]`Geslacht(t-11s)`[/C][C]+0.4961[/C][C] 0.3376[/C][C]+1.4700e+00[/C][C] 0.1757[/C][C] 0.08785[/C][/ROW]
[ROW][C]`Geslacht(t-12s)`[/C][C]-0.6378[/C][C] 0.3111[/C][C]-2.0500e+00[/C][C] 0.0706[/C][C] 0.0353[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=299205&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=299205&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)	+3.905	1.967	+1.9850e+00	0.07845	0.03922
Belang	-0.2019	0.1918	-1.0530e+00	0.3199	0.1599
`Geslacht(t-1s)`	-0.03495	0.2665	-1.3120e-01	0.8985	0.4493
`Geslacht(t-2s)`	+0.2048	0.2302	+8.8970e-01	0.3968	0.1984
`Geslacht(t-3s)`	-0.2427	0.3579	-6.7820e-01	0.5147	0.2574
`Geslacht(t-4s)`	+0.6019	0.2944	+2.0440e+00	0.07125	0.03562
`Geslacht(t-5s)`	+0.1179	0.2345	+5.0260e-01	0.6273	0.3136
`Geslacht(t-6s)`	-0.06931	0.2512	-2.7590e-01	0.7888	0.3944
`Geslacht(t-7s)`	-0.1236	0.2888	-4.2790e-01	0.6788	0.3394
`Geslacht(t-8s)`	-0.1866	0.252	-7.4050e-01	0.4779	0.2389
`Geslacht(t-9s)`	-0.898	0.368	-2.4400e+00	0.03735	0.01867
`Geslacht(t-10s)`	-0.07715	0.2393	-3.2240e-01	0.7545	0.3772
`Geslacht(t-11s)`	+0.4961	0.3376	+1.4700e+00	0.1757	0.08785
`Geslacht(t-12s)`	-0.6378	0.3111	-2.0500e+00	0.0706	0.0353

Multiple Linear Regression - Regression Statistics
Multiple R	0.8199
R-squared	0.6722
Adjusted R-squared	0.1988
F-TEST (value)	1.42
F-TEST (DF numerator)	13
F-TEST (DF denominator)	9
p-value	0.3036
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.4572
Sum Squared Residuals	1.881

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.8199 \tabularnewline
R-squared &  0.6722 \tabularnewline
Adjusted R-squared &  0.1988 \tabularnewline
F-TEST (value) &  1.42 \tabularnewline
F-TEST (DF numerator) & 13 \tabularnewline
F-TEST (DF denominator) & 9 \tabularnewline
p-value &  0.3036 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  0.4572 \tabularnewline
Sum Squared Residuals &  1.881 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=299205&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.8199[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.6722[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.1988[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 1.42[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]13[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]9[/C][/ROW]
[ROW][C]p-value[/C][C] 0.3036[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C] 0.4572[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 1.881[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=299205&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=299205&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	0.8199
R-squared	0.6722
Adjusted R-squared	0.1988
F-TEST (value)	1.42
F-TEST (DF numerator)	13
F-TEST (DF denominator)	9
p-value	0.3036
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.4572
Sum Squared Residuals	1.881

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	2	1.841	0.1588
2	2	1.379	0.6207
3	2	2.098	-0.09848
4	1	1.501	-0.501
5	1	1.175	-0.1747
6	1	1.294	-0.2935
7	1	0.9083	0.09173
8	2	1.538	0.4617
9	2	1.831	0.1694
10	2	1.987	0.01281
11	2	1.843	0.1573
12	1	0.9745	0.02554
13	2	1.877	0.1235
14	1	1.216	-0.2158
15	2	2.31	-0.3099
16	1	0.8402	0.1598
17	1	1.123	-0.1231
18	1	1.425	-0.4252
19	1	1.338	-0.3382
20	1	1.29	-0.2898
21	2	1.824	0.1762
22	2	1.591	0.4092
23	1	0.7969	0.2031

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 &  2 &  1.841 &  0.1588 \tabularnewline
2 &  2 &  1.379 &  0.6207 \tabularnewline
3 &  2 &  2.098 & -0.09848 \tabularnewline
4 &  1 &  1.501 & -0.501 \tabularnewline
5 &  1 &  1.175 & -0.1747 \tabularnewline
6 &  1 &  1.294 & -0.2935 \tabularnewline
7 &  1 &  0.9083 &  0.09173 \tabularnewline
8 &  2 &  1.538 &  0.4617 \tabularnewline
9 &  2 &  1.831 &  0.1694 \tabularnewline
10 &  2 &  1.987 &  0.01281 \tabularnewline
11 &  2 &  1.843 &  0.1573 \tabularnewline
12 &  1 &  0.9745 &  0.02554 \tabularnewline
13 &  2 &  1.877 &  0.1235 \tabularnewline
14 &  1 &  1.216 & -0.2158 \tabularnewline
15 &  2 &  2.31 & -0.3099 \tabularnewline
16 &  1 &  0.8402 &  0.1598 \tabularnewline
17 &  1 &  1.123 & -0.1231 \tabularnewline
18 &  1 &  1.425 & -0.4252 \tabularnewline
19 &  1 &  1.338 & -0.3382 \tabularnewline
20 &  1 &  1.29 & -0.2898 \tabularnewline
21 &  2 &  1.824 &  0.1762 \tabularnewline
22 &  2 &  1.591 &  0.4092 \tabularnewline
23 &  1 &  0.7969 &  0.2031 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=299205&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] 2[/C][C] 1.841[/C][C] 0.1588[/C][/ROW]
[ROW][C]2[/C][C] 2[/C][C] 1.379[/C][C] 0.6207[/C][/ROW]
[ROW][C]3[/C][C] 2[/C][C] 2.098[/C][C]-0.09848[/C][/ROW]
[ROW][C]4[/C][C] 1[/C][C] 1.501[/C][C]-0.501[/C][/ROW]
[ROW][C]5[/C][C] 1[/C][C] 1.175[/C][C]-0.1747[/C][/ROW]
[ROW][C]6[/C][C] 1[/C][C] 1.294[/C][C]-0.2935[/C][/ROW]
[ROW][C]7[/C][C] 1[/C][C] 0.9083[/C][C] 0.09173[/C][/ROW]
[ROW][C]8[/C][C] 2[/C][C] 1.538[/C][C] 0.4617[/C][/ROW]
[ROW][C]9[/C][C] 2[/C][C] 1.831[/C][C] 0.1694[/C][/ROW]
[ROW][C]10[/C][C] 2[/C][C] 1.987[/C][C] 0.01281[/C][/ROW]
[ROW][C]11[/C][C] 2[/C][C] 1.843[/C][C] 0.1573[/C][/ROW]
[ROW][C]12[/C][C] 1[/C][C] 0.9745[/C][C] 0.02554[/C][/ROW]
[ROW][C]13[/C][C] 2[/C][C] 1.877[/C][C] 0.1235[/C][/ROW]
[ROW][C]14[/C][C] 1[/C][C] 1.216[/C][C]-0.2158[/C][/ROW]
[ROW][C]15[/C][C] 2[/C][C] 2.31[/C][C]-0.3099[/C][/ROW]
[ROW][C]16[/C][C] 1[/C][C] 0.8402[/C][C] 0.1598[/C][/ROW]
[ROW][C]17[/C][C] 1[/C][C] 1.123[/C][C]-0.1231[/C][/ROW]
[ROW][C]18[/C][C] 1[/C][C] 1.425[/C][C]-0.4252[/C][/ROW]
[ROW][C]19[/C][C] 1[/C][C] 1.338[/C][C]-0.3382[/C][/ROW]
[ROW][C]20[/C][C] 1[/C][C] 1.29[/C][C]-0.2898[/C][/ROW]
[ROW][C]21[/C][C] 2[/C][C] 1.824[/C][C] 0.1762[/C][/ROW]
[ROW][C]22[/C][C] 2[/C][C] 1.591[/C][C] 0.4092[/C][/ROW]
[ROW][C]23[/C][C] 1[/C][C] 0.7969[/C][C] 0.2031[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=299205&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=299205&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	2	1.841	0.1588
2	2	1.379	0.6207
3	2	2.098	-0.09848
4	1	1.501	-0.501
5	1	1.175	-0.1747
6	1	1.294	-0.2935
7	1	0.9083	0.09173
8	2	1.538	0.4617
9	2	1.831	0.1694
10	2	1.987	0.01281
11	2	1.843	0.1573
12	1	0.9745	0.02554
13	2	1.877	0.1235
14	1	1.216	-0.2158
15	2	2.31	-0.3099
16	1	0.8402	0.1598
17	1	1.123	-0.1231
18	1	1.425	-0.4252
19	1	1.338	-0.3382
20	1	1.29	-0.2898
21	2	1.824	0.1762
22	2	1.591	0.4092
23	1	0.7969	0.2031

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted RESET test data: mylm RESET = 2.7567, df1 = 2, df2 = 7, p-value = 0.1309
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = -0.02491, df1 = 26, df2 = -17, p-value = NA
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 0.19146, df1 = 2, df2 = 7, p-value = 0.8299

\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 = 2.7567, df1 = 2, df2 = 7, p-value = 0.1309
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of regressors \tabularnewline
> reset_test_regressors
	RESET test
data:  mylm
RESET = -0.02491, df1 = 26, df2 = -17, p-value = NA
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of principal components \tabularnewline
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 0.19146, df1 = 2, df2 = 7, p-value = 0.8299
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=299205&T=5

[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 = 2.7567, df1 = 2, df2 = 7, p-value = 0.1309
[/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 = -0.02491, df1 = 26, df2 = -17, p-value = NA
[/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 = 0.19146, df1 = 2, df2 = 7, p-value = 0.8299
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=299205&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=299205&T=5

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 = 2.7567, df1 = 2, df2 = 7, p-value = 0.1309
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = -0.02491, df1 = 26, df2 = -17, p-value = NA
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 0.19146, df1 = 2, df2 = 7, p-value = 0.8299

Variance Inflation Factors (Multicollinearity)

> vif
           Belang  `Geslacht(t-1s)`  `Geslacht(t-2s)`  `Geslacht(t-3s)` 
         2.065946          1.949610          1.455233          3.464190 
 `Geslacht(t-4s)`  `Geslacht(t-5s)`  `Geslacht(t-6s)`  `Geslacht(t-7s)` 
         2.379873          1.441274          1.732663          2.256328 
 `Geslacht(t-8s)`  `Geslacht(t-9s)` `Geslacht(t-10s)` `Geslacht(t-11s)` 
         1.664250          3.380580          1.572239          2.844737 
`Geslacht(t-12s)` 
         2.658009

\begin{tabular}{lllllllll}
\hline
Variance Inflation Factors (Multicollinearity) \tabularnewline
> vif
           Belang  `Geslacht(t-1s)`  `Geslacht(t-2s)`  `Geslacht(t-3s)` 
         2.065946          1.949610          1.455233          3.464190 
 `Geslacht(t-4s)`  `Geslacht(t-5s)`  `Geslacht(t-6s)`  `Geslacht(t-7s)` 
         2.379873          1.441274          1.732663          2.256328 
 `Geslacht(t-8s)`  `Geslacht(t-9s)` `Geslacht(t-10s)` `Geslacht(t-11s)` 
         1.664250          3.380580          1.572239          2.844737 
`Geslacht(t-12s)` 
         2.658009 
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=299205&T=6

[TABLE]
[ROW][C]Variance Inflation Factors (Multicollinearity)[/C][/ROW]
[ROW][C]> vif
           Belang  `Geslacht(t-1s)`  `Geslacht(t-2s)`  `Geslacht(t-3s)` 
         2.065946          1.949610          1.455233          3.464190 
 `Geslacht(t-4s)`  `Geslacht(t-5s)`  `Geslacht(t-6s)`  `Geslacht(t-7s)` 
         2.379873          1.441274          1.732663          2.256328 
 `Geslacht(t-8s)`  `Geslacht(t-9s)` `Geslacht(t-10s)` `Geslacht(t-11s)` 
         1.664250          3.380580          1.572239          2.844737 
`Geslacht(t-12s)` 
         2.658009 
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=299205&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=299205&T=6

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
           Belang  `Geslacht(t-1s)`  `Geslacht(t-2s)`  `Geslacht(t-3s)` 
         2.065946          1.949610          1.455233          3.464190 
 `Geslacht(t-4s)`  `Geslacht(t-5s)`  `Geslacht(t-6s)`  `Geslacht(t-7s)` 
         2.379873          1.441274          1.732663          2.256328 
 `Geslacht(t-8s)`  `Geslacht(t-9s)` `Geslacht(t-10s)` `Geslacht(t-11s)` 
         1.664250          3.380580          1.572239          2.844737 
`Geslacht(t-12s)` 
         2.658009

Figure 1

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Figure 2

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Figure 3

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Figure 4

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Figure 5

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Figure 6

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Figure 7

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Figure 8

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Figure 9

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Figure 10

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Parameters (Session):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ; par4 = 0 ; par5 = 12 ;

Parameters (R input):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ; par4 = 0 ; par5 = 12 ;

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

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