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R Software Module

rwasp_multipleregression.wasp

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Multiple Regression

Date of computation

Mon, 23 Jan 2017 10:44:16 +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/t1485164724by070psdcnwm9s2.htm/, Retrieved Wed, 15 May 2024 09:54:58 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=304623, Retrieved Wed, 15 May 2024 09:54:58 +0000

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Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)

-       [Multiple Regression] [Multiple Regressi...] [2017-01-23 09:44:16] [7b02c9ca65294818d9c418453f92ae83] [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	10 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 time10 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=304623&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]

10 seconds[/C][/ROW] [ROW]

R Server[/C]

Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=304623&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=304623&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	10 seconds
R Server	Big Analytics Cloud Computing Center

Multiple Linear Regression - Estimated Regression Equation
ITHSUM[t] = + 5.76873 -0.0237248Bevr_Leeftijd[t] + 0.00592575TVDC[t] + 0.464441SKEOU1[t] -0.128696SKEOU2[t] + 0.565992SKEOU3[t] + 0.745118SKEOU4[t] + 0.762374SKEOU5[t] + 0.36521SKEOU6[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
ITHSUM[t] =  +  5.76873 -0.0237248Bevr_Leeftijd[t] +  0.00592575TVDC[t] +  0.464441SKEOU1[t] -0.128696SKEOU2[t] +  0.565992SKEOU3[t] +  0.745118SKEOU4[t] +  0.762374SKEOU5[t] +  0.36521SKEOU6[t]  + e[t] \tabularnewline
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=304623&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]ITHSUM[t] =  +  5.76873 -0.0237248Bevr_Leeftijd[t] +  0.00592575TVDC[t] +  0.464441SKEOU1[t] -0.128696SKEOU2[t] +  0.565992SKEOU3[t] +  0.745118SKEOU4[t] +  0.762374SKEOU5[t] +  0.36521SKEOU6[t]  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=304623&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=304623&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] = + 5.76873 -0.0237248Bevr_Leeftijd[t] + 0.00592575TVDC[t] + 0.464441SKEOU1[t] -0.128696SKEOU2[t] + 0.565992SKEOU3[t] + 0.745118SKEOU4[t] + 0.762374SKEOU5[t] + 0.36521SKEOU6[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)	+5.769	4.204	+1.3720e+00	0.1734	0.0867
Bevr_Leeftijd	-0.02372	0.1372	-1.7290e-01	0.8631	0.4316
TVDC	+0.005926	0.1497	+3.9580e-02	0.9685	0.4843
SKEOU1	+0.4644	0.3296	+1.4090e+00	0.1622	0.08112
SKEOU2	-0.1287	0.3935	-3.2710e-01	0.7444	0.3722
SKEOU3	+0.566	0.2993	+1.8910e+00	0.06179	0.0309
SKEOU4	+0.7451	0.4302	+1.7320e+00	0.08672	0.04336
SKEOU5	+0.7624	0.3514	+2.1690e+00	0.0327	0.01635
SKEOU6	+0.3652	0.3771	+9.6830e-01	0.3355	0.1677

\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) & +5.769 &  4.204 & +1.3720e+00 &  0.1734 &  0.0867 \tabularnewline
Bevr_Leeftijd & -0.02372 &  0.1372 & -1.7290e-01 &  0.8631 &  0.4316 \tabularnewline
TVDC & +0.005926 &  0.1497 & +3.9580e-02 &  0.9685 &  0.4843 \tabularnewline
SKEOU1 & +0.4644 &  0.3296 & +1.4090e+00 &  0.1622 &  0.08112 \tabularnewline
SKEOU2 & -0.1287 &  0.3935 & -3.2710e-01 &  0.7444 &  0.3722 \tabularnewline
SKEOU3 & +0.566 &  0.2993 & +1.8910e+00 &  0.06179 &  0.0309 \tabularnewline
SKEOU4 & +0.7451 &  0.4302 & +1.7320e+00 &  0.08672 &  0.04336 \tabularnewline
SKEOU5 & +0.7624 &  0.3514 & +2.1690e+00 &  0.0327 &  0.01635 \tabularnewline
SKEOU6 & +0.3652 &  0.3771 & +9.6830e-01 &  0.3355 &  0.1677 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=304623&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]+5.769[/C][C] 4.204[/C][C]+1.3720e+00[/C][C] 0.1734[/C][C] 0.0867[/C][/ROW]
[ROW][C]Bevr_Leeftijd[/C][C]-0.02372[/C][C] 0.1372[/C][C]-1.7290e-01[/C][C] 0.8631[/C][C] 0.4316[/C][/ROW]
[ROW][C]TVDC[/C][C]+0.005926[/C][C] 0.1497[/C][C]+3.9580e-02[/C][C] 0.9685[/C][C] 0.4843[/C][/ROW]
[ROW][C]SKEOU1[/C][C]+0.4644[/C][C] 0.3296[/C][C]+1.4090e+00[/C][C] 0.1622[/C][C] 0.08112[/C][/ROW]
[ROW][C]SKEOU2[/C][C]-0.1287[/C][C] 0.3935[/C][C]-3.2710e-01[/C][C] 0.7444[/C][C] 0.3722[/C][/ROW]
[ROW][C]SKEOU3[/C][C]+0.566[/C][C] 0.2993[/C][C]+1.8910e+00[/C][C] 0.06179[/C][C] 0.0309[/C][/ROW]
[ROW][C]SKEOU4[/C][C]+0.7451[/C][C] 0.4302[/C][C]+1.7320e+00[/C][C] 0.08672[/C][C] 0.04336[/C][/ROW]
[ROW][C]SKEOU5[/C][C]+0.7624[/C][C] 0.3514[/C][C]+2.1690e+00[/C][C] 0.0327[/C][C] 0.01635[/C][/ROW]
[ROW][C]SKEOU6[/C][C]+0.3652[/C][C] 0.3771[/C][C]+9.6830e-01[/C][C] 0.3355[/C][C] 0.1677[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=304623&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=304623&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)	+5.769	4.204	+1.3720e+00	0.1734	0.0867
Bevr_Leeftijd	-0.02372	0.1372	-1.7290e-01	0.8631	0.4316
TVDC	+0.005926	0.1497	+3.9580e-02	0.9685	0.4843
SKEOU1	+0.4644	0.3296	+1.4090e+00	0.1622	0.08112
SKEOU2	-0.1287	0.3935	-3.2710e-01	0.7444	0.3722
SKEOU3	+0.566	0.2993	+1.8910e+00	0.06179	0.0309
SKEOU4	+0.7451	0.4302	+1.7320e+00	0.08672	0.04336
SKEOU5	+0.7624	0.3514	+2.1690e+00	0.0327	0.01635
SKEOU6	+0.3652	0.3771	+9.6830e-01	0.3355	0.1677

Multiple Linear Regression - Regression Statistics
Multiple R	0.3759
R-squared	0.1413
Adjusted R-squared	0.06501
F-TEST (value)	1.852
F-TEST (DF numerator)	8
F-TEST (DF denominator)	90
p-value	0.07766
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	2.209
Sum Squared Residuals	439.1

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.3759 \tabularnewline
R-squared &  0.1413 \tabularnewline
Adjusted R-squared &  0.06501 \tabularnewline
F-TEST (value) &  1.852 \tabularnewline
F-TEST (DF numerator) & 8 \tabularnewline
F-TEST (DF denominator) & 90 \tabularnewline
p-value &  0.07766 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  2.209 \tabularnewline
Sum Squared Residuals &  439.1 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=304623&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.3759[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.1413[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.06501[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 1.852[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]8[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]90[/C][/ROW]
[ROW][C]p-value[/C][C] 0.07766[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C] 2.209[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 439.1[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=304623&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=304623&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	0.3759
R-squared	0.1413
Adjusted R-squared	0.06501
F-TEST (value)	1.852
F-TEST (DF numerator)	8
F-TEST (DF denominator)	90
p-value	0.07766
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	2.209
Sum Squared Residuals	439.1

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	14	16.7	-2.696
2	19	17.18	1.819
3	17	17.8	-0.7974
4	20	17.15	2.85
5	15	17.99	-2.986
6	19	16.1	2.901
7	20	16.13	3.869
8	18	17.05	0.9533
9	15	15.87	-0.8695
10	14	16.91	-2.912
11	16	16.18	-0.1847
12	19	17.37	1.629
13	18	16.43	1.571
14	17	16.5	0.4954
15	19	17.18	1.82
16	17	16.93	0.07248
17	19	18.07	0.9305
18	20	17.86	2.135
19	19	16.97	2.027
20	16	15.21	0.7917
21	16	16.81	-0.8135
22	18	16.06	1.939
23	16	17.09	-1.088
24	17	18.01	-1.01
25	20	17.72	2.276
26	19	16.81	2.186
27	7	16.05	-9.054
28	16	14.97	1.03
29	16	17.18	-1.184
30	18	16.95	1.046
31	17	18.3	-1.303
32	19	16.2	2.8
33	16	17.01	-1.011
34	13	15.69	-2.687
35	16	16.53	-0.5278
36	12	17.25	-5.248
37	17	16.71	0.2908
38	17	17.59	-0.5907
39	17	14.47	2.526
40	16	18.17	-2.174
41	16	16.65	-0.6493
42	14	16.1	-2.097
43	16	16.44	-0.4448
44	13	16.32	-3.316
45	16	16.38	-0.3811
46	14	15.4	-1.403
47	19	17.23	1.775
48	18	16.94	1.065
49	14	17.05	-3.053
50	18	17.12	0.8838
51	15	15.48	-0.4783
52	17	17.08	-0.07805
53	13	16.95	-3.951
54	19	17.48	1.522
55	18	18.12	-0.1211
56	15	15.42	-0.4212
57	20	17.35	2.647
58	19	16.78	2.215
59	18	17.37	0.6294
60	15	16.71	-1.715
61	20	18.62	1.384
62	17	17.46	-0.462
63	19	17.36	1.635
64	20	17.18	2.82
65	18	17.03	0.9709
66	17	16.44	0.5614
67	18	16.56	1.435
68	17	16.2	0.8045
69	20	18.67	1.331
70	16	16.16	-0.1572
71	14	16.33	-2.325
72	15	15.63	-0.6306
73	20	17.2	2.804
74	17	15.97	1.031
75	17	15.65	1.353
76	18	14.69	3.311
77	20	16.91	3.091
78	16	16.07	-0.07398
79	18	17.62	0.3795
80	15	16.89	-1.894
81	18	18.84	-0.8426
82	20	18.01	1.99
83	14	17.36	-3.363
84	15	15.29	-0.2896
85	17	17.03	-0.0306
86	18	17.6	0.3975
87	20	17.48	2.519
88	17	17.12	-0.1222
89	16	16.54	-0.5352
90	11	15.74	-4.742
91	15	16.89	-1.894
92	18	16.99	1.005
93	16	18.31	-2.309
94	18	16.89	1.112
95	15	16.99	-1.992
96	17	16.16	0.8428
97	19	17.59	1.408
98	16	16.83	-0.8283
99	14	17.15	-3.147

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 &  14 &  16.7 & -2.696 \tabularnewline
2 &  19 &  17.18 &  1.819 \tabularnewline
3 &  17 &  17.8 & -0.7974 \tabularnewline
4 &  20 &  17.15 &  2.85 \tabularnewline
5 &  15 &  17.99 & -2.986 \tabularnewline
6 &  19 &  16.1 &  2.901 \tabularnewline
7 &  20 &  16.13 &  3.869 \tabularnewline
8 &  18 &  17.05 &  0.9533 \tabularnewline
9 &  15 &  15.87 & -0.8695 \tabularnewline
10 &  14 &  16.91 & -2.912 \tabularnewline
11 &  16 &  16.18 & -0.1847 \tabularnewline
12 &  19 &  17.37 &  1.629 \tabularnewline
13 &  18 &  16.43 &  1.571 \tabularnewline
14 &  17 &  16.5 &  0.4954 \tabularnewline
15 &  19 &  17.18 &  1.82 \tabularnewline
16 &  17 &  16.93 &  0.07248 \tabularnewline
17 &  19 &  18.07 &  0.9305 \tabularnewline
18 &  20 &  17.86 &  2.135 \tabularnewline
19 &  19 &  16.97 &  2.027 \tabularnewline
20 &  16 &  15.21 &  0.7917 \tabularnewline
21 &  16 &  16.81 & -0.8135 \tabularnewline
22 &  18 &  16.06 &  1.939 \tabularnewline
23 &  16 &  17.09 & -1.088 \tabularnewline
24 &  17 &  18.01 & -1.01 \tabularnewline
25 &  20 &  17.72 &  2.276 \tabularnewline
26 &  19 &  16.81 &  2.186 \tabularnewline
27 &  7 &  16.05 & -9.054 \tabularnewline
28 &  16 &  14.97 &  1.03 \tabularnewline
29 &  16 &  17.18 & -1.184 \tabularnewline
30 &  18 &  16.95 &  1.046 \tabularnewline
31 &  17 &  18.3 & -1.303 \tabularnewline
32 &  19 &  16.2 &  2.8 \tabularnewline
33 &  16 &  17.01 & -1.011 \tabularnewline
34 &  13 &  15.69 & -2.687 \tabularnewline
35 &  16 &  16.53 & -0.5278 \tabularnewline
36 &  12 &  17.25 & -5.248 \tabularnewline
37 &  17 &  16.71 &  0.2908 \tabularnewline
38 &  17 &  17.59 & -0.5907 \tabularnewline
39 &  17 &  14.47 &  2.526 \tabularnewline
40 &  16 &  18.17 & -2.174 \tabularnewline
41 &  16 &  16.65 & -0.6493 \tabularnewline
42 &  14 &  16.1 & -2.097 \tabularnewline
43 &  16 &  16.44 & -0.4448 \tabularnewline
44 &  13 &  16.32 & -3.316 \tabularnewline
45 &  16 &  16.38 & -0.3811 \tabularnewline
46 &  14 &  15.4 & -1.403 \tabularnewline
47 &  19 &  17.23 &  1.775 \tabularnewline
48 &  18 &  16.94 &  1.065 \tabularnewline
49 &  14 &  17.05 & -3.053 \tabularnewline
50 &  18 &  17.12 &  0.8838 \tabularnewline
51 &  15 &  15.48 & -0.4783 \tabularnewline
52 &  17 &  17.08 & -0.07805 \tabularnewline
53 &  13 &  16.95 & -3.951 \tabularnewline
54 &  19 &  17.48 &  1.522 \tabularnewline
55 &  18 &  18.12 & -0.1211 \tabularnewline
56 &  15 &  15.42 & -0.4212 \tabularnewline
57 &  20 &  17.35 &  2.647 \tabularnewline
58 &  19 &  16.78 &  2.215 \tabularnewline
59 &  18 &  17.37 &  0.6294 \tabularnewline
60 &  15 &  16.71 & -1.715 \tabularnewline
61 &  20 &  18.62 &  1.384 \tabularnewline
62 &  17 &  17.46 & -0.462 \tabularnewline
63 &  19 &  17.36 &  1.635 \tabularnewline
64 &  20 &  17.18 &  2.82 \tabularnewline
65 &  18 &  17.03 &  0.9709 \tabularnewline
66 &  17 &  16.44 &  0.5614 \tabularnewline
67 &  18 &  16.56 &  1.435 \tabularnewline
68 &  17 &  16.2 &  0.8045 \tabularnewline
69 &  20 &  18.67 &  1.331 \tabularnewline
70 &  16 &  16.16 & -0.1572 \tabularnewline
71 &  14 &  16.33 & -2.325 \tabularnewline
72 &  15 &  15.63 & -0.6306 \tabularnewline
73 &  20 &  17.2 &  2.804 \tabularnewline
74 &  17 &  15.97 &  1.031 \tabularnewline
75 &  17 &  15.65 &  1.353 \tabularnewline
76 &  18 &  14.69 &  3.311 \tabularnewline
77 &  20 &  16.91 &  3.091 \tabularnewline
78 &  16 &  16.07 & -0.07398 \tabularnewline
79 &  18 &  17.62 &  0.3795 \tabularnewline
80 &  15 &  16.89 & -1.894 \tabularnewline
81 &  18 &  18.84 & -0.8426 \tabularnewline
82 &  20 &  18.01 &  1.99 \tabularnewline
83 &  14 &  17.36 & -3.363 \tabularnewline
84 &  15 &  15.29 & -0.2896 \tabularnewline
85 &  17 &  17.03 & -0.0306 \tabularnewline
86 &  18 &  17.6 &  0.3975 \tabularnewline
87 &  20 &  17.48 &  2.519 \tabularnewline
88 &  17 &  17.12 & -0.1222 \tabularnewline
89 &  16 &  16.54 & -0.5352 \tabularnewline
90 &  11 &  15.74 & -4.742 \tabularnewline
91 &  15 &  16.89 & -1.894 \tabularnewline
92 &  18 &  16.99 &  1.005 \tabularnewline
93 &  16 &  18.31 & -2.309 \tabularnewline
94 &  18 &  16.89 &  1.112 \tabularnewline
95 &  15 &  16.99 & -1.992 \tabularnewline
96 &  17 &  16.16 &  0.8428 \tabularnewline
97 &  19 &  17.59 &  1.408 \tabularnewline
98 &  16 &  16.83 & -0.8283 \tabularnewline
99 &  14 &  17.15 & -3.147 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=304623&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] 14[/C][C] 16.7[/C][C]-2.696[/C][/ROW]
[ROW][C]2[/C][C] 19[/C][C] 17.18[/C][C] 1.819[/C][/ROW]
[ROW][C]3[/C][C] 17[/C][C] 17.8[/C][C]-0.7974[/C][/ROW]
[ROW][C]4[/C][C] 20[/C][C] 17.15[/C][C] 2.85[/C][/ROW]
[ROW][C]5[/C][C] 15[/C][C] 17.99[/C][C]-2.986[/C][/ROW]
[ROW][C]6[/C][C] 19[/C][C] 16.1[/C][C] 2.901[/C][/ROW]
[ROW][C]7[/C][C] 20[/C][C] 16.13[/C][C] 3.869[/C][/ROW]
[ROW][C]8[/C][C] 18[/C][C] 17.05[/C][C] 0.9533[/C][/ROW]
[ROW][C]9[/C][C] 15[/C][C] 15.87[/C][C]-0.8695[/C][/ROW]
[ROW][C]10[/C][C] 14[/C][C] 16.91[/C][C]-2.912[/C][/ROW]
[ROW][C]11[/C][C] 16[/C][C] 16.18[/C][C]-0.1847[/C][/ROW]
[ROW][C]12[/C][C] 19[/C][C] 17.37[/C][C] 1.629[/C][/ROW]
[ROW][C]13[/C][C] 18[/C][C] 16.43[/C][C] 1.571[/C][/ROW]
[ROW][C]14[/C][C] 17[/C][C] 16.5[/C][C] 0.4954[/C][/ROW]
[ROW][C]15[/C][C] 19[/C][C] 17.18[/C][C] 1.82[/C][/ROW]
[ROW][C]16[/C][C] 17[/C][C] 16.93[/C][C] 0.07248[/C][/ROW]
[ROW][C]17[/C][C] 19[/C][C] 18.07[/C][C] 0.9305[/C][/ROW]
[ROW][C]18[/C][C] 20[/C][C] 17.86[/C][C] 2.135[/C][/ROW]
[ROW][C]19[/C][C] 19[/C][C] 16.97[/C][C] 2.027[/C][/ROW]
[ROW][C]20[/C][C] 16[/C][C] 15.21[/C][C] 0.7917[/C][/ROW]
[ROW][C]21[/C][C] 16[/C][C] 16.81[/C][C]-0.8135[/C][/ROW]
[ROW][C]22[/C][C] 18[/C][C] 16.06[/C][C] 1.939[/C][/ROW]
[ROW][C]23[/C][C] 16[/C][C] 17.09[/C][C]-1.088[/C][/ROW]
[ROW][C]24[/C][C] 17[/C][C] 18.01[/C][C]-1.01[/C][/ROW]
[ROW][C]25[/C][C] 20[/C][C] 17.72[/C][C] 2.276[/C][/ROW]
[ROW][C]26[/C][C] 19[/C][C] 16.81[/C][C] 2.186[/C][/ROW]
[ROW][C]27[/C][C] 7[/C][C] 16.05[/C][C]-9.054[/C][/ROW]
[ROW][C]28[/C][C] 16[/C][C] 14.97[/C][C] 1.03[/C][/ROW]
[ROW][C]29[/C][C] 16[/C][C] 17.18[/C][C]-1.184[/C][/ROW]
[ROW][C]30[/C][C] 18[/C][C] 16.95[/C][C] 1.046[/C][/ROW]
[ROW][C]31[/C][C] 17[/C][C] 18.3[/C][C]-1.303[/C][/ROW]
[ROW][C]32[/C][C] 19[/C][C] 16.2[/C][C] 2.8[/C][/ROW]
[ROW][C]33[/C][C] 16[/C][C] 17.01[/C][C]-1.011[/C][/ROW]
[ROW][C]34[/C][C] 13[/C][C] 15.69[/C][C]-2.687[/C][/ROW]
[ROW][C]35[/C][C] 16[/C][C] 16.53[/C][C]-0.5278[/C][/ROW]
[ROW][C]36[/C][C] 12[/C][C] 17.25[/C][C]-5.248[/C][/ROW]
[ROW][C]37[/C][C] 17[/C][C] 16.71[/C][C] 0.2908[/C][/ROW]
[ROW][C]38[/C][C] 17[/C][C] 17.59[/C][C]-0.5907[/C][/ROW]
[ROW][C]39[/C][C] 17[/C][C] 14.47[/C][C] 2.526[/C][/ROW]
[ROW][C]40[/C][C] 16[/C][C] 18.17[/C][C]-2.174[/C][/ROW]
[ROW][C]41[/C][C] 16[/C][C] 16.65[/C][C]-0.6493[/C][/ROW]
[ROW][C]42[/C][C] 14[/C][C] 16.1[/C][C]-2.097[/C][/ROW]
[ROW][C]43[/C][C] 16[/C][C] 16.44[/C][C]-0.4448[/C][/ROW]
[ROW][C]44[/C][C] 13[/C][C] 16.32[/C][C]-3.316[/C][/ROW]
[ROW][C]45[/C][C] 16[/C][C] 16.38[/C][C]-0.3811[/C][/ROW]
[ROW][C]46[/C][C] 14[/C][C] 15.4[/C][C]-1.403[/C][/ROW]
[ROW][C]47[/C][C] 19[/C][C] 17.23[/C][C] 1.775[/C][/ROW]
[ROW][C]48[/C][C] 18[/C][C] 16.94[/C][C] 1.065[/C][/ROW]
[ROW][C]49[/C][C] 14[/C][C] 17.05[/C][C]-3.053[/C][/ROW]
[ROW][C]50[/C][C] 18[/C][C] 17.12[/C][C] 0.8838[/C][/ROW]
[ROW][C]51[/C][C] 15[/C][C] 15.48[/C][C]-0.4783[/C][/ROW]
[ROW][C]52[/C][C] 17[/C][C] 17.08[/C][C]-0.07805[/C][/ROW]
[ROW][C]53[/C][C] 13[/C][C] 16.95[/C][C]-3.951[/C][/ROW]
[ROW][C]54[/C][C] 19[/C][C] 17.48[/C][C] 1.522[/C][/ROW]
[ROW][C]55[/C][C] 18[/C][C] 18.12[/C][C]-0.1211[/C][/ROW]
[ROW][C]56[/C][C] 15[/C][C] 15.42[/C][C]-0.4212[/C][/ROW]
[ROW][C]57[/C][C] 20[/C][C] 17.35[/C][C] 2.647[/C][/ROW]
[ROW][C]58[/C][C] 19[/C][C] 16.78[/C][C] 2.215[/C][/ROW]
[ROW][C]59[/C][C] 18[/C][C] 17.37[/C][C] 0.6294[/C][/ROW]
[ROW][C]60[/C][C] 15[/C][C] 16.71[/C][C]-1.715[/C][/ROW]
[ROW][C]61[/C][C] 20[/C][C] 18.62[/C][C] 1.384[/C][/ROW]
[ROW][C]62[/C][C] 17[/C][C] 17.46[/C][C]-0.462[/C][/ROW]
[ROW][C]63[/C][C] 19[/C][C] 17.36[/C][C] 1.635[/C][/ROW]
[ROW][C]64[/C][C] 20[/C][C] 17.18[/C][C] 2.82[/C][/ROW]
[ROW][C]65[/C][C] 18[/C][C] 17.03[/C][C] 0.9709[/C][/ROW]
[ROW][C]66[/C][C] 17[/C][C] 16.44[/C][C] 0.5614[/C][/ROW]
[ROW][C]67[/C][C] 18[/C][C] 16.56[/C][C] 1.435[/C][/ROW]
[ROW][C]68[/C][C] 17[/C][C] 16.2[/C][C] 0.8045[/C][/ROW]
[ROW][C]69[/C][C] 20[/C][C] 18.67[/C][C] 1.331[/C][/ROW]
[ROW][C]70[/C][C] 16[/C][C] 16.16[/C][C]-0.1572[/C][/ROW]
[ROW][C]71[/C][C] 14[/C][C] 16.33[/C][C]-2.325[/C][/ROW]
[ROW][C]72[/C][C] 15[/C][C] 15.63[/C][C]-0.6306[/C][/ROW]
[ROW][C]73[/C][C] 20[/C][C] 17.2[/C][C] 2.804[/C][/ROW]
[ROW][C]74[/C][C] 17[/C][C] 15.97[/C][C] 1.031[/C][/ROW]
[ROW][C]75[/C][C] 17[/C][C] 15.65[/C][C] 1.353[/C][/ROW]
[ROW][C]76[/C][C] 18[/C][C] 14.69[/C][C] 3.311[/C][/ROW]
[ROW][C]77[/C][C] 20[/C][C] 16.91[/C][C] 3.091[/C][/ROW]
[ROW][C]78[/C][C] 16[/C][C] 16.07[/C][C]-0.07398[/C][/ROW]
[ROW][C]79[/C][C] 18[/C][C] 17.62[/C][C] 0.3795[/C][/ROW]
[ROW][C]80[/C][C] 15[/C][C] 16.89[/C][C]-1.894[/C][/ROW]
[ROW][C]81[/C][C] 18[/C][C] 18.84[/C][C]-0.8426[/C][/ROW]
[ROW][C]82[/C][C] 20[/C][C] 18.01[/C][C] 1.99[/C][/ROW]
[ROW][C]83[/C][C] 14[/C][C] 17.36[/C][C]-3.363[/C][/ROW]
[ROW][C]84[/C][C] 15[/C][C] 15.29[/C][C]-0.2896[/C][/ROW]
[ROW][C]85[/C][C] 17[/C][C] 17.03[/C][C]-0.0306[/C][/ROW]
[ROW][C]86[/C][C] 18[/C][C] 17.6[/C][C] 0.3975[/C][/ROW]
[ROW][C]87[/C][C] 20[/C][C] 17.48[/C][C] 2.519[/C][/ROW]
[ROW][C]88[/C][C] 17[/C][C] 17.12[/C][C]-0.1222[/C][/ROW]
[ROW][C]89[/C][C] 16[/C][C] 16.54[/C][C]-0.5352[/C][/ROW]
[ROW][C]90[/C][C] 11[/C][C] 15.74[/C][C]-4.742[/C][/ROW]
[ROW][C]91[/C][C] 15[/C][C] 16.89[/C][C]-1.894[/C][/ROW]
[ROW][C]92[/C][C] 18[/C][C] 16.99[/C][C] 1.005[/C][/ROW]
[ROW][C]93[/C][C] 16[/C][C] 18.31[/C][C]-2.309[/C][/ROW]
[ROW][C]94[/C][C] 18[/C][C] 16.89[/C][C] 1.112[/C][/ROW]
[ROW][C]95[/C][C] 15[/C][C] 16.99[/C][C]-1.992[/C][/ROW]
[ROW][C]96[/C][C] 17[/C][C] 16.16[/C][C] 0.8428[/C][/ROW]
[ROW][C]97[/C][C] 19[/C][C] 17.59[/C][C] 1.408[/C][/ROW]
[ROW][C]98[/C][C] 16[/C][C] 16.83[/C][C]-0.8283[/C][/ROW]
[ROW][C]99[/C][C] 14[/C][C] 17.15[/C][C]-3.147[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=304623&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=304623&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	14	16.7	-2.696
2	19	17.18	1.819
3	17	17.8	-0.7974
4	20	17.15	2.85
5	15	17.99	-2.986
6	19	16.1	2.901
7	20	16.13	3.869
8	18	17.05	0.9533
9	15	15.87	-0.8695
10	14	16.91	-2.912
11	16	16.18	-0.1847
12	19	17.37	1.629
13	18	16.43	1.571
14	17	16.5	0.4954
15	19	17.18	1.82
16	17	16.93	0.07248
17	19	18.07	0.9305
18	20	17.86	2.135
19	19	16.97	2.027
20	16	15.21	0.7917
21	16	16.81	-0.8135
22	18	16.06	1.939
23	16	17.09	-1.088
24	17	18.01	-1.01
25	20	17.72	2.276
26	19	16.81	2.186
27	7	16.05	-9.054
28	16	14.97	1.03
29	16	17.18	-1.184
30	18	16.95	1.046
31	17	18.3	-1.303
32	19	16.2	2.8
33	16	17.01	-1.011
34	13	15.69	-2.687
35	16	16.53	-0.5278
36	12	17.25	-5.248
37	17	16.71	0.2908
38	17	17.59	-0.5907
39	17	14.47	2.526
40	16	18.17	-2.174
41	16	16.65	-0.6493
42	14	16.1	-2.097
43	16	16.44	-0.4448
44	13	16.32	-3.316
45	16	16.38	-0.3811
46	14	15.4	-1.403
47	19	17.23	1.775
48	18	16.94	1.065
49	14	17.05	-3.053
50	18	17.12	0.8838
51	15	15.48	-0.4783
52	17	17.08	-0.07805
53	13	16.95	-3.951
54	19	17.48	1.522
55	18	18.12	-0.1211
56	15	15.42	-0.4212
57	20	17.35	2.647
58	19	16.78	2.215
59	18	17.37	0.6294
60	15	16.71	-1.715
61	20	18.62	1.384
62	17	17.46	-0.462
63	19	17.36	1.635
64	20	17.18	2.82
65	18	17.03	0.9709
66	17	16.44	0.5614
67	18	16.56	1.435
68	17	16.2	0.8045
69	20	18.67	1.331
70	16	16.16	-0.1572
71	14	16.33	-2.325
72	15	15.63	-0.6306
73	20	17.2	2.804
74	17	15.97	1.031
75	17	15.65	1.353
76	18	14.69	3.311
77	20	16.91	3.091
78	16	16.07	-0.07398
79	18	17.62	0.3795
80	15	16.89	-1.894
81	18	18.84	-0.8426
82	20	18.01	1.99
83	14	17.36	-3.363
84	15	15.29	-0.2896
85	17	17.03	-0.0306
86	18	17.6	0.3975
87	20	17.48	2.519
88	17	17.12	-0.1222
89	16	16.54	-0.5352
90	11	15.74	-4.742
91	15	16.89	-1.894
92	18	16.99	1.005
93	16	18.31	-2.309
94	18	16.89	1.112
95	15	16.99	-1.992
96	17	16.16	0.8428
97	19	17.59	1.408
98	16	16.83	-0.8283
99	14	17.15	-3.147

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
12	0.595	0.81	0.405
13	0.4739	0.9478	0.5261
14	0.3274	0.6547	0.6726
15	0.6129	0.7742	0.3871
16	0.4996	0.9992	0.5004
17	0.3891	0.7782	0.6109
18	0.3077	0.6155	0.6923
19	0.3855	0.771	0.6145
20	0.303	0.6061	0.697
21	0.272	0.544	0.728
22	0.2255	0.4509	0.7745
23	0.1782	0.3564	0.8218
24	0.1337	0.2675	0.8663
25	0.2132	0.4264	0.7868
26	0.2236	0.4473	0.7764
27	0.9638	0.07244	0.03622
28	0.9621	0.07572	0.03786
29	0.9492	0.1017	0.05084
30	0.9312	0.1376	0.06878
31	0.9242	0.1516	0.07579
32	0.9445	0.1109	0.05547
33	0.9254	0.1492	0.07459
34	0.9437	0.1126	0.05632
35	0.9274	0.1452	0.07258
36	0.9818	0.03639	0.01819
37	0.9783	0.04347	0.02174
38	0.9707	0.0586	0.0293
39	0.9819	0.03617	0.01808
40	0.9834	0.03323	0.01661
41	0.9769	0.04625	0.02313
42	0.9765	0.04699	0.0235
43	0.9715	0.05696	0.02848
44	0.983	0.03404	0.01702
45	0.9752	0.04957	0.02478
46	0.9676	0.06489	0.03245
47	0.9632	0.07366	0.03683
48	0.9542	0.09158	0.04579
49	0.9635	0.07298	0.03649
50	0.9548	0.09035	0.04518
51	0.9426	0.1148	0.05742
52	0.9305	0.1389	0.06946
53	0.9686	0.06284	0.03142
54	0.9636	0.07276	0.03638
55	0.954	0.09201	0.046
56	0.9369	0.1261	0.06307
57	0.9462	0.1076	0.05382
58	0.9587	0.08257	0.04129
59	0.9433	0.1135	0.05673
60	0.9461	0.1079	0.05395
61	0.9308	0.1383	0.06917
62	0.9081	0.1837	0.09186
63	0.8907	0.2186	0.1093
64	0.89	0.22	0.11
65	0.864	0.2721	0.136
66	0.8304	0.3392	0.1696
67	0.8349	0.3302	0.1651
68	0.792	0.416	0.208
69	0.7682	0.4636	0.2318
70	0.7508	0.4985	0.2492
71	0.7272	0.5456	0.2728
72	0.6653	0.6694	0.3347
73	0.6982	0.6036	0.3018
74	0.6481	0.7037	0.3519
75	0.5997	0.8006	0.4003
76	0.6324	0.7353	0.3676
77	0.6354	0.7292	0.3646
78	0.5841	0.8318	0.4159
79	0.4998	0.9996	0.5002
80	0.4862	0.9724	0.5138
81	0.4678	0.9356	0.5322
82	0.3745	0.7489	0.6255
83	0.3555	0.711	0.6445
84	0.3025	0.6051	0.6975
85	0.2263	0.4527	0.7737
86	0.2625	0.5249	0.7375
87	0.2008	0.4016	0.7992

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
12 &  0.595 &  0.81 &  0.405 \tabularnewline
13 &  0.4739 &  0.9478 &  0.5261 \tabularnewline
14 &  0.3274 &  0.6547 &  0.6726 \tabularnewline
15 &  0.6129 &  0.7742 &  0.3871 \tabularnewline
16 &  0.4996 &  0.9992 &  0.5004 \tabularnewline
17 &  0.3891 &  0.7782 &  0.6109 \tabularnewline
18 &  0.3077 &  0.6155 &  0.6923 \tabularnewline
19 &  0.3855 &  0.771 &  0.6145 \tabularnewline
20 &  0.303 &  0.6061 &  0.697 \tabularnewline
21 &  0.272 &  0.544 &  0.728 \tabularnewline
22 &  0.2255 &  0.4509 &  0.7745 \tabularnewline
23 &  0.1782 &  0.3564 &  0.8218 \tabularnewline
24 &  0.1337 &  0.2675 &  0.8663 \tabularnewline
25 &  0.2132 &  0.4264 &  0.7868 \tabularnewline
26 &  0.2236 &  0.4473 &  0.7764 \tabularnewline
27 &  0.9638 &  0.07244 &  0.03622 \tabularnewline
28 &  0.9621 &  0.07572 &  0.03786 \tabularnewline
29 &  0.9492 &  0.1017 &  0.05084 \tabularnewline
30 &  0.9312 &  0.1376 &  0.06878 \tabularnewline
31 &  0.9242 &  0.1516 &  0.07579 \tabularnewline
32 &  0.9445 &  0.1109 &  0.05547 \tabularnewline
33 &  0.9254 &  0.1492 &  0.07459 \tabularnewline
34 &  0.9437 &  0.1126 &  0.05632 \tabularnewline
35 &  0.9274 &  0.1452 &  0.07258 \tabularnewline
36 &  0.9818 &  0.03639 &  0.01819 \tabularnewline
37 &  0.9783 &  0.04347 &  0.02174 \tabularnewline
38 &  0.9707 &  0.0586 &  0.0293 \tabularnewline
39 &  0.9819 &  0.03617 &  0.01808 \tabularnewline
40 &  0.9834 &  0.03323 &  0.01661 \tabularnewline
41 &  0.9769 &  0.04625 &  0.02313 \tabularnewline
42 &  0.9765 &  0.04699 &  0.0235 \tabularnewline
43 &  0.9715 &  0.05696 &  0.02848 \tabularnewline
44 &  0.983 &  0.03404 &  0.01702 \tabularnewline
45 &  0.9752 &  0.04957 &  0.02478 \tabularnewline
46 &  0.9676 &  0.06489 &  0.03245 \tabularnewline
47 &  0.9632 &  0.07366 &  0.03683 \tabularnewline
48 &  0.9542 &  0.09158 &  0.04579 \tabularnewline
49 &  0.9635 &  0.07298 &  0.03649 \tabularnewline
50 &  0.9548 &  0.09035 &  0.04518 \tabularnewline
51 &  0.9426 &  0.1148 &  0.05742 \tabularnewline
52 &  0.9305 &  0.1389 &  0.06946 \tabularnewline
53 &  0.9686 &  0.06284 &  0.03142 \tabularnewline
54 &  0.9636 &  0.07276 &  0.03638 \tabularnewline
55 &  0.954 &  0.09201 &  0.046 \tabularnewline
56 &  0.9369 &  0.1261 &  0.06307 \tabularnewline
57 &  0.9462 &  0.1076 &  0.05382 \tabularnewline
58 &  0.9587 &  0.08257 &  0.04129 \tabularnewline
59 &  0.9433 &  0.1135 &  0.05673 \tabularnewline
60 &  0.9461 &  0.1079 &  0.05395 \tabularnewline
61 &  0.9308 &  0.1383 &  0.06917 \tabularnewline
62 &  0.9081 &  0.1837 &  0.09186 \tabularnewline
63 &  0.8907 &  0.2186 &  0.1093 \tabularnewline
64 &  0.89 &  0.22 &  0.11 \tabularnewline
65 &  0.864 &  0.2721 &  0.136 \tabularnewline
66 &  0.8304 &  0.3392 &  0.1696 \tabularnewline
67 &  0.8349 &  0.3302 &  0.1651 \tabularnewline
68 &  0.792 &  0.416 &  0.208 \tabularnewline
69 &  0.7682 &  0.4636 &  0.2318 \tabularnewline
70 &  0.7508 &  0.4985 &  0.2492 \tabularnewline
71 &  0.7272 &  0.5456 &  0.2728 \tabularnewline
72 &  0.6653 &  0.6694 &  0.3347 \tabularnewline
73 &  0.6982 &  0.6036 &  0.3018 \tabularnewline
74 &  0.6481 &  0.7037 &  0.3519 \tabularnewline
75 &  0.5997 &  0.8006 &  0.4003 \tabularnewline
76 &  0.6324 &  0.7353 &  0.3676 \tabularnewline
77 &  0.6354 &  0.7292 &  0.3646 \tabularnewline
78 &  0.5841 &  0.8318 &  0.4159 \tabularnewline
79 &  0.4998 &  0.9996 &  0.5002 \tabularnewline
80 &  0.4862 &  0.9724 &  0.5138 \tabularnewline
81 &  0.4678 &  0.9356 &  0.5322 \tabularnewline
82 &  0.3745 &  0.7489 &  0.6255 \tabularnewline
83 &  0.3555 &  0.711 &  0.6445 \tabularnewline
84 &  0.3025 &  0.6051 &  0.6975 \tabularnewline
85 &  0.2263 &  0.4527 &  0.7737 \tabularnewline
86 &  0.2625 &  0.5249 &  0.7375 \tabularnewline
87 &  0.2008 &  0.4016 &  0.7992 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=304623&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]12[/C][C] 0.595[/C][C] 0.81[/C][C] 0.405[/C][/ROW]
[ROW][C]13[/C][C] 0.4739[/C][C] 0.9478[/C][C] 0.5261[/C][/ROW]
[ROW][C]14[/C][C] 0.3274[/C][C] 0.6547[/C][C] 0.6726[/C][/ROW]
[ROW][C]15[/C][C] 0.6129[/C][C] 0.7742[/C][C] 0.3871[/C][/ROW]
[ROW][C]16[/C][C] 0.4996[/C][C] 0.9992[/C][C] 0.5004[/C][/ROW]
[ROW][C]17[/C][C] 0.3891[/C][C] 0.7782[/C][C] 0.6109[/C][/ROW]
[ROW][C]18[/C][C] 0.3077[/C][C] 0.6155[/C][C] 0.6923[/C][/ROW]
[ROW][C]19[/C][C] 0.3855[/C][C] 0.771[/C][C] 0.6145[/C][/ROW]
[ROW][C]20[/C][C] 0.303[/C][C] 0.6061[/C][C] 0.697[/C][/ROW]
[ROW][C]21[/C][C] 0.272[/C][C] 0.544[/C][C] 0.728[/C][/ROW]
[ROW][C]22[/C][C] 0.2255[/C][C] 0.4509[/C][C] 0.7745[/C][/ROW]
[ROW][C]23[/C][C] 0.1782[/C][C] 0.3564[/C][C] 0.8218[/C][/ROW]
[ROW][C]24[/C][C] 0.1337[/C][C] 0.2675[/C][C] 0.8663[/C][/ROW]
[ROW][C]25[/C][C] 0.2132[/C][C] 0.4264[/C][C] 0.7868[/C][/ROW]
[ROW][C]26[/C][C] 0.2236[/C][C] 0.4473[/C][C] 0.7764[/C][/ROW]
[ROW][C]27[/C][C] 0.9638[/C][C] 0.07244[/C][C] 0.03622[/C][/ROW]
[ROW][C]28[/C][C] 0.9621[/C][C] 0.07572[/C][C] 0.03786[/C][/ROW]
[ROW][C]29[/C][C] 0.9492[/C][C] 0.1017[/C][C] 0.05084[/C][/ROW]
[ROW][C]30[/C][C] 0.9312[/C][C] 0.1376[/C][C] 0.06878[/C][/ROW]
[ROW][C]31[/C][C] 0.9242[/C][C] 0.1516[/C][C] 0.07579[/C][/ROW]
[ROW][C]32[/C][C] 0.9445[/C][C] 0.1109[/C][C] 0.05547[/C][/ROW]
[ROW][C]33[/C][C] 0.9254[/C][C] 0.1492[/C][C] 0.07459[/C][/ROW]
[ROW][C]34[/C][C] 0.9437[/C][C] 0.1126[/C][C] 0.05632[/C][/ROW]
[ROW][C]35[/C][C] 0.9274[/C][C] 0.1452[/C][C] 0.07258[/C][/ROW]
[ROW][C]36[/C][C] 0.9818[/C][C] 0.03639[/C][C] 0.01819[/C][/ROW]
[ROW][C]37[/C][C] 0.9783[/C][C] 0.04347[/C][C] 0.02174[/C][/ROW]
[ROW][C]38[/C][C] 0.9707[/C][C] 0.0586[/C][C] 0.0293[/C][/ROW]
[ROW][C]39[/C][C] 0.9819[/C][C] 0.03617[/C][C] 0.01808[/C][/ROW]
[ROW][C]40[/C][C] 0.9834[/C][C] 0.03323[/C][C] 0.01661[/C][/ROW]
[ROW][C]41[/C][C] 0.9769[/C][C] 0.04625[/C][C] 0.02313[/C][/ROW]
[ROW][C]42[/C][C] 0.9765[/C][C] 0.04699[/C][C] 0.0235[/C][/ROW]
[ROW][C]43[/C][C] 0.9715[/C][C] 0.05696[/C][C] 0.02848[/C][/ROW]
[ROW][C]44[/C][C] 0.983[/C][C] 0.03404[/C][C] 0.01702[/C][/ROW]
[ROW][C]45[/C][C] 0.9752[/C][C] 0.04957[/C][C] 0.02478[/C][/ROW]
[ROW][C]46[/C][C] 0.9676[/C][C] 0.06489[/C][C] 0.03245[/C][/ROW]
[ROW][C]47[/C][C] 0.9632[/C][C] 0.07366[/C][C] 0.03683[/C][/ROW]
[ROW][C]48[/C][C] 0.9542[/C][C] 0.09158[/C][C] 0.04579[/C][/ROW]
[ROW][C]49[/C][C] 0.9635[/C][C] 0.07298[/C][C] 0.03649[/C][/ROW]
[ROW][C]50[/C][C] 0.9548[/C][C] 0.09035[/C][C] 0.04518[/C][/ROW]
[ROW][C]51[/C][C] 0.9426[/C][C] 0.1148[/C][C] 0.05742[/C][/ROW]
[ROW][C]52[/C][C] 0.9305[/C][C] 0.1389[/C][C] 0.06946[/C][/ROW]
[ROW][C]53[/C][C] 0.9686[/C][C] 0.06284[/C][C] 0.03142[/C][/ROW]
[ROW][C]54[/C][C] 0.9636[/C][C] 0.07276[/C][C] 0.03638[/C][/ROW]
[ROW][C]55[/C][C] 0.954[/C][C] 0.09201[/C][C] 0.046[/C][/ROW]
[ROW][C]56[/C][C] 0.9369[/C][C] 0.1261[/C][C] 0.06307[/C][/ROW]
[ROW][C]57[/C][C] 0.9462[/C][C] 0.1076[/C][C] 0.05382[/C][/ROW]
[ROW][C]58[/C][C] 0.9587[/C][C] 0.08257[/C][C] 0.04129[/C][/ROW]
[ROW][C]59[/C][C] 0.9433[/C][C] 0.1135[/C][C] 0.05673[/C][/ROW]
[ROW][C]60[/C][C] 0.9461[/C][C] 0.1079[/C][C] 0.05395[/C][/ROW]
[ROW][C]61[/C][C] 0.9308[/C][C] 0.1383[/C][C] 0.06917[/C][/ROW]
[ROW][C]62[/C][C] 0.9081[/C][C] 0.1837[/C][C] 0.09186[/C][/ROW]
[ROW][C]63[/C][C] 0.8907[/C][C] 0.2186[/C][C] 0.1093[/C][/ROW]
[ROW][C]64[/C][C] 0.89[/C][C] 0.22[/C][C] 0.11[/C][/ROW]
[ROW][C]65[/C][C] 0.864[/C][C] 0.2721[/C][C] 0.136[/C][/ROW]
[ROW][C]66[/C][C] 0.8304[/C][C] 0.3392[/C][C] 0.1696[/C][/ROW]
[ROW][C]67[/C][C] 0.8349[/C][C] 0.3302[/C][C] 0.1651[/C][/ROW]
[ROW][C]68[/C][C] 0.792[/C][C] 0.416[/C][C] 0.208[/C][/ROW]
[ROW][C]69[/C][C] 0.7682[/C][C] 0.4636[/C][C] 0.2318[/C][/ROW]
[ROW][C]70[/C][C] 0.7508[/C][C] 0.4985[/C][C] 0.2492[/C][/ROW]
[ROW][C]71[/C][C] 0.7272[/C][C] 0.5456[/C][C] 0.2728[/C][/ROW]
[ROW][C]72[/C][C] 0.6653[/C][C] 0.6694[/C][C] 0.3347[/C][/ROW]
[ROW][C]73[/C][C] 0.6982[/C][C] 0.6036[/C][C] 0.3018[/C][/ROW]
[ROW][C]74[/C][C] 0.6481[/C][C] 0.7037[/C][C] 0.3519[/C][/ROW]
[ROW][C]75[/C][C] 0.5997[/C][C] 0.8006[/C][C] 0.4003[/C][/ROW]
[ROW][C]76[/C][C] 0.6324[/C][C] 0.7353[/C][C] 0.3676[/C][/ROW]
[ROW][C]77[/C][C] 0.6354[/C][C] 0.7292[/C][C] 0.3646[/C][/ROW]
[ROW][C]78[/C][C] 0.5841[/C][C] 0.8318[/C][C] 0.4159[/C][/ROW]
[ROW][C]79[/C][C] 0.4998[/C][C] 0.9996[/C][C] 0.5002[/C][/ROW]
[ROW][C]80[/C][C] 0.4862[/C][C] 0.9724[/C][C] 0.5138[/C][/ROW]
[ROW][C]81[/C][C] 0.4678[/C][C] 0.9356[/C][C] 0.5322[/C][/ROW]
[ROW][C]82[/C][C] 0.3745[/C][C] 0.7489[/C][C] 0.6255[/C][/ROW]
[ROW][C]83[/C][C] 0.3555[/C][C] 0.711[/C][C] 0.6445[/C][/ROW]
[ROW][C]84[/C][C] 0.3025[/C][C] 0.6051[/C][C] 0.6975[/C][/ROW]
[ROW][C]85[/C][C] 0.2263[/C][C] 0.4527[/C][C] 0.7737[/C][/ROW]
[ROW][C]86[/C][C] 0.2625[/C][C] 0.5249[/C][C] 0.7375[/C][/ROW]
[ROW][C]87[/C][C] 0.2008[/C][C] 0.4016[/C][C] 0.7992[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=304623&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=304623&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
12	0.595	0.81	0.405
13	0.4739	0.9478	0.5261
14	0.3274	0.6547	0.6726
15	0.6129	0.7742	0.3871
16	0.4996	0.9992	0.5004
17	0.3891	0.7782	0.6109
18	0.3077	0.6155	0.6923
19	0.3855	0.771	0.6145
20	0.303	0.6061	0.697
21	0.272	0.544	0.728
22	0.2255	0.4509	0.7745
23	0.1782	0.3564	0.8218
24	0.1337	0.2675	0.8663
25	0.2132	0.4264	0.7868
26	0.2236	0.4473	0.7764
27	0.9638	0.07244	0.03622
28	0.9621	0.07572	0.03786
29	0.9492	0.1017	0.05084
30	0.9312	0.1376	0.06878
31	0.9242	0.1516	0.07579
32	0.9445	0.1109	0.05547
33	0.9254	0.1492	0.07459
34	0.9437	0.1126	0.05632
35	0.9274	0.1452	0.07258
36	0.9818	0.03639	0.01819
37	0.9783	0.04347	0.02174
38	0.9707	0.0586	0.0293
39	0.9819	0.03617	0.01808
40	0.9834	0.03323	0.01661
41	0.9769	0.04625	0.02313
42	0.9765	0.04699	0.0235
43	0.9715	0.05696	0.02848
44	0.983	0.03404	0.01702
45	0.9752	0.04957	0.02478
46	0.9676	0.06489	0.03245
47	0.9632	0.07366	0.03683
48	0.9542	0.09158	0.04579
49	0.9635	0.07298	0.03649
50	0.9548	0.09035	0.04518
51	0.9426	0.1148	0.05742
52	0.9305	0.1389	0.06946
53	0.9686	0.06284	0.03142
54	0.9636	0.07276	0.03638
55	0.954	0.09201	0.046
56	0.9369	0.1261	0.06307
57	0.9462	0.1076	0.05382
58	0.9587	0.08257	0.04129
59	0.9433	0.1135	0.05673
60	0.9461	0.1079	0.05395
61	0.9308	0.1383	0.06917
62	0.9081	0.1837	0.09186
63	0.8907	0.2186	0.1093
64	0.89	0.22	0.11
65	0.864	0.2721	0.136
66	0.8304	0.3392	0.1696
67	0.8349	0.3302	0.1651
68	0.792	0.416	0.208
69	0.7682	0.4636	0.2318
70	0.7508	0.4985	0.2492
71	0.7272	0.5456	0.2728
72	0.6653	0.6694	0.3347
73	0.6982	0.6036	0.3018
74	0.6481	0.7037	0.3519
75	0.5997	0.8006	0.4003
76	0.6324	0.7353	0.3676
77	0.6354	0.7292	0.3646
78	0.5841	0.8318	0.4159
79	0.4998	0.9996	0.5002
80	0.4862	0.9724	0.5138
81	0.4678	0.9356	0.5322
82	0.3745	0.7489	0.6255
83	0.3555	0.711	0.6445
84	0.3025	0.6051	0.6975
85	0.2263	0.4527	0.7737
86	0.2625	0.5249	0.7375
87	0.2008	0.4016	0.7992

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	8	0.105263	NOK
10% type I error level	21	0.276316	NOK

\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 & 8 & 0.105263 & NOK \tabularnewline
10% type I error level & 21 & 0.276316 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=304623&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]8[/C][C]0.105263[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]21[/C][C]0.276316[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=304623&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=304623&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	8	0.105263	NOK
10% type I error level	21	0.276316	NOK

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted RESET test data: mylm RESET = 2.4174, df1 = 2, df2 = 88, p-value = 0.09505
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 0.71063, df1 = 16, df2 = 74, p-value = 0.7745
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 1.1466, df1 = 2, df2 = 88, p-value = 0.3224

\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 = 2.4174, df1 = 2, df2 = 88, p-value = 0.09505
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of regressors \tabularnewline
> reset_test_regressors
	RESET test
data:  mylm
RESET = 0.71063, df1 = 16, df2 = 74, p-value = 0.7745
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of principal components \tabularnewline
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 1.1466, df1 = 2, df2 = 88, p-value = 0.3224
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=304623&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 = 2.4174, df1 = 2, df2 = 88, p-value = 0.09505
[/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 = 0.71063, df1 = 16, df2 = 74, p-value = 0.7745
[/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 = 1.1466, df1 = 2, df2 = 88, p-value = 0.3224
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=304623&T=7

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=304623&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 = 2.4174, df1 = 2, df2 = 88, p-value = 0.09505
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 0.71063, df1 = 16, df2 = 74, p-value = 0.7745
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 1.1466, df1 = 2, df2 = 88, p-value = 0.3224

Variance Inflation Factors (Multicollinearity)
> vif Bevr_Leeftijd TVDC SKEOU1 SKEOU2 SKEOU3 1.049195 1.574693 1.197348 1.475813 1.073781 SKEOU4 SKEOU5 SKEOU6 1.125052 1.035594 1.050069

\begin{tabular}{lllllllll}
\hline
Variance Inflation Factors (Multicollinearity) \tabularnewline
> vif
Bevr_Leeftijd          TVDC        SKEOU1        SKEOU2        SKEOU3 
     1.049195      1.574693      1.197348      1.475813      1.073781 
       SKEOU4        SKEOU5        SKEOU6 
     1.125052      1.035594      1.050069 
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=304623&T=8

[TABLE]
[ROW][C]Variance Inflation Factors (Multicollinearity)[/C][/ROW]
[ROW][C]> vif
Bevr_Leeftijd          TVDC        SKEOU1        SKEOU2        SKEOU3 
     1.049195      1.574693      1.197348      1.475813      1.073781 
       SKEOU4        SKEOU5        SKEOU6 
     1.125052      1.035594      1.050069 
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=304623&T=8

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=304623&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 1.049195 1.574693 1.197348 1.475813 1.073781 SKEOU4 SKEOU5 SKEOU6 1.125052 1.035594 1.050069

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 = 0 ; par3 = 1 ; par4 = 1 ;

Parameters (R input):

par1 = 1 ; par2 = 0 ; par3 = 1 ; par4 = 0 ; par5 = ;

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