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Author's title

Multiple regression Duurzame consumptiegoederen zonder dummies, zonder tren...

Author

*The author of this computation has been verified*

R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Tue, 23 Dec 2008 12:36:18 -0700

Cite this page as follows

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2008/Dec/23/t1230061072j28tmlyxwyvamwm.htm/, Retrieved Fri, 24 May 2024 11:11:56 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=36379, Retrieved Fri, 24 May 2024 11:11:56 +0000

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IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

153

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

-     [Multiple Regression] [Q1 The Seatbeltlaw] [2007-11-14 19:27:43] [8cd6641b921d30ebe00b648d1481bba0]
F R  D  [Multiple Regression] [Q1 seatbelt law] [2008-11-24 10:40:19] [7a4703cb85a198d9845d72899eff0288]
F   P     [Multiple Regression] [The seatbelt law ...] [2008-11-27 16:39:47] [7a4703cb85a198d9845d72899eff0288]
-   PD        [Multiple Regression] [Multiple regressi...] [2008-12-23 19:36:18] [9f72e095d5529918bf5b0810c01bf6ce] [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	3 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 3 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=36379&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]3 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=36379&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=36379&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	3 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

Multiple Linear Regression - Estimated Regression Equation
Cons[t] = + 94.675 -4.28928571428569Dummy[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Cons[t] =  +  94.675 -4.28928571428569Dummy[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=36379&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Cons[t] =  +  94.675 -4.28928571428569Dummy[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=36379&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=36379&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
Cons[t] = + 94.675 -4.28928571428569Dummy[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)	94.675	5.044424	18.7682	0	0
Dummy	-4.28928571428569	5.719839	-0.7499	0.458475	0.229238

\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) & 94.675 & 5.044424 & 18.7682 & 0 & 0 \tabularnewline
Dummy & -4.28928571428569 & 5.719839 & -0.7499 & 0.458475 & 0.229238 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=36379&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]94.675[/C][C]5.044424[/C][C]18.7682[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Dummy[/C][C]-4.28928571428569[/C][C]5.719839[/C][C]-0.7499[/C][C]0.458475[/C][C]0.229238[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=36379&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=36379&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)	94.675	5.044424	18.7682	0	0
Dummy	-4.28928571428569	5.719839	-0.7499	0.458475	0.229238

Multiple Linear Regression - Regression Statistics
Multiple R	0.127555616468756
R-squared	0.0162704352927245
Adjusted R-squared	-0.0126627871986658
F-TEST (value)	0.562344387928653
F-TEST (DF numerator)	1
F-TEST (DF denominator)	34
p-value	0.458475113127893
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	14.2677848621974
Sum Squared Residuals	6921.36928571428

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.127555616468756 \tabularnewline
R-squared & 0.0162704352927245 \tabularnewline
Adjusted R-squared & -0.0126627871986658 \tabularnewline
F-TEST (value) & 0.562344387928653 \tabularnewline
F-TEST (DF numerator) & 1 \tabularnewline
F-TEST (DF denominator) & 34 \tabularnewline
p-value & 0.458475113127893 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 14.2677848621974 \tabularnewline
Sum Squared Residuals & 6921.36928571428 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=36379&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.127555616468756[/C][/ROW]
[ROW][C]R-squared[/C][C]0.0162704352927245[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]-0.0126627871986658[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]0.562344387928653[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]1[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]34[/C][/ROW]
[ROW][C]p-value[/C][C]0.458475113127893[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]14.2677848621974[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]6921.36928571428[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=36379&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=36379&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	0.127555616468756
R-squared	0.0162704352927245
Adjusted R-squared	-0.0126627871986658
F-TEST (value)	0.562344387928653
F-TEST (DF numerator)	1
F-TEST (DF denominator)	34
p-value	0.458475113127893
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	14.2677848621974
Sum Squared Residuals	6921.36928571428

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	98.1	94.6750000000002	3.42499999999976
2	101.1	94.675	6.42500000000004
3	111.1	94.675	16.4250000000000
4	93.3	94.675	-1.37499999999997
5	100	94.675	5.32500000000003
6	108	94.675	13.3250000000000
7	70.4	94.675	-24.2750000000000
8	75.4	94.675	-19.2750000000000
9	105.5	90.3857142857143	15.1142857142857
10	112.3	90.3857142857143	21.9142857142857
11	102.5	90.3857142857143	12.1142857142857
12	93.5	90.3857142857143	3.11428571428571
13	86.7	90.3857142857143	-3.68571428571428
14	95.2	90.3857142857143	4.81428571428572
15	103.8	90.3857142857143	13.4142857142857
16	97	90.3857142857143	6.61428571428571
17	95.5	90.3857142857143	5.11428571428571
18	101	90.3857142857143	10.6142857142857
19	67.5	90.3857142857143	-22.8857142857143
20	64	90.3857142857143	-26.3857142857143
21	106.7	90.3857142857143	16.3142857142857
22	100.6	90.3857142857143	10.2142857142857
23	101.2	90.3857142857143	10.8142857142857
24	93.1	90.3857142857143	2.71428571428571
25	84.2	90.3857142857143	-6.18571428571428
26	85.8	90.3857142857143	-4.58571428571429
27	91.8	90.3857142857143	1.41428571428571
28	92.4	90.3857142857143	2.01428571428572
29	80.3	90.3857142857143	-10.0857142857143
30	79.7	90.3857142857143	-10.6857142857143
31	62.5	90.3857142857143	-27.8857142857143
32	57.1	90.3857142857143	-33.2857142857143
33	100.8	90.3857142857143	10.4142857142857
34	100.7	90.3857142857143	10.3142857142857
35	86.2	90.3857142857143	-4.18571428571428
36	83.2	90.3857142857143	-7.18571428571428

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 98.1 & 94.6750000000002 & 3.42499999999976 \tabularnewline
2 & 101.1 & 94.675 & 6.42500000000004 \tabularnewline
3 & 111.1 & 94.675 & 16.4250000000000 \tabularnewline
4 & 93.3 & 94.675 & -1.37499999999997 \tabularnewline
5 & 100 & 94.675 & 5.32500000000003 \tabularnewline
6 & 108 & 94.675 & 13.3250000000000 \tabularnewline
7 & 70.4 & 94.675 & -24.2750000000000 \tabularnewline
8 & 75.4 & 94.675 & -19.2750000000000 \tabularnewline
9 & 105.5 & 90.3857142857143 & 15.1142857142857 \tabularnewline
10 & 112.3 & 90.3857142857143 & 21.9142857142857 \tabularnewline
11 & 102.5 & 90.3857142857143 & 12.1142857142857 \tabularnewline
12 & 93.5 & 90.3857142857143 & 3.11428571428571 \tabularnewline
13 & 86.7 & 90.3857142857143 & -3.68571428571428 \tabularnewline
14 & 95.2 & 90.3857142857143 & 4.81428571428572 \tabularnewline
15 & 103.8 & 90.3857142857143 & 13.4142857142857 \tabularnewline
16 & 97 & 90.3857142857143 & 6.61428571428571 \tabularnewline
17 & 95.5 & 90.3857142857143 & 5.11428571428571 \tabularnewline
18 & 101 & 90.3857142857143 & 10.6142857142857 \tabularnewline
19 & 67.5 & 90.3857142857143 & -22.8857142857143 \tabularnewline
20 & 64 & 90.3857142857143 & -26.3857142857143 \tabularnewline
21 & 106.7 & 90.3857142857143 & 16.3142857142857 \tabularnewline
22 & 100.6 & 90.3857142857143 & 10.2142857142857 \tabularnewline
23 & 101.2 & 90.3857142857143 & 10.8142857142857 \tabularnewline
24 & 93.1 & 90.3857142857143 & 2.71428571428571 \tabularnewline
25 & 84.2 & 90.3857142857143 & -6.18571428571428 \tabularnewline
26 & 85.8 & 90.3857142857143 & -4.58571428571429 \tabularnewline
27 & 91.8 & 90.3857142857143 & 1.41428571428571 \tabularnewline
28 & 92.4 & 90.3857142857143 & 2.01428571428572 \tabularnewline
29 & 80.3 & 90.3857142857143 & -10.0857142857143 \tabularnewline
30 & 79.7 & 90.3857142857143 & -10.6857142857143 \tabularnewline
31 & 62.5 & 90.3857142857143 & -27.8857142857143 \tabularnewline
32 & 57.1 & 90.3857142857143 & -33.2857142857143 \tabularnewline
33 & 100.8 & 90.3857142857143 & 10.4142857142857 \tabularnewline
34 & 100.7 & 90.3857142857143 & 10.3142857142857 \tabularnewline
35 & 86.2 & 90.3857142857143 & -4.18571428571428 \tabularnewline
36 & 83.2 & 90.3857142857143 & -7.18571428571428 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=36379&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]98.1[/C][C]94.6750000000002[/C][C]3.42499999999976[/C][/ROW]
[ROW][C]2[/C][C]101.1[/C][C]94.675[/C][C]6.42500000000004[/C][/ROW]
[ROW][C]3[/C][C]111.1[/C][C]94.675[/C][C]16.4250000000000[/C][/ROW]
[ROW][C]4[/C][C]93.3[/C][C]94.675[/C][C]-1.37499999999997[/C][/ROW]
[ROW][C]5[/C][C]100[/C][C]94.675[/C][C]5.32500000000003[/C][/ROW]
[ROW][C]6[/C][C]108[/C][C]94.675[/C][C]13.3250000000000[/C][/ROW]
[ROW][C]7[/C][C]70.4[/C][C]94.675[/C][C]-24.2750000000000[/C][/ROW]
[ROW][C]8[/C][C]75.4[/C][C]94.675[/C][C]-19.2750000000000[/C][/ROW]
[ROW][C]9[/C][C]105.5[/C][C]90.3857142857143[/C][C]15.1142857142857[/C][/ROW]
[ROW][C]10[/C][C]112.3[/C][C]90.3857142857143[/C][C]21.9142857142857[/C][/ROW]
[ROW][C]11[/C][C]102.5[/C][C]90.3857142857143[/C][C]12.1142857142857[/C][/ROW]
[ROW][C]12[/C][C]93.5[/C][C]90.3857142857143[/C][C]3.11428571428571[/C][/ROW]
[ROW][C]13[/C][C]86.7[/C][C]90.3857142857143[/C][C]-3.68571428571428[/C][/ROW]
[ROW][C]14[/C][C]95.2[/C][C]90.3857142857143[/C][C]4.81428571428572[/C][/ROW]
[ROW][C]15[/C][C]103.8[/C][C]90.3857142857143[/C][C]13.4142857142857[/C][/ROW]
[ROW][C]16[/C][C]97[/C][C]90.3857142857143[/C][C]6.61428571428571[/C][/ROW]
[ROW][C]17[/C][C]95.5[/C][C]90.3857142857143[/C][C]5.11428571428571[/C][/ROW]
[ROW][C]18[/C][C]101[/C][C]90.3857142857143[/C][C]10.6142857142857[/C][/ROW]
[ROW][C]19[/C][C]67.5[/C][C]90.3857142857143[/C][C]-22.8857142857143[/C][/ROW]
[ROW][C]20[/C][C]64[/C][C]90.3857142857143[/C][C]-26.3857142857143[/C][/ROW]
[ROW][C]21[/C][C]106.7[/C][C]90.3857142857143[/C][C]16.3142857142857[/C][/ROW]
[ROW][C]22[/C][C]100.6[/C][C]90.3857142857143[/C][C]10.2142857142857[/C][/ROW]
[ROW][C]23[/C][C]101.2[/C][C]90.3857142857143[/C][C]10.8142857142857[/C][/ROW]
[ROW][C]24[/C][C]93.1[/C][C]90.3857142857143[/C][C]2.71428571428571[/C][/ROW]
[ROW][C]25[/C][C]84.2[/C][C]90.3857142857143[/C][C]-6.18571428571428[/C][/ROW]
[ROW][C]26[/C][C]85.8[/C][C]90.3857142857143[/C][C]-4.58571428571429[/C][/ROW]
[ROW][C]27[/C][C]91.8[/C][C]90.3857142857143[/C][C]1.41428571428571[/C][/ROW]
[ROW][C]28[/C][C]92.4[/C][C]90.3857142857143[/C][C]2.01428571428572[/C][/ROW]
[ROW][C]29[/C][C]80.3[/C][C]90.3857142857143[/C][C]-10.0857142857143[/C][/ROW]
[ROW][C]30[/C][C]79.7[/C][C]90.3857142857143[/C][C]-10.6857142857143[/C][/ROW]
[ROW][C]31[/C][C]62.5[/C][C]90.3857142857143[/C][C]-27.8857142857143[/C][/ROW]
[ROW][C]32[/C][C]57.1[/C][C]90.3857142857143[/C][C]-33.2857142857143[/C][/ROW]
[ROW][C]33[/C][C]100.8[/C][C]90.3857142857143[/C][C]10.4142857142857[/C][/ROW]
[ROW][C]34[/C][C]100.7[/C][C]90.3857142857143[/C][C]10.3142857142857[/C][/ROW]
[ROW][C]35[/C][C]86.2[/C][C]90.3857142857143[/C][C]-4.18571428571428[/C][/ROW]
[ROW][C]36[/C][C]83.2[/C][C]90.3857142857143[/C][C]-7.18571428571428[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=36379&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=36379&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	98.1	94.6750000000002	3.42499999999976
2	101.1	94.675	6.42500000000004
3	111.1	94.675	16.4250000000000
4	93.3	94.675	-1.37499999999997
5	100	94.675	5.32500000000003
6	108	94.675	13.3250000000000
7	70.4	94.675	-24.2750000000000
8	75.4	94.675	-19.2750000000000
9	105.5	90.3857142857143	15.1142857142857
10	112.3	90.3857142857143	21.9142857142857
11	102.5	90.3857142857143	12.1142857142857
12	93.5	90.3857142857143	3.11428571428571
13	86.7	90.3857142857143	-3.68571428571428
14	95.2	90.3857142857143	4.81428571428572
15	103.8	90.3857142857143	13.4142857142857
16	97	90.3857142857143	6.61428571428571
17	95.5	90.3857142857143	5.11428571428571
18	101	90.3857142857143	10.6142857142857
19	67.5	90.3857142857143	-22.8857142857143
20	64	90.3857142857143	-26.3857142857143
21	106.7	90.3857142857143	16.3142857142857
22	100.6	90.3857142857143	10.2142857142857
23	101.2	90.3857142857143	10.8142857142857
24	93.1	90.3857142857143	2.71428571428571
25	84.2	90.3857142857143	-6.18571428571428
26	85.8	90.3857142857143	-4.58571428571429
27	91.8	90.3857142857143	1.41428571428571
28	92.4	90.3857142857143	2.01428571428572
29	80.3	90.3857142857143	-10.0857142857143
30	79.7	90.3857142857143	-10.6857142857143
31	62.5	90.3857142857143	-27.8857142857143
32	57.1	90.3857142857143	-33.2857142857143
33	100.8	90.3857142857143	10.4142857142857
34	100.7	90.3857142857143	10.3142857142857
35	86.2	90.3857142857143	-4.18571428571428
36	83.2	90.3857142857143	-7.18571428571428

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
5	0.148439561875408	0.296879123750816	0.851560438124592
6	0.108942774180334	0.217885548360669	0.891057225819666
7	0.596504020524448	0.806991958951103	0.403495979475552
8	0.657710848623192	0.684578302753616	0.342289151376808
9	0.561917046053035	0.87616590789393	0.438082953946965
10	0.520381809724897	0.959236380550207	0.479618190275103
11	0.443481028676382	0.886962057352764	0.556518971323618
12	0.392279553195748	0.784559106391496	0.607720446804252
13	0.373123930284203	0.746247860568405	0.626876069715797
14	0.289286077338024	0.578572154676047	0.710713922661976
15	0.244633118175774	0.489266236351549	0.755366881824226
16	0.183597806882488	0.367195613764976	0.816402193117512
17	0.133427783623774	0.266855567247547	0.866572216376226
18	0.105229230190993	0.210458460381986	0.894770769809007
19	0.283133510996664	0.566267021993328	0.716866489003336
20	0.529675204513443	0.940649590973114	0.470324795486557
21	0.565898246140029	0.868203507719942	0.434101753859971
22	0.534047254999107	0.931905490001786	0.465952745000893
23	0.524762404384827	0.950475191230346	0.475237595615173
24	0.446763590069472	0.893527180138944	0.553236409930528
25	0.358360689585083	0.716721379170166	0.641639310414917
26	0.269339859436244	0.538679718872488	0.730660140563756
27	0.201046407419209	0.402092814838418	0.798953592580791
28	0.148847912564597	0.297695825129194	0.851152087435403
29	0.0971910326447632	0.194382065289526	0.902808967355237
30	0.05728822533467	0.11457645066934	0.94271177466533
31	0.104391992683007	0.208783985366014	0.895608007316993

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
5 & 0.148439561875408 & 0.296879123750816 & 0.851560438124592 \tabularnewline
6 & 0.108942774180334 & 0.217885548360669 & 0.891057225819666 \tabularnewline
7 & 0.596504020524448 & 0.806991958951103 & 0.403495979475552 \tabularnewline
8 & 0.657710848623192 & 0.684578302753616 & 0.342289151376808 \tabularnewline
9 & 0.561917046053035 & 0.87616590789393 & 0.438082953946965 \tabularnewline
10 & 0.520381809724897 & 0.959236380550207 & 0.479618190275103 \tabularnewline
11 & 0.443481028676382 & 0.886962057352764 & 0.556518971323618 \tabularnewline
12 & 0.392279553195748 & 0.784559106391496 & 0.607720446804252 \tabularnewline
13 & 0.373123930284203 & 0.746247860568405 & 0.626876069715797 \tabularnewline
14 & 0.289286077338024 & 0.578572154676047 & 0.710713922661976 \tabularnewline
15 & 0.244633118175774 & 0.489266236351549 & 0.755366881824226 \tabularnewline
16 & 0.183597806882488 & 0.367195613764976 & 0.816402193117512 \tabularnewline
17 & 0.133427783623774 & 0.266855567247547 & 0.866572216376226 \tabularnewline
18 & 0.105229230190993 & 0.210458460381986 & 0.894770769809007 \tabularnewline
19 & 0.283133510996664 & 0.566267021993328 & 0.716866489003336 \tabularnewline
20 & 0.529675204513443 & 0.940649590973114 & 0.470324795486557 \tabularnewline
21 & 0.565898246140029 & 0.868203507719942 & 0.434101753859971 \tabularnewline
22 & 0.534047254999107 & 0.931905490001786 & 0.465952745000893 \tabularnewline
23 & 0.524762404384827 & 0.950475191230346 & 0.475237595615173 \tabularnewline
24 & 0.446763590069472 & 0.893527180138944 & 0.553236409930528 \tabularnewline
25 & 0.358360689585083 & 0.716721379170166 & 0.641639310414917 \tabularnewline
26 & 0.269339859436244 & 0.538679718872488 & 0.730660140563756 \tabularnewline
27 & 0.201046407419209 & 0.402092814838418 & 0.798953592580791 \tabularnewline
28 & 0.148847912564597 & 0.297695825129194 & 0.851152087435403 \tabularnewline
29 & 0.0971910326447632 & 0.194382065289526 & 0.902808967355237 \tabularnewline
30 & 0.05728822533467 & 0.11457645066934 & 0.94271177466533 \tabularnewline
31 & 0.104391992683007 & 0.208783985366014 & 0.895608007316993 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=36379&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]5[/C][C]0.148439561875408[/C][C]0.296879123750816[/C][C]0.851560438124592[/C][/ROW]
[ROW][C]6[/C][C]0.108942774180334[/C][C]0.217885548360669[/C][C]0.891057225819666[/C][/ROW]
[ROW][C]7[/C][C]0.596504020524448[/C][C]0.806991958951103[/C][C]0.403495979475552[/C][/ROW]
[ROW][C]8[/C][C]0.657710848623192[/C][C]0.684578302753616[/C][C]0.342289151376808[/C][/ROW]
[ROW][C]9[/C][C]0.561917046053035[/C][C]0.87616590789393[/C][C]0.438082953946965[/C][/ROW]
[ROW][C]10[/C][C]0.520381809724897[/C][C]0.959236380550207[/C][C]0.479618190275103[/C][/ROW]
[ROW][C]11[/C][C]0.443481028676382[/C][C]0.886962057352764[/C][C]0.556518971323618[/C][/ROW]
[ROW][C]12[/C][C]0.392279553195748[/C][C]0.784559106391496[/C][C]0.607720446804252[/C][/ROW]
[ROW][C]13[/C][C]0.373123930284203[/C][C]0.746247860568405[/C][C]0.626876069715797[/C][/ROW]
[ROW][C]14[/C][C]0.289286077338024[/C][C]0.578572154676047[/C][C]0.710713922661976[/C][/ROW]
[ROW][C]15[/C][C]0.244633118175774[/C][C]0.489266236351549[/C][C]0.755366881824226[/C][/ROW]
[ROW][C]16[/C][C]0.183597806882488[/C][C]0.367195613764976[/C][C]0.816402193117512[/C][/ROW]
[ROW][C]17[/C][C]0.133427783623774[/C][C]0.266855567247547[/C][C]0.866572216376226[/C][/ROW]
[ROW][C]18[/C][C]0.105229230190993[/C][C]0.210458460381986[/C][C]0.894770769809007[/C][/ROW]
[ROW][C]19[/C][C]0.283133510996664[/C][C]0.566267021993328[/C][C]0.716866489003336[/C][/ROW]
[ROW][C]20[/C][C]0.529675204513443[/C][C]0.940649590973114[/C][C]0.470324795486557[/C][/ROW]
[ROW][C]21[/C][C]0.565898246140029[/C][C]0.868203507719942[/C][C]0.434101753859971[/C][/ROW]
[ROW][C]22[/C][C]0.534047254999107[/C][C]0.931905490001786[/C][C]0.465952745000893[/C][/ROW]
[ROW][C]23[/C][C]0.524762404384827[/C][C]0.950475191230346[/C][C]0.475237595615173[/C][/ROW]
[ROW][C]24[/C][C]0.446763590069472[/C][C]0.893527180138944[/C][C]0.553236409930528[/C][/ROW]
[ROW][C]25[/C][C]0.358360689585083[/C][C]0.716721379170166[/C][C]0.641639310414917[/C][/ROW]
[ROW][C]26[/C][C]0.269339859436244[/C][C]0.538679718872488[/C][C]0.730660140563756[/C][/ROW]
[ROW][C]27[/C][C]0.201046407419209[/C][C]0.402092814838418[/C][C]0.798953592580791[/C][/ROW]
[ROW][C]28[/C][C]0.148847912564597[/C][C]0.297695825129194[/C][C]0.851152087435403[/C][/ROW]
[ROW][C]29[/C][C]0.0971910326447632[/C][C]0.194382065289526[/C][C]0.902808967355237[/C][/ROW]
[ROW][C]30[/C][C]0.05728822533467[/C][C]0.11457645066934[/C][C]0.94271177466533[/C][/ROW]
[ROW][C]31[/C][C]0.104391992683007[/C][C]0.208783985366014[/C][C]0.895608007316993[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=36379&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=36379&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
5	0.148439561875408	0.296879123750816	0.851560438124592
6	0.108942774180334	0.217885548360669	0.891057225819666
7	0.596504020524448	0.806991958951103	0.403495979475552
8	0.657710848623192	0.684578302753616	0.342289151376808
9	0.561917046053035	0.87616590789393	0.438082953946965
10	0.520381809724897	0.959236380550207	0.479618190275103
11	0.443481028676382	0.886962057352764	0.556518971323618
12	0.392279553195748	0.784559106391496	0.607720446804252
13	0.373123930284203	0.746247860568405	0.626876069715797
14	0.289286077338024	0.578572154676047	0.710713922661976
15	0.244633118175774	0.489266236351549	0.755366881824226
16	0.183597806882488	0.367195613764976	0.816402193117512
17	0.133427783623774	0.266855567247547	0.866572216376226
18	0.105229230190993	0.210458460381986	0.894770769809007
19	0.283133510996664	0.566267021993328	0.716866489003336
20	0.529675204513443	0.940649590973114	0.470324795486557
21	0.565898246140029	0.868203507719942	0.434101753859971
22	0.534047254999107	0.931905490001786	0.465952745000893
23	0.524762404384827	0.950475191230346	0.475237595615173
24	0.446763590069472	0.893527180138944	0.553236409930528
25	0.358360689585083	0.716721379170166	0.641639310414917
26	0.269339859436244	0.538679718872488	0.730660140563756
27	0.201046407419209	0.402092814838418	0.798953592580791
28	0.148847912564597	0.297695825129194	0.851152087435403
29	0.0971910326447632	0.194382065289526	0.902808967355237
30	0.05728822533467	0.11457645066934	0.94271177466533
31	0.104391992683007	0.208783985366014	0.895608007316993

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

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 0 & 0 & OK \tabularnewline
10% type I error level & 0 & 0 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=36379&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=36379&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=36379&T=6

As an alternative you can also use a QR Code:

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

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

Figure 1

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Figure 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 = 2 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 2 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

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

library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- 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'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
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[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,mysum$coefficients[i,1])
a<-table.element(a, round(mysum$coefficients[i,2],6))
a<-table.element(a, round(mysum$coefficients[i,3],4))
a<-table.element(a, round(mysum$coefficients[i,4],6))
a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, sqrt(mysum$r.squared))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, mysum$r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, mysum$adj.r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, mysum$fstatistic[1])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[3])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
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, mysum$sigma)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, sum(myerror*myerror))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,x[i])
a<-table.element(a,x[i]-mysum$resid[i])
a<-table.element(a,mysum$resid[i])
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,gqarr[mypoint-kp3+1,1])
a<-table.element(a,gqarr[mypoint-kp3+1,2])
a<-table.element(a,gqarr[mypoint-kp3+1,3])
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,numsignificant1)
a<-table.element(a,numsignificant1/numgqtests)
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,numsignificant5)
a<-table.element(a,numsignificant5/numgqtests)
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,numsignificant10)
a<-table.element(a,numsignificant10/numgqtests)
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}

Free Statistics

Description of Statistical Computation

Multiple regression Duurzame consumptiegoederen zonder dummies, zonder tren...

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code