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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Wed, 20 Nov 2013 16:02:53 -0500

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2013/Nov/20/t1384981391roiwrm5c6eh7db8.htm/, Retrieved Wed, 01 May 2024 22:47:33 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=226730, Retrieved Wed, 01 May 2024 22:47:33 +0000

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Estimated Impact

103

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-       [Multiple Regression] [] [2013-11-20 21:02:53] [d4f4c31d85c50f8f61914fc31b2124f2] [Current]

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2.939217848	3	0.38	0.3	0.18	0.033717848
2.897889829	2	0.33	0.03	0.16	-0.049760171
2.821384807	3	0.14	0.47	0.92	0.000734807
2.976828188	1	0.13	0.79	0.11	0.028678188
2.98658494	1	0.24	0.81	0.43	0.04813494
3.08609928	1	0.96	0.42	0.09	0.02769928
2.970331644	2	0.4	0.17	0.34	0.030381644
3.001422707	3	0.98	0.75	0.69	0.070872707
2.896078452	2	0.01	0.72	0.25	0.010378452
3.048743903	1	0.57	0.77	0.65	0.085093903
2.951881247	1	0.08	0.06	0.13	-0.007118753
2.856323604	4	0.24	0.25	0.42	0.023173604
2.879911503	2	0.02	0.16	0.91	0.025211503
3.040257383	4	0.87	0.16	0.2	0.113857383
3.003077554	0	0.01	0.53	0.12	0.023527554
2.959791865	2	0.43	0.81	0.71	0.058141865
2.917990764	1	0.15	0.69	0.48	-0.009159236
2.818941548	2	0.65	0.86	0.85	-0.098058452
2.914778377	4	0.78	0.34	0.67	0.036578377
2.915101361	4	0.57	0.84	0.5	0.062701361
2.847462114	4	0.24	0.57	0.18	0.005512114
2.914117188	2	0.87	0.27	0.45	-0.072032812
2.923740777	4	0.5	0.58	0.66	0.084440777
2.913471302	4	0.26	0.04	0.31	0.064971302
2.768332582	4	0.13	0.78	0.32	-0.045367418
2.780356645	3	0.1	0.29	0.75	-0.051893355
2.872434637	2	0.16	0.43	0.69	-0.007715363
2.957005147	3	0.67	0.17	0.61	0.043555147
2.877572021	3	0.38	0.44	0.15	-0.026527979
2.892788186	0	0.03	0.14	0.83	-0.049211814
2.938104209	4	0.8	0.12	0.86	0.065304209
2.963092457	1	0.63	0.06	0.81	-0.014307543
2.947805711	2	0.31	0.1	0.18	0.005705711
2.929058982	2	0.1	0.33	0.78	0.059908982
2.798268951	3	0.22	0.76	0.81	-0.032431049
3.046956202	0	0.79	0.79	0.47	0.004806202
2.909470599	4	0.7	0.71	0.52	0.039620599
2.916937627	2	0.93	0.89	0.94	-0.026612373
2.936951397	0	0.19	0.35	0.67	-0.030198603
2.935183006	2	0.48	0.23	0.54	0.001133006
3.071599792	0	0.87	0.55	0.8	0.036949792
2.944966979	1	0.08	0.91	0.12	0.006516979
2.997397751	0	0.41	0.44	0.06	-0.036602249
3.048360762	1	0.8	0.5	0.18	0.017460762
2.859827106	1	0.13	0.64	0.37	-0.073872894
2.885496007	4	0.56	0.24	0.41	0.012996007
2.837868559	3	0.23	0.69	0.72	-0.002081441
2.879873437	2	0.34	0.83	0.78	-0.005576563
2.987063453	0	0.17	0.32	0.78	0.029263453
2.899181373	4	0.6	0.64	0.49	0.037481373
2.731576475	4	0.26	0.34	0.65	-0.085623525
2.908615135	2	0.9	0.17	0.48	-0.081534865
2.726225589	4	0.24	0.05	0.17	-0.129424411
2.883899312	3	0.91	0.77	0.81	-0.029350688
2.926219759	4	0.98	0.16	0.11	-0.019680241
2.933021763	1	0.54	0.48	0.74	-0.027978237
3.099391718	0	0.65	0.01	0.67	0.068541718
2.929512226	2	0.45	0.8	0.77	0.029412226
2.948690227	2	0.47	0.67	0.02	-0.009559773
2.997111915	4	0.57	0.85	0.31	0.131661915

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	7 seconds
R Server	'Sir Ronald Aylmer Fisher' @ fisher.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 7 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ fisher.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=226730&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]7 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Ronald Aylmer Fisher' @ fisher.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=226730&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=226730&T=0

As an alternative you can also use a QR Code:

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

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	7 seconds
R Server	'Sir Ronald Aylmer Fisher' @ fisher.wessa.net

Multiple Linear Regression - Estimated Regression Equation
echtscheiding[t] = + 3 -0.04aantalkinderen[t] + 0.12stresserendejob[t] -0.025seksleven[t] -0.07openkunnenpraten[t] + 1storingsterm[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
echtscheiding[t] =  +  3 -0.04aantalkinderen[t] +  0.12stresserendejob[t] -0.025seksleven[t] -0.07openkunnenpraten[t] +  1storingsterm[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=226730&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]echtscheiding[t] =  +  3 -0.04aantalkinderen[t] +  0.12stresserendejob[t] -0.025seksleven[t] -0.07openkunnenpraten[t] +  1storingsterm[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=226730&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=226730&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
echtscheiding[t] = + 3 -0.04aantalkinderen[t] + 0.12stresserendejob[t] -0.025seksleven[t] -0.07openkunnenpraten[t] + 1storingsterm[t] + e[t]

Multiple Linear Regression - Ordinary Least Squares
Variable	Parameter	S.D.	T-STATH0: parameter = 0	2-tail p-value	1-tail p-value
(Intercept)	3	1.87748e-16	1.598e+16	0	0
aantalkinderen	-0.04	4.22436e-17	-9.469e+14	0	0
stresserendejob	0.12	1.99169e-16	6.025e+14	0	0
seksleven	-0.025	2.06817e-16	-1.209e+14	0	0
openkunnenpraten	-0.07	2.10732e-16	-3.322e+14	0	0
storingsterm	1	1.14321e-15	8.747e+14	0	0

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Ordinary Least Squares \tabularnewline
Variable & Parameter & S.D. & T-STATH0: parameter = 0 & 2-tail p-value & 1-tail p-value \tabularnewline
(Intercept) & 3 & 1.87748e-16 & 1.598e+16 & 0 & 0 \tabularnewline
aantalkinderen & -0.04 & 4.22436e-17 & -9.469e+14 & 0 & 0 \tabularnewline
stresserendejob & 0.12 & 1.99169e-16 & 6.025e+14 & 0 & 0 \tabularnewline
seksleven & -0.025 & 2.06817e-16 & -1.209e+14 & 0 & 0 \tabularnewline
openkunnenpraten & -0.07 & 2.10732e-16 & -3.322e+14 & 0 & 0 \tabularnewline
storingsterm & 1 & 1.14321e-15 & 8.747e+14 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=226730&T=2

[TABLE]
[ROW][C]Multiple Linear Regression - Ordinary Least Squares[/C][/ROW]
[ROW][C]Variable[/C][C]Parameter[/C][C]S.D.[/C][C]T-STATH0: parameter = 0[/C][C]2-tail p-value[/C][C]1-tail p-value[/C][/ROW]
[ROW][C](Intercept)[/C][C]3[/C][C]1.87748e-16[/C][C]1.598e+16[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]aantalkinderen[/C][C]-0.04[/C][C]4.22436e-17[/C][C]-9.469e+14[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]stresserendejob[/C][C]0.12[/C][C]1.99169e-16[/C][C]6.025e+14[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]seksleven[/C][C]-0.025[/C][C]2.06817e-16[/C][C]-1.209e+14[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]openkunnenpraten[/C][C]-0.07[/C][C]2.10732e-16[/C][C]-3.322e+14[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]storingsterm[/C][C]1[/C][C]1.14321e-15[/C][C]8.747e+14[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=226730&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=226730&T=2

As an alternative you can also use a QR Code:

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

Multiple Linear Regression - Ordinary Least Squares
Variable	Parameter	S.D.	T-STATH0: parameter = 0	2-tail p-value	1-tail p-value
(Intercept)	3	1.87748e-16	1.598e+16	0	0
aantalkinderen	-0.04	4.22436e-17	-9.469e+14	0	0
stresserendejob	0.12	1.99169e-16	6.025e+14	0	0
seksleven	-0.025	2.06817e-16	-1.209e+14	0	0
openkunnenpraten	-0.07	2.10732e-16	-3.322e+14	0	0
storingsterm	1	1.14321e-15	8.747e+14	0	0

Multiple Linear Regression - Regression Statistics
Multiple R	1
R-squared	1
Adjusted R-squared	1
F-TEST (value)	3.91015e+29
F-TEST (DF numerator)	5
F-TEST (DF denominator)	54
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	4.39454e-16
Sum Squared Residuals	1.04285e-29

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 1 \tabularnewline
R-squared & 1 \tabularnewline
Adjusted R-squared & 1 \tabularnewline
F-TEST (value) & 3.91015e+29 \tabularnewline
F-TEST (DF numerator) & 5 \tabularnewline
F-TEST (DF denominator) & 54 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 4.39454e-16 \tabularnewline
Sum Squared Residuals & 1.04285e-29 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=226730&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]1[/C][/ROW]
[ROW][C]R-squared[/C][C]1[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]1[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]3.91015e+29[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]5[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]54[/C][/ROW]
[ROW][C]p-value[/C][C]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]4.39454e-16[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]1.04285e-29[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=226730&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=226730&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	1
R-squared	1
Adjusted R-squared	1
F-TEST (value)	3.91015e+29
F-TEST (DF numerator)	5
F-TEST (DF denominator)	54
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	4.39454e-16
Sum Squared Residuals	1.04285e-29

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	2.93922	2.93922	-2.97494e-15
2	2.89789	2.89789	1.2786e-16
3	2.82138	2.82138	-2.01861e-16
4	2.97683	2.97683	-8.41566e-17
5	2.98658	2.98658	1.94475e-16
6	3.0861	3.0861	7.68369e-17
7	2.97033	2.97033	-6.7337e-17
8	3.00142	3.00142	8.09235e-17
9	2.89608	2.89608	-8.05664e-17
10	3.04874	3.04874	1.04386e-17
11	2.95188	2.95188	3.74245e-16
12	2.85632	2.85632	1.48041e-16
13	2.87991	2.87991	2.12184e-16
14	3.04026	3.04026	3.19728e-16
15	3.00308	3.00308	-7.52195e-17
16	2.95979	2.95979	8.2969e-17
17	2.91799	2.91799	-2.07232e-16
18	2.81894	2.81894	6.80413e-17
19	2.91478	2.91478	2.61458e-16
20	2.9151	2.9151	1.50292e-16
21	2.84746	2.84746	4.63622e-17
22	2.91412	2.91412	8.4319e-17
23	2.92374	2.92374	-1.30824e-17
24	2.91347	2.91347	2.76574e-16
25	2.76833	2.76833	3.93433e-16
26	2.78036	2.78036	1.72821e-18
27	2.87243	2.87243	2.18175e-17
28	2.95701	2.95701	-1.05359e-16
29	2.87757	2.87757	3.73782e-16
30	2.89279	2.89279	1.21432e-16
31	2.9381	2.9381	4.70593e-17
32	2.96309	2.96309	5.75886e-17
33	2.94781	2.94781	1.69129e-16
34	2.92906	2.92906	-6.07139e-17
35	2.79827	2.79827	-1.47597e-16
36	3.04696	3.04696	-8.49833e-17
37	2.90947	2.90947	1.29181e-16
38	2.91694	2.91694	-3.86157e-16
39	2.93695	2.93695	4.06797e-18
40	2.93518	2.93518	-2.89131e-17
41	3.0716	3.0716	2.11483e-17
42	2.94497	2.94497	2.34561e-16
43	2.9974	2.9974	-1.23429e-16
44	3.04836	3.04836	1.79629e-16
45	2.85983	2.85983	2.01481e-17
46	2.8855	2.8855	1.24452e-16
47	2.83787	2.83787	-7.87854e-17
48	2.87987	2.87987	1.97239e-17
49	2.98706	2.98706	1.02779e-16
50	2.89918	2.89918	-1.11038e-16
51	2.73158	2.73158	-7.9014e-17
52	2.90862	2.90862	-2.06043e-16
53	2.72623	2.72623	6.78595e-17
54	2.8839	2.8839	-2.8839e-17
55	2.92622	2.92622	6.0462e-17
56	2.93302	2.93302	8.11853e-17
57	3.09939	3.09939	3.81951e-17
58	2.92951	2.92951	-1.2359e-16
59	2.94869	2.94869	2.93187e-16
60	2.99711	2.99711	1.91558e-16

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 2.93922 & 2.93922 & -2.97494e-15 \tabularnewline
2 & 2.89789 & 2.89789 & 1.2786e-16 \tabularnewline
3 & 2.82138 & 2.82138 & -2.01861e-16 \tabularnewline
4 & 2.97683 & 2.97683 & -8.41566e-17 \tabularnewline
5 & 2.98658 & 2.98658 & 1.94475e-16 \tabularnewline
6 & 3.0861 & 3.0861 & 7.68369e-17 \tabularnewline
7 & 2.97033 & 2.97033 & -6.7337e-17 \tabularnewline
8 & 3.00142 & 3.00142 & 8.09235e-17 \tabularnewline
9 & 2.89608 & 2.89608 & -8.05664e-17 \tabularnewline
10 & 3.04874 & 3.04874 & 1.04386e-17 \tabularnewline
11 & 2.95188 & 2.95188 & 3.74245e-16 \tabularnewline
12 & 2.85632 & 2.85632 & 1.48041e-16 \tabularnewline
13 & 2.87991 & 2.87991 & 2.12184e-16 \tabularnewline
14 & 3.04026 & 3.04026 & 3.19728e-16 \tabularnewline
15 & 3.00308 & 3.00308 & -7.52195e-17 \tabularnewline
16 & 2.95979 & 2.95979 & 8.2969e-17 \tabularnewline
17 & 2.91799 & 2.91799 & -2.07232e-16 \tabularnewline
18 & 2.81894 & 2.81894 & 6.80413e-17 \tabularnewline
19 & 2.91478 & 2.91478 & 2.61458e-16 \tabularnewline
20 & 2.9151 & 2.9151 & 1.50292e-16 \tabularnewline
21 & 2.84746 & 2.84746 & 4.63622e-17 \tabularnewline
22 & 2.91412 & 2.91412 & 8.4319e-17 \tabularnewline
23 & 2.92374 & 2.92374 & -1.30824e-17 \tabularnewline
24 & 2.91347 & 2.91347 & 2.76574e-16 \tabularnewline
25 & 2.76833 & 2.76833 & 3.93433e-16 \tabularnewline
26 & 2.78036 & 2.78036 & 1.72821e-18 \tabularnewline
27 & 2.87243 & 2.87243 & 2.18175e-17 \tabularnewline
28 & 2.95701 & 2.95701 & -1.05359e-16 \tabularnewline
29 & 2.87757 & 2.87757 & 3.73782e-16 \tabularnewline
30 & 2.89279 & 2.89279 & 1.21432e-16 \tabularnewline
31 & 2.9381 & 2.9381 & 4.70593e-17 \tabularnewline
32 & 2.96309 & 2.96309 & 5.75886e-17 \tabularnewline
33 & 2.94781 & 2.94781 & 1.69129e-16 \tabularnewline
34 & 2.92906 & 2.92906 & -6.07139e-17 \tabularnewline
35 & 2.79827 & 2.79827 & -1.47597e-16 \tabularnewline
36 & 3.04696 & 3.04696 & -8.49833e-17 \tabularnewline
37 & 2.90947 & 2.90947 & 1.29181e-16 \tabularnewline
38 & 2.91694 & 2.91694 & -3.86157e-16 \tabularnewline
39 & 2.93695 & 2.93695 & 4.06797e-18 \tabularnewline
40 & 2.93518 & 2.93518 & -2.89131e-17 \tabularnewline
41 & 3.0716 & 3.0716 & 2.11483e-17 \tabularnewline
42 & 2.94497 & 2.94497 & 2.34561e-16 \tabularnewline
43 & 2.9974 & 2.9974 & -1.23429e-16 \tabularnewline
44 & 3.04836 & 3.04836 & 1.79629e-16 \tabularnewline
45 & 2.85983 & 2.85983 & 2.01481e-17 \tabularnewline
46 & 2.8855 & 2.8855 & 1.24452e-16 \tabularnewline
47 & 2.83787 & 2.83787 & -7.87854e-17 \tabularnewline
48 & 2.87987 & 2.87987 & 1.97239e-17 \tabularnewline
49 & 2.98706 & 2.98706 & 1.02779e-16 \tabularnewline
50 & 2.89918 & 2.89918 & -1.11038e-16 \tabularnewline
51 & 2.73158 & 2.73158 & -7.9014e-17 \tabularnewline
52 & 2.90862 & 2.90862 & -2.06043e-16 \tabularnewline
53 & 2.72623 & 2.72623 & 6.78595e-17 \tabularnewline
54 & 2.8839 & 2.8839 & -2.8839e-17 \tabularnewline
55 & 2.92622 & 2.92622 & 6.0462e-17 \tabularnewline
56 & 2.93302 & 2.93302 & 8.11853e-17 \tabularnewline
57 & 3.09939 & 3.09939 & 3.81951e-17 \tabularnewline
58 & 2.92951 & 2.92951 & -1.2359e-16 \tabularnewline
59 & 2.94869 & 2.94869 & 2.93187e-16 \tabularnewline
60 & 2.99711 & 2.99711 & 1.91558e-16 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=226730&T=4

[TABLE]
[ROW][C]Multiple Linear Regression - Actuals, Interpolation, and Residuals[/C][/ROW]
[ROW][C]Time or Index[/C][C]Actuals[/C][C]InterpolationForecast[/C][C]ResidualsPrediction Error[/C][/ROW]
[ROW][C]1[/C][C]2.93922[/C][C]2.93922[/C][C]-2.97494e-15[/C][/ROW]
[ROW][C]2[/C][C]2.89789[/C][C]2.89789[/C][C]1.2786e-16[/C][/ROW]
[ROW][C]3[/C][C]2.82138[/C][C]2.82138[/C][C]-2.01861e-16[/C][/ROW]
[ROW][C]4[/C][C]2.97683[/C][C]2.97683[/C][C]-8.41566e-17[/C][/ROW]
[ROW][C]5[/C][C]2.98658[/C][C]2.98658[/C][C]1.94475e-16[/C][/ROW]
[ROW][C]6[/C][C]3.0861[/C][C]3.0861[/C][C]7.68369e-17[/C][/ROW]
[ROW][C]7[/C][C]2.97033[/C][C]2.97033[/C][C]-6.7337e-17[/C][/ROW]
[ROW][C]8[/C][C]3.00142[/C][C]3.00142[/C][C]8.09235e-17[/C][/ROW]
[ROW][C]9[/C][C]2.89608[/C][C]2.89608[/C][C]-8.05664e-17[/C][/ROW]
[ROW][C]10[/C][C]3.04874[/C][C]3.04874[/C][C]1.04386e-17[/C][/ROW]
[ROW][C]11[/C][C]2.95188[/C][C]2.95188[/C][C]3.74245e-16[/C][/ROW]
[ROW][C]12[/C][C]2.85632[/C][C]2.85632[/C][C]1.48041e-16[/C][/ROW]
[ROW][C]13[/C][C]2.87991[/C][C]2.87991[/C][C]2.12184e-16[/C][/ROW]
[ROW][C]14[/C][C]3.04026[/C][C]3.04026[/C][C]3.19728e-16[/C][/ROW]
[ROW][C]15[/C][C]3.00308[/C][C]3.00308[/C][C]-7.52195e-17[/C][/ROW]
[ROW][C]16[/C][C]2.95979[/C][C]2.95979[/C][C]8.2969e-17[/C][/ROW]
[ROW][C]17[/C][C]2.91799[/C][C]2.91799[/C][C]-2.07232e-16[/C][/ROW]
[ROW][C]18[/C][C]2.81894[/C][C]2.81894[/C][C]6.80413e-17[/C][/ROW]
[ROW][C]19[/C][C]2.91478[/C][C]2.91478[/C][C]2.61458e-16[/C][/ROW]
[ROW][C]20[/C][C]2.9151[/C][C]2.9151[/C][C]1.50292e-16[/C][/ROW]
[ROW][C]21[/C][C]2.84746[/C][C]2.84746[/C][C]4.63622e-17[/C][/ROW]
[ROW][C]22[/C][C]2.91412[/C][C]2.91412[/C][C]8.4319e-17[/C][/ROW]
[ROW][C]23[/C][C]2.92374[/C][C]2.92374[/C][C]-1.30824e-17[/C][/ROW]
[ROW][C]24[/C][C]2.91347[/C][C]2.91347[/C][C]2.76574e-16[/C][/ROW]
[ROW][C]25[/C][C]2.76833[/C][C]2.76833[/C][C]3.93433e-16[/C][/ROW]
[ROW][C]26[/C][C]2.78036[/C][C]2.78036[/C][C]1.72821e-18[/C][/ROW]
[ROW][C]27[/C][C]2.87243[/C][C]2.87243[/C][C]2.18175e-17[/C][/ROW]
[ROW][C]28[/C][C]2.95701[/C][C]2.95701[/C][C]-1.05359e-16[/C][/ROW]
[ROW][C]29[/C][C]2.87757[/C][C]2.87757[/C][C]3.73782e-16[/C][/ROW]
[ROW][C]30[/C][C]2.89279[/C][C]2.89279[/C][C]1.21432e-16[/C][/ROW]
[ROW][C]31[/C][C]2.9381[/C][C]2.9381[/C][C]4.70593e-17[/C][/ROW]
[ROW][C]32[/C][C]2.96309[/C][C]2.96309[/C][C]5.75886e-17[/C][/ROW]
[ROW][C]33[/C][C]2.94781[/C][C]2.94781[/C][C]1.69129e-16[/C][/ROW]
[ROW][C]34[/C][C]2.92906[/C][C]2.92906[/C][C]-6.07139e-17[/C][/ROW]
[ROW][C]35[/C][C]2.79827[/C][C]2.79827[/C][C]-1.47597e-16[/C][/ROW]
[ROW][C]36[/C][C]3.04696[/C][C]3.04696[/C][C]-8.49833e-17[/C][/ROW]
[ROW][C]37[/C][C]2.90947[/C][C]2.90947[/C][C]1.29181e-16[/C][/ROW]
[ROW][C]38[/C][C]2.91694[/C][C]2.91694[/C][C]-3.86157e-16[/C][/ROW]
[ROW][C]39[/C][C]2.93695[/C][C]2.93695[/C][C]4.06797e-18[/C][/ROW]
[ROW][C]40[/C][C]2.93518[/C][C]2.93518[/C][C]-2.89131e-17[/C][/ROW]
[ROW][C]41[/C][C]3.0716[/C][C]3.0716[/C][C]2.11483e-17[/C][/ROW]
[ROW][C]42[/C][C]2.94497[/C][C]2.94497[/C][C]2.34561e-16[/C][/ROW]
[ROW][C]43[/C][C]2.9974[/C][C]2.9974[/C][C]-1.23429e-16[/C][/ROW]
[ROW][C]44[/C][C]3.04836[/C][C]3.04836[/C][C]1.79629e-16[/C][/ROW]
[ROW][C]45[/C][C]2.85983[/C][C]2.85983[/C][C]2.01481e-17[/C][/ROW]
[ROW][C]46[/C][C]2.8855[/C][C]2.8855[/C][C]1.24452e-16[/C][/ROW]
[ROW][C]47[/C][C]2.83787[/C][C]2.83787[/C][C]-7.87854e-17[/C][/ROW]
[ROW][C]48[/C][C]2.87987[/C][C]2.87987[/C][C]1.97239e-17[/C][/ROW]
[ROW][C]49[/C][C]2.98706[/C][C]2.98706[/C][C]1.02779e-16[/C][/ROW]
[ROW][C]50[/C][C]2.89918[/C][C]2.89918[/C][C]-1.11038e-16[/C][/ROW]
[ROW][C]51[/C][C]2.73158[/C][C]2.73158[/C][C]-7.9014e-17[/C][/ROW]
[ROW][C]52[/C][C]2.90862[/C][C]2.90862[/C][C]-2.06043e-16[/C][/ROW]
[ROW][C]53[/C][C]2.72623[/C][C]2.72623[/C][C]6.78595e-17[/C][/ROW]
[ROW][C]54[/C][C]2.8839[/C][C]2.8839[/C][C]-2.8839e-17[/C][/ROW]
[ROW][C]55[/C][C]2.92622[/C][C]2.92622[/C][C]6.0462e-17[/C][/ROW]
[ROW][C]56[/C][C]2.93302[/C][C]2.93302[/C][C]8.11853e-17[/C][/ROW]
[ROW][C]57[/C][C]3.09939[/C][C]3.09939[/C][C]3.81951e-17[/C][/ROW]
[ROW][C]58[/C][C]2.92951[/C][C]2.92951[/C][C]-1.2359e-16[/C][/ROW]
[ROW][C]59[/C][C]2.94869[/C][C]2.94869[/C][C]2.93187e-16[/C][/ROW]
[ROW][C]60[/C][C]2.99711[/C][C]2.99711[/C][C]1.91558e-16[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=226730&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=226730&T=4

As an alternative you can also use a QR Code:

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	2.93922	2.93922	-2.97494e-15
2	2.89789	2.89789	1.2786e-16
3	2.82138	2.82138	-2.01861e-16
4	2.97683	2.97683	-8.41566e-17
5	2.98658	2.98658	1.94475e-16
6	3.0861	3.0861	7.68369e-17
7	2.97033	2.97033	-6.7337e-17
8	3.00142	3.00142	8.09235e-17
9	2.89608	2.89608	-8.05664e-17
10	3.04874	3.04874	1.04386e-17
11	2.95188	2.95188	3.74245e-16
12	2.85632	2.85632	1.48041e-16
13	2.87991	2.87991	2.12184e-16
14	3.04026	3.04026	3.19728e-16
15	3.00308	3.00308	-7.52195e-17
16	2.95979	2.95979	8.2969e-17
17	2.91799	2.91799	-2.07232e-16
18	2.81894	2.81894	6.80413e-17
19	2.91478	2.91478	2.61458e-16
20	2.9151	2.9151	1.50292e-16
21	2.84746	2.84746	4.63622e-17
22	2.91412	2.91412	8.4319e-17
23	2.92374	2.92374	-1.30824e-17
24	2.91347	2.91347	2.76574e-16
25	2.76833	2.76833	3.93433e-16
26	2.78036	2.78036	1.72821e-18
27	2.87243	2.87243	2.18175e-17
28	2.95701	2.95701	-1.05359e-16
29	2.87757	2.87757	3.73782e-16
30	2.89279	2.89279	1.21432e-16
31	2.9381	2.9381	4.70593e-17
32	2.96309	2.96309	5.75886e-17
33	2.94781	2.94781	1.69129e-16
34	2.92906	2.92906	-6.07139e-17
35	2.79827	2.79827	-1.47597e-16
36	3.04696	3.04696	-8.49833e-17
37	2.90947	2.90947	1.29181e-16
38	2.91694	2.91694	-3.86157e-16
39	2.93695	2.93695	4.06797e-18
40	2.93518	2.93518	-2.89131e-17
41	3.0716	3.0716	2.11483e-17
42	2.94497	2.94497	2.34561e-16
43	2.9974	2.9974	-1.23429e-16
44	3.04836	3.04836	1.79629e-16
45	2.85983	2.85983	2.01481e-17
46	2.8855	2.8855	1.24452e-16
47	2.83787	2.83787	-7.87854e-17
48	2.87987	2.87987	1.97239e-17
49	2.98706	2.98706	1.02779e-16
50	2.89918	2.89918	-1.11038e-16
51	2.73158	2.73158	-7.9014e-17
52	2.90862	2.90862	-2.06043e-16
53	2.72623	2.72623	6.78595e-17
54	2.8839	2.8839	-2.8839e-17
55	2.92622	2.92622	6.0462e-17
56	2.93302	2.93302	8.11853e-17
57	3.09939	3.09939	3.81951e-17
58	2.92951	2.92951	-1.2359e-16
59	2.94869	2.94869	2.93187e-16
60	2.99711	2.99711	1.91558e-16

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
9	0.526851	0.946299	0.473149
10	0.714118	0.571763	0.285882
11	0.671242	0.657517	0.328758
12	0.999279	0.00144155	0.000720774
13	0.263744	0.527487	0.736256
14	1	2.68857e-13	1.34428e-13
15	0.9999	0.000199653	9.98264e-05
16	0.450638	0.901276	0.549362
17	0.351352	0.702704	0.648648
18	0.999981	3.76458e-05	1.88229e-05
19	1	3.45426e-16	1.72713e-16
20	0.0443641	0.0887282	0.955636
21	0.0700408	0.140082	0.929959
22	0.116365	0.232729	0.883635
23	1	2.54634e-10	1.27317e-10
24	0.873907	0.252185	0.126093
25	0.00473143	0.00946286	0.995269
26	0.912734	0.174532	0.0872658
27	1.29795e-07	2.59591e-07	1
28	0.139701	0.279402	0.860299
29	0.288648	0.577295	0.711352
30	0.825265	0.34947	0.174735
31	0.999999	1.86243e-06	9.31214e-07
32	1	3.10175e-07	1.55087e-07
33	0.934354	0.131292	0.0656461
34	0.719612	0.560776	0.280388
35	1.33017e-09	2.66033e-09	1
36	0.978572	0.0428552	0.0214276
37	0.999996	8.49936e-06	4.24968e-06
38	0.000451748	0.000903496	0.999548
39	0.265036	0.530073	0.734964
40	0.19741	0.394819	0.80259
41	3.74778e-06	7.49556e-06	0.999996
42	0.0367568	0.0735136	0.963243
43	0.0395275	0.079055	0.960472
44	0.343821	0.687641	0.656179
45	0.0146023	0.0292047	0.985398
46	5.87227e-08	1.17445e-07	1
47	0.871847	0.256307	0.128153
48	0.532258	0.935485	0.467742
49	0.82932	0.341359	0.17068
50	0.276045	0.55209	0.723955
51	0.0590573	0.118115	0.940943

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
9 & 0.526851 & 0.946299 & 0.473149 \tabularnewline
10 & 0.714118 & 0.571763 & 0.285882 \tabularnewline
11 & 0.671242 & 0.657517 & 0.328758 \tabularnewline
12 & 0.999279 & 0.00144155 & 0.000720774 \tabularnewline
13 & 0.263744 & 0.527487 & 0.736256 \tabularnewline
14 & 1 & 2.68857e-13 & 1.34428e-13 \tabularnewline
15 & 0.9999 & 0.000199653 & 9.98264e-05 \tabularnewline
16 & 0.450638 & 0.901276 & 0.549362 \tabularnewline
17 & 0.351352 & 0.702704 & 0.648648 \tabularnewline
18 & 0.999981 & 3.76458e-05 & 1.88229e-05 \tabularnewline
19 & 1 & 3.45426e-16 & 1.72713e-16 \tabularnewline
20 & 0.0443641 & 0.0887282 & 0.955636 \tabularnewline
21 & 0.0700408 & 0.140082 & 0.929959 \tabularnewline
22 & 0.116365 & 0.232729 & 0.883635 \tabularnewline
23 & 1 & 2.54634e-10 & 1.27317e-10 \tabularnewline
24 & 0.873907 & 0.252185 & 0.126093 \tabularnewline
25 & 0.00473143 & 0.00946286 & 0.995269 \tabularnewline
26 & 0.912734 & 0.174532 & 0.0872658 \tabularnewline
27 & 1.29795e-07 & 2.59591e-07 & 1 \tabularnewline
28 & 0.139701 & 0.279402 & 0.860299 \tabularnewline
29 & 0.288648 & 0.577295 & 0.711352 \tabularnewline
30 & 0.825265 & 0.34947 & 0.174735 \tabularnewline
31 & 0.999999 & 1.86243e-06 & 9.31214e-07 \tabularnewline
32 & 1 & 3.10175e-07 & 1.55087e-07 \tabularnewline
33 & 0.934354 & 0.131292 & 0.0656461 \tabularnewline
34 & 0.719612 & 0.560776 & 0.280388 \tabularnewline
35 & 1.33017e-09 & 2.66033e-09 & 1 \tabularnewline
36 & 0.978572 & 0.0428552 & 0.0214276 \tabularnewline
37 & 0.999996 & 8.49936e-06 & 4.24968e-06 \tabularnewline
38 & 0.000451748 & 0.000903496 & 0.999548 \tabularnewline
39 & 0.265036 & 0.530073 & 0.734964 \tabularnewline
40 & 0.19741 & 0.394819 & 0.80259 \tabularnewline
41 & 3.74778e-06 & 7.49556e-06 & 0.999996 \tabularnewline
42 & 0.0367568 & 0.0735136 & 0.963243 \tabularnewline
43 & 0.0395275 & 0.079055 & 0.960472 \tabularnewline
44 & 0.343821 & 0.687641 & 0.656179 \tabularnewline
45 & 0.0146023 & 0.0292047 & 0.985398 \tabularnewline
46 & 5.87227e-08 & 1.17445e-07 & 1 \tabularnewline
47 & 0.871847 & 0.256307 & 0.128153 \tabularnewline
48 & 0.532258 & 0.935485 & 0.467742 \tabularnewline
49 & 0.82932 & 0.341359 & 0.17068 \tabularnewline
50 & 0.276045 & 0.55209 & 0.723955 \tabularnewline
51 & 0.0590573 & 0.118115 & 0.940943 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=226730&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]9[/C][C]0.526851[/C][C]0.946299[/C][C]0.473149[/C][/ROW]
[ROW][C]10[/C][C]0.714118[/C][C]0.571763[/C][C]0.285882[/C][/ROW]
[ROW][C]11[/C][C]0.671242[/C][C]0.657517[/C][C]0.328758[/C][/ROW]
[ROW][C]12[/C][C]0.999279[/C][C]0.00144155[/C][C]0.000720774[/C][/ROW]
[ROW][C]13[/C][C]0.263744[/C][C]0.527487[/C][C]0.736256[/C][/ROW]
[ROW][C]14[/C][C]1[/C][C]2.68857e-13[/C][C]1.34428e-13[/C][/ROW]
[ROW][C]15[/C][C]0.9999[/C][C]0.000199653[/C][C]9.98264e-05[/C][/ROW]
[ROW][C]16[/C][C]0.450638[/C][C]0.901276[/C][C]0.549362[/C][/ROW]
[ROW][C]17[/C][C]0.351352[/C][C]0.702704[/C][C]0.648648[/C][/ROW]
[ROW][C]18[/C][C]0.999981[/C][C]3.76458e-05[/C][C]1.88229e-05[/C][/ROW]
[ROW][C]19[/C][C]1[/C][C]3.45426e-16[/C][C]1.72713e-16[/C][/ROW]
[ROW][C]20[/C][C]0.0443641[/C][C]0.0887282[/C][C]0.955636[/C][/ROW]
[ROW][C]21[/C][C]0.0700408[/C][C]0.140082[/C][C]0.929959[/C][/ROW]
[ROW][C]22[/C][C]0.116365[/C][C]0.232729[/C][C]0.883635[/C][/ROW]
[ROW][C]23[/C][C]1[/C][C]2.54634e-10[/C][C]1.27317e-10[/C][/ROW]
[ROW][C]24[/C][C]0.873907[/C][C]0.252185[/C][C]0.126093[/C][/ROW]
[ROW][C]25[/C][C]0.00473143[/C][C]0.00946286[/C][C]0.995269[/C][/ROW]
[ROW][C]26[/C][C]0.912734[/C][C]0.174532[/C][C]0.0872658[/C][/ROW]
[ROW][C]27[/C][C]1.29795e-07[/C][C]2.59591e-07[/C][C]1[/C][/ROW]
[ROW][C]28[/C][C]0.139701[/C][C]0.279402[/C][C]0.860299[/C][/ROW]
[ROW][C]29[/C][C]0.288648[/C][C]0.577295[/C][C]0.711352[/C][/ROW]
[ROW][C]30[/C][C]0.825265[/C][C]0.34947[/C][C]0.174735[/C][/ROW]
[ROW][C]31[/C][C]0.999999[/C][C]1.86243e-06[/C][C]9.31214e-07[/C][/ROW]
[ROW][C]32[/C][C]1[/C][C]3.10175e-07[/C][C]1.55087e-07[/C][/ROW]
[ROW][C]33[/C][C]0.934354[/C][C]0.131292[/C][C]0.0656461[/C][/ROW]
[ROW][C]34[/C][C]0.719612[/C][C]0.560776[/C][C]0.280388[/C][/ROW]
[ROW][C]35[/C][C]1.33017e-09[/C][C]2.66033e-09[/C][C]1[/C][/ROW]
[ROW][C]36[/C][C]0.978572[/C][C]0.0428552[/C][C]0.0214276[/C][/ROW]
[ROW][C]37[/C][C]0.999996[/C][C]8.49936e-06[/C][C]4.24968e-06[/C][/ROW]
[ROW][C]38[/C][C]0.000451748[/C][C]0.000903496[/C][C]0.999548[/C][/ROW]
[ROW][C]39[/C][C]0.265036[/C][C]0.530073[/C][C]0.734964[/C][/ROW]
[ROW][C]40[/C][C]0.19741[/C][C]0.394819[/C][C]0.80259[/C][/ROW]
[ROW][C]41[/C][C]3.74778e-06[/C][C]7.49556e-06[/C][C]0.999996[/C][/ROW]
[ROW][C]42[/C][C]0.0367568[/C][C]0.0735136[/C][C]0.963243[/C][/ROW]
[ROW][C]43[/C][C]0.0395275[/C][C]0.079055[/C][C]0.960472[/C][/ROW]
[ROW][C]44[/C][C]0.343821[/C][C]0.687641[/C][C]0.656179[/C][/ROW]
[ROW][C]45[/C][C]0.0146023[/C][C]0.0292047[/C][C]0.985398[/C][/ROW]
[ROW][C]46[/C][C]5.87227e-08[/C][C]1.17445e-07[/C][C]1[/C][/ROW]
[ROW][C]47[/C][C]0.871847[/C][C]0.256307[/C][C]0.128153[/C][/ROW]
[ROW][C]48[/C][C]0.532258[/C][C]0.935485[/C][C]0.467742[/C][/ROW]
[ROW][C]49[/C][C]0.82932[/C][C]0.341359[/C][C]0.17068[/C][/ROW]
[ROW][C]50[/C][C]0.276045[/C][C]0.55209[/C][C]0.723955[/C][/ROW]
[ROW][C]51[/C][C]0.0590573[/C][C]0.118115[/C][C]0.940943[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=226730&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=226730&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
9	0.526851	0.946299	0.473149
10	0.714118	0.571763	0.285882
11	0.671242	0.657517	0.328758
12	0.999279	0.00144155	0.000720774
13	0.263744	0.527487	0.736256
14	1	2.68857e-13	1.34428e-13
15	0.9999	0.000199653	9.98264e-05
16	0.450638	0.901276	0.549362
17	0.351352	0.702704	0.648648
18	0.999981	3.76458e-05	1.88229e-05
19	1	3.45426e-16	1.72713e-16
20	0.0443641	0.0887282	0.955636
21	0.0700408	0.140082	0.929959
22	0.116365	0.232729	0.883635
23	1	2.54634e-10	1.27317e-10
24	0.873907	0.252185	0.126093
25	0.00473143	0.00946286	0.995269
26	0.912734	0.174532	0.0872658
27	1.29795e-07	2.59591e-07	1
28	0.139701	0.279402	0.860299
29	0.288648	0.577295	0.711352
30	0.825265	0.34947	0.174735
31	0.999999	1.86243e-06	9.31214e-07
32	1	3.10175e-07	1.55087e-07
33	0.934354	0.131292	0.0656461
34	0.719612	0.560776	0.280388
35	1.33017e-09	2.66033e-09	1
36	0.978572	0.0428552	0.0214276
37	0.999996	8.49936e-06	4.24968e-06
38	0.000451748	0.000903496	0.999548
39	0.265036	0.530073	0.734964
40	0.19741	0.394819	0.80259
41	3.74778e-06	7.49556e-06	0.999996
42	0.0367568	0.0735136	0.963243
43	0.0395275	0.079055	0.960472
44	0.343821	0.687641	0.656179
45	0.0146023	0.0292047	0.985398
46	5.87227e-08	1.17445e-07	1
47	0.871847	0.256307	0.128153
48	0.532258	0.935485	0.467742
49	0.82932	0.341359	0.17068
50	0.276045	0.55209	0.723955
51	0.0590573	0.118115	0.940943

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	15	0.348837	NOK
5% type I error level	17	0.395349	NOK
10% type I error level	20	0.465116	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 & 15 & 0.348837 & NOK \tabularnewline
5% type I error level & 17 & 0.395349 & NOK \tabularnewline
10% type I error level & 20 & 0.465116 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=226730&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]15[/C][C]0.348837[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]17[/C][C]0.395349[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]20[/C][C]0.465116[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=226730&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=226730&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	15	0.348837	NOK
5% type I error level	17	0.395349	NOK
10% type I error level	20	0.465116	NOK

Figure 1

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Figure 2

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Figure 3

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Figure 4

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Figure 5

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Figure 6

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Figure 7

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Figure 8

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Figure 9

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Figure 10

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Parameters (Session):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 1 ; 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$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, signif(mysum$coefficients[i,1],6), sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,signif(mysum$coefficients[i,1],6))
a<-table.element(a, signif(mysum$coefficients[i,2],6))
a<-table.element(a, signif(mysum$coefficients[i,3],4))
a<-table.element(a, signif(mysum$coefficients[i,4],6))
a<-table.element(a, signif(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, signif(sqrt(mysum$r.squared),6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, signif(mysum$r.squared,6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, signif(mysum$adj.r.squared,6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[1],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, signif(mysum$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$fstatistic[3],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, signif(1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]),6))
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, signif(mysum$sigma,6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, signif(sum(myerror*myerror),6))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,signif(x[i],6))
a<-table.element(a,signif(x[i]-mysum$resid[i],6))
a<-table.element(a,signif(mysum$resid[i],6))
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,signif(gqarr[mypoint-kp3+1,1],6))
a<-table.element(a,signif(gqarr[mypoint-kp3+1,2],6))
a<-table.element(a,signif(gqarr[mypoint-kp3+1,3],6))
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,signif(numsignificant1/numgqtests,6))
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,signif(numsignificant5,6))
a<-table.element(a,signif(numsignificant5/numgqtests,6))
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,signif(numsignificant10,6))
a<-table.element(a,signif(numsignificant10/numgqtests,6))
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code