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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Sun, 04 Nov 2012 06:04:10 -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/2012/Nov/04/t1352027108ic6212gbla8etvk.htm/, Retrieved Tue, 23 Apr 2024 23:42:10 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=185790, Retrieved Tue, 23 Apr 2024 23:42:10 +0000

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Estimated Impact

169

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

-     [Multiple Regression] [Competence to learn] [2010-11-17 07:43:53] [b98453cac15ba1066b407e146608df68]
- R PD  [Multiple Regression] [workshop 7: regre...] [2012-11-02 15:42:59] [40b341cf5fb1ddfd74e4c5704837f48c]
-   PD    [Multiple Regression] [workshop 7: Y_t m...] [2012-11-02 16:52:03] [40b341cf5fb1ddfd74e4c5704837f48c]
- R         [Multiple Regression] [WS 7 multiple reg...] [2012-11-04 10:48:57] [e01c78beec4051e03ee053d8bc2c6384]
-   P           [Multiple Regression] [WS 7 multiple reg...] [2012-11-04 11:04:10] [074a00bbc2315ea54a3f557bcf69eecf] [Current]

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Dataseries X:

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Histogram

Boxplots

31/12/1961	9190	2514	2550	1512	1591	472	551
31/12/1962	9251	2537	2572	1517	1595	476	554
31/12/1963	9328	2564	2597	1525	1602	483	558
31/12/1964	9428	2595	2623	1540	1613	493	565
31/12/1965	9499	2617	2647	1547	1622	498	568
31/12/1966	9556	2638	2670	1547	1627	502	572
31/12/1967	9606	2657	2690	1547	1632	504	575
31/12/1968	9632	2668	2705	1547	1634	503	574
31/12/1969	9660	2683	2721	1546	1637	501	572
31/12/1970	9651	2687	2729	1533	1627	502	573
31/12/1971	9695	2705	2747	1538	1632	502	572
31/12/1972	9727	2717	2761	1543	1637	500	569
31/12/1973	9757	2728	2773	1549	1643	498	566
31/12/1974	9788	2741	2786	1556	1650	495	560
31/12/1975	9813	2752	2796	1559	1654	494	557
31/12/1976	9823	2759	2807	1559	1656	490	552
31/12/1977	9837	2767	2817	1563	1661	484	545
31/12/1978	9842	2774	2827	1563	1662	477	539
31/12/1979	9855	2781	2838	1564	1664	474	535
31/12/1980	9863	2788	2847	1564	1665	469	531
31/12/1981	9855	2789	2853	1557	1661	466	528
31/12/1982	9858	2795	2860	1554	1659	464	526
31/12/1983	9853	2798	2864	1552	1656	460	523
31/12/1984	9858	2801	2869	1552	1656	458	521
31/12/1985	9859	2803	2873	1551	1655	457	519
31/12/1986	9865	2808	2877	1552	1654	456	517
31/12/1987	9876	2813	2883	1554	1656	455	515
31/12/1988	9928	2826	2896	1567	1668	456	514
31/12/1989	9948	2835	2905	1572	1672	453	511
31/12/1990	9987	2849	2919	1579	1680	453	508
31/12/1991	10022	2862	2933	1588	1688	449	502
31/12/1992	10068	2877	2948	1597	1696	449	501
31/12/1993	10101	2888	2959	1603	1702	449	500
31/12/1994	10131	2897	2969	1607	1706	452	500
31/12/1995	10143	2902	2978	1607	1708	450	498
31/12/1996	10170	2911	2988	1609	1711	452	499
31/12/1997	10192	2917	2996	1612	1714	454	499
31/12/1998	10214	2924	3003	1615	1717	455	500
31/12/1999	10239	2930	3011	1619	1721	458	501
31/12/2000	10263	2935	3018	1622	1724	461	503
31/12/2001	10310	2945	3028	1628	1730	469	510
31/12/2002	10355	2957	3038	1634	1735	477	515
31/12/2003	10396	2967	3049	1640	1740	480	520
31/12/2004	10446	2980	3063	1648	1748	484	523
31/12/2005	10511	2997	3081	1657	1757	490	529
31/12/2006	10585	3017	3100	1668	1768	497	534
31/12/2007	10667	3040	3122	1678	1778	506	543
31/12/2008	10753	3064	3145	1687	1789	516	553
31/12/2009	10840	3085	3167	1700	1798	527	563
31/12/2010	10951	3113	3193	1714	1811	542	577

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	9 seconds
R Server	'Gertrude Mary Cox' @ cox.wessa.net
R Framework error message	Warning: there are blank lines in the 'Data X' field. Please, use NA for missing data - blank lines are simply deleted and are NOT treated as missing values.

\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 & 9 seconds \tabularnewline
R Server & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
R Framework error message & Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values. \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185790&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]9 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[ROW][C]R Framework error message[/C][C]Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185790&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185790&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	9 seconds
R Server	'Gertrude Mary Cox' @ cox.wessa.net
R Framework error message	Warning: there are blank lines in the 'Data X' field. Please, use NA for missing data - blank lines are simply deleted and are NOT treated as missing values.

Multiple Linear Regression - Estimated Regression Equation
jaar[t] = + 0.00131729825631988 -3.03481194081606e-09totaal[t] -3.69292016553485e-10vlaamsm[t] + 4.56677402633589e-09vlaamsvr[t] + 5.00117340245021e-09waalsm[t] + 3.87504151472791e-09waalsvr[t] + 1.34813310231096e-09brusselm[t] + 6.37005796506606e-09brusselvr[t] -6.45150461363158e-07t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
jaar[t] =  +  0.00131729825631988 -3.03481194081606e-09totaal[t] -3.69292016553485e-10vlaamsm[t] +  4.56677402633589e-09vlaamsvr[t] +  5.00117340245021e-09waalsm[t] +  3.87504151472791e-09waalsvr[t] +  1.34813310231096e-09brusselm[t] +  6.37005796506606e-09brusselvr[t] -6.45150461363158e-07t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185790&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]jaar[t] =  +  0.00131729825631988 -3.03481194081606e-09totaal[t] -3.69292016553485e-10vlaamsm[t] +  4.56677402633589e-09vlaamsvr[t] +  5.00117340245021e-09waalsm[t] +  3.87504151472791e-09waalsvr[t] +  1.34813310231096e-09brusselm[t] +  6.37005796506606e-09brusselvr[t] -6.45150461363158e-07t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185790&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185790&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
jaar[t] = + 0.00131729825631988 -3.03481194081606e-09totaal[t] -3.69292016553485e-10vlaamsm[t] + 4.56677402633589e-09vlaamsvr[t] + 5.00117340245021e-09waalsm[t] + 3.87504151472791e-09waalsvr[t] + 1.34813310231096e-09brusselm[t] + 6.37005796506606e-09brusselvr[t] -6.45150461363158e-07t + e[t]

Multiple Linear Regression - Ordinary Least Squares
Variable	Parameter	S.D.	T-STATH0: parameter = 0	2-tail p-value	1-tail p-value
(Intercept)	0.00131729825631988	0	4051.7266	0	0
totaal	-3.03481194081606e-09	0	-1.7704	0.084094	0.042047
vlaamsm	-3.69292016553485e-10	0	-0.2031	0.840097	0.420048
vlaamsvr	4.56677402633589e-09	0	2.2318	0.031156	0.015578
waalsm	5.00117340245021e-09	0	2.6096	0.012596	0.006298
waalsvr	3.87504151472791e-09	0	2.1216	0.039966	0.019983
brusselm	1.34813310231096e-09	0	0.757	0.453394	0.226697
brusselvr	6.37005796506606e-09	0	3.2792	0.002128	0.001064
t	-6.45150461363158e-07	0	-302.2881	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) & 0.00131729825631988 & 0 & 4051.7266 & 0 & 0 \tabularnewline
totaal & -3.03481194081606e-09 & 0 & -1.7704 & 0.084094 & 0.042047 \tabularnewline
vlaamsm & -3.69292016553485e-10 & 0 & -0.2031 & 0.840097 & 0.420048 \tabularnewline
vlaamsvr & 4.56677402633589e-09 & 0 & 2.2318 & 0.031156 & 0.015578 \tabularnewline
waalsm & 5.00117340245021e-09 & 0 & 2.6096 & 0.012596 & 0.006298 \tabularnewline
waalsvr & 3.87504151472791e-09 & 0 & 2.1216 & 0.039966 & 0.019983 \tabularnewline
brusselm & 1.34813310231096e-09 & 0 & 0.757 & 0.453394 & 0.226697 \tabularnewline
brusselvr & 6.37005796506606e-09 & 0 & 3.2792 & 0.002128 & 0.001064 \tabularnewline
t & -6.45150461363158e-07 & 0 & -302.2881 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185790&T=2

[TABLE]
[ROW][C]Multiple Linear Regression - Ordinary Least Squares[/C][/ROW]
[ROW][C]Variable[/C][C]Parameter[/C][C]S.D.[/C][C]T-STATH0: parameter = 0[/C][C]2-tail p-value[/C][C]1-tail p-value[/C][/ROW]
[ROW][C](Intercept)[/C][C]0.00131729825631988[/C][C]0[/C][C]4051.7266[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]totaal[/C][C]-3.03481194081606e-09[/C][C]0[/C][C]-1.7704[/C][C]0.084094[/C][C]0.042047[/C][/ROW]
[ROW][C]vlaamsm[/C][C]-3.69292016553485e-10[/C][C]0[/C][C]-0.2031[/C][C]0.840097[/C][C]0.420048[/C][/ROW]
[ROW][C]vlaamsvr[/C][C]4.56677402633589e-09[/C][C]0[/C][C]2.2318[/C][C]0.031156[/C][C]0.015578[/C][/ROW]
[ROW][C]waalsm[/C][C]5.00117340245021e-09[/C][C]0[/C][C]2.6096[/C][C]0.012596[/C][C]0.006298[/C][/ROW]
[ROW][C]waalsvr[/C][C]3.87504151472791e-09[/C][C]0[/C][C]2.1216[/C][C]0.039966[/C][C]0.019983[/C][/ROW]
[ROW][C]brusselm[/C][C]1.34813310231096e-09[/C][C]0[/C][C]0.757[/C][C]0.453394[/C][C]0.226697[/C][/ROW]
[ROW][C]brusselvr[/C][C]6.37005796506606e-09[/C][C]0[/C][C]3.2792[/C][C]0.002128[/C][C]0.001064[/C][/ROW]
[ROW][C]t[/C][C]-6.45150461363158e-07[/C][C]0[/C][C]-302.2881[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185790&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185790&T=2

As an alternative you can also use a QR Code:

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

Multiple Linear Regression - Ordinary Least Squares
Variable	Parameter	S.D.	T-STATH0: parameter = 0	2-tail p-value	1-tail p-value
(Intercept)	0.00131729825631988	0	4051.7266	0	0
totaal	-3.03481194081606e-09	0	-1.7704	0.084094	0.042047
vlaamsm	-3.69292016553485e-10	0	-0.2031	0.840097	0.420048
vlaamsvr	4.56677402633589e-09	0	2.2318	0.031156	0.015578
waalsm	5.00117340245021e-09	0	2.6096	0.012596	0.006298
waalsvr	3.87504151472791e-09	0	2.1216	0.039966	0.019983
brusselm	1.34813310231096e-09	0	0.757	0.453394	0.226697
brusselvr	6.37005796506606e-09	0	3.2792	0.002128	0.001064
t	-6.45150461363158e-07	0	-302.2881	0	0

Multiple Linear Regression - Regression Statistics
Multiple R	0.999999778232433
R-squared	0.999999556464916
Adjusted R-squared	0.999999469921485
F-TEST (value)	11554886.880063
F-TEST (DF numerator)	8
F-TEST (DF denominator)	41
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	6.95569733332192e-09
Sum Squared Residuals	1.98365074110405e-15

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.999999778232433 \tabularnewline
R-squared & 0.999999556464916 \tabularnewline
Adjusted R-squared & 0.999999469921485 \tabularnewline
F-TEST (value) & 11554886.880063 \tabularnewline
F-TEST (DF numerator) & 8 \tabularnewline
F-TEST (DF denominator) & 41 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 6.95569733332192e-09 \tabularnewline
Sum Squared Residuals & 1.98365074110405e-15 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185790&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.999999778232433[/C][/ROW]
[ROW][C]R-squared[/C][C]0.999999556464916[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.999999469921485[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]11554886.880063[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]8[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]41[/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]6.95569733332192e-09[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]1.98365074110405e-15[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185790&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185790&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	0.999999778232433
R-squared	0.999999556464916
Adjusted R-squared	0.999999469921485
F-TEST (value)	11554886.880063
F-TEST (DF numerator)	8
F-TEST (DF denominator)	41
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	6.95569733332192e-09
Sum Squared Residuals	1.98365074110405e-15

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	0.00131735509094	0.00131735324375744	1.84718255963141e-09
2	0.0013166836561332	0.00131667995381926	3.70231393923294e-09
3	0.00131601290541688	0.00131600737314606	5.53227081618118e-09
4	0.0013153428377461	0.00131534174435727	1.0933888295074e-09
5	0.00131467345207803	0.00131467833482723	-4.88274919785928e-09
6	0.00131400474737199	0.00131400772872734	-2.9813553511131e-09
7	0.00131333672258939	0.00131333633824882	3.84340568361696e-10
8	0.00131266937669377	0.00131267675396717	-7.37727340713629e-09
9	0.00131200270865075	0.00131200534534465	-2.63669389474355e-09
10	0.00131133671742809	0.00131132651773658	1.01996915030359e-08
11	0.0013106714019956	0.00131066140124262	1.00007529802032e-08
12	0.00131000676132522	0.00131000121476581	5.54655941001658e-09
13	0.00130934279439094	0.00130934720987176	-4.41548081472657e-09
14	0.00130867950016886	0.00130868241626368	-2.91609481895961e-09
15	0.00130801687763713	0.00130801304641115	3.83122598353473e-09
16	0.00130735492577598	0.00130735570456134	-7.78785366354438e-10
17	0.0013066936435677	0.00130669748083376	-3.8372660599239e-09
18	0.00130603302999663	0.00130603645677084	-3.426774214008e-09
19	0.00130537308404918	0.00130538272984969	-9.64580050530537e-09
20	0.0013047138047138	0.00130472347095906	-9.6662452579312e-09
21	0.00130405519098099	0.00130405596739229	-7.76411303210684e-10
22	0.00130339724184326	0.00130339327417582	3.96766743888206e-09
23	0.00130273995629518	0.00130273432681656	5.6294786153514e-09
24	0.00130208333333333	0.00130208029190744	3.04142589313673e-09
25	0.00130142737195634	0.00130142667068226	7.01274080409921e-10
26	0.00130077207116482	0.00130076676986813	5.30129669173382e-09
27	0.00130011742996142	0.00130011745484029	-2.48788777898484e-11
28	0.00129946344735077	0.00129947555525168	-1.21079009099425e-08
29	0.00129881012233953	0.00129882483734946	-1.47150099246246e-08
30	0.00129815745393635	0.00129816699234258	-9.53840623210895e-09
31	0.00129750544115185	0.00129750715551598	-1.71436413249068e-09
32	0.00129685408299866	0.00129685500677026	-9.23771602036491e-10
33	0.00129620337849139	0.0012962027670485	6.11442887616187e-10
34	0.00129555332664661	0.0012955484656	4.86104660520245e-09
35	0.00129490392648287	0.0012948984656024	5.46088047661426e-09
36	0.00129425517702071	0.00129424441312627	1.07638944429229e-08
37	0.00129360707728259	0.00129359614015327	1.09371293189964e-08
38	0.00129295962629296	0.0012929479530391	1.16732538617759e-08
39	0.00129231282307821	0.00129230717003627	5.65304193923788e-09
40	0.00129166666666667	0.00129166271820641	3.94846025271622e-09
41	0.00129102115608862	0.00129102553916401	-4.3830753849401e-09
42	0.00129037629037629	0.001290377076004	-7.85627713782408e-10
43	0.00128973206856382	0.00128973931678431	-7.24822049131172e-09
44	0.00128908848968729	0.0012890970721917	-8.58250441172545e-09
45	0.0012884455527847	0.00128845677800304	-1.12252183341383e-08
46	0.00128780325689598	0.00128780535990986	-2.10301387656799e-09
47	0.00128716160106295	0.00128716155605032	4.50126242515424e-11
48	0.00128652058432935	0.00128651640248422	4.1818451343801e-09
49	0.00128588020574083	0.00128587835795187	1.84778895974323e-09
50	0.00128524046434494	0.00128523453408867	5.930256270055e-09

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 0.00131735509094 & 0.00131735324375744 & 1.84718255963141e-09 \tabularnewline
2 & 0.0013166836561332 & 0.00131667995381926 & 3.70231393923294e-09 \tabularnewline
3 & 0.00131601290541688 & 0.00131600737314606 & 5.53227081618118e-09 \tabularnewline
4 & 0.0013153428377461 & 0.00131534174435727 & 1.0933888295074e-09 \tabularnewline
5 & 0.00131467345207803 & 0.00131467833482723 & -4.88274919785928e-09 \tabularnewline
6 & 0.00131400474737199 & 0.00131400772872734 & -2.9813553511131e-09 \tabularnewline
7 & 0.00131333672258939 & 0.00131333633824882 & 3.84340568361696e-10 \tabularnewline
8 & 0.00131266937669377 & 0.00131267675396717 & -7.37727340713629e-09 \tabularnewline
9 & 0.00131200270865075 & 0.00131200534534465 & -2.63669389474355e-09 \tabularnewline
10 & 0.00131133671742809 & 0.00131132651773658 & 1.01996915030359e-08 \tabularnewline
11 & 0.0013106714019956 & 0.00131066140124262 & 1.00007529802032e-08 \tabularnewline
12 & 0.00131000676132522 & 0.00131000121476581 & 5.54655941001658e-09 \tabularnewline
13 & 0.00130934279439094 & 0.00130934720987176 & -4.41548081472657e-09 \tabularnewline
14 & 0.00130867950016886 & 0.00130868241626368 & -2.91609481895961e-09 \tabularnewline
15 & 0.00130801687763713 & 0.00130801304641115 & 3.83122598353473e-09 \tabularnewline
16 & 0.00130735492577598 & 0.00130735570456134 & -7.78785366354438e-10 \tabularnewline
17 & 0.0013066936435677 & 0.00130669748083376 & -3.8372660599239e-09 \tabularnewline
18 & 0.00130603302999663 & 0.00130603645677084 & -3.426774214008e-09 \tabularnewline
19 & 0.00130537308404918 & 0.00130538272984969 & -9.64580050530537e-09 \tabularnewline
20 & 0.0013047138047138 & 0.00130472347095906 & -9.6662452579312e-09 \tabularnewline
21 & 0.00130405519098099 & 0.00130405596739229 & -7.76411303210684e-10 \tabularnewline
22 & 0.00130339724184326 & 0.00130339327417582 & 3.96766743888206e-09 \tabularnewline
23 & 0.00130273995629518 & 0.00130273432681656 & 5.6294786153514e-09 \tabularnewline
24 & 0.00130208333333333 & 0.00130208029190744 & 3.04142589313673e-09 \tabularnewline
25 & 0.00130142737195634 & 0.00130142667068226 & 7.01274080409921e-10 \tabularnewline
26 & 0.00130077207116482 & 0.00130076676986813 & 5.30129669173382e-09 \tabularnewline
27 & 0.00130011742996142 & 0.00130011745484029 & -2.48788777898484e-11 \tabularnewline
28 & 0.00129946344735077 & 0.00129947555525168 & -1.21079009099425e-08 \tabularnewline
29 & 0.00129881012233953 & 0.00129882483734946 & -1.47150099246246e-08 \tabularnewline
30 & 0.00129815745393635 & 0.00129816699234258 & -9.53840623210895e-09 \tabularnewline
31 & 0.00129750544115185 & 0.00129750715551598 & -1.71436413249068e-09 \tabularnewline
32 & 0.00129685408299866 & 0.00129685500677026 & -9.23771602036491e-10 \tabularnewline
33 & 0.00129620337849139 & 0.0012962027670485 & 6.11442887616187e-10 \tabularnewline
34 & 0.00129555332664661 & 0.0012955484656 & 4.86104660520245e-09 \tabularnewline
35 & 0.00129490392648287 & 0.0012948984656024 & 5.46088047661426e-09 \tabularnewline
36 & 0.00129425517702071 & 0.00129424441312627 & 1.07638944429229e-08 \tabularnewline
37 & 0.00129360707728259 & 0.00129359614015327 & 1.09371293189964e-08 \tabularnewline
38 & 0.00129295962629296 & 0.0012929479530391 & 1.16732538617759e-08 \tabularnewline
39 & 0.00129231282307821 & 0.00129230717003627 & 5.65304193923788e-09 \tabularnewline
40 & 0.00129166666666667 & 0.00129166271820641 & 3.94846025271622e-09 \tabularnewline
41 & 0.00129102115608862 & 0.00129102553916401 & -4.3830753849401e-09 \tabularnewline
42 & 0.00129037629037629 & 0.001290377076004 & -7.85627713782408e-10 \tabularnewline
43 & 0.00128973206856382 & 0.00128973931678431 & -7.24822049131172e-09 \tabularnewline
44 & 0.00128908848968729 & 0.0012890970721917 & -8.58250441172545e-09 \tabularnewline
45 & 0.0012884455527847 & 0.00128845677800304 & -1.12252183341383e-08 \tabularnewline
46 & 0.00128780325689598 & 0.00128780535990986 & -2.10301387656799e-09 \tabularnewline
47 & 0.00128716160106295 & 0.00128716155605032 & 4.50126242515424e-11 \tabularnewline
48 & 0.00128652058432935 & 0.00128651640248422 & 4.1818451343801e-09 \tabularnewline
49 & 0.00128588020574083 & 0.00128587835795187 & 1.84778895974323e-09 \tabularnewline
50 & 0.00128524046434494 & 0.00128523453408867 & 5.930256270055e-09 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185790&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]0.00131735509094[/C][C]0.00131735324375744[/C][C]1.84718255963141e-09[/C][/ROW]
[ROW][C]2[/C][C]0.0013166836561332[/C][C]0.00131667995381926[/C][C]3.70231393923294e-09[/C][/ROW]
[ROW][C]3[/C][C]0.00131601290541688[/C][C]0.00131600737314606[/C][C]5.53227081618118e-09[/C][/ROW]
[ROW][C]4[/C][C]0.0013153428377461[/C][C]0.00131534174435727[/C][C]1.0933888295074e-09[/C][/ROW]
[ROW][C]5[/C][C]0.00131467345207803[/C][C]0.00131467833482723[/C][C]-4.88274919785928e-09[/C][/ROW]
[ROW][C]6[/C][C]0.00131400474737199[/C][C]0.00131400772872734[/C][C]-2.9813553511131e-09[/C][/ROW]
[ROW][C]7[/C][C]0.00131333672258939[/C][C]0.00131333633824882[/C][C]3.84340568361696e-10[/C][/ROW]
[ROW][C]8[/C][C]0.00131266937669377[/C][C]0.00131267675396717[/C][C]-7.37727340713629e-09[/C][/ROW]
[ROW][C]9[/C][C]0.00131200270865075[/C][C]0.00131200534534465[/C][C]-2.63669389474355e-09[/C][/ROW]
[ROW][C]10[/C][C]0.00131133671742809[/C][C]0.00131132651773658[/C][C]1.01996915030359e-08[/C][/ROW]
[ROW][C]11[/C][C]0.0013106714019956[/C][C]0.00131066140124262[/C][C]1.00007529802032e-08[/C][/ROW]
[ROW][C]12[/C][C]0.00131000676132522[/C][C]0.00131000121476581[/C][C]5.54655941001658e-09[/C][/ROW]
[ROW][C]13[/C][C]0.00130934279439094[/C][C]0.00130934720987176[/C][C]-4.41548081472657e-09[/C][/ROW]
[ROW][C]14[/C][C]0.00130867950016886[/C][C]0.00130868241626368[/C][C]-2.91609481895961e-09[/C][/ROW]
[ROW][C]15[/C][C]0.00130801687763713[/C][C]0.00130801304641115[/C][C]3.83122598353473e-09[/C][/ROW]
[ROW][C]16[/C][C]0.00130735492577598[/C][C]0.00130735570456134[/C][C]-7.78785366354438e-10[/C][/ROW]
[ROW][C]17[/C][C]0.0013066936435677[/C][C]0.00130669748083376[/C][C]-3.8372660599239e-09[/C][/ROW]
[ROW][C]18[/C][C]0.00130603302999663[/C][C]0.00130603645677084[/C][C]-3.426774214008e-09[/C][/ROW]
[ROW][C]19[/C][C]0.00130537308404918[/C][C]0.00130538272984969[/C][C]-9.64580050530537e-09[/C][/ROW]
[ROW][C]20[/C][C]0.0013047138047138[/C][C]0.00130472347095906[/C][C]-9.6662452579312e-09[/C][/ROW]
[ROW][C]21[/C][C]0.00130405519098099[/C][C]0.00130405596739229[/C][C]-7.76411303210684e-10[/C][/ROW]
[ROW][C]22[/C][C]0.00130339724184326[/C][C]0.00130339327417582[/C][C]3.96766743888206e-09[/C][/ROW]
[ROW][C]23[/C][C]0.00130273995629518[/C][C]0.00130273432681656[/C][C]5.6294786153514e-09[/C][/ROW]
[ROW][C]24[/C][C]0.00130208333333333[/C][C]0.00130208029190744[/C][C]3.04142589313673e-09[/C][/ROW]
[ROW][C]25[/C][C]0.00130142737195634[/C][C]0.00130142667068226[/C][C]7.01274080409921e-10[/C][/ROW]
[ROW][C]26[/C][C]0.00130077207116482[/C][C]0.00130076676986813[/C][C]5.30129669173382e-09[/C][/ROW]
[ROW][C]27[/C][C]0.00130011742996142[/C][C]0.00130011745484029[/C][C]-2.48788777898484e-11[/C][/ROW]
[ROW][C]28[/C][C]0.00129946344735077[/C][C]0.00129947555525168[/C][C]-1.21079009099425e-08[/C][/ROW]
[ROW][C]29[/C][C]0.00129881012233953[/C][C]0.00129882483734946[/C][C]-1.47150099246246e-08[/C][/ROW]
[ROW][C]30[/C][C]0.00129815745393635[/C][C]0.00129816699234258[/C][C]-9.53840623210895e-09[/C][/ROW]
[ROW][C]31[/C][C]0.00129750544115185[/C][C]0.00129750715551598[/C][C]-1.71436413249068e-09[/C][/ROW]
[ROW][C]32[/C][C]0.00129685408299866[/C][C]0.00129685500677026[/C][C]-9.23771602036491e-10[/C][/ROW]
[ROW][C]33[/C][C]0.00129620337849139[/C][C]0.0012962027670485[/C][C]6.11442887616187e-10[/C][/ROW]
[ROW][C]34[/C][C]0.00129555332664661[/C][C]0.0012955484656[/C][C]4.86104660520245e-09[/C][/ROW]
[ROW][C]35[/C][C]0.00129490392648287[/C][C]0.0012948984656024[/C][C]5.46088047661426e-09[/C][/ROW]
[ROW][C]36[/C][C]0.00129425517702071[/C][C]0.00129424441312627[/C][C]1.07638944429229e-08[/C][/ROW]
[ROW][C]37[/C][C]0.00129360707728259[/C][C]0.00129359614015327[/C][C]1.09371293189964e-08[/C][/ROW]
[ROW][C]38[/C][C]0.00129295962629296[/C][C]0.0012929479530391[/C][C]1.16732538617759e-08[/C][/ROW]
[ROW][C]39[/C][C]0.00129231282307821[/C][C]0.00129230717003627[/C][C]5.65304193923788e-09[/C][/ROW]
[ROW][C]40[/C][C]0.00129166666666667[/C][C]0.00129166271820641[/C][C]3.94846025271622e-09[/C][/ROW]
[ROW][C]41[/C][C]0.00129102115608862[/C][C]0.00129102553916401[/C][C]-4.3830753849401e-09[/C][/ROW]
[ROW][C]42[/C][C]0.00129037629037629[/C][C]0.001290377076004[/C][C]-7.85627713782408e-10[/C][/ROW]
[ROW][C]43[/C][C]0.00128973206856382[/C][C]0.00128973931678431[/C][C]-7.24822049131172e-09[/C][/ROW]
[ROW][C]44[/C][C]0.00128908848968729[/C][C]0.0012890970721917[/C][C]-8.58250441172545e-09[/C][/ROW]
[ROW][C]45[/C][C]0.0012884455527847[/C][C]0.00128845677800304[/C][C]-1.12252183341383e-08[/C][/ROW]
[ROW][C]46[/C][C]0.00128780325689598[/C][C]0.00128780535990986[/C][C]-2.10301387656799e-09[/C][/ROW]
[ROW][C]47[/C][C]0.00128716160106295[/C][C]0.00128716155605032[/C][C]4.50126242515424e-11[/C][/ROW]
[ROW][C]48[/C][C]0.00128652058432935[/C][C]0.00128651640248422[/C][C]4.1818451343801e-09[/C][/ROW]
[ROW][C]49[/C][C]0.00128588020574083[/C][C]0.00128587835795187[/C][C]1.84778895974323e-09[/C][/ROW]
[ROW][C]50[/C][C]0.00128524046434494[/C][C]0.00128523453408867[/C][C]5.930256270055e-09[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185790&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185790&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	0.00131735509094	0.00131735324375744	1.84718255963141e-09
2	0.0013166836561332	0.00131667995381926	3.70231393923294e-09
3	0.00131601290541688	0.00131600737314606	5.53227081618118e-09
4	0.0013153428377461	0.00131534174435727	1.0933888295074e-09
5	0.00131467345207803	0.00131467833482723	-4.88274919785928e-09
6	0.00131400474737199	0.00131400772872734	-2.9813553511131e-09
7	0.00131333672258939	0.00131333633824882	3.84340568361696e-10
8	0.00131266937669377	0.00131267675396717	-7.37727340713629e-09
9	0.00131200270865075	0.00131200534534465	-2.63669389474355e-09
10	0.00131133671742809	0.00131132651773658	1.01996915030359e-08
11	0.0013106714019956	0.00131066140124262	1.00007529802032e-08
12	0.00131000676132522	0.00131000121476581	5.54655941001658e-09
13	0.00130934279439094	0.00130934720987176	-4.41548081472657e-09
14	0.00130867950016886	0.00130868241626368	-2.91609481895961e-09
15	0.00130801687763713	0.00130801304641115	3.83122598353473e-09
16	0.00130735492577598	0.00130735570456134	-7.78785366354438e-10
17	0.0013066936435677	0.00130669748083376	-3.8372660599239e-09
18	0.00130603302999663	0.00130603645677084	-3.426774214008e-09
19	0.00130537308404918	0.00130538272984969	-9.64580050530537e-09
20	0.0013047138047138	0.00130472347095906	-9.6662452579312e-09
21	0.00130405519098099	0.00130405596739229	-7.76411303210684e-10
22	0.00130339724184326	0.00130339327417582	3.96766743888206e-09
23	0.00130273995629518	0.00130273432681656	5.6294786153514e-09
24	0.00130208333333333	0.00130208029190744	3.04142589313673e-09
25	0.00130142737195634	0.00130142667068226	7.01274080409921e-10
26	0.00130077207116482	0.00130076676986813	5.30129669173382e-09
27	0.00130011742996142	0.00130011745484029	-2.48788777898484e-11
28	0.00129946344735077	0.00129947555525168	-1.21079009099425e-08
29	0.00129881012233953	0.00129882483734946	-1.47150099246246e-08
30	0.00129815745393635	0.00129816699234258	-9.53840623210895e-09
31	0.00129750544115185	0.00129750715551598	-1.71436413249068e-09
32	0.00129685408299866	0.00129685500677026	-9.23771602036491e-10
33	0.00129620337849139	0.0012962027670485	6.11442887616187e-10
34	0.00129555332664661	0.0012955484656	4.86104660520245e-09
35	0.00129490392648287	0.0012948984656024	5.46088047661426e-09
36	0.00129425517702071	0.00129424441312627	1.07638944429229e-08
37	0.00129360707728259	0.00129359614015327	1.09371293189964e-08
38	0.00129295962629296	0.0012929479530391	1.16732538617759e-08
39	0.00129231282307821	0.00129230717003627	5.65304193923788e-09
40	0.00129166666666667	0.00129166271820641	3.94846025271622e-09
41	0.00129102115608862	0.00129102553916401	-4.3830753849401e-09
42	0.00129037629037629	0.001290377076004	-7.85627713782408e-10
43	0.00128973206856382	0.00128973931678431	-7.24822049131172e-09
44	0.00128908848968729	0.0012890970721917	-8.58250441172545e-09
45	0.0012884455527847	0.00128845677800304	-1.12252183341383e-08
46	0.00128780325689598	0.00128780535990986	-2.10301387656799e-09
47	0.00128716160106295	0.00128716155605032	4.50126242515424e-11
48	0.00128652058432935	0.00128651640248422	4.1818451343801e-09
49	0.00128588020574083	0.00128587835795187	1.84778895974323e-09
50	0.00128524046434494	0.00128523453408867	5.930256270055e-09

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
12	0.0221939854027487	0.0443879708054973	0.977806014597251
13	0.0113995517464751	0.0227991034929502	0.988600448253525
14	0.00925249931345372	0.0185049986269074	0.990747500686546
15	0.00395811997283212	0.00791623994566425	0.996041880027168
16	0.00184541162969736	0.00369082325939472	0.998154588370303
17	0.000813416281317772	0.00162683256263554	0.999186583718682
18	0.00870313909700151	0.017406278194003	0.991296860902999
19	0.0218670518288832	0.0437341036577664	0.978132948171117
20	0.0645459082546348	0.12909181650927	0.935454091745365
21	0.202291046868575	0.404582093737149	0.797708953131425
22	0.215606496231782	0.431212992463563	0.784393503768218
23	0.289668049712623	0.579336099425247	0.710331950287377
24	0.387005383999668	0.774010767999335	0.612994616000332
25	0.423881419104037	0.847762838208074	0.576118580895963
26	0.627834373687411	0.744331252625179	0.372165626312589
27	0.937973337369292	0.124053325261416	0.0620266626307078
28	0.917561297020309	0.164877405959383	0.0824387029796914
29	0.90447180291894	0.19105639416212	0.0955281970810598
30	0.983373156947152	0.0332536861056953	0.0166268430528477
31	0.999916858545078	0.000166282909844171	8.31414549220857e-05
32	0.999999823259004	3.53481992277166e-07	1.76740996138583e-07
33	0.999999944401111	1.11197777128063e-07	5.55988885640313e-08
34	0.999999717618906	5.64762188509697e-07	2.82381094254849e-07
35	0.999998235952732	3.52809453663568e-06	1.76404726831784e-06
36	0.999990299022556	1.94019548888317e-05	9.70097744441583e-06
37	0.999985826229101	2.83475417974234e-05	1.41737708987117e-05
38	0.999808534921726	0.000382930156547864	0.000191465078273932

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
12 & 0.0221939854027487 & 0.0443879708054973 & 0.977806014597251 \tabularnewline
13 & 0.0113995517464751 & 0.0227991034929502 & 0.988600448253525 \tabularnewline
14 & 0.00925249931345372 & 0.0185049986269074 & 0.990747500686546 \tabularnewline
15 & 0.00395811997283212 & 0.00791623994566425 & 0.996041880027168 \tabularnewline
16 & 0.00184541162969736 & 0.00369082325939472 & 0.998154588370303 \tabularnewline
17 & 0.000813416281317772 & 0.00162683256263554 & 0.999186583718682 \tabularnewline
18 & 0.00870313909700151 & 0.017406278194003 & 0.991296860902999 \tabularnewline
19 & 0.0218670518288832 & 0.0437341036577664 & 0.978132948171117 \tabularnewline
20 & 0.0645459082546348 & 0.12909181650927 & 0.935454091745365 \tabularnewline
21 & 0.202291046868575 & 0.404582093737149 & 0.797708953131425 \tabularnewline
22 & 0.215606496231782 & 0.431212992463563 & 0.784393503768218 \tabularnewline
23 & 0.289668049712623 & 0.579336099425247 & 0.710331950287377 \tabularnewline
24 & 0.387005383999668 & 0.774010767999335 & 0.612994616000332 \tabularnewline
25 & 0.423881419104037 & 0.847762838208074 & 0.576118580895963 \tabularnewline
26 & 0.627834373687411 & 0.744331252625179 & 0.372165626312589 \tabularnewline
27 & 0.937973337369292 & 0.124053325261416 & 0.0620266626307078 \tabularnewline
28 & 0.917561297020309 & 0.164877405959383 & 0.0824387029796914 \tabularnewline
29 & 0.90447180291894 & 0.19105639416212 & 0.0955281970810598 \tabularnewline
30 & 0.983373156947152 & 0.0332536861056953 & 0.0166268430528477 \tabularnewline
31 & 0.999916858545078 & 0.000166282909844171 & 8.31414549220857e-05 \tabularnewline
32 & 0.999999823259004 & 3.53481992277166e-07 & 1.76740996138583e-07 \tabularnewline
33 & 0.999999944401111 & 1.11197777128063e-07 & 5.55988885640313e-08 \tabularnewline
34 & 0.999999717618906 & 5.64762188509697e-07 & 2.82381094254849e-07 \tabularnewline
35 & 0.999998235952732 & 3.52809453663568e-06 & 1.76404726831784e-06 \tabularnewline
36 & 0.999990299022556 & 1.94019548888317e-05 & 9.70097744441583e-06 \tabularnewline
37 & 0.999985826229101 & 2.83475417974234e-05 & 1.41737708987117e-05 \tabularnewline
38 & 0.999808534921726 & 0.000382930156547864 & 0.000191465078273932 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185790&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]0.0221939854027487[/C][C]0.0443879708054973[/C][C]0.977806014597251[/C][/ROW]
[ROW][C]13[/C][C]0.0113995517464751[/C][C]0.0227991034929502[/C][C]0.988600448253525[/C][/ROW]
[ROW][C]14[/C][C]0.00925249931345372[/C][C]0.0185049986269074[/C][C]0.990747500686546[/C][/ROW]
[ROW][C]15[/C][C]0.00395811997283212[/C][C]0.00791623994566425[/C][C]0.996041880027168[/C][/ROW]
[ROW][C]16[/C][C]0.00184541162969736[/C][C]0.00369082325939472[/C][C]0.998154588370303[/C][/ROW]
[ROW][C]17[/C][C]0.000813416281317772[/C][C]0.00162683256263554[/C][C]0.999186583718682[/C][/ROW]
[ROW][C]18[/C][C]0.00870313909700151[/C][C]0.017406278194003[/C][C]0.991296860902999[/C][/ROW]
[ROW][C]19[/C][C]0.0218670518288832[/C][C]0.0437341036577664[/C][C]0.978132948171117[/C][/ROW]
[ROW][C]20[/C][C]0.0645459082546348[/C][C]0.12909181650927[/C][C]0.935454091745365[/C][/ROW]
[ROW][C]21[/C][C]0.202291046868575[/C][C]0.404582093737149[/C][C]0.797708953131425[/C][/ROW]
[ROW][C]22[/C][C]0.215606496231782[/C][C]0.431212992463563[/C][C]0.784393503768218[/C][/ROW]
[ROW][C]23[/C][C]0.289668049712623[/C][C]0.579336099425247[/C][C]0.710331950287377[/C][/ROW]
[ROW][C]24[/C][C]0.387005383999668[/C][C]0.774010767999335[/C][C]0.612994616000332[/C][/ROW]
[ROW][C]25[/C][C]0.423881419104037[/C][C]0.847762838208074[/C][C]0.576118580895963[/C][/ROW]
[ROW][C]26[/C][C]0.627834373687411[/C][C]0.744331252625179[/C][C]0.372165626312589[/C][/ROW]
[ROW][C]27[/C][C]0.937973337369292[/C][C]0.124053325261416[/C][C]0.0620266626307078[/C][/ROW]
[ROW][C]28[/C][C]0.917561297020309[/C][C]0.164877405959383[/C][C]0.0824387029796914[/C][/ROW]
[ROW][C]29[/C][C]0.90447180291894[/C][C]0.19105639416212[/C][C]0.0955281970810598[/C][/ROW]
[ROW][C]30[/C][C]0.983373156947152[/C][C]0.0332536861056953[/C][C]0.0166268430528477[/C][/ROW]
[ROW][C]31[/C][C]0.999916858545078[/C][C]0.000166282909844171[/C][C]8.31414549220857e-05[/C][/ROW]
[ROW][C]32[/C][C]0.999999823259004[/C][C]3.53481992277166e-07[/C][C]1.76740996138583e-07[/C][/ROW]
[ROW][C]33[/C][C]0.999999944401111[/C][C]1.11197777128063e-07[/C][C]5.55988885640313e-08[/C][/ROW]
[ROW][C]34[/C][C]0.999999717618906[/C][C]5.64762188509697e-07[/C][C]2.82381094254849e-07[/C][/ROW]
[ROW][C]35[/C][C]0.999998235952732[/C][C]3.52809453663568e-06[/C][C]1.76404726831784e-06[/C][/ROW]
[ROW][C]36[/C][C]0.999990299022556[/C][C]1.94019548888317e-05[/C][C]9.70097744441583e-06[/C][/ROW]
[ROW][C]37[/C][C]0.999985826229101[/C][C]2.83475417974234e-05[/C][C]1.41737708987117e-05[/C][/ROW]
[ROW][C]38[/C][C]0.999808534921726[/C][C]0.000382930156547864[/C][C]0.000191465078273932[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185790&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185790&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	0.0221939854027487	0.0443879708054973	0.977806014597251
13	0.0113995517464751	0.0227991034929502	0.988600448253525
14	0.00925249931345372	0.0185049986269074	0.990747500686546
15	0.00395811997283212	0.00791623994566425	0.996041880027168
16	0.00184541162969736	0.00369082325939472	0.998154588370303
17	0.000813416281317772	0.00162683256263554	0.999186583718682
18	0.00870313909700151	0.017406278194003	0.991296860902999
19	0.0218670518288832	0.0437341036577664	0.978132948171117
20	0.0645459082546348	0.12909181650927	0.935454091745365
21	0.202291046868575	0.404582093737149	0.797708953131425
22	0.215606496231782	0.431212992463563	0.784393503768218
23	0.289668049712623	0.579336099425247	0.710331950287377
24	0.387005383999668	0.774010767999335	0.612994616000332
25	0.423881419104037	0.847762838208074	0.576118580895963
26	0.627834373687411	0.744331252625179	0.372165626312589
27	0.937973337369292	0.124053325261416	0.0620266626307078
28	0.917561297020309	0.164877405959383	0.0824387029796914
29	0.90447180291894	0.19105639416212	0.0955281970810598
30	0.983373156947152	0.0332536861056953	0.0166268430528477
31	0.999916858545078	0.000166282909844171	8.31414549220857e-05
32	0.999999823259004	3.53481992277166e-07	1.76740996138583e-07
33	0.999999944401111	1.11197777128063e-07	5.55988885640313e-08
34	0.999999717618906	5.64762188509697e-07	2.82381094254849e-07
35	0.999998235952732	3.52809453663568e-06	1.76404726831784e-06
36	0.999990299022556	1.94019548888317e-05	9.70097744441583e-06
37	0.999985826229101	2.83475417974234e-05	1.41737708987117e-05
38	0.999808534921726	0.000382930156547864	0.000191465078273932

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	11	0.407407407407407	NOK
5% type I error level	17	0.62962962962963	NOK
10% type I error level	17	0.62962962962963	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 & 11 & 0.407407407407407 & NOK \tabularnewline
5% type I error level & 17 & 0.62962962962963 & NOK \tabularnewline
10% type I error level & 17 & 0.62962962962963 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185790&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]11[/C][C]0.407407407407407[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]17[/C][C]0.62962962962963[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]17[/C][C]0.62962962962963[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185790&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185790&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	11	0.407407407407407	NOK
5% type I error level	17	0.62962962962963	NOK
10% type I error level	17	0.62962962962963	NOK

Figure 1

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Figure 3

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Figure 4

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Figure 5

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Figure 6

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Figure 7

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Figure 8

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Figure 9

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Figure 10

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Parameters (Session):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = 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

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code