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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationFri, 02 Nov 2012 13:20:14 -0400
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/Nov/02/t1351876895avn4kxvzz2prh7j.htm/, Retrieved Mon, 27 Jun 2022 06:26:21 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=185654, Retrieved Mon, 27 Jun 2022 06:26:21 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact133
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]
-             [Multiple Regression] [workshop 7: deter...] [2012-11-02 17:20:14] [7a9100b3135ff0dae36397155af309d9] [Current]
-   PD          [Multiple Regression] [workshop 7: berek...] [2012-11-02 19:46:17] [40b341cf5fb1ddfd74e4c5704837f48c]
- R P             [Multiple Regression] [WS 7 multiple reg...] [2012-11-04 13:01:29] [e01c78beec4051e03ee053d8bc2c6384]
-    D            [Multiple Regression] [Paper 2012: invoe...] [2012-12-06 20:03:52] [40b341cf5fb1ddfd74e4c5704837f48c]
-    D              [Multiple Regression] [Paper 2012: invo...] [2012-12-06 20:11:38] [40b341cf5fb1ddfd74e4c5704837f48c]
-   PD            [Multiple Regression] [Paper 2012: rfc m...] [2012-12-12 13:09:31] [40b341cf5fb1ddfd74e4c5704837f48c]
- R             [Multiple Regression] [Multiple regressi...] [2012-12-21 15:06:33] [426d1a1037dab69a05582f8f9c03d6e4]
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Dataseries X:
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 Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time8 seconds
R Server'George Udny Yule' @ yule.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 & 8 seconds \tabularnewline
R Server & 'George Udny Yule' @ yule.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=185654&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]8 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ yule.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=185654&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185654&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 Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time8 seconds
R Server'George Udny Yule' @ yule.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
totaal[t] = + 30804.5369680775 -23401252.9732768jaar[t] + 0.870436647176309vlaams_man[t] + 1.09619421051282vlaams_vrouw[t] + 1.08567272568283waals_man[t] + 0.972768756511784waals_vrouw[t] + 0.909537250570316brussel_man[t] + 1.13627288054705brussel_vrouw[t] -15.1769126220416t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
totaal[t] =  +  30804.5369680775 -23401252.9732768jaar[t] +  0.870436647176309vlaams_man[t] +  1.09619421051282vlaams_vrouw[t] +  1.08567272568283waals_man[t] +  0.972768756511784waals_vrouw[t] +  0.909537250570316brussel_man[t] +  1.13627288054705brussel_vrouw[t] -15.1769126220416t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185654&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]totaal[t] =  +  30804.5369680775 -23401252.9732768jaar[t] +  0.870436647176309vlaams_man[t] +  1.09619421051282vlaams_vrouw[t] +  1.08567272568283waals_man[t] +  0.972768756511784waals_vrouw[t] +  0.909537250570316brussel_man[t] +  1.13627288054705brussel_vrouw[t] -15.1769126220416t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185654&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185654&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
totaal[t] = + 30804.5369680775 -23401252.9732768jaar[t] + 0.870436647176309vlaams_man[t] + 1.09619421051282vlaams_vrouw[t] + 1.08567272568283waals_man[t] + 0.972768756511784waals_vrouw[t] + 0.909537250570316brussel_man[t] + 1.13627288054705brussel_vrouw[t] -15.1769126220416t + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)30804.536968077517412.9984331.76910.0843230.042162
jaar-23401252.973276813217986.10236-1.77040.0840940.042047
vlaams_man0.8704366471763090.0839710.366100
vlaams_vrouw1.096194210512820.08306913.196100
waals_man1.085672725682830.06538716.603700
waals_vrouw0.9727687565117840.07395213.154100
brussel_man0.9095372505703160.06798713.378100
brussel_vrouw1.136272880547050.07237815.699200
t-15.17691262204168.526204-1.780.0824830.041242

\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) & 30804.5369680775 & 17412.998433 & 1.7691 & 0.084323 & 0.042162 \tabularnewline
jaar & -23401252.9732768 & 13217986.10236 & -1.7704 & 0.084094 & 0.042047 \tabularnewline
vlaams_man & 0.870436647176309 & 0.08397 & 10.3661 & 0 & 0 \tabularnewline
vlaams_vrouw & 1.09619421051282 & 0.083069 & 13.1961 & 0 & 0 \tabularnewline
waals_man & 1.08567272568283 & 0.065387 & 16.6037 & 0 & 0 \tabularnewline
waals_vrouw & 0.972768756511784 & 0.073952 & 13.1541 & 0 & 0 \tabularnewline
brussel_man & 0.909537250570316 & 0.067987 & 13.3781 & 0 & 0 \tabularnewline
brussel_vrouw & 1.13627288054705 & 0.072378 & 15.6992 & 0 & 0 \tabularnewline
t & -15.1769126220416 & 8.526204 & -1.78 & 0.082483 & 0.041242 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185654&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]30804.5369680775[/C][C]17412.998433[/C][C]1.7691[/C][C]0.084323[/C][C]0.042162[/C][/ROW]
[ROW][C]jaar[/C][C]-23401252.9732768[/C][C]13217986.10236[/C][C]-1.7704[/C][C]0.084094[/C][C]0.042047[/C][/ROW]
[ROW][C]vlaams_man[/C][C]0.870436647176309[/C][C]0.08397[/C][C]10.3661[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]vlaams_vrouw[/C][C]1.09619421051282[/C][C]0.083069[/C][C]13.1961[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]waals_man[/C][C]1.08567272568283[/C][C]0.065387[/C][C]16.6037[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]waals_vrouw[/C][C]0.972768756511784[/C][C]0.073952[/C][C]13.1541[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]brussel_man[/C][C]0.909537250570316[/C][C]0.067987[/C][C]13.3781[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]brussel_vrouw[/C][C]1.13627288054705[/C][C]0.072378[/C][C]15.6992[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]t[/C][C]-15.1769126220416[/C][C]8.526204[/C][C]-1.78[/C][C]0.082483[/C][C]0.041242[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185654&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185654&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
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)30804.536968077517412.9984331.76910.0843230.042162
jaar-23401252.973276813217986.10236-1.77040.0840940.042047
vlaams_man0.8704366471763090.0839710.366100
vlaams_vrouw1.096194210512820.08306913.196100
waals_man1.085672725682830.06538716.603700
waals_vrouw0.9727687565117840.07395213.154100
brussel_man0.9095372505703160.06798713.378100
brussel_vrouw1.136272880547050.07237815.699200
t-15.17691262204168.526204-1.780.0824830.041242







Multiple Linear Regression - Regression Statistics
Multiple R0.999998996517776
R-squared0.999997993036559
Adjusted R-squared0.999997601433936
F-TEST (value)2553603.92142985
F-TEST (DF numerator)8
F-TEST (DF denominator)41
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.61079340580206
Sum Squared Residuals15.2958119674225

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.999998996517776 \tabularnewline
R-squared & 0.999997993036559 \tabularnewline
Adjusted R-squared & 0.999997601433936 \tabularnewline
F-TEST (value) & 2553603.92142985 \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 & 0.61079340580206 \tabularnewline
Sum Squared Residuals & 15.2958119674225 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185654&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.999998996517776[/C][/ROW]
[ROW][C]R-squared[/C][C]0.999997993036559[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.999997601433936[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]2553603.92142985[/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]0.61079340580206[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]15.2958119674225[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185654&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185654&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 R0.999998996517776
R-squared0.999997993036559
Adjusted R-squared0.999997601433936
F-TEST (value)2553603.92142985
F-TEST (DF numerator)8
F-TEST (DF denominator)41
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.61079340580206
Sum Squared Residuals15.2958119674225







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
191909189.773476836710.226523163287704
292519250.811701798360.188298201637033
393289328.6444564847-0.644456484702895
494289428.66738234806-0.66738234806104
594999498.924333154140.0756668458610427
695569555.934669262130.0653307378749467
796069604.944291002041.0557089979562
896329631.301552162730.698447837264957
996609660.06217742192-0.0621774219207797
1096519650.925971284380.0740287156173079
1196959695.87356337378-0.873563373777729
1297279727.10634819719-0.106348197191145
1397579757.31898316549-0.318983165493528
1497889788.09302892045-0.0930289204474411
1598139811.788796324191.21120367581114
1698239822.879603614990.120396385009302
1798379836.898359536290.101640463713074
1898429842.02400156293-0.024001562932786
1998559856.19935081646-1.19935081645882
2098639864.28919610798-1.28919610797912
2198559854.344057470370.655942529625127
2298589858.16578246824-0.165782468244185
2398539853.22964265127-0.229642651268907
2498589857.419190799220.58080920078305
2598599858.477721943460.522278056542202
2698659864.303449952780.696550047222603
2798769876.31011019002-0.310110190018058
2899289927.56364611180.436353888201801
2999489948.5570433318-0.55704333179597
3099879988.15926161825-1.1592616182492
311002210022.0000787662-7.8766211996077e-05
321006810067.98215763120.0178423688248848
331010110100.87986124190.120138758075812
341013110130.67322589550.326774104470233
351014310142.76493916720.235060832832552
361017010169.61044785780.389552142152874
371019210191.58645367430.413546325707174
381021410213.54825602990.451743970071526
391023910239.5981731885-0.598173188471221
401026310262.7441548820.255845117979388
411031010309.92016423220.0798357678287411
421035510355.5766416934-0.576641693397184
431039610396.0256857544-0.0256857543607167
441044610446.0862200922-0.0862200921823565
451051110511.2845894911-0.284589491120514
461058510584.0656098490.934390151005746
471066710667.0372692159-0.0372692158626008
481075310753.8935098748-0.89350987480154
491084010840.3340036275-0.334003627516723
501095110950.39740792290.602592077110022

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 9190 & 9189.77347683671 & 0.226523163287704 \tabularnewline
2 & 9251 & 9250.81170179836 & 0.188298201637033 \tabularnewline
3 & 9328 & 9328.6444564847 & -0.644456484702895 \tabularnewline
4 & 9428 & 9428.66738234806 & -0.66738234806104 \tabularnewline
5 & 9499 & 9498.92433315414 & 0.0756668458610427 \tabularnewline
6 & 9556 & 9555.93466926213 & 0.0653307378749467 \tabularnewline
7 & 9606 & 9604.94429100204 & 1.0557089979562 \tabularnewline
8 & 9632 & 9631.30155216273 & 0.698447837264957 \tabularnewline
9 & 9660 & 9660.06217742192 & -0.0621774219207797 \tabularnewline
10 & 9651 & 9650.92597128438 & 0.0740287156173079 \tabularnewline
11 & 9695 & 9695.87356337378 & -0.873563373777729 \tabularnewline
12 & 9727 & 9727.10634819719 & -0.106348197191145 \tabularnewline
13 & 9757 & 9757.31898316549 & -0.318983165493528 \tabularnewline
14 & 9788 & 9788.09302892045 & -0.0930289204474411 \tabularnewline
15 & 9813 & 9811.78879632419 & 1.21120367581114 \tabularnewline
16 & 9823 & 9822.87960361499 & 0.120396385009302 \tabularnewline
17 & 9837 & 9836.89835953629 & 0.101640463713074 \tabularnewline
18 & 9842 & 9842.02400156293 & -0.024001562932786 \tabularnewline
19 & 9855 & 9856.19935081646 & -1.19935081645882 \tabularnewline
20 & 9863 & 9864.28919610798 & -1.28919610797912 \tabularnewline
21 & 9855 & 9854.34405747037 & 0.655942529625127 \tabularnewline
22 & 9858 & 9858.16578246824 & -0.165782468244185 \tabularnewline
23 & 9853 & 9853.22964265127 & -0.229642651268907 \tabularnewline
24 & 9858 & 9857.41919079922 & 0.58080920078305 \tabularnewline
25 & 9859 & 9858.47772194346 & 0.522278056542202 \tabularnewline
26 & 9865 & 9864.30344995278 & 0.696550047222603 \tabularnewline
27 & 9876 & 9876.31011019002 & -0.310110190018058 \tabularnewline
28 & 9928 & 9927.5636461118 & 0.436353888201801 \tabularnewline
29 & 9948 & 9948.5570433318 & -0.55704333179597 \tabularnewline
30 & 9987 & 9988.15926161825 & -1.1592616182492 \tabularnewline
31 & 10022 & 10022.0000787662 & -7.8766211996077e-05 \tabularnewline
32 & 10068 & 10067.9821576312 & 0.0178423688248848 \tabularnewline
33 & 10101 & 10100.8798612419 & 0.120138758075812 \tabularnewline
34 & 10131 & 10130.6732258955 & 0.326774104470233 \tabularnewline
35 & 10143 & 10142.7649391672 & 0.235060832832552 \tabularnewline
36 & 10170 & 10169.6104478578 & 0.389552142152874 \tabularnewline
37 & 10192 & 10191.5864536743 & 0.413546325707174 \tabularnewline
38 & 10214 & 10213.5482560299 & 0.451743970071526 \tabularnewline
39 & 10239 & 10239.5981731885 & -0.598173188471221 \tabularnewline
40 & 10263 & 10262.744154882 & 0.255845117979388 \tabularnewline
41 & 10310 & 10309.9201642322 & 0.0798357678287411 \tabularnewline
42 & 10355 & 10355.5766416934 & -0.576641693397184 \tabularnewline
43 & 10396 & 10396.0256857544 & -0.0256857543607167 \tabularnewline
44 & 10446 & 10446.0862200922 & -0.0862200921823565 \tabularnewline
45 & 10511 & 10511.2845894911 & -0.284589491120514 \tabularnewline
46 & 10585 & 10584.065609849 & 0.934390151005746 \tabularnewline
47 & 10667 & 10667.0372692159 & -0.0372692158626008 \tabularnewline
48 & 10753 & 10753.8935098748 & -0.89350987480154 \tabularnewline
49 & 10840 & 10840.3340036275 & -0.334003627516723 \tabularnewline
50 & 10951 & 10950.3974079229 & 0.602592077110022 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185654&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]9190[/C][C]9189.77347683671[/C][C]0.226523163287704[/C][/ROW]
[ROW][C]2[/C][C]9251[/C][C]9250.81170179836[/C][C]0.188298201637033[/C][/ROW]
[ROW][C]3[/C][C]9328[/C][C]9328.6444564847[/C][C]-0.644456484702895[/C][/ROW]
[ROW][C]4[/C][C]9428[/C][C]9428.66738234806[/C][C]-0.66738234806104[/C][/ROW]
[ROW][C]5[/C][C]9499[/C][C]9498.92433315414[/C][C]0.0756668458610427[/C][/ROW]
[ROW][C]6[/C][C]9556[/C][C]9555.93466926213[/C][C]0.0653307378749467[/C][/ROW]
[ROW][C]7[/C][C]9606[/C][C]9604.94429100204[/C][C]1.0557089979562[/C][/ROW]
[ROW][C]8[/C][C]9632[/C][C]9631.30155216273[/C][C]0.698447837264957[/C][/ROW]
[ROW][C]9[/C][C]9660[/C][C]9660.06217742192[/C][C]-0.0621774219207797[/C][/ROW]
[ROW][C]10[/C][C]9651[/C][C]9650.92597128438[/C][C]0.0740287156173079[/C][/ROW]
[ROW][C]11[/C][C]9695[/C][C]9695.87356337378[/C][C]-0.873563373777729[/C][/ROW]
[ROW][C]12[/C][C]9727[/C][C]9727.10634819719[/C][C]-0.106348197191145[/C][/ROW]
[ROW][C]13[/C][C]9757[/C][C]9757.31898316549[/C][C]-0.318983165493528[/C][/ROW]
[ROW][C]14[/C][C]9788[/C][C]9788.09302892045[/C][C]-0.0930289204474411[/C][/ROW]
[ROW][C]15[/C][C]9813[/C][C]9811.78879632419[/C][C]1.21120367581114[/C][/ROW]
[ROW][C]16[/C][C]9823[/C][C]9822.87960361499[/C][C]0.120396385009302[/C][/ROW]
[ROW][C]17[/C][C]9837[/C][C]9836.89835953629[/C][C]0.101640463713074[/C][/ROW]
[ROW][C]18[/C][C]9842[/C][C]9842.02400156293[/C][C]-0.024001562932786[/C][/ROW]
[ROW][C]19[/C][C]9855[/C][C]9856.19935081646[/C][C]-1.19935081645882[/C][/ROW]
[ROW][C]20[/C][C]9863[/C][C]9864.28919610798[/C][C]-1.28919610797912[/C][/ROW]
[ROW][C]21[/C][C]9855[/C][C]9854.34405747037[/C][C]0.655942529625127[/C][/ROW]
[ROW][C]22[/C][C]9858[/C][C]9858.16578246824[/C][C]-0.165782468244185[/C][/ROW]
[ROW][C]23[/C][C]9853[/C][C]9853.22964265127[/C][C]-0.229642651268907[/C][/ROW]
[ROW][C]24[/C][C]9858[/C][C]9857.41919079922[/C][C]0.58080920078305[/C][/ROW]
[ROW][C]25[/C][C]9859[/C][C]9858.47772194346[/C][C]0.522278056542202[/C][/ROW]
[ROW][C]26[/C][C]9865[/C][C]9864.30344995278[/C][C]0.696550047222603[/C][/ROW]
[ROW][C]27[/C][C]9876[/C][C]9876.31011019002[/C][C]-0.310110190018058[/C][/ROW]
[ROW][C]28[/C][C]9928[/C][C]9927.5636461118[/C][C]0.436353888201801[/C][/ROW]
[ROW][C]29[/C][C]9948[/C][C]9948.5570433318[/C][C]-0.55704333179597[/C][/ROW]
[ROW][C]30[/C][C]9987[/C][C]9988.15926161825[/C][C]-1.1592616182492[/C][/ROW]
[ROW][C]31[/C][C]10022[/C][C]10022.0000787662[/C][C]-7.8766211996077e-05[/C][/ROW]
[ROW][C]32[/C][C]10068[/C][C]10067.9821576312[/C][C]0.0178423688248848[/C][/ROW]
[ROW][C]33[/C][C]10101[/C][C]10100.8798612419[/C][C]0.120138758075812[/C][/ROW]
[ROW][C]34[/C][C]10131[/C][C]10130.6732258955[/C][C]0.326774104470233[/C][/ROW]
[ROW][C]35[/C][C]10143[/C][C]10142.7649391672[/C][C]0.235060832832552[/C][/ROW]
[ROW][C]36[/C][C]10170[/C][C]10169.6104478578[/C][C]0.389552142152874[/C][/ROW]
[ROW][C]37[/C][C]10192[/C][C]10191.5864536743[/C][C]0.413546325707174[/C][/ROW]
[ROW][C]38[/C][C]10214[/C][C]10213.5482560299[/C][C]0.451743970071526[/C][/ROW]
[ROW][C]39[/C][C]10239[/C][C]10239.5981731885[/C][C]-0.598173188471221[/C][/ROW]
[ROW][C]40[/C][C]10263[/C][C]10262.744154882[/C][C]0.255845117979388[/C][/ROW]
[ROW][C]41[/C][C]10310[/C][C]10309.9201642322[/C][C]0.0798357678287411[/C][/ROW]
[ROW][C]42[/C][C]10355[/C][C]10355.5766416934[/C][C]-0.576641693397184[/C][/ROW]
[ROW][C]43[/C][C]10396[/C][C]10396.0256857544[/C][C]-0.0256857543607167[/C][/ROW]
[ROW][C]44[/C][C]10446[/C][C]10446.0862200922[/C][C]-0.0862200921823565[/C][/ROW]
[ROW][C]45[/C][C]10511[/C][C]10511.2845894911[/C][C]-0.284589491120514[/C][/ROW]
[ROW][C]46[/C][C]10585[/C][C]10584.065609849[/C][C]0.934390151005746[/C][/ROW]
[ROW][C]47[/C][C]10667[/C][C]10667.0372692159[/C][C]-0.0372692158626008[/C][/ROW]
[ROW][C]48[/C][C]10753[/C][C]10753.8935098748[/C][C]-0.89350987480154[/C][/ROW]
[ROW][C]49[/C][C]10840[/C][C]10840.3340036275[/C][C]-0.334003627516723[/C][/ROW]
[ROW][C]50[/C][C]10951[/C][C]10950.3974079229[/C][C]0.602592077110022[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185654&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185654&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 IndexActualsInterpolationForecastResidualsPrediction Error
191909189.773476836710.226523163287704
292519250.811701798360.188298201637033
393289328.6444564847-0.644456484702895
494289428.66738234806-0.66738234806104
594999498.924333154140.0756668458610427
695569555.934669262130.0653307378749467
796069604.944291002041.0557089979562
896329631.301552162730.698447837264957
996609660.06217742192-0.0621774219207797
1096519650.925971284380.0740287156173079
1196959695.87356337378-0.873563373777729
1297279727.10634819719-0.106348197191145
1397579757.31898316549-0.318983165493528
1497889788.09302892045-0.0930289204474411
1598139811.788796324191.21120367581114
1698239822.879603614990.120396385009302
1798379836.898359536290.101640463713074
1898429842.02400156293-0.024001562932786
1998559856.19935081646-1.19935081645882
2098639864.28919610798-1.28919610797912
2198559854.344057470370.655942529625127
2298589858.16578246824-0.165782468244185
2398539853.22964265127-0.229642651268907
2498589857.419190799220.58080920078305
2598599858.477721943460.522278056542202
2698659864.303449952780.696550047222603
2798769876.31011019002-0.310110190018058
2899289927.56364611180.436353888201801
2999489948.5570433318-0.55704333179597
3099879988.15926161825-1.1592616182492
311002210022.0000787662-7.8766211996077e-05
321006810067.98215763120.0178423688248848
331010110100.87986124190.120138758075812
341013110130.67322589550.326774104470233
351014310142.76493916720.235060832832552
361017010169.61044785780.389552142152874
371019210191.58645367430.413546325707174
381021410213.54825602990.451743970071526
391023910239.5981731885-0.598173188471221
401026310262.7441548820.255845117979388
411031010309.92016423220.0798357678287411
421035510355.5766416934-0.576641693397184
431039610396.0256857544-0.0256857543607167
441044610446.0862200922-0.0862200921823565
451051110511.2845894911-0.284589491120514
461058510584.0656098490.934390151005746
471066710667.0372692159-0.0372692158626008
481075310753.8935098748-0.89350987480154
491084010840.3340036275-0.334003627516723
501095110950.39740792290.602592077110022







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
120.4261556244480770.8523112488961540.573844375551923
130.2790843122384620.5581686244769240.720915687761538
140.2882198599769130.5764397199538260.711780140023087
150.307459344641370.6149186892827390.69254065535863
160.2761776093225770.5523552186451550.723822390677423
170.2242577668979380.4485155337958750.775742233102062
180.3078211092358390.6156422184716780.692178890764161
190.2696434151911220.5392868303822440.730356584808878
200.2926679362913260.5853358725826530.707332063708674
210.5634917889579370.8730164220841270.436508211042064
220.5540982267571330.8918035464857340.445901773242867
230.6204632559312960.7590734881374070.379536744068704
240.5566349643844870.8867300712310270.443365035615513
250.6066556807409530.7866886385180930.393344319259047
260.5954722786614840.8090554426770320.404527721338516
270.763585432298520.472829135402960.23641456770148
280.8685598847912570.2628802304174850.131440115208743
290.8709609145470670.2580781709058650.129039085452933
300.808123105969330.3837537880613410.19187689403067
310.867441971161060.265116057677880.13255802883894
320.8752952883832510.2494094232334970.124704711616749
330.8333623156002160.3332753687995680.166637684399784
340.7606345052207770.4787309895584460.239365494779223
350.6933262229893810.6133475540212380.306673777010619
360.5757835313856410.8484329372287170.424216468614359
370.504877492892380.9902450142152390.49512250710762
380.3603427078443560.7206854156887110.639657292155644

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
12 & 0.426155624448077 & 0.852311248896154 & 0.573844375551923 \tabularnewline
13 & 0.279084312238462 & 0.558168624476924 & 0.720915687761538 \tabularnewline
14 & 0.288219859976913 & 0.576439719953826 & 0.711780140023087 \tabularnewline
15 & 0.30745934464137 & 0.614918689282739 & 0.69254065535863 \tabularnewline
16 & 0.276177609322577 & 0.552355218645155 & 0.723822390677423 \tabularnewline
17 & 0.224257766897938 & 0.448515533795875 & 0.775742233102062 \tabularnewline
18 & 0.307821109235839 & 0.615642218471678 & 0.692178890764161 \tabularnewline
19 & 0.269643415191122 & 0.539286830382244 & 0.730356584808878 \tabularnewline
20 & 0.292667936291326 & 0.585335872582653 & 0.707332063708674 \tabularnewline
21 & 0.563491788957937 & 0.873016422084127 & 0.436508211042064 \tabularnewline
22 & 0.554098226757133 & 0.891803546485734 & 0.445901773242867 \tabularnewline
23 & 0.620463255931296 & 0.759073488137407 & 0.379536744068704 \tabularnewline
24 & 0.556634964384487 & 0.886730071231027 & 0.443365035615513 \tabularnewline
25 & 0.606655680740953 & 0.786688638518093 & 0.393344319259047 \tabularnewline
26 & 0.595472278661484 & 0.809055442677032 & 0.404527721338516 \tabularnewline
27 & 0.76358543229852 & 0.47282913540296 & 0.23641456770148 \tabularnewline
28 & 0.868559884791257 & 0.262880230417485 & 0.131440115208743 \tabularnewline
29 & 0.870960914547067 & 0.258078170905865 & 0.129039085452933 \tabularnewline
30 & 0.80812310596933 & 0.383753788061341 & 0.19187689403067 \tabularnewline
31 & 0.86744197116106 & 0.26511605767788 & 0.13255802883894 \tabularnewline
32 & 0.875295288383251 & 0.249409423233497 & 0.124704711616749 \tabularnewline
33 & 0.833362315600216 & 0.333275368799568 & 0.166637684399784 \tabularnewline
34 & 0.760634505220777 & 0.478730989558446 & 0.239365494779223 \tabularnewline
35 & 0.693326222989381 & 0.613347554021238 & 0.306673777010619 \tabularnewline
36 & 0.575783531385641 & 0.848432937228717 & 0.424216468614359 \tabularnewline
37 & 0.50487749289238 & 0.990245014215239 & 0.49512250710762 \tabularnewline
38 & 0.360342707844356 & 0.720685415688711 & 0.639657292155644 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185654&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]0.426155624448077[/C][C]0.852311248896154[/C][C]0.573844375551923[/C][/ROW]
[ROW][C]13[/C][C]0.279084312238462[/C][C]0.558168624476924[/C][C]0.720915687761538[/C][/ROW]
[ROW][C]14[/C][C]0.288219859976913[/C][C]0.576439719953826[/C][C]0.711780140023087[/C][/ROW]
[ROW][C]15[/C][C]0.30745934464137[/C][C]0.614918689282739[/C][C]0.69254065535863[/C][/ROW]
[ROW][C]16[/C][C]0.276177609322577[/C][C]0.552355218645155[/C][C]0.723822390677423[/C][/ROW]
[ROW][C]17[/C][C]0.224257766897938[/C][C]0.448515533795875[/C][C]0.775742233102062[/C][/ROW]
[ROW][C]18[/C][C]0.307821109235839[/C][C]0.615642218471678[/C][C]0.692178890764161[/C][/ROW]
[ROW][C]19[/C][C]0.269643415191122[/C][C]0.539286830382244[/C][C]0.730356584808878[/C][/ROW]
[ROW][C]20[/C][C]0.292667936291326[/C][C]0.585335872582653[/C][C]0.707332063708674[/C][/ROW]
[ROW][C]21[/C][C]0.563491788957937[/C][C]0.873016422084127[/C][C]0.436508211042064[/C][/ROW]
[ROW][C]22[/C][C]0.554098226757133[/C][C]0.891803546485734[/C][C]0.445901773242867[/C][/ROW]
[ROW][C]23[/C][C]0.620463255931296[/C][C]0.759073488137407[/C][C]0.379536744068704[/C][/ROW]
[ROW][C]24[/C][C]0.556634964384487[/C][C]0.886730071231027[/C][C]0.443365035615513[/C][/ROW]
[ROW][C]25[/C][C]0.606655680740953[/C][C]0.786688638518093[/C][C]0.393344319259047[/C][/ROW]
[ROW][C]26[/C][C]0.595472278661484[/C][C]0.809055442677032[/C][C]0.404527721338516[/C][/ROW]
[ROW][C]27[/C][C]0.76358543229852[/C][C]0.47282913540296[/C][C]0.23641456770148[/C][/ROW]
[ROW][C]28[/C][C]0.868559884791257[/C][C]0.262880230417485[/C][C]0.131440115208743[/C][/ROW]
[ROW][C]29[/C][C]0.870960914547067[/C][C]0.258078170905865[/C][C]0.129039085452933[/C][/ROW]
[ROW][C]30[/C][C]0.80812310596933[/C][C]0.383753788061341[/C][C]0.19187689403067[/C][/ROW]
[ROW][C]31[/C][C]0.86744197116106[/C][C]0.26511605767788[/C][C]0.13255802883894[/C][/ROW]
[ROW][C]32[/C][C]0.875295288383251[/C][C]0.249409423233497[/C][C]0.124704711616749[/C][/ROW]
[ROW][C]33[/C][C]0.833362315600216[/C][C]0.333275368799568[/C][C]0.166637684399784[/C][/ROW]
[ROW][C]34[/C][C]0.760634505220777[/C][C]0.478730989558446[/C][C]0.239365494779223[/C][/ROW]
[ROW][C]35[/C][C]0.693326222989381[/C][C]0.613347554021238[/C][C]0.306673777010619[/C][/ROW]
[ROW][C]36[/C][C]0.575783531385641[/C][C]0.848432937228717[/C][C]0.424216468614359[/C][/ROW]
[ROW][C]37[/C][C]0.50487749289238[/C][C]0.990245014215239[/C][C]0.49512250710762[/C][/ROW]
[ROW][C]38[/C][C]0.360342707844356[/C][C]0.720685415688711[/C][C]0.639657292155644[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185654&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185654&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-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
120.4261556244480770.8523112488961540.573844375551923
130.2790843122384620.5581686244769240.720915687761538
140.2882198599769130.5764397199538260.711780140023087
150.307459344641370.6149186892827390.69254065535863
160.2761776093225770.5523552186451550.723822390677423
170.2242577668979380.4485155337958750.775742233102062
180.3078211092358390.6156422184716780.692178890764161
190.2696434151911220.5392868303822440.730356584808878
200.2926679362913260.5853358725826530.707332063708674
210.5634917889579370.8730164220841270.436508211042064
220.5540982267571330.8918035464857340.445901773242867
230.6204632559312960.7590734881374070.379536744068704
240.5566349643844870.8867300712310270.443365035615513
250.6066556807409530.7866886385180930.393344319259047
260.5954722786614840.8090554426770320.404527721338516
270.763585432298520.472829135402960.23641456770148
280.8685598847912570.2628802304174850.131440115208743
290.8709609145470670.2580781709058650.129039085452933
300.808123105969330.3837537880613410.19187689403067
310.867441971161060.265116057677880.13255802883894
320.8752952883832510.2494094232334970.124704711616749
330.8333623156002160.3332753687995680.166637684399784
340.7606345052207770.4787309895584460.239365494779223
350.6933262229893810.6133475540212380.306673777010619
360.5757835313856410.8484329372287170.424216468614359
370.504877492892380.9902450142152390.49512250710762
380.3603427078443560.7206854156887110.639657292155644







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level00OK
10% type I error level00OK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 0 & 0 & OK \tabularnewline
10% type I error level & 0 & 0 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185654&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185654&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185654&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 testsOK/NOK
1% type I error level00OK
5% type I error level00OK
10% type I error level00OK



Parameters (Session):
par1 = 2 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 2 ; par2 = Do not include Seasonal Dummies ; par3 = 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')
}