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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 12:52:03 -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/t1351875258wxj72hccxm490p6.htm/, Retrieved Mon, 27 Jun 2022 05:01:59 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=185653, Retrieved Mon, 27 Jun 2022 05:01:59 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact167
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] [7a9100b3135ff0dae36397155af309d9] [Current]
-             [Multiple Regression] [workshop 7: deter...] [2012-11-02 17:20:14] [40b341cf5fb1ddfd74e4c5704837f48c]
-   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]
- 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] [e01c78beec4051e03ee053d8bc2c6384]
-   P           [Multiple Regression] [WS 7 multiple reg...] [2012-11-04 11:08:13] [e01c78beec4051e03ee053d8bc2c6384]
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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 time9 seconds
R Server'Gwilym Jenkins' @ jenkins.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 & 'Gwilym Jenkins' @ jenkins.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=185653&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]'Gwilym Jenkins' @ jenkins.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=185653&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185653&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 time9 seconds
R Server'Gwilym Jenkins' @ jenkins.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] = -182.979537835799 + 121523.779182633jaar[t] + 0.949247856899833vlaams_man[t] + 1.06181457703044vlaams_vrouw[t] + 1.04052668979494waals_man[t] + 0.951487691077562waals_vrouw[t] + 0.944015429707455brussel_man[t] + 1.06350547762047brussel_vrouw[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
totaal[t] =  -182.979537835799 +  121523.779182633jaar[t] +  0.949247856899833vlaams_man[t] +  1.06181457703044vlaams_vrouw[t] +  1.04052668979494waals_man[t] +  0.951487691077562waals_vrouw[t] +  0.944015429707455brussel_man[t] +  1.06350547762047brussel_vrouw[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185653&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]totaal[t] =  -182.979537835799 +  121523.779182633jaar[t] +  0.949247856899833vlaams_man[t] +  1.06181457703044vlaams_vrouw[t] +  1.04052668979494waals_man[t] +  0.951487691077562waals_vrouw[t] +  0.944015429707455brussel_man[t] +  1.06350547762047brussel_vrouw[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185653&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185653&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] = -182.979537835799 + 121523.779182633jaar[t] + 0.949247856899833vlaams_man[t] + 1.06181457703044vlaams_vrouw[t] + 1.04052668979494waals_man[t] + 0.951487691077562waals_vrouw[t] + 0.944015429707455brussel_man[t] + 1.06350547762047brussel_vrouw[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-182.979537835799410.039367-0.44620.657710.328855
jaar121523.779182633297238.0863790.40880.6847320.342366
vlaams_man0.9492478568998330.07316712.973700
vlaams_vrouw1.061814577030440.08285212.815700
waals_man1.040526689794940.06180516.835800
waals_vrouw0.9514876910775620.07483912.713700
brussel_man0.9440154297074550.06683114.125500
brussel_vrouw1.063505477620470.0612517.363300

\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) & -182.979537835799 & 410.039367 & -0.4462 & 0.65771 & 0.328855 \tabularnewline
jaar & 121523.779182633 & 297238.086379 & 0.4088 & 0.684732 & 0.342366 \tabularnewline
vlaams_man & 0.949247856899833 & 0.073167 & 12.9737 & 0 & 0 \tabularnewline
vlaams_vrouw & 1.06181457703044 & 0.082852 & 12.8157 & 0 & 0 \tabularnewline
waals_man & 1.04052668979494 & 0.061805 & 16.8358 & 0 & 0 \tabularnewline
waals_vrouw & 0.951487691077562 & 0.074839 & 12.7137 & 0 & 0 \tabularnewline
brussel_man & 0.944015429707455 & 0.066831 & 14.1255 & 0 & 0 \tabularnewline
brussel_vrouw & 1.06350547762047 & 0.06125 & 17.3633 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185653&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]-182.979537835799[/C][C]410.039367[/C][C]-0.4462[/C][C]0.65771[/C][C]0.328855[/C][/ROW]
[ROW][C]jaar[/C][C]121523.779182633[/C][C]297238.086379[/C][C]0.4088[/C][C]0.684732[/C][C]0.342366[/C][/ROW]
[ROW][C]vlaams_man[/C][C]0.949247856899833[/C][C]0.073167[/C][C]12.9737[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]vlaams_vrouw[/C][C]1.06181457703044[/C][C]0.082852[/C][C]12.8157[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]waals_man[/C][C]1.04052668979494[/C][C]0.061805[/C][C]16.8358[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]waals_vrouw[/C][C]0.951487691077562[/C][C]0.074839[/C][C]12.7137[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]brussel_man[/C][C]0.944015429707455[/C][C]0.066831[/C][C]14.1255[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]brussel_vrouw[/C][C]1.06350547762047[/C][C]0.06125[/C][C]17.3633[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185653&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185653&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)-182.979537835799410.039367-0.44620.657710.328855
jaar121523.779182633297238.0863790.40880.6847320.342366
vlaams_man0.9492478568998330.07316712.973700
vlaams_vrouw1.061814577030440.08285212.815700
waals_man1.040526689794940.06180516.835800
waals_vrouw0.9514876910775620.07483912.713700
brussel_man0.9440154297074550.06683114.125500
brussel_vrouw1.063505477620470.0612517.363300







Multiple Linear Regression - Regression Statistics
Multiple R0.999998918967823
R-squared0.999997837936815
Adjusted R-squared0.999997477592951
F-TEST (value)2775121.03670975
F-TEST (DF numerator)7
F-TEST (DF denominator)42
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.626362981975182
Sum Squared Residuals16.4778845779314

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.999998918967823 \tabularnewline
R-squared & 0.999997837936815 \tabularnewline
Adjusted R-squared & 0.999997477592951 \tabularnewline
F-TEST (value) & 2775121.03670975 \tabularnewline
F-TEST (DF numerator) & 7 \tabularnewline
F-TEST (DF denominator) & 42 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.626362981975182 \tabularnewline
Sum Squared Residuals & 16.4778845779314 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185653&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.999998918967823[/C][/ROW]
[ROW][C]R-squared[/C][C]0.999997837936815[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.999997477592951[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]2775121.03670975[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]7[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]42[/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]0.626362981975182[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]16.4778845779314[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185653&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185653&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 R0.999998918967823
R-squared0.999997837936815
Adjusted R-squared0.999997477592951
F-TEST (value)2775121.03670975
F-TEST (DF numerator)7
F-TEST (DF denominator)42
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.626362981975182
Sum Squared Residuals16.4778845779314







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
191909189.806787479650.193212520348749
292519250.89297595280.107024047200173
393289328.83327762726-0.833277627256444
494289428.74466862748-0.744668627476104
594999498.787994681550.212005318449579
695569555.850193509810.149806490188848
796069604.876999183011.12300081698817
896329631.060300343940.939699656064565
996609660.00592997866-0.00592997865459314
1096519651.18230128143-0.182301281434822
1196959696.19713987322-1.19713987321767
1297279727.25427320046-0.254273200464552
1397579757.23062577258-0.230625772581267
1497889788.02485290436-0.0248529043596578
1598139811.797199677461.20280032253908
1698239822.850838406510.149161593486654
1798379836.793519811940.206480188060833
1898429841.938467140210.0615328597902657
1998559856.0403972326-1.04039723259927
2098639864.13873393986-1.13873393985831
2198559854.266631714250.733368285751476
2298589858.27526716294-0.275267162937924
2398539853.38829861344-0.388298613440543
2498589857.450277950810.549722049184736
2598599858.453276301380.546723698614362
2698659864.385151879090.614848120911181
2798769876.33572652952-0.33572652952084
2899289927.22527294460.774727055398973
2999489948.23146181687-0.231461816874587
3099879988.01209308531-1.01209308531383
311002210021.9580313980.0419686019875126
321006810067.95794866170.0420513382558687
331010110100.88914017170.110859828263561
341013110130.77162370990.228376290090589
351014310142.88321019310.116789806916784
361017010169.8528009790.147199021041021
371019210191.82811920920.171880790829415
381021410213.81043960550.189560394467558
391023910239.7854506824-0.785450682363405
401026310262.8209690240.179030976016871
411031010309.80189654460.198103455422551
421035510355.6028994992-0.602899499171384
431039610395.84722241760.152777582396436
441044610445.87733169710.122668302879144
451051110511.0223304006-0.0223304006075853
461058510583.94150386411.05849613591686
471066710667.0439808004-0.0439808003605646
481075310754.076079745-1.07607974495746
491084010840.4018448984-0.401844898358375
501095110950.49624187570.503758124347383

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 9190 & 9189.80678747965 & 0.193212520348749 \tabularnewline
2 & 9251 & 9250.8929759528 & 0.107024047200173 \tabularnewline
3 & 9328 & 9328.83327762726 & -0.833277627256444 \tabularnewline
4 & 9428 & 9428.74466862748 & -0.744668627476104 \tabularnewline
5 & 9499 & 9498.78799468155 & 0.212005318449579 \tabularnewline
6 & 9556 & 9555.85019350981 & 0.149806490188848 \tabularnewline
7 & 9606 & 9604.87699918301 & 1.12300081698817 \tabularnewline
8 & 9632 & 9631.06030034394 & 0.939699656064565 \tabularnewline
9 & 9660 & 9660.00592997866 & -0.00592997865459314 \tabularnewline
10 & 9651 & 9651.18230128143 & -0.182301281434822 \tabularnewline
11 & 9695 & 9696.19713987322 & -1.19713987321767 \tabularnewline
12 & 9727 & 9727.25427320046 & -0.254273200464552 \tabularnewline
13 & 9757 & 9757.23062577258 & -0.230625772581267 \tabularnewline
14 & 9788 & 9788.02485290436 & -0.0248529043596578 \tabularnewline
15 & 9813 & 9811.79719967746 & 1.20280032253908 \tabularnewline
16 & 9823 & 9822.85083840651 & 0.149161593486654 \tabularnewline
17 & 9837 & 9836.79351981194 & 0.206480188060833 \tabularnewline
18 & 9842 & 9841.93846714021 & 0.0615328597902657 \tabularnewline
19 & 9855 & 9856.0403972326 & -1.04039723259927 \tabularnewline
20 & 9863 & 9864.13873393986 & -1.13873393985831 \tabularnewline
21 & 9855 & 9854.26663171425 & 0.733368285751476 \tabularnewline
22 & 9858 & 9858.27526716294 & -0.275267162937924 \tabularnewline
23 & 9853 & 9853.38829861344 & -0.388298613440543 \tabularnewline
24 & 9858 & 9857.45027795081 & 0.549722049184736 \tabularnewline
25 & 9859 & 9858.45327630138 & 0.546723698614362 \tabularnewline
26 & 9865 & 9864.38515187909 & 0.614848120911181 \tabularnewline
27 & 9876 & 9876.33572652952 & -0.33572652952084 \tabularnewline
28 & 9928 & 9927.2252729446 & 0.774727055398973 \tabularnewline
29 & 9948 & 9948.23146181687 & -0.231461816874587 \tabularnewline
30 & 9987 & 9988.01209308531 & -1.01209308531383 \tabularnewline
31 & 10022 & 10021.958031398 & 0.0419686019875126 \tabularnewline
32 & 10068 & 10067.9579486617 & 0.0420513382558687 \tabularnewline
33 & 10101 & 10100.8891401717 & 0.110859828263561 \tabularnewline
34 & 10131 & 10130.7716237099 & 0.228376290090589 \tabularnewline
35 & 10143 & 10142.8832101931 & 0.116789806916784 \tabularnewline
36 & 10170 & 10169.852800979 & 0.147199021041021 \tabularnewline
37 & 10192 & 10191.8281192092 & 0.171880790829415 \tabularnewline
38 & 10214 & 10213.8104396055 & 0.189560394467558 \tabularnewline
39 & 10239 & 10239.7854506824 & -0.785450682363405 \tabularnewline
40 & 10263 & 10262.820969024 & 0.179030976016871 \tabularnewline
41 & 10310 & 10309.8018965446 & 0.198103455422551 \tabularnewline
42 & 10355 & 10355.6028994992 & -0.602899499171384 \tabularnewline
43 & 10396 & 10395.8472224176 & 0.152777582396436 \tabularnewline
44 & 10446 & 10445.8773316971 & 0.122668302879144 \tabularnewline
45 & 10511 & 10511.0223304006 & -0.0223304006075853 \tabularnewline
46 & 10585 & 10583.9415038641 & 1.05849613591686 \tabularnewline
47 & 10667 & 10667.0439808004 & -0.0439808003605646 \tabularnewline
48 & 10753 & 10754.076079745 & -1.07607974495746 \tabularnewline
49 & 10840 & 10840.4018448984 & -0.401844898358375 \tabularnewline
50 & 10951 & 10950.4962418757 & 0.503758124347383 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185653&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]9189.80678747965[/C][C]0.193212520348749[/C][/ROW]
[ROW][C]2[/C][C]9251[/C][C]9250.8929759528[/C][C]0.107024047200173[/C][/ROW]
[ROW][C]3[/C][C]9328[/C][C]9328.83327762726[/C][C]-0.833277627256444[/C][/ROW]
[ROW][C]4[/C][C]9428[/C][C]9428.74466862748[/C][C]-0.744668627476104[/C][/ROW]
[ROW][C]5[/C][C]9499[/C][C]9498.78799468155[/C][C]0.212005318449579[/C][/ROW]
[ROW][C]6[/C][C]9556[/C][C]9555.85019350981[/C][C]0.149806490188848[/C][/ROW]
[ROW][C]7[/C][C]9606[/C][C]9604.87699918301[/C][C]1.12300081698817[/C][/ROW]
[ROW][C]8[/C][C]9632[/C][C]9631.06030034394[/C][C]0.939699656064565[/C][/ROW]
[ROW][C]9[/C][C]9660[/C][C]9660.00592997866[/C][C]-0.00592997865459314[/C][/ROW]
[ROW][C]10[/C][C]9651[/C][C]9651.18230128143[/C][C]-0.182301281434822[/C][/ROW]
[ROW][C]11[/C][C]9695[/C][C]9696.19713987322[/C][C]-1.19713987321767[/C][/ROW]
[ROW][C]12[/C][C]9727[/C][C]9727.25427320046[/C][C]-0.254273200464552[/C][/ROW]
[ROW][C]13[/C][C]9757[/C][C]9757.23062577258[/C][C]-0.230625772581267[/C][/ROW]
[ROW][C]14[/C][C]9788[/C][C]9788.02485290436[/C][C]-0.0248529043596578[/C][/ROW]
[ROW][C]15[/C][C]9813[/C][C]9811.79719967746[/C][C]1.20280032253908[/C][/ROW]
[ROW][C]16[/C][C]9823[/C][C]9822.85083840651[/C][C]0.149161593486654[/C][/ROW]
[ROW][C]17[/C][C]9837[/C][C]9836.79351981194[/C][C]0.206480188060833[/C][/ROW]
[ROW][C]18[/C][C]9842[/C][C]9841.93846714021[/C][C]0.0615328597902657[/C][/ROW]
[ROW][C]19[/C][C]9855[/C][C]9856.0403972326[/C][C]-1.04039723259927[/C][/ROW]
[ROW][C]20[/C][C]9863[/C][C]9864.13873393986[/C][C]-1.13873393985831[/C][/ROW]
[ROW][C]21[/C][C]9855[/C][C]9854.26663171425[/C][C]0.733368285751476[/C][/ROW]
[ROW][C]22[/C][C]9858[/C][C]9858.27526716294[/C][C]-0.275267162937924[/C][/ROW]
[ROW][C]23[/C][C]9853[/C][C]9853.38829861344[/C][C]-0.388298613440543[/C][/ROW]
[ROW][C]24[/C][C]9858[/C][C]9857.45027795081[/C][C]0.549722049184736[/C][/ROW]
[ROW][C]25[/C][C]9859[/C][C]9858.45327630138[/C][C]0.546723698614362[/C][/ROW]
[ROW][C]26[/C][C]9865[/C][C]9864.38515187909[/C][C]0.614848120911181[/C][/ROW]
[ROW][C]27[/C][C]9876[/C][C]9876.33572652952[/C][C]-0.33572652952084[/C][/ROW]
[ROW][C]28[/C][C]9928[/C][C]9927.2252729446[/C][C]0.774727055398973[/C][/ROW]
[ROW][C]29[/C][C]9948[/C][C]9948.23146181687[/C][C]-0.231461816874587[/C][/ROW]
[ROW][C]30[/C][C]9987[/C][C]9988.01209308531[/C][C]-1.01209308531383[/C][/ROW]
[ROW][C]31[/C][C]10022[/C][C]10021.958031398[/C][C]0.0419686019875126[/C][/ROW]
[ROW][C]32[/C][C]10068[/C][C]10067.9579486617[/C][C]0.0420513382558687[/C][/ROW]
[ROW][C]33[/C][C]10101[/C][C]10100.8891401717[/C][C]0.110859828263561[/C][/ROW]
[ROW][C]34[/C][C]10131[/C][C]10130.7716237099[/C][C]0.228376290090589[/C][/ROW]
[ROW][C]35[/C][C]10143[/C][C]10142.8832101931[/C][C]0.116789806916784[/C][/ROW]
[ROW][C]36[/C][C]10170[/C][C]10169.852800979[/C][C]0.147199021041021[/C][/ROW]
[ROW][C]37[/C][C]10192[/C][C]10191.8281192092[/C][C]0.171880790829415[/C][/ROW]
[ROW][C]38[/C][C]10214[/C][C]10213.8104396055[/C][C]0.189560394467558[/C][/ROW]
[ROW][C]39[/C][C]10239[/C][C]10239.7854506824[/C][C]-0.785450682363405[/C][/ROW]
[ROW][C]40[/C][C]10263[/C][C]10262.820969024[/C][C]0.179030976016871[/C][/ROW]
[ROW][C]41[/C][C]10310[/C][C]10309.8018965446[/C][C]0.198103455422551[/C][/ROW]
[ROW][C]42[/C][C]10355[/C][C]10355.6028994992[/C][C]-0.602899499171384[/C][/ROW]
[ROW][C]43[/C][C]10396[/C][C]10395.8472224176[/C][C]0.152777582396436[/C][/ROW]
[ROW][C]44[/C][C]10446[/C][C]10445.8773316971[/C][C]0.122668302879144[/C][/ROW]
[ROW][C]45[/C][C]10511[/C][C]10511.0223304006[/C][C]-0.0223304006075853[/C][/ROW]
[ROW][C]46[/C][C]10585[/C][C]10583.9415038641[/C][C]1.05849613591686[/C][/ROW]
[ROW][C]47[/C][C]10667[/C][C]10667.0439808004[/C][C]-0.0439808003605646[/C][/ROW]
[ROW][C]48[/C][C]10753[/C][C]10754.076079745[/C][C]-1.07607974495746[/C][/ROW]
[ROW][C]49[/C][C]10840[/C][C]10840.4018448984[/C][C]-0.401844898358375[/C][/ROW]
[ROW][C]50[/C][C]10951[/C][C]10950.4962418757[/C][C]0.503758124347383[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185653&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185653&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
191909189.806787479650.193212520348749
292519250.89297595280.107024047200173
393289328.83327762726-0.833277627256444
494289428.74466862748-0.744668627476104
594999498.787994681550.212005318449579
695569555.850193509810.149806490188848
796069604.876999183011.12300081698817
896329631.060300343940.939699656064565
996609660.00592997866-0.00592997865459314
1096519651.18230128143-0.182301281434822
1196959696.19713987322-1.19713987321767
1297279727.25427320046-0.254273200464552
1397579757.23062577258-0.230625772581267
1497889788.02485290436-0.0248529043596578
1598139811.797199677461.20280032253908
1698239822.850838406510.149161593486654
1798379836.793519811940.206480188060833
1898429841.938467140210.0615328597902657
1998559856.0403972326-1.04039723259927
2098639864.13873393986-1.13873393985831
2198559854.266631714250.733368285751476
2298589858.27526716294-0.275267162937924
2398539853.38829861344-0.388298613440543
2498589857.450277950810.549722049184736
2598599858.453276301380.546723698614362
2698659864.385151879090.614848120911181
2798769876.33572652952-0.33572652952084
2899289927.22527294460.774727055398973
2999489948.23146181687-0.231461816874587
3099879988.01209308531-1.01209308531383
311002210021.9580313980.0419686019875126
321006810067.95794866170.0420513382558687
331010110100.88914017170.110859828263561
341013110130.77162370990.228376290090589
351014310142.88321019310.116789806916784
361017010169.8528009790.147199021041021
371019210191.82811920920.171880790829415
381021410213.81043960550.189560394467558
391023910239.7854506824-0.785450682363405
401026310262.8209690240.179030976016871
411031010309.80189654460.198103455422551
421035510355.6028994992-0.602899499171384
431039610395.84722241760.152777582396436
441044610445.87733169710.122668302879144
451051110511.0223304006-0.0223304006075853
461058510583.94150386411.05849613591686
471066710667.0439808004-0.0439808003605646
481075310754.076079745-1.07607974495746
491084010840.4018448984-0.401844898358375
501095110950.49624187570.503758124347383







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
110.179330818900220.358661637800440.82066918109978
120.2820259994022910.5640519988045820.717974000597709
130.1774932603240340.3549865206480690.822506739675966
140.1776157622915660.3552315245831310.822384237708434
150.2285475218593540.4570950437187080.771452478140646
160.236500015677490.4730000313549810.76349998432251
170.21079154682380.4215830936476010.7892084531762
180.2373313851764920.4746627703529840.762668614823508
190.2805239137690850.561047827538170.719476086230915
200.41230798383270.82461596766540.5876920161673
210.5422111551268610.9155776897462770.457788844873139
220.579583718931530.8408325621369390.42041628106847
230.6208323820298050.7583352359403890.379167617970195
240.5361671324053720.9276657351892550.463832867594628
250.5648803907112880.8702392185774250.435119609288712
260.6116302151733190.7767395696533620.388369784826681
270.8015767975116980.3968464049766030.198423202488302
280.9034788496246420.1930423007507160.0965211503753579
290.9267543084123020.1464913831753950.0732456915876977
300.9443176336785940.1113647326428110.0556823663214057
310.9127080034296480.1745839931407030.0872919965703517
320.8593092517476710.2813814965046580.140690748252329
330.7938838114569740.4122323770860520.206116188543026
340.7313835768613710.5372328462772590.268616423138629
350.7592442553341650.4815114893316690.240755744665835
360.7047129256676390.5905741486647210.295287074332361
370.6490720106481960.7018559787036090.350927989351804
380.5454595551005170.9090808897989650.454540444899482
390.9207827426251440.1584345147497110.0792172573748556

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
11 & 0.17933081890022 & 0.35866163780044 & 0.82066918109978 \tabularnewline
12 & 0.282025999402291 & 0.564051998804582 & 0.717974000597709 \tabularnewline
13 & 0.177493260324034 & 0.354986520648069 & 0.822506739675966 \tabularnewline
14 & 0.177615762291566 & 0.355231524583131 & 0.822384237708434 \tabularnewline
15 & 0.228547521859354 & 0.457095043718708 & 0.771452478140646 \tabularnewline
16 & 0.23650001567749 & 0.473000031354981 & 0.76349998432251 \tabularnewline
17 & 0.2107915468238 & 0.421583093647601 & 0.7892084531762 \tabularnewline
18 & 0.237331385176492 & 0.474662770352984 & 0.762668614823508 \tabularnewline
19 & 0.280523913769085 & 0.56104782753817 & 0.719476086230915 \tabularnewline
20 & 0.4123079838327 & 0.8246159676654 & 0.5876920161673 \tabularnewline
21 & 0.542211155126861 & 0.915577689746277 & 0.457788844873139 \tabularnewline
22 & 0.57958371893153 & 0.840832562136939 & 0.42041628106847 \tabularnewline
23 & 0.620832382029805 & 0.758335235940389 & 0.379167617970195 \tabularnewline
24 & 0.536167132405372 & 0.927665735189255 & 0.463832867594628 \tabularnewline
25 & 0.564880390711288 & 0.870239218577425 & 0.435119609288712 \tabularnewline
26 & 0.611630215173319 & 0.776739569653362 & 0.388369784826681 \tabularnewline
27 & 0.801576797511698 & 0.396846404976603 & 0.198423202488302 \tabularnewline
28 & 0.903478849624642 & 0.193042300750716 & 0.0965211503753579 \tabularnewline
29 & 0.926754308412302 & 0.146491383175395 & 0.0732456915876977 \tabularnewline
30 & 0.944317633678594 & 0.111364732642811 & 0.0556823663214057 \tabularnewline
31 & 0.912708003429648 & 0.174583993140703 & 0.0872919965703517 \tabularnewline
32 & 0.859309251747671 & 0.281381496504658 & 0.140690748252329 \tabularnewline
33 & 0.793883811456974 & 0.412232377086052 & 0.206116188543026 \tabularnewline
34 & 0.731383576861371 & 0.537232846277259 & 0.268616423138629 \tabularnewline
35 & 0.759244255334165 & 0.481511489331669 & 0.240755744665835 \tabularnewline
36 & 0.704712925667639 & 0.590574148664721 & 0.295287074332361 \tabularnewline
37 & 0.649072010648196 & 0.701855978703609 & 0.350927989351804 \tabularnewline
38 & 0.545459555100517 & 0.909080889798965 & 0.454540444899482 \tabularnewline
39 & 0.920782742625144 & 0.158434514749711 & 0.0792172573748556 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185653&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]11[/C][C]0.17933081890022[/C][C]0.35866163780044[/C][C]0.82066918109978[/C][/ROW]
[ROW][C]12[/C][C]0.282025999402291[/C][C]0.564051998804582[/C][C]0.717974000597709[/C][/ROW]
[ROW][C]13[/C][C]0.177493260324034[/C][C]0.354986520648069[/C][C]0.822506739675966[/C][/ROW]
[ROW][C]14[/C][C]0.177615762291566[/C][C]0.355231524583131[/C][C]0.822384237708434[/C][/ROW]
[ROW][C]15[/C][C]0.228547521859354[/C][C]0.457095043718708[/C][C]0.771452478140646[/C][/ROW]
[ROW][C]16[/C][C]0.23650001567749[/C][C]0.473000031354981[/C][C]0.76349998432251[/C][/ROW]
[ROW][C]17[/C][C]0.2107915468238[/C][C]0.421583093647601[/C][C]0.7892084531762[/C][/ROW]
[ROW][C]18[/C][C]0.237331385176492[/C][C]0.474662770352984[/C][C]0.762668614823508[/C][/ROW]
[ROW][C]19[/C][C]0.280523913769085[/C][C]0.56104782753817[/C][C]0.719476086230915[/C][/ROW]
[ROW][C]20[/C][C]0.4123079838327[/C][C]0.8246159676654[/C][C]0.5876920161673[/C][/ROW]
[ROW][C]21[/C][C]0.542211155126861[/C][C]0.915577689746277[/C][C]0.457788844873139[/C][/ROW]
[ROW][C]22[/C][C]0.57958371893153[/C][C]0.840832562136939[/C][C]0.42041628106847[/C][/ROW]
[ROW][C]23[/C][C]0.620832382029805[/C][C]0.758335235940389[/C][C]0.379167617970195[/C][/ROW]
[ROW][C]24[/C][C]0.536167132405372[/C][C]0.927665735189255[/C][C]0.463832867594628[/C][/ROW]
[ROW][C]25[/C][C]0.564880390711288[/C][C]0.870239218577425[/C][C]0.435119609288712[/C][/ROW]
[ROW][C]26[/C][C]0.611630215173319[/C][C]0.776739569653362[/C][C]0.388369784826681[/C][/ROW]
[ROW][C]27[/C][C]0.801576797511698[/C][C]0.396846404976603[/C][C]0.198423202488302[/C][/ROW]
[ROW][C]28[/C][C]0.903478849624642[/C][C]0.193042300750716[/C][C]0.0965211503753579[/C][/ROW]
[ROW][C]29[/C][C]0.926754308412302[/C][C]0.146491383175395[/C][C]0.0732456915876977[/C][/ROW]
[ROW][C]30[/C][C]0.944317633678594[/C][C]0.111364732642811[/C][C]0.0556823663214057[/C][/ROW]
[ROW][C]31[/C][C]0.912708003429648[/C][C]0.174583993140703[/C][C]0.0872919965703517[/C][/ROW]
[ROW][C]32[/C][C]0.859309251747671[/C][C]0.281381496504658[/C][C]0.140690748252329[/C][/ROW]
[ROW][C]33[/C][C]0.793883811456974[/C][C]0.412232377086052[/C][C]0.206116188543026[/C][/ROW]
[ROW][C]34[/C][C]0.731383576861371[/C][C]0.537232846277259[/C][C]0.268616423138629[/C][/ROW]
[ROW][C]35[/C][C]0.759244255334165[/C][C]0.481511489331669[/C][C]0.240755744665835[/C][/ROW]
[ROW][C]36[/C][C]0.704712925667639[/C][C]0.590574148664721[/C][C]0.295287074332361[/C][/ROW]
[ROW][C]37[/C][C]0.649072010648196[/C][C]0.701855978703609[/C][C]0.350927989351804[/C][/ROW]
[ROW][C]38[/C][C]0.545459555100517[/C][C]0.909080889798965[/C][C]0.454540444899482[/C][/ROW]
[ROW][C]39[/C][C]0.920782742625144[/C][C]0.158434514749711[/C][C]0.0792172573748556[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185653&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185653&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
110.179330818900220.358661637800440.82066918109978
120.2820259994022910.5640519988045820.717974000597709
130.1774932603240340.3549865206480690.822506739675966
140.1776157622915660.3552315245831310.822384237708434
150.2285475218593540.4570950437187080.771452478140646
160.236500015677490.4730000313549810.76349998432251
170.21079154682380.4215830936476010.7892084531762
180.2373313851764920.4746627703529840.762668614823508
190.2805239137690850.561047827538170.719476086230915
200.41230798383270.82461596766540.5876920161673
210.5422111551268610.9155776897462770.457788844873139
220.579583718931530.8408325621369390.42041628106847
230.6208323820298050.7583352359403890.379167617970195
240.5361671324053720.9276657351892550.463832867594628
250.5648803907112880.8702392185774250.435119609288712
260.6116302151733190.7767395696533620.388369784826681
270.8015767975116980.3968464049766030.198423202488302
280.9034788496246420.1930423007507160.0965211503753579
290.9267543084123020.1464913831753950.0732456915876977
300.9443176336785940.1113647326428110.0556823663214057
310.9127080034296480.1745839931407030.0872919965703517
320.8593092517476710.2813814965046580.140690748252329
330.7938838114569740.4122323770860520.206116188543026
340.7313835768613710.5372328462772590.268616423138629
350.7592442553341650.4815114893316690.240755744665835
360.7047129256676390.5905741486647210.295287074332361
370.6490720106481960.7018559787036090.350927989351804
380.5454595551005170.9090808897989650.454540444899482
390.9207827426251440.1584345147497110.0792172573748556







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=185653&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=185653&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185653&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 = No Linear Trend ;
R code (references can be found in the software module):
library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,mysum$coefficients[i,1])
a<-table.element(a, round(mysum$coefficients[i,2],6))
a<-table.element(a, round(mysum$coefficients[i,3],4))
a<-table.element(a, round(mysum$coefficients[i,4],6))
a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, sqrt(mysum$r.squared))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, mysum$r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, mysum$adj.r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, mysum$fstatistic[1])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[3])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a, mysum$sigma)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, sum(myerror*myerror))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,x[i])
a<-table.element(a,x[i]-mysum$resid[i])
a<-table.element(a,mysum$resid[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
if (n > n25) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-values',header=TRUE)
a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'breakpoint index',header=TRUE)
a<-table.element(a,'greater',header=TRUE)
a<-table.element(a,'2-sided',header=TRUE)
a<-table.element(a,'less',header=TRUE)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
a<-table.element(a,mypoint,header=TRUE)
a<-table.element(a,gqarr[mypoint-kp3+1,1])
a<-table.element(a,gqarr[mypoint-kp3+1,2])
a<-table.element(a,gqarr[mypoint-kp3+1,3])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,numsignificant1)
a<-table.element(a,numsignificant1/numgqtests)
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,numsignificant5)
a<-table.element(a,numsignificant5/numgqtests)
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,numsignificant10)
a<-table.element(a,numsignificant10/numgqtests)
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}