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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 computationSat, 03 Nov 2012 09:54:54 -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/03/t1351950987bltcw5nqqsl3o06.htm/, Retrieved Sun, 03 Jul 2022 14:14:44 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=185724, Retrieved Sun, 03 Jul 2022 14:14:44 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact117
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  D    [Multiple Regression] [WS 7 multiple reg...] [2012-11-03 13:54:54] [074a00bbc2315ea54a3f557bcf69eecf] [Current]
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Dataseries X:
2514	2550	1512	1591	472	551
2537	2572	1517	1595	476	554
2564	2597	1525	1602	483	558
2595	2623	1540	1613	493	565
2617	2647	1547	1622	498	568
2638	2670	1547	1627	502	572
2657	2690	1547	1632	504	575
2668	2705	1547	1634	503	574
2683	2721	1546	1637	501	572
2687	2729	1533	1627	502	573
2705	2747	1538	1632	502	572
2717	2761	1543	1637	500	569
2728	2773	1549	1643	498	566
2741	2786	1556	1650	495	560
2752	2796	1559	1654	494	557
2759	2807	1559	1656	490	552
2767	2817	1563	1661	484	545
2774	2827	1563	1662	477	539
2781	2838	1564	1664	474	535
2788	2847	1564	1665	469	531
2789	2853	1557	1661	466	528
2795	2860	1554	1659	464	526
2798	2864	1552	1656	460	523
2801	2869	1552	1656	458	521
2803	2873	1551	1655	457	519
2808	2877	1552	1654	456	517
2813	2883	1554	1656	455	515
2826	2896	1567	1668	456	514
2835	2905	1572	1672	453	511
2849	2919	1579	1680	453	508
2862	2933	1588	1688	449	502
2877	2948	1597	1696	449	501
2888	2959	1603	1702	449	500
2897	2969	1607	1706	452	500
2902	2978	1607	1708	450	498
2911	2988	1609	1711	452	499
2917	2996	1612	1714	454	499
2924	3003	1615	1717	455	500
2930	3011	1619	1721	458	501
2935	3018	1622	1724	461	503
2945	3028	1628	1730	469	510
2957	3038	1634	1735	477	515
2967	3049	1640	1740	480	520
2980	3063	1648	1748	484	523
2997	3081	1657	1757	490	529
3017	3100	1668	1768	497	534
3040	3122	1678	1778	506	543
3064	3145	1687	1789	516	553
3085	3167	1700	1798	527	563
3113	3193	1714	1811	542	577




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time7 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 7 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185724&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]7 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185724&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185724&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 time7 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net







Multiple Linear Regression - Estimated Regression Equation
Waalsm[t] = -130.339838114327 + 0.034984131069266Vlaamsm[t] -0.206430397593295Vlaamsvr[t] + 1.33218483391395Waalsvr[t] + 0.334891195292699Brusselm[t] -0.352391285465773Brusselvr[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Waalsm[t] =  -130.339838114327 +  0.034984131069266Vlaamsm[t] -0.206430397593295Vlaamsvr[t] +  1.33218483391395Waalsvr[t] +  0.334891195292699Brusselm[t] -0.352391285465773Brusselvr[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185724&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Waalsm[t] =  -130.339838114327 +  0.034984131069266Vlaamsm[t] -0.206430397593295Vlaamsvr[t] +  1.33218483391395Waalsvr[t] +  0.334891195292699Brusselm[t] -0.352391285465773Brusselvr[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185724&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185724&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
Waalsm[t] = -130.339838114327 + 0.034984131069266Vlaamsm[t] -0.206430397593295Vlaamsvr[t] + 1.33218483391395Waalsvr[t] + 0.334891195292699Brusselm[t] -0.352391285465773Brusselvr[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-130.33983811432794.597577-1.37780.1752220.087611
Vlaamsm0.0349841310692660.1275840.27420.7852110.392605
Vlaamsvr-0.2064303975932950.114762-1.79880.0789180.039459
Waalsvr1.332184833913950.07181618.549900
Brusselm0.3348911952926990.2369571.41330.1646040.082302
Brusselvr-0.3523912854657730.211367-1.66720.1025780.051289

\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) & -130.339838114327 & 94.597577 & -1.3778 & 0.175222 & 0.087611 \tabularnewline
Vlaamsm & 0.034984131069266 & 0.127584 & 0.2742 & 0.785211 & 0.392605 \tabularnewline
Vlaamsvr & -0.206430397593295 & 0.114762 & -1.7988 & 0.078918 & 0.039459 \tabularnewline
Waalsvr & 1.33218483391395 & 0.071816 & 18.5499 & 0 & 0 \tabularnewline
Brusselm & 0.334891195292699 & 0.236957 & 1.4133 & 0.164604 & 0.082302 \tabularnewline
Brusselvr & -0.352391285465773 & 0.211367 & -1.6672 & 0.102578 & 0.051289 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185724&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]-130.339838114327[/C][C]94.597577[/C][C]-1.3778[/C][C]0.175222[/C][C]0.087611[/C][/ROW]
[ROW][C]Vlaamsm[/C][C]0.034984131069266[/C][C]0.127584[/C][C]0.2742[/C][C]0.785211[/C][C]0.392605[/C][/ROW]
[ROW][C]Vlaamsvr[/C][C]-0.206430397593295[/C][C]0.114762[/C][C]-1.7988[/C][C]0.078918[/C][C]0.039459[/C][/ROW]
[ROW][C]Waalsvr[/C][C]1.33218483391395[/C][C]0.071816[/C][C]18.5499[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Brusselm[/C][C]0.334891195292699[/C][C]0.236957[/C][C]1.4133[/C][C]0.164604[/C][C]0.082302[/C][/ROW]
[ROW][C]Brusselvr[/C][C]-0.352391285465773[/C][C]0.211367[/C][C]-1.6672[/C][C]0.102578[/C][C]0.051289[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185724&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185724&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)-130.33983811432794.597577-1.37780.1752220.087611
Vlaamsm0.0349841310692660.1275840.27420.7852110.392605
Vlaamsvr-0.2064303975932950.114762-1.79880.0789180.039459
Waalsvr1.332184833913950.07181618.549900
Brusselm0.3348911952926990.2369571.41330.1646040.082302
Brusselvr-0.3523912854657730.211367-1.66720.1025780.051289







Multiple Linear Regression - Regression Statistics
Multiple R0.99873293121104
R-squared0.997467467885395
Adjusted R-squared0.997179680145099
F-TEST (value)3465.98318211721
F-TEST (DF numerator)5
F-TEST (DF denominator)44
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.63857676401103
Sum Squared Residuals306.331842941472

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.99873293121104 \tabularnewline
R-squared & 0.997467467885395 \tabularnewline
Adjusted R-squared & 0.997179680145099 \tabularnewline
F-TEST (value) & 3465.98318211721 \tabularnewline
F-TEST (DF numerator) & 5 \tabularnewline
F-TEST (DF denominator) & 44 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.63857676401103 \tabularnewline
Sum Squared Residuals & 306.331842941472 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185724&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.99873293121104[/C][/ROW]
[ROW][C]R-squared[/C][C]0.997467467885395[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.997179680145099[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]3465.98318211721[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]5[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]44[/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]2.63857676401103[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]306.331842941472[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185724&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185724&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 R0.99873293121104
R-squared0.997467467885395
Adjusted R-squared0.997179680145099
F-TEST (value)3465.98318211721
F-TEST (DF numerator)5
F-TEST (DF denominator)44
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.63857676401103
Sum Squared Residuals306.331842941472







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
115121514.61987017451-2.61987017450808
215171516.494166702480.505833297520852
315251522.53794536412.4620546358996
415401533.791469217546.20853078245799
515471542.213736184124.78626381588153
615471544.79142760082.20857239919528
715471547.60105084301-0.601050843012691
815471547.57129007888-0.571290078876164
915461548.82472036551-2.82472036551043
1015331533.97386527973-0.973865279728585
1115381537.901147937330.0988520626684176
1215431542.479247579240.520752420761695
1315491548.767408719180.232591280823706
1415561556.97357521868-0.973575218678098
1515591561.34511868127-2.34511868126749
1615591562.40603453921-3.40603453921207
1715631567.73991960791-4.73991960790721
1815631567.02279872912-4.02279872911882
1915641568.06621449689-4.06621449689033
2015641567.52052383535-3.52052383534908
2115571561.04068651572-4.04068651572201
2215541557.1762090315-3.17620903150279
2315521552.17649440782-0.176494407822091
2415521551.284294993410.715705006590435
2515511549.56624820691.43375179310018
2615521547.95315381364.04684618640213
2715541549.923753126854.07624687314883
2815671564.368452149762.63154785023535
2915721568.206675357223.7933246427766
3015791577.52108015361.4789198464041
3115881586.518109894131.48189010587435
3215971594.956285853042.04371414695742
3316031601.415877210231.58412278977228
3416071605.999843335451.00015666454794
3516071607.01626026063-0.0162602606327763
3616091609.58075907118-0.580759071184693
3716121612.80555756918-0.805557569181171
3816151615.58448811508-0.584488115081739
3916191620.12397135682-1.12397135681909
4016221623.1503247457-1.15032474570075
4116281629.64136164803-1.64136164802533
4216341635.57496454951-1.57496454950602
4316401639.557712814790.442287185208588
4416481648.06235054847-0.0623505484707959
4516571656.825996584160.174003415843905
4616681668.83981676404-0.839816764042268
4716781678.26733055916-0.267330559164706
4816871688.8380828315-1.83808283150399
4917001697.180834635692.81916536430642
5017141710.201492741963.79850725804165

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 1512 & 1514.61987017451 & -2.61987017450808 \tabularnewline
2 & 1517 & 1516.49416670248 & 0.505833297520852 \tabularnewline
3 & 1525 & 1522.5379453641 & 2.4620546358996 \tabularnewline
4 & 1540 & 1533.79146921754 & 6.20853078245799 \tabularnewline
5 & 1547 & 1542.21373618412 & 4.78626381588153 \tabularnewline
6 & 1547 & 1544.7914276008 & 2.20857239919528 \tabularnewline
7 & 1547 & 1547.60105084301 & -0.601050843012691 \tabularnewline
8 & 1547 & 1547.57129007888 & -0.571290078876164 \tabularnewline
9 & 1546 & 1548.82472036551 & -2.82472036551043 \tabularnewline
10 & 1533 & 1533.97386527973 & -0.973865279728585 \tabularnewline
11 & 1538 & 1537.90114793733 & 0.0988520626684176 \tabularnewline
12 & 1543 & 1542.47924757924 & 0.520752420761695 \tabularnewline
13 & 1549 & 1548.76740871918 & 0.232591280823706 \tabularnewline
14 & 1556 & 1556.97357521868 & -0.973575218678098 \tabularnewline
15 & 1559 & 1561.34511868127 & -2.34511868126749 \tabularnewline
16 & 1559 & 1562.40603453921 & -3.40603453921207 \tabularnewline
17 & 1563 & 1567.73991960791 & -4.73991960790721 \tabularnewline
18 & 1563 & 1567.02279872912 & -4.02279872911882 \tabularnewline
19 & 1564 & 1568.06621449689 & -4.06621449689033 \tabularnewline
20 & 1564 & 1567.52052383535 & -3.52052383534908 \tabularnewline
21 & 1557 & 1561.04068651572 & -4.04068651572201 \tabularnewline
22 & 1554 & 1557.1762090315 & -3.17620903150279 \tabularnewline
23 & 1552 & 1552.17649440782 & -0.176494407822091 \tabularnewline
24 & 1552 & 1551.28429499341 & 0.715705006590435 \tabularnewline
25 & 1551 & 1549.5662482069 & 1.43375179310018 \tabularnewline
26 & 1552 & 1547.9531538136 & 4.04684618640213 \tabularnewline
27 & 1554 & 1549.92375312685 & 4.07624687314883 \tabularnewline
28 & 1567 & 1564.36845214976 & 2.63154785023535 \tabularnewline
29 & 1572 & 1568.20667535722 & 3.7933246427766 \tabularnewline
30 & 1579 & 1577.5210801536 & 1.4789198464041 \tabularnewline
31 & 1588 & 1586.51810989413 & 1.48189010587435 \tabularnewline
32 & 1597 & 1594.95628585304 & 2.04371414695742 \tabularnewline
33 & 1603 & 1601.41587721023 & 1.58412278977228 \tabularnewline
34 & 1607 & 1605.99984333545 & 1.00015666454794 \tabularnewline
35 & 1607 & 1607.01626026063 & -0.0162602606327763 \tabularnewline
36 & 1609 & 1609.58075907118 & -0.580759071184693 \tabularnewline
37 & 1612 & 1612.80555756918 & -0.805557569181171 \tabularnewline
38 & 1615 & 1615.58448811508 & -0.584488115081739 \tabularnewline
39 & 1619 & 1620.12397135682 & -1.12397135681909 \tabularnewline
40 & 1622 & 1623.1503247457 & -1.15032474570075 \tabularnewline
41 & 1628 & 1629.64136164803 & -1.64136164802533 \tabularnewline
42 & 1634 & 1635.57496454951 & -1.57496454950602 \tabularnewline
43 & 1640 & 1639.55771281479 & 0.442287185208588 \tabularnewline
44 & 1648 & 1648.06235054847 & -0.0623505484707959 \tabularnewline
45 & 1657 & 1656.82599658416 & 0.174003415843905 \tabularnewline
46 & 1668 & 1668.83981676404 & -0.839816764042268 \tabularnewline
47 & 1678 & 1678.26733055916 & -0.267330559164706 \tabularnewline
48 & 1687 & 1688.8380828315 & -1.83808283150399 \tabularnewline
49 & 1700 & 1697.18083463569 & 2.81916536430642 \tabularnewline
50 & 1714 & 1710.20149274196 & 3.79850725804165 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185724&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]1512[/C][C]1514.61987017451[/C][C]-2.61987017450808[/C][/ROW]
[ROW][C]2[/C][C]1517[/C][C]1516.49416670248[/C][C]0.505833297520852[/C][/ROW]
[ROW][C]3[/C][C]1525[/C][C]1522.5379453641[/C][C]2.4620546358996[/C][/ROW]
[ROW][C]4[/C][C]1540[/C][C]1533.79146921754[/C][C]6.20853078245799[/C][/ROW]
[ROW][C]5[/C][C]1547[/C][C]1542.21373618412[/C][C]4.78626381588153[/C][/ROW]
[ROW][C]6[/C][C]1547[/C][C]1544.7914276008[/C][C]2.20857239919528[/C][/ROW]
[ROW][C]7[/C][C]1547[/C][C]1547.60105084301[/C][C]-0.601050843012691[/C][/ROW]
[ROW][C]8[/C][C]1547[/C][C]1547.57129007888[/C][C]-0.571290078876164[/C][/ROW]
[ROW][C]9[/C][C]1546[/C][C]1548.82472036551[/C][C]-2.82472036551043[/C][/ROW]
[ROW][C]10[/C][C]1533[/C][C]1533.97386527973[/C][C]-0.973865279728585[/C][/ROW]
[ROW][C]11[/C][C]1538[/C][C]1537.90114793733[/C][C]0.0988520626684176[/C][/ROW]
[ROW][C]12[/C][C]1543[/C][C]1542.47924757924[/C][C]0.520752420761695[/C][/ROW]
[ROW][C]13[/C][C]1549[/C][C]1548.76740871918[/C][C]0.232591280823706[/C][/ROW]
[ROW][C]14[/C][C]1556[/C][C]1556.97357521868[/C][C]-0.973575218678098[/C][/ROW]
[ROW][C]15[/C][C]1559[/C][C]1561.34511868127[/C][C]-2.34511868126749[/C][/ROW]
[ROW][C]16[/C][C]1559[/C][C]1562.40603453921[/C][C]-3.40603453921207[/C][/ROW]
[ROW][C]17[/C][C]1563[/C][C]1567.73991960791[/C][C]-4.73991960790721[/C][/ROW]
[ROW][C]18[/C][C]1563[/C][C]1567.02279872912[/C][C]-4.02279872911882[/C][/ROW]
[ROW][C]19[/C][C]1564[/C][C]1568.06621449689[/C][C]-4.06621449689033[/C][/ROW]
[ROW][C]20[/C][C]1564[/C][C]1567.52052383535[/C][C]-3.52052383534908[/C][/ROW]
[ROW][C]21[/C][C]1557[/C][C]1561.04068651572[/C][C]-4.04068651572201[/C][/ROW]
[ROW][C]22[/C][C]1554[/C][C]1557.1762090315[/C][C]-3.17620903150279[/C][/ROW]
[ROW][C]23[/C][C]1552[/C][C]1552.17649440782[/C][C]-0.176494407822091[/C][/ROW]
[ROW][C]24[/C][C]1552[/C][C]1551.28429499341[/C][C]0.715705006590435[/C][/ROW]
[ROW][C]25[/C][C]1551[/C][C]1549.5662482069[/C][C]1.43375179310018[/C][/ROW]
[ROW][C]26[/C][C]1552[/C][C]1547.9531538136[/C][C]4.04684618640213[/C][/ROW]
[ROW][C]27[/C][C]1554[/C][C]1549.92375312685[/C][C]4.07624687314883[/C][/ROW]
[ROW][C]28[/C][C]1567[/C][C]1564.36845214976[/C][C]2.63154785023535[/C][/ROW]
[ROW][C]29[/C][C]1572[/C][C]1568.20667535722[/C][C]3.7933246427766[/C][/ROW]
[ROW][C]30[/C][C]1579[/C][C]1577.5210801536[/C][C]1.4789198464041[/C][/ROW]
[ROW][C]31[/C][C]1588[/C][C]1586.51810989413[/C][C]1.48189010587435[/C][/ROW]
[ROW][C]32[/C][C]1597[/C][C]1594.95628585304[/C][C]2.04371414695742[/C][/ROW]
[ROW][C]33[/C][C]1603[/C][C]1601.41587721023[/C][C]1.58412278977228[/C][/ROW]
[ROW][C]34[/C][C]1607[/C][C]1605.99984333545[/C][C]1.00015666454794[/C][/ROW]
[ROW][C]35[/C][C]1607[/C][C]1607.01626026063[/C][C]-0.0162602606327763[/C][/ROW]
[ROW][C]36[/C][C]1609[/C][C]1609.58075907118[/C][C]-0.580759071184693[/C][/ROW]
[ROW][C]37[/C][C]1612[/C][C]1612.80555756918[/C][C]-0.805557569181171[/C][/ROW]
[ROW][C]38[/C][C]1615[/C][C]1615.58448811508[/C][C]-0.584488115081739[/C][/ROW]
[ROW][C]39[/C][C]1619[/C][C]1620.12397135682[/C][C]-1.12397135681909[/C][/ROW]
[ROW][C]40[/C][C]1622[/C][C]1623.1503247457[/C][C]-1.15032474570075[/C][/ROW]
[ROW][C]41[/C][C]1628[/C][C]1629.64136164803[/C][C]-1.64136164802533[/C][/ROW]
[ROW][C]42[/C][C]1634[/C][C]1635.57496454951[/C][C]-1.57496454950602[/C][/ROW]
[ROW][C]43[/C][C]1640[/C][C]1639.55771281479[/C][C]0.442287185208588[/C][/ROW]
[ROW][C]44[/C][C]1648[/C][C]1648.06235054847[/C][C]-0.0623505484707959[/C][/ROW]
[ROW][C]45[/C][C]1657[/C][C]1656.82599658416[/C][C]0.174003415843905[/C][/ROW]
[ROW][C]46[/C][C]1668[/C][C]1668.83981676404[/C][C]-0.839816764042268[/C][/ROW]
[ROW][C]47[/C][C]1678[/C][C]1678.26733055916[/C][C]-0.267330559164706[/C][/ROW]
[ROW][C]48[/C][C]1687[/C][C]1688.8380828315[/C][C]-1.83808283150399[/C][/ROW]
[ROW][C]49[/C][C]1700[/C][C]1697.18083463569[/C][C]2.81916536430642[/C][/ROW]
[ROW][C]50[/C][C]1714[/C][C]1710.20149274196[/C][C]3.79850725804165[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185724&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185724&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
115121514.61987017451-2.61987017450808
215171516.494166702480.505833297520852
315251522.53794536412.4620546358996
415401533.791469217546.20853078245799
515471542.213736184124.78626381588153
615471544.79142760082.20857239919528
715471547.60105084301-0.601050843012691
815471547.57129007888-0.571290078876164
915461548.82472036551-2.82472036551043
1015331533.97386527973-0.973865279728585
1115381537.901147937330.0988520626684176
1215431542.479247579240.520752420761695
1315491548.767408719180.232591280823706
1415561556.97357521868-0.973575218678098
1515591561.34511868127-2.34511868126749
1615591562.40603453921-3.40603453921207
1715631567.73991960791-4.73991960790721
1815631567.02279872912-4.02279872911882
1915641568.06621449689-4.06621449689033
2015641567.52052383535-3.52052383534908
2115571561.04068651572-4.04068651572201
2215541557.1762090315-3.17620903150279
2315521552.17649440782-0.176494407822091
2415521551.284294993410.715705006590435
2515511549.56624820691.43375179310018
2615521547.95315381364.04684618640213
2715541549.923753126854.07624687314883
2815671564.368452149762.63154785023535
2915721568.206675357223.7933246427766
3015791577.52108015361.4789198464041
3115881586.518109894131.48189010587435
3215971594.956285853042.04371414695742
3316031601.415877210231.58412278977228
3416071605.999843335451.00015666454794
3516071607.01626026063-0.0162602606327763
3616091609.58075907118-0.580759071184693
3716121612.80555756918-0.805557569181171
3816151615.58448811508-0.584488115081739
3916191620.12397135682-1.12397135681909
4016221623.1503247457-1.15032474570075
4116281629.64136164803-1.64136164802533
4216341635.57496454951-1.57496454950602
4316401639.557712814790.442287185208588
4416481648.06235054847-0.0623505484707959
4516571656.825996584160.174003415843905
4616681668.83981676404-0.839816764042268
4716781678.26733055916-0.267330559164706
4816871688.8380828315-1.83808283150399
4917001697.180834635692.81916536430642
5017141710.201492741963.79850725804165







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
90.3682156457347270.7364312914694540.631784354265273
100.3030028463354390.6060056926708780.696997153664561
110.1939501932053030.3879003864106060.806049806794697
120.1636525230575570.3273050461151150.836347476942443
130.1610937641572180.3221875283144370.838906235842781
140.572705039282450.8545899214351010.42729496071755
150.8642373466884170.2715253066231650.135762653311583
160.82400429341660.35199141316680.1759957065834
170.7497665429663460.5004669140673090.250233457033654
180.7740211657853980.4519576684292030.225978834214602
190.8749821714816060.2500356570367880.125017828518394
200.9313953906758840.1372092186482330.0686046093241163
210.9451518302298760.1096963395402490.0548481697701244
220.9589243547131480.08215129057370340.0410756452868517
230.9798284837759350.04034303244812990.0201715162240649
240.992130497356110.01573900528778090.00786950264389047
250.9977693927727860.004461214454427810.00223060722721391
260.9965204203719040.006959159256192110.00347957962809605
270.9942045531679680.01159089366406320.0057954468320316
280.9966848086597260.006630382680548410.00331519134027421
290.9979114909349230.004177018130153410.0020885090650767
300.9987091484018580.002581703196283290.00129085159814164
310.9994827581779230.001034483644154790.000517241822077394
320.9995273914621970.0009452170756058140.000472608537802907
330.9989816762446760.002036647510648840.00101832375532442
340.998492825309450.003014349381099490.00150717469054974
350.9968325626558470.006334874688305180.00316743734415259
360.9918653459274720.01626930814505570.00813465407252785
370.9799994965570870.04000100688582580.0200005034429129
380.9670314603227360.06593707935452870.0329685396772644
390.9785535249743930.04289295005121410.021446475025607
400.9708568156487350.05828636870252910.0291431843512646
410.9175891971145310.1648216057709380.0824108028854691

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
9 & 0.368215645734727 & 0.736431291469454 & 0.631784354265273 \tabularnewline
10 & 0.303002846335439 & 0.606005692670878 & 0.696997153664561 \tabularnewline
11 & 0.193950193205303 & 0.387900386410606 & 0.806049806794697 \tabularnewline
12 & 0.163652523057557 & 0.327305046115115 & 0.836347476942443 \tabularnewline
13 & 0.161093764157218 & 0.322187528314437 & 0.838906235842781 \tabularnewline
14 & 0.57270503928245 & 0.854589921435101 & 0.42729496071755 \tabularnewline
15 & 0.864237346688417 & 0.271525306623165 & 0.135762653311583 \tabularnewline
16 & 0.8240042934166 & 0.3519914131668 & 0.1759957065834 \tabularnewline
17 & 0.749766542966346 & 0.500466914067309 & 0.250233457033654 \tabularnewline
18 & 0.774021165785398 & 0.451957668429203 & 0.225978834214602 \tabularnewline
19 & 0.874982171481606 & 0.250035657036788 & 0.125017828518394 \tabularnewline
20 & 0.931395390675884 & 0.137209218648233 & 0.0686046093241163 \tabularnewline
21 & 0.945151830229876 & 0.109696339540249 & 0.0548481697701244 \tabularnewline
22 & 0.958924354713148 & 0.0821512905737034 & 0.0410756452868517 \tabularnewline
23 & 0.979828483775935 & 0.0403430324481299 & 0.0201715162240649 \tabularnewline
24 & 0.99213049735611 & 0.0157390052877809 & 0.00786950264389047 \tabularnewline
25 & 0.997769392772786 & 0.00446121445442781 & 0.00223060722721391 \tabularnewline
26 & 0.996520420371904 & 0.00695915925619211 & 0.00347957962809605 \tabularnewline
27 & 0.994204553167968 & 0.0115908936640632 & 0.0057954468320316 \tabularnewline
28 & 0.996684808659726 & 0.00663038268054841 & 0.00331519134027421 \tabularnewline
29 & 0.997911490934923 & 0.00417701813015341 & 0.0020885090650767 \tabularnewline
30 & 0.998709148401858 & 0.00258170319628329 & 0.00129085159814164 \tabularnewline
31 & 0.999482758177923 & 0.00103448364415479 & 0.000517241822077394 \tabularnewline
32 & 0.999527391462197 & 0.000945217075605814 & 0.000472608537802907 \tabularnewline
33 & 0.998981676244676 & 0.00203664751064884 & 0.00101832375532442 \tabularnewline
34 & 0.99849282530945 & 0.00301434938109949 & 0.00150717469054974 \tabularnewline
35 & 0.996832562655847 & 0.00633487468830518 & 0.00316743734415259 \tabularnewline
36 & 0.991865345927472 & 0.0162693081450557 & 0.00813465407252785 \tabularnewline
37 & 0.979999496557087 & 0.0400010068858258 & 0.0200005034429129 \tabularnewline
38 & 0.967031460322736 & 0.0659370793545287 & 0.0329685396772644 \tabularnewline
39 & 0.978553524974393 & 0.0428929500512141 & 0.021446475025607 \tabularnewline
40 & 0.970856815648735 & 0.0582863687025291 & 0.0291431843512646 \tabularnewline
41 & 0.917589197114531 & 0.164821605770938 & 0.0824108028854691 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185724&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]9[/C][C]0.368215645734727[/C][C]0.736431291469454[/C][C]0.631784354265273[/C][/ROW]
[ROW][C]10[/C][C]0.303002846335439[/C][C]0.606005692670878[/C][C]0.696997153664561[/C][/ROW]
[ROW][C]11[/C][C]0.193950193205303[/C][C]0.387900386410606[/C][C]0.806049806794697[/C][/ROW]
[ROW][C]12[/C][C]0.163652523057557[/C][C]0.327305046115115[/C][C]0.836347476942443[/C][/ROW]
[ROW][C]13[/C][C]0.161093764157218[/C][C]0.322187528314437[/C][C]0.838906235842781[/C][/ROW]
[ROW][C]14[/C][C]0.57270503928245[/C][C]0.854589921435101[/C][C]0.42729496071755[/C][/ROW]
[ROW][C]15[/C][C]0.864237346688417[/C][C]0.271525306623165[/C][C]0.135762653311583[/C][/ROW]
[ROW][C]16[/C][C]0.8240042934166[/C][C]0.3519914131668[/C][C]0.1759957065834[/C][/ROW]
[ROW][C]17[/C][C]0.749766542966346[/C][C]0.500466914067309[/C][C]0.250233457033654[/C][/ROW]
[ROW][C]18[/C][C]0.774021165785398[/C][C]0.451957668429203[/C][C]0.225978834214602[/C][/ROW]
[ROW][C]19[/C][C]0.874982171481606[/C][C]0.250035657036788[/C][C]0.125017828518394[/C][/ROW]
[ROW][C]20[/C][C]0.931395390675884[/C][C]0.137209218648233[/C][C]0.0686046093241163[/C][/ROW]
[ROW][C]21[/C][C]0.945151830229876[/C][C]0.109696339540249[/C][C]0.0548481697701244[/C][/ROW]
[ROW][C]22[/C][C]0.958924354713148[/C][C]0.0821512905737034[/C][C]0.0410756452868517[/C][/ROW]
[ROW][C]23[/C][C]0.979828483775935[/C][C]0.0403430324481299[/C][C]0.0201715162240649[/C][/ROW]
[ROW][C]24[/C][C]0.99213049735611[/C][C]0.0157390052877809[/C][C]0.00786950264389047[/C][/ROW]
[ROW][C]25[/C][C]0.997769392772786[/C][C]0.00446121445442781[/C][C]0.00223060722721391[/C][/ROW]
[ROW][C]26[/C][C]0.996520420371904[/C][C]0.00695915925619211[/C][C]0.00347957962809605[/C][/ROW]
[ROW][C]27[/C][C]0.994204553167968[/C][C]0.0115908936640632[/C][C]0.0057954468320316[/C][/ROW]
[ROW][C]28[/C][C]0.996684808659726[/C][C]0.00663038268054841[/C][C]0.00331519134027421[/C][/ROW]
[ROW][C]29[/C][C]0.997911490934923[/C][C]0.00417701813015341[/C][C]0.0020885090650767[/C][/ROW]
[ROW][C]30[/C][C]0.998709148401858[/C][C]0.00258170319628329[/C][C]0.00129085159814164[/C][/ROW]
[ROW][C]31[/C][C]0.999482758177923[/C][C]0.00103448364415479[/C][C]0.000517241822077394[/C][/ROW]
[ROW][C]32[/C][C]0.999527391462197[/C][C]0.000945217075605814[/C][C]0.000472608537802907[/C][/ROW]
[ROW][C]33[/C][C]0.998981676244676[/C][C]0.00203664751064884[/C][C]0.00101832375532442[/C][/ROW]
[ROW][C]34[/C][C]0.99849282530945[/C][C]0.00301434938109949[/C][C]0.00150717469054974[/C][/ROW]
[ROW][C]35[/C][C]0.996832562655847[/C][C]0.00633487468830518[/C][C]0.00316743734415259[/C][/ROW]
[ROW][C]36[/C][C]0.991865345927472[/C][C]0.0162693081450557[/C][C]0.00813465407252785[/C][/ROW]
[ROW][C]37[/C][C]0.979999496557087[/C][C]0.0400010068858258[/C][C]0.0200005034429129[/C][/ROW]
[ROW][C]38[/C][C]0.967031460322736[/C][C]0.0659370793545287[/C][C]0.0329685396772644[/C][/ROW]
[ROW][C]39[/C][C]0.978553524974393[/C][C]0.0428929500512141[/C][C]0.021446475025607[/C][/ROW]
[ROW][C]40[/C][C]0.970856815648735[/C][C]0.0582863687025291[/C][C]0.0291431843512646[/C][/ROW]
[ROW][C]41[/C][C]0.917589197114531[/C][C]0.164821605770938[/C][C]0.0824108028854691[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185724&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185724&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
90.3682156457347270.7364312914694540.631784354265273
100.3030028463354390.6060056926708780.696997153664561
110.1939501932053030.3879003864106060.806049806794697
120.1636525230575570.3273050461151150.836347476942443
130.1610937641572180.3221875283144370.838906235842781
140.572705039282450.8545899214351010.42729496071755
150.8642373466884170.2715253066231650.135762653311583
160.82400429341660.35199141316680.1759957065834
170.7497665429663460.5004669140673090.250233457033654
180.7740211657853980.4519576684292030.225978834214602
190.8749821714816060.2500356570367880.125017828518394
200.9313953906758840.1372092186482330.0686046093241163
210.9451518302298760.1096963395402490.0548481697701244
220.9589243547131480.08215129057370340.0410756452868517
230.9798284837759350.04034303244812990.0201715162240649
240.992130497356110.01573900528778090.00786950264389047
250.9977693927727860.004461214454427810.00223060722721391
260.9965204203719040.006959159256192110.00347957962809605
270.9942045531679680.01159089366406320.0057954468320316
280.9966848086597260.006630382680548410.00331519134027421
290.9979114909349230.004177018130153410.0020885090650767
300.9987091484018580.002581703196283290.00129085159814164
310.9994827581779230.001034483644154790.000517241822077394
320.9995273914621970.0009452170756058140.000472608537802907
330.9989816762446760.002036647510648840.00101832375532442
340.998492825309450.003014349381099490.00150717469054974
350.9968325626558470.006334874688305180.00316743734415259
360.9918653459274720.01626930814505570.00813465407252785
370.9799994965570870.04000100688582580.0200005034429129
380.9670314603227360.06593707935452870.0329685396772644
390.9785535249743930.04289295005121410.021446475025607
400.9708568156487350.05828636870252910.0291431843512646
410.9175891971145310.1648216057709380.0824108028854691







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level100.303030303030303NOK
5% type I error level160.484848484848485NOK
10% type I error level190.575757575757576NOK

\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 & 10 & 0.303030303030303 & NOK \tabularnewline
5% type I error level & 16 & 0.484848484848485 & NOK \tabularnewline
10% type I error level & 19 & 0.575757575757576 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185724&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]10[/C][C]0.303030303030303[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]16[/C][C]0.484848484848485[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]19[/C][C]0.575757575757576[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185724&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185724&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 level100.303030303030303NOK
5% type I error level160.484848484848485NOK
10% type I error level190.575757575757576NOK



Parameters (Session):
par1 = 3 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 3 ; 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')
}