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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 computationThu, 24 Nov 2011 17:57:48 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Nov/24/t1322175496i6jehajax6rdg5p.htm/, Retrieved Thu, 28 Mar 2024 18:24:57 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=147232, Retrieved Thu, 28 Mar 2024 18:24:57 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact68
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [Ws7] [2011-11-24 22:57:48] [8ccb8599b802845e9be7b9afcc742f78] [Current]
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Dataseries X:
1	162556	1081		213118	6282929
1	29790	309		81767	4324047
1	87550	458		153198	4108272
0	84738	588		-26007	-1212617
1	54660	299		126942	1485329
1	42634	156		157214	1779876
0	40949	481		129352	1367203
1	42312	323		234817	2519076
1	37704	452		60448	912684
1	16275	109		47818	1443586
0	25830	115		245546	1220017
0	12679	110		48020	984885
1	18014	239		-1710	1457425
0	43556	247		32648	-572920
1	24524	497		95350	929144
0	6532	103		151352	1151176
0	7123	109		288170	790090
1	20813	502		114337	774497
1	37597	248		37884	990576
0	17821	373		122844	454195
1	12988	119		82340	876607
1	22330	84		79801	711969
0	13326	102		165548	702380
0	16189	295		116384	264449
0	7146	105		134028	450033
0	15824	64		63838	541063
1	26088	267		74996	588864
0	11326	129		31080	-37216
0	8568	37		32168	783310
0	14416	361		49857	467359
1	3369	28		87161	688779
1	11819	85		106113	608419
1	6620	44		80570	696348
1	4519	49		102129	597793
0	2220	22		301670	821730
0	18562	155		102313	377934
0	10327	91		88577	651939
1	5336	81		112477	697458
1	2365	79		191778	700368
0	4069	145		79804	225986
0	7710	816		128294	348695
0	13718	61		96448	373683
0	4525	226		93811	501709
0	6869	105		117520	413743
0	4628	62		69159	379825
1	3653	24		101792	336260
1	1265	26		210568	636765
1	7489	322		136996	481231
0	4901	84		121920	469107




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 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 & 3 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=147232&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]3 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=147232&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147232&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 time3 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
Group[t] = + 0.487205492139385 -9.87926596117904e-07Costs[t] -0.000234395473004616Trades[t] -1.30804671140543e-06Dividends[t] + 2.34279226279435e-07Wealth[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Group[t] =  +  0.487205492139385 -9.87926596117904e-07Costs[t] -0.000234395473004616Trades[t] -1.30804671140543e-06Dividends[t] +  2.34279226279435e-07Wealth[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147232&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Group[t] =  +  0.487205492139385 -9.87926596117904e-07Costs[t] -0.000234395473004616Trades[t] -1.30804671140543e-06Dividends[t] +  2.34279226279435e-07Wealth[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147232&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147232&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
Group[t] = + 0.487205492139385 -9.87926596117904e-07Costs[t] -0.000234395473004616Trades[t] -1.30804671140543e-06Dividends[t] + 2.34279226279435e-07Wealth[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)0.4872054921393850.1490393.2690.0020990.00105
Costs-9.87926596117904e-074e-06-0.23050.8187490.409374
Trades-0.0002343954730046160.000468-0.5010.6188890.309444
Dividends-1.30804671140543e-061e-06-1.19490.2385110.119255
Wealth2.34279226279435e-0702.84440.0067270.003364

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Ordinary Least Squares \tabularnewline
Variable & Parameter & S.D. & T-STATH0: parameter = 0 & 2-tail p-value & 1-tail p-value \tabularnewline
(Intercept) & 0.487205492139385 & 0.149039 & 3.269 & 0.002099 & 0.00105 \tabularnewline
Costs & -9.87926596117904e-07 & 4e-06 & -0.2305 & 0.818749 & 0.409374 \tabularnewline
Trades & -0.000234395473004616 & 0.000468 & -0.501 & 0.618889 & 0.309444 \tabularnewline
Dividends & -1.30804671140543e-06 & 1e-06 & -1.1949 & 0.238511 & 0.119255 \tabularnewline
Wealth & 2.34279226279435e-07 & 0 & 2.8444 & 0.006727 & 0.003364 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147232&T=2

[TABLE]
[ROW][C]Multiple Linear Regression - Ordinary Least Squares[/C][/ROW]
[ROW][C]Variable[/C][C]Parameter[/C][C]S.D.[/C][C]T-STATH0: parameter = 0[/C][C]2-tail p-value[/C][C]1-tail p-value[/C][/ROW]
[ROW][C](Intercept)[/C][C]0.487205492139385[/C][C]0.149039[/C][C]3.269[/C][C]0.002099[/C][C]0.00105[/C][/ROW]
[ROW][C]Costs[/C][C]-9.87926596117904e-07[/C][C]4e-06[/C][C]-0.2305[/C][C]0.818749[/C][C]0.409374[/C][/ROW]
[ROW][C]Trades[/C][C]-0.000234395473004616[/C][C]0.000468[/C][C]-0.501[/C][C]0.618889[/C][C]0.309444[/C][/ROW]
[ROW][C]Dividends[/C][C]-1.30804671140543e-06[/C][C]1e-06[/C][C]-1.1949[/C][C]0.238511[/C][C]0.119255[/C][/ROW]
[ROW][C]Wealth[/C][C]2.34279226279435e-07[/C][C]0[/C][C]2.8444[/C][C]0.006727[/C][C]0.003364[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147232&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147232&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)0.4872054921393850.1490393.2690.0020990.00105
Costs-9.87926596117904e-074e-06-0.23050.8187490.409374
Trades-0.0002343954730046160.000468-0.5010.6188890.309444
Dividends-1.30804671140543e-061e-06-1.19490.2385110.119255
Wealth2.34279226279435e-0702.84440.0067270.003364







Multiple Linear Regression - Regression Statistics
Multiple R0.450228000079615
R-squared0.20270525205569
Adjusted R-squared0.13022391133348
F-TEST (value)2.79665428420498
F-TEST (DF numerator)4
F-TEST (DF denominator)44
p-value0.0373912495309497
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.471043156485764
Sum Squared Residuals9.76279283197115

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.450228000079615 \tabularnewline
R-squared & 0.20270525205569 \tabularnewline
Adjusted R-squared & 0.13022391133348 \tabularnewline
F-TEST (value) & 2.79665428420498 \tabularnewline
F-TEST (DF numerator) & 4 \tabularnewline
F-TEST (DF denominator) & 44 \tabularnewline
p-value & 0.0373912495309497 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.471043156485764 \tabularnewline
Sum Squared Residuals & 9.76279283197115 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147232&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.450228000079615[/C][/ROW]
[ROW][C]R-squared[/C][C]0.20270525205569[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.13022391133348[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]2.79665428420498[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]4[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]44[/C][/ROW]
[ROW][C]p-value[/C][C]0.0373912495309497[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.471043156485764[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]9.76279283197115[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147232&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147232&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 R0.450228000079615
R-squared0.20270525205569
Adjusted R-squared0.13022391133348
F-TEST (value)2.79665428420498
F-TEST (DF numerator)4
F-TEST (DF denominator)44
p-value0.0373912495309497
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.471043156485764
Sum Squared Residuals9.76279283197115







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
111.26642203591018-0.266422035910177
211.29142628778703-0.291426287787032
311.05545203742473-0.0554520374247277
400.015593428401063-0.015593428401063
510.545056841218380.45494315878162
610.6198652523182170.380134747681783
700.48511546623294-0.48511546623294
810.6527101758109590.347289824189041
910.4787640477078540.521235952292146
1010.7212319137319030.278768086268097
1100.399370869775129-0.399370869775129
1200.616821761489231-0.616821761489231
1310.7570696256256230.242930374374377
1400.209351316132755-0.209351316132755
1510.4394399137025670.560560086297433
1600.528330758620888-0.528330758620888
1700.26278123750315-0.26278123750315
1810.3808676695198470.619132330480153
1910.574449675836090.42555032416391
2000.327892903803345-0.327892903803345
2110.544147483715470.45585251628453
2210.5078719823537630.492128017646237
2300.398140570047451-0.398140570047451
2400.311784482588631-0.311784482588631
2500.385652742422008-0.385652742422008
2600.49982796645585-0.49982796645585
2710.4387092029428810.561290797057119
2800.396406192018462-0.396406192018462
2900.611504318687131-0.611504318687131
3000.432623996599273-0.432623996599273
3110.5246700459776490.475329954022351
3210.4593447443808180.540655255619182
3310.5281025643841780.471897435615822
3410.4777166526004390.522283347399561
3500.277771411870826-0.277771411870826
3600.387248202267197-0.387248202267197
3700.492546097083085-0.492546097083085
3810.479222633152780.52077736684722
3910.3795788943031660.620421105696834
4000.397754740707097-0.397754740707097
4100.202199322126009-0.202199322126009
4200.420742666132705-0.420742666132705
4300.424592573686683-0.424592573686683
4400.399017640079333-0.399017640079333
4500.466622753133764-0.466622753133764
4610.4236011467129970.576398853287003
4710.3536095142917820.646390485708218
4810.3378768266195520.662123173380448
4900.41309941410714-0.41309941410714

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 1 & 1.26642203591018 & -0.266422035910177 \tabularnewline
2 & 1 & 1.29142628778703 & -0.291426287787032 \tabularnewline
3 & 1 & 1.05545203742473 & -0.0554520374247277 \tabularnewline
4 & 0 & 0.015593428401063 & -0.015593428401063 \tabularnewline
5 & 1 & 0.54505684121838 & 0.45494315878162 \tabularnewline
6 & 1 & 0.619865252318217 & 0.380134747681783 \tabularnewline
7 & 0 & 0.48511546623294 & -0.48511546623294 \tabularnewline
8 & 1 & 0.652710175810959 & 0.347289824189041 \tabularnewline
9 & 1 & 0.478764047707854 & 0.521235952292146 \tabularnewline
10 & 1 & 0.721231913731903 & 0.278768086268097 \tabularnewline
11 & 0 & 0.399370869775129 & -0.399370869775129 \tabularnewline
12 & 0 & 0.616821761489231 & -0.616821761489231 \tabularnewline
13 & 1 & 0.757069625625623 & 0.242930374374377 \tabularnewline
14 & 0 & 0.209351316132755 & -0.209351316132755 \tabularnewline
15 & 1 & 0.439439913702567 & 0.560560086297433 \tabularnewline
16 & 0 & 0.528330758620888 & -0.528330758620888 \tabularnewline
17 & 0 & 0.26278123750315 & -0.26278123750315 \tabularnewline
18 & 1 & 0.380867669519847 & 0.619132330480153 \tabularnewline
19 & 1 & 0.57444967583609 & 0.42555032416391 \tabularnewline
20 & 0 & 0.327892903803345 & -0.327892903803345 \tabularnewline
21 & 1 & 0.54414748371547 & 0.45585251628453 \tabularnewline
22 & 1 & 0.507871982353763 & 0.492128017646237 \tabularnewline
23 & 0 & 0.398140570047451 & -0.398140570047451 \tabularnewline
24 & 0 & 0.311784482588631 & -0.311784482588631 \tabularnewline
25 & 0 & 0.385652742422008 & -0.385652742422008 \tabularnewline
26 & 0 & 0.49982796645585 & -0.49982796645585 \tabularnewline
27 & 1 & 0.438709202942881 & 0.561290797057119 \tabularnewline
28 & 0 & 0.396406192018462 & -0.396406192018462 \tabularnewline
29 & 0 & 0.611504318687131 & -0.611504318687131 \tabularnewline
30 & 0 & 0.432623996599273 & -0.432623996599273 \tabularnewline
31 & 1 & 0.524670045977649 & 0.475329954022351 \tabularnewline
32 & 1 & 0.459344744380818 & 0.540655255619182 \tabularnewline
33 & 1 & 0.528102564384178 & 0.471897435615822 \tabularnewline
34 & 1 & 0.477716652600439 & 0.522283347399561 \tabularnewline
35 & 0 & 0.277771411870826 & -0.277771411870826 \tabularnewline
36 & 0 & 0.387248202267197 & -0.387248202267197 \tabularnewline
37 & 0 & 0.492546097083085 & -0.492546097083085 \tabularnewline
38 & 1 & 0.47922263315278 & 0.52077736684722 \tabularnewline
39 & 1 & 0.379578894303166 & 0.620421105696834 \tabularnewline
40 & 0 & 0.397754740707097 & -0.397754740707097 \tabularnewline
41 & 0 & 0.202199322126009 & -0.202199322126009 \tabularnewline
42 & 0 & 0.420742666132705 & -0.420742666132705 \tabularnewline
43 & 0 & 0.424592573686683 & -0.424592573686683 \tabularnewline
44 & 0 & 0.399017640079333 & -0.399017640079333 \tabularnewline
45 & 0 & 0.466622753133764 & -0.466622753133764 \tabularnewline
46 & 1 & 0.423601146712997 & 0.576398853287003 \tabularnewline
47 & 1 & 0.353609514291782 & 0.646390485708218 \tabularnewline
48 & 1 & 0.337876826619552 & 0.662123173380448 \tabularnewline
49 & 0 & 0.41309941410714 & -0.41309941410714 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147232&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]1[/C][C]1.26642203591018[/C][C]-0.266422035910177[/C][/ROW]
[ROW][C]2[/C][C]1[/C][C]1.29142628778703[/C][C]-0.291426287787032[/C][/ROW]
[ROW][C]3[/C][C]1[/C][C]1.05545203742473[/C][C]-0.0554520374247277[/C][/ROW]
[ROW][C]4[/C][C]0[/C][C]0.015593428401063[/C][C]-0.015593428401063[/C][/ROW]
[ROW][C]5[/C][C]1[/C][C]0.54505684121838[/C][C]0.45494315878162[/C][/ROW]
[ROW][C]6[/C][C]1[/C][C]0.619865252318217[/C][C]0.380134747681783[/C][/ROW]
[ROW][C]7[/C][C]0[/C][C]0.48511546623294[/C][C]-0.48511546623294[/C][/ROW]
[ROW][C]8[/C][C]1[/C][C]0.652710175810959[/C][C]0.347289824189041[/C][/ROW]
[ROW][C]9[/C][C]1[/C][C]0.478764047707854[/C][C]0.521235952292146[/C][/ROW]
[ROW][C]10[/C][C]1[/C][C]0.721231913731903[/C][C]0.278768086268097[/C][/ROW]
[ROW][C]11[/C][C]0[/C][C]0.399370869775129[/C][C]-0.399370869775129[/C][/ROW]
[ROW][C]12[/C][C]0[/C][C]0.616821761489231[/C][C]-0.616821761489231[/C][/ROW]
[ROW][C]13[/C][C]1[/C][C]0.757069625625623[/C][C]0.242930374374377[/C][/ROW]
[ROW][C]14[/C][C]0[/C][C]0.209351316132755[/C][C]-0.209351316132755[/C][/ROW]
[ROW][C]15[/C][C]1[/C][C]0.439439913702567[/C][C]0.560560086297433[/C][/ROW]
[ROW][C]16[/C][C]0[/C][C]0.528330758620888[/C][C]-0.528330758620888[/C][/ROW]
[ROW][C]17[/C][C]0[/C][C]0.26278123750315[/C][C]-0.26278123750315[/C][/ROW]
[ROW][C]18[/C][C]1[/C][C]0.380867669519847[/C][C]0.619132330480153[/C][/ROW]
[ROW][C]19[/C][C]1[/C][C]0.57444967583609[/C][C]0.42555032416391[/C][/ROW]
[ROW][C]20[/C][C]0[/C][C]0.327892903803345[/C][C]-0.327892903803345[/C][/ROW]
[ROW][C]21[/C][C]1[/C][C]0.54414748371547[/C][C]0.45585251628453[/C][/ROW]
[ROW][C]22[/C][C]1[/C][C]0.507871982353763[/C][C]0.492128017646237[/C][/ROW]
[ROW][C]23[/C][C]0[/C][C]0.398140570047451[/C][C]-0.398140570047451[/C][/ROW]
[ROW][C]24[/C][C]0[/C][C]0.311784482588631[/C][C]-0.311784482588631[/C][/ROW]
[ROW][C]25[/C][C]0[/C][C]0.385652742422008[/C][C]-0.385652742422008[/C][/ROW]
[ROW][C]26[/C][C]0[/C][C]0.49982796645585[/C][C]-0.49982796645585[/C][/ROW]
[ROW][C]27[/C][C]1[/C][C]0.438709202942881[/C][C]0.561290797057119[/C][/ROW]
[ROW][C]28[/C][C]0[/C][C]0.396406192018462[/C][C]-0.396406192018462[/C][/ROW]
[ROW][C]29[/C][C]0[/C][C]0.611504318687131[/C][C]-0.611504318687131[/C][/ROW]
[ROW][C]30[/C][C]0[/C][C]0.432623996599273[/C][C]-0.432623996599273[/C][/ROW]
[ROW][C]31[/C][C]1[/C][C]0.524670045977649[/C][C]0.475329954022351[/C][/ROW]
[ROW][C]32[/C][C]1[/C][C]0.459344744380818[/C][C]0.540655255619182[/C][/ROW]
[ROW][C]33[/C][C]1[/C][C]0.528102564384178[/C][C]0.471897435615822[/C][/ROW]
[ROW][C]34[/C][C]1[/C][C]0.477716652600439[/C][C]0.522283347399561[/C][/ROW]
[ROW][C]35[/C][C]0[/C][C]0.277771411870826[/C][C]-0.277771411870826[/C][/ROW]
[ROW][C]36[/C][C]0[/C][C]0.387248202267197[/C][C]-0.387248202267197[/C][/ROW]
[ROW][C]37[/C][C]0[/C][C]0.492546097083085[/C][C]-0.492546097083085[/C][/ROW]
[ROW][C]38[/C][C]1[/C][C]0.47922263315278[/C][C]0.52077736684722[/C][/ROW]
[ROW][C]39[/C][C]1[/C][C]0.379578894303166[/C][C]0.620421105696834[/C][/ROW]
[ROW][C]40[/C][C]0[/C][C]0.397754740707097[/C][C]-0.397754740707097[/C][/ROW]
[ROW][C]41[/C][C]0[/C][C]0.202199322126009[/C][C]-0.202199322126009[/C][/ROW]
[ROW][C]42[/C][C]0[/C][C]0.420742666132705[/C][C]-0.420742666132705[/C][/ROW]
[ROW][C]43[/C][C]0[/C][C]0.424592573686683[/C][C]-0.424592573686683[/C][/ROW]
[ROW][C]44[/C][C]0[/C][C]0.399017640079333[/C][C]-0.399017640079333[/C][/ROW]
[ROW][C]45[/C][C]0[/C][C]0.466622753133764[/C][C]-0.466622753133764[/C][/ROW]
[ROW][C]46[/C][C]1[/C][C]0.423601146712997[/C][C]0.576398853287003[/C][/ROW]
[ROW][C]47[/C][C]1[/C][C]0.353609514291782[/C][C]0.646390485708218[/C][/ROW]
[ROW][C]48[/C][C]1[/C][C]0.337876826619552[/C][C]0.662123173380448[/C][/ROW]
[ROW][C]49[/C][C]0[/C][C]0.41309941410714[/C][C]-0.41309941410714[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147232&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147232&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
111.26642203591018-0.266422035910177
211.29142628778703-0.291426287787032
311.05545203742473-0.0554520374247277
400.015593428401063-0.015593428401063
510.545056841218380.45494315878162
610.6198652523182170.380134747681783
700.48511546623294-0.48511546623294
810.6527101758109590.347289824189041
910.4787640477078540.521235952292146
1010.7212319137319030.278768086268097
1100.399370869775129-0.399370869775129
1200.616821761489231-0.616821761489231
1310.7570696256256230.242930374374377
1400.209351316132755-0.209351316132755
1510.4394399137025670.560560086297433
1600.528330758620888-0.528330758620888
1700.26278123750315-0.26278123750315
1810.3808676695198470.619132330480153
1910.574449675836090.42555032416391
2000.327892903803345-0.327892903803345
2110.544147483715470.45585251628453
2210.5078719823537630.492128017646237
2300.398140570047451-0.398140570047451
2400.311784482588631-0.311784482588631
2500.385652742422008-0.385652742422008
2600.49982796645585-0.49982796645585
2710.4387092029428810.561290797057119
2800.396406192018462-0.396406192018462
2900.611504318687131-0.611504318687131
3000.432623996599273-0.432623996599273
3110.5246700459776490.475329954022351
3210.4593447443808180.540655255619182
3310.5281025643841780.471897435615822
3410.4777166526004390.522283347399561
3500.277771411870826-0.277771411870826
3600.387248202267197-0.387248202267197
3700.492546097083085-0.492546097083085
3810.479222633152780.52077736684722
3910.3795788943031660.620421105696834
4000.397754740707097-0.397754740707097
4100.202199322126009-0.202199322126009
4200.420742666132705-0.420742666132705
4300.424592573686683-0.424592573686683
4400.399017640079333-0.399017640079333
4500.466622753133764-0.466622753133764
4610.4236011467129970.576398853287003
4710.3536095142917820.646390485708218
4810.3378768266195520.662123173380448
4900.41309941410714-0.41309941410714







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
80.1384739875186250.2769479750372490.861526012481375
90.4019897371033790.8039794742067580.598010262896621
100.2621998057793260.5243996115586510.737800194220675
110.4216400051710490.8432800103420980.578359994828951
120.5854725061752870.8290549876494270.414527493824713
130.4987975103665850.9975950207331690.501202489633415
140.433013907169060.866027814338120.56698609283094
150.4191558136541290.8383116273082590.580844186345871
160.5321514568775230.9356970862449540.467848543122477
170.4703247157583160.9406494315166320.529675284241684
180.4451680706944330.8903361413888670.554831929305567
190.4011270456274010.8022540912548030.598872954372598
200.3779113576437860.7558227152875720.622088642356214
210.3422075109108570.6844150218217140.657792489089143
220.3412393669687890.6824787339375780.658760633031211
230.3267720672189240.6535441344378480.673227932781076
240.2758977956935720.5517955913871440.724102204306428
250.243807570403270.4876151408065390.75619242959673
260.245146413642110.4902928272842190.75485358635789
270.3250040303215910.6500080606431810.674995969678409
280.2825761150803810.5651522301607620.717423884919619
290.4018786962435620.8037573924871240.598121303756438
300.3734449724114120.7468899448228230.626555027588588
310.3328522597749790.6657045195499580.667147740225021
320.3667847740252910.7335695480505810.633215225974709
330.3227515246748850.6455030493497710.677248475325115
340.3103262030545620.6206524061091230.689673796945438
350.5866521499702970.8266957000594060.413347850029703
360.4946694344988030.9893388689976050.505330565501197
370.4390261442429170.8780522884858340.560973855757083
380.4779726465573210.9559452931146420.522027353442679
390.3869651837697720.7739303675395440.613034816230228
400.3189719333497320.6379438666994640.681028066650268
410.534527569765670.9309448604686610.46547243023433

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
8 & 0.138473987518625 & 0.276947975037249 & 0.861526012481375 \tabularnewline
9 & 0.401989737103379 & 0.803979474206758 & 0.598010262896621 \tabularnewline
10 & 0.262199805779326 & 0.524399611558651 & 0.737800194220675 \tabularnewline
11 & 0.421640005171049 & 0.843280010342098 & 0.578359994828951 \tabularnewline
12 & 0.585472506175287 & 0.829054987649427 & 0.414527493824713 \tabularnewline
13 & 0.498797510366585 & 0.997595020733169 & 0.501202489633415 \tabularnewline
14 & 0.43301390716906 & 0.86602781433812 & 0.56698609283094 \tabularnewline
15 & 0.419155813654129 & 0.838311627308259 & 0.580844186345871 \tabularnewline
16 & 0.532151456877523 & 0.935697086244954 & 0.467848543122477 \tabularnewline
17 & 0.470324715758316 & 0.940649431516632 & 0.529675284241684 \tabularnewline
18 & 0.445168070694433 & 0.890336141388867 & 0.554831929305567 \tabularnewline
19 & 0.401127045627401 & 0.802254091254803 & 0.598872954372598 \tabularnewline
20 & 0.377911357643786 & 0.755822715287572 & 0.622088642356214 \tabularnewline
21 & 0.342207510910857 & 0.684415021821714 & 0.657792489089143 \tabularnewline
22 & 0.341239366968789 & 0.682478733937578 & 0.658760633031211 \tabularnewline
23 & 0.326772067218924 & 0.653544134437848 & 0.673227932781076 \tabularnewline
24 & 0.275897795693572 & 0.551795591387144 & 0.724102204306428 \tabularnewline
25 & 0.24380757040327 & 0.487615140806539 & 0.75619242959673 \tabularnewline
26 & 0.24514641364211 & 0.490292827284219 & 0.75485358635789 \tabularnewline
27 & 0.325004030321591 & 0.650008060643181 & 0.674995969678409 \tabularnewline
28 & 0.282576115080381 & 0.565152230160762 & 0.717423884919619 \tabularnewline
29 & 0.401878696243562 & 0.803757392487124 & 0.598121303756438 \tabularnewline
30 & 0.373444972411412 & 0.746889944822823 & 0.626555027588588 \tabularnewline
31 & 0.332852259774979 & 0.665704519549958 & 0.667147740225021 \tabularnewline
32 & 0.366784774025291 & 0.733569548050581 & 0.633215225974709 \tabularnewline
33 & 0.322751524674885 & 0.645503049349771 & 0.677248475325115 \tabularnewline
34 & 0.310326203054562 & 0.620652406109123 & 0.689673796945438 \tabularnewline
35 & 0.586652149970297 & 0.826695700059406 & 0.413347850029703 \tabularnewline
36 & 0.494669434498803 & 0.989338868997605 & 0.505330565501197 \tabularnewline
37 & 0.439026144242917 & 0.878052288485834 & 0.560973855757083 \tabularnewline
38 & 0.477972646557321 & 0.955945293114642 & 0.522027353442679 \tabularnewline
39 & 0.386965183769772 & 0.773930367539544 & 0.613034816230228 \tabularnewline
40 & 0.318971933349732 & 0.637943866699464 & 0.681028066650268 \tabularnewline
41 & 0.53452756976567 & 0.930944860468661 & 0.46547243023433 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147232&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]8[/C][C]0.138473987518625[/C][C]0.276947975037249[/C][C]0.861526012481375[/C][/ROW]
[ROW][C]9[/C][C]0.401989737103379[/C][C]0.803979474206758[/C][C]0.598010262896621[/C][/ROW]
[ROW][C]10[/C][C]0.262199805779326[/C][C]0.524399611558651[/C][C]0.737800194220675[/C][/ROW]
[ROW][C]11[/C][C]0.421640005171049[/C][C]0.843280010342098[/C][C]0.578359994828951[/C][/ROW]
[ROW][C]12[/C][C]0.585472506175287[/C][C]0.829054987649427[/C][C]0.414527493824713[/C][/ROW]
[ROW][C]13[/C][C]0.498797510366585[/C][C]0.997595020733169[/C][C]0.501202489633415[/C][/ROW]
[ROW][C]14[/C][C]0.43301390716906[/C][C]0.86602781433812[/C][C]0.56698609283094[/C][/ROW]
[ROW][C]15[/C][C]0.419155813654129[/C][C]0.838311627308259[/C][C]0.580844186345871[/C][/ROW]
[ROW][C]16[/C][C]0.532151456877523[/C][C]0.935697086244954[/C][C]0.467848543122477[/C][/ROW]
[ROW][C]17[/C][C]0.470324715758316[/C][C]0.940649431516632[/C][C]0.529675284241684[/C][/ROW]
[ROW][C]18[/C][C]0.445168070694433[/C][C]0.890336141388867[/C][C]0.554831929305567[/C][/ROW]
[ROW][C]19[/C][C]0.401127045627401[/C][C]0.802254091254803[/C][C]0.598872954372598[/C][/ROW]
[ROW][C]20[/C][C]0.377911357643786[/C][C]0.755822715287572[/C][C]0.622088642356214[/C][/ROW]
[ROW][C]21[/C][C]0.342207510910857[/C][C]0.684415021821714[/C][C]0.657792489089143[/C][/ROW]
[ROW][C]22[/C][C]0.341239366968789[/C][C]0.682478733937578[/C][C]0.658760633031211[/C][/ROW]
[ROW][C]23[/C][C]0.326772067218924[/C][C]0.653544134437848[/C][C]0.673227932781076[/C][/ROW]
[ROW][C]24[/C][C]0.275897795693572[/C][C]0.551795591387144[/C][C]0.724102204306428[/C][/ROW]
[ROW][C]25[/C][C]0.24380757040327[/C][C]0.487615140806539[/C][C]0.75619242959673[/C][/ROW]
[ROW][C]26[/C][C]0.24514641364211[/C][C]0.490292827284219[/C][C]0.75485358635789[/C][/ROW]
[ROW][C]27[/C][C]0.325004030321591[/C][C]0.650008060643181[/C][C]0.674995969678409[/C][/ROW]
[ROW][C]28[/C][C]0.282576115080381[/C][C]0.565152230160762[/C][C]0.717423884919619[/C][/ROW]
[ROW][C]29[/C][C]0.401878696243562[/C][C]0.803757392487124[/C][C]0.598121303756438[/C][/ROW]
[ROW][C]30[/C][C]0.373444972411412[/C][C]0.746889944822823[/C][C]0.626555027588588[/C][/ROW]
[ROW][C]31[/C][C]0.332852259774979[/C][C]0.665704519549958[/C][C]0.667147740225021[/C][/ROW]
[ROW][C]32[/C][C]0.366784774025291[/C][C]0.733569548050581[/C][C]0.633215225974709[/C][/ROW]
[ROW][C]33[/C][C]0.322751524674885[/C][C]0.645503049349771[/C][C]0.677248475325115[/C][/ROW]
[ROW][C]34[/C][C]0.310326203054562[/C][C]0.620652406109123[/C][C]0.689673796945438[/C][/ROW]
[ROW][C]35[/C][C]0.586652149970297[/C][C]0.826695700059406[/C][C]0.413347850029703[/C][/ROW]
[ROW][C]36[/C][C]0.494669434498803[/C][C]0.989338868997605[/C][C]0.505330565501197[/C][/ROW]
[ROW][C]37[/C][C]0.439026144242917[/C][C]0.878052288485834[/C][C]0.560973855757083[/C][/ROW]
[ROW][C]38[/C][C]0.477972646557321[/C][C]0.955945293114642[/C][C]0.522027353442679[/C][/ROW]
[ROW][C]39[/C][C]0.386965183769772[/C][C]0.773930367539544[/C][C]0.613034816230228[/C][/ROW]
[ROW][C]40[/C][C]0.318971933349732[/C][C]0.637943866699464[/C][C]0.681028066650268[/C][/ROW]
[ROW][C]41[/C][C]0.53452756976567[/C][C]0.930944860468661[/C][C]0.46547243023433[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147232&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147232&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
80.1384739875186250.2769479750372490.861526012481375
90.4019897371033790.8039794742067580.598010262896621
100.2621998057793260.5243996115586510.737800194220675
110.4216400051710490.8432800103420980.578359994828951
120.5854725061752870.8290549876494270.414527493824713
130.4987975103665850.9975950207331690.501202489633415
140.433013907169060.866027814338120.56698609283094
150.4191558136541290.8383116273082590.580844186345871
160.5321514568775230.9356970862449540.467848543122477
170.4703247157583160.9406494315166320.529675284241684
180.4451680706944330.8903361413888670.554831929305567
190.4011270456274010.8022540912548030.598872954372598
200.3779113576437860.7558227152875720.622088642356214
210.3422075109108570.6844150218217140.657792489089143
220.3412393669687890.6824787339375780.658760633031211
230.3267720672189240.6535441344378480.673227932781076
240.2758977956935720.5517955913871440.724102204306428
250.243807570403270.4876151408065390.75619242959673
260.245146413642110.4902928272842190.75485358635789
270.3250040303215910.6500080606431810.674995969678409
280.2825761150803810.5651522301607620.717423884919619
290.4018786962435620.8037573924871240.598121303756438
300.3734449724114120.7468899448228230.626555027588588
310.3328522597749790.6657045195499580.667147740225021
320.3667847740252910.7335695480505810.633215225974709
330.3227515246748850.6455030493497710.677248475325115
340.3103262030545620.6206524061091230.689673796945438
350.5866521499702970.8266957000594060.413347850029703
360.4946694344988030.9893388689976050.505330565501197
370.4390261442429170.8780522884858340.560973855757083
380.4779726465573210.9559452931146420.522027353442679
390.3869651837697720.7739303675395440.613034816230228
400.3189719333497320.6379438666994640.681028066650268
410.534527569765670.9309448604686610.46547243023433







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=147232&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=147232&T=6

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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 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
R code (references can be found in the software module):
library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$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')
}