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Description of Statistical Computation

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, 11 Dec 2010 16:27:00 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Dec/11/t12920850089qay0zp9c89ia4t.htm/, Retrieved Mon, 06 May 2024 17:45:54 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=108248, Retrieved Mon, 06 May 2024 17:45:54 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact112

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [] [2010-12-11 16:27:00] [558c060a42ec367ec2c020fab85c25c7] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
2	4,5	1	7	42	3	1	3
1,8	69	2.547	4.603	624	3	5	4
0,7	27	11	180	180	4	4	4
3,9	19	0,023	0,3	35	1	1	1
1	30,4	160	169	392	4	5	4
3,6	28	3	26	63	1	2	1
1,4	50	52	440	230	1	1	1
1,5	7	0,425	6	112	5	4	4
0,7	30	465	423	281	5	5	5
2,1	3,5	0,075	1	42	1	1	1
0	50	3	25	28	2	2	2
4,1	6	0,785	4	42	2	2	2
1,2	10,4	0,2	5	120	2	2	2
0,5	20	28	115	148	5	5	5
3,4	3,9	0,12	1	16	3	1	2
1,5	41	85	325	310	1	3	1
3,4	9	0,101	4	28	5	1	3
0,8	7,6	1	6	68	5	3	4
0,8	46	521	655	336	5	5	5
1,4	2,6	0,005	0,14	21,5	5	2	4
2	24	0,01	0,25	50	1	1	1
1,9	100	62	1.320	267	1	1	1
1,3	3,2	0,023	0,4	19	4	1	3
2	2	0,048	0,33	30	4	1	3
5,6	5	2	6	12	2	1	1
3,1	6,5	4	11	120	2	1	1
1,8	12	0,48	16	140	2	2	2
0,9	20,2	10	115	170	4	4	4
1,8	13	2	11	17	2	1	2
1,9	27	192	180	115	4	4	4
0,9	18	3	12	31	5	5	5
2,6	4,7	0,28	2	21	3	1	3
2,4	9,8	4	50	52	1	1	1
1,2	29	7	179	164	2	3	2
0,9	7	0,75	12	225	2	2	2
0,5	6	4	21	225	3	2	3
0,6	20	56	175	151	5	5	5
2,3	4,5	0,9	3	60	2	1	2
0,5	7,5	2	12	200	3	1	3
2,6	2,3	0,104	3	46	3	2	2
0,6	24	4	58	210	4	3	4
6,6	3	4	4	14	2	1	1

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time13 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135

\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 & 13 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=108248&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]13 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=108248&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=108248&T=0

As an alternative you can also use a QR Code:  
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 time13 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135






Multiple Linear Regression - Estimated Regression Equation
PS[t] = + 3.58737153871768 -0.0094560197051181L[t] + 0.00508884300409277Wb[t] -0.00390837673295982Wbr[t] -0.000786575888854624Tg[t] + 0.810406161455018P[t] + 0.327261431599046S[t] -1.66026239739375D[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
PS[t] =  +  3.58737153871768 -0.0094560197051181L[t] +  0.00508884300409277Wb[t] -0.00390837673295982Wbr[t] -0.000786575888854624Tg[t] +  0.810406161455018P[t] +  0.327261431599046S[t] -1.66026239739375D[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=108248&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]PS[t] =  +  3.58737153871768 -0.0094560197051181L[t] +  0.00508884300409277Wb[t] -0.00390837673295982Wbr[t] -0.000786575888854624Tg[t] +  0.810406161455018P[t] +  0.327261431599046S[t] -1.66026239739375D[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=108248&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=108248&T=1

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Estimated Regression Equation
PS[t] = + 3.58737153871768 -0.0094560197051181L[t] + 0.00508884300409277Wb[t] -0.00390837673295982Wbr[t] -0.000786575888854624Tg[t] + 0.810406161455018P[t] + 0.327261431599046S[t] -1.66026239739375D[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)3.587371538717680.4678057.668500
L-0.00945601970511810.011399-0.82950.4125870.206293
Wb0.005088843004092770.0027561.84620.0735790.03679
Wbr-0.003908376732959820.002151-1.81740.077980.03899
Tg-0.0007865758888546240.002026-0.38820.7002880.350144
P0.8104061614550180.3738082.1680.0372520.018626
S0.3272614315990460.2258621.44890.1565140.078257
D-1.660262397393750.463823-3.57950.0010610.00053

\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) & 3.58737153871768 & 0.467805 & 7.6685 & 0 & 0 \tabularnewline
L & -0.0094560197051181 & 0.011399 & -0.8295 & 0.412587 & 0.206293 \tabularnewline
Wb & 0.00508884300409277 & 0.002756 & 1.8462 & 0.073579 & 0.03679 \tabularnewline
Wbr & -0.00390837673295982 & 0.002151 & -1.8174 & 0.07798 & 0.03899 \tabularnewline
Tg & -0.000786575888854624 & 0.002026 & -0.3882 & 0.700288 & 0.350144 \tabularnewline
P & 0.810406161455018 & 0.373808 & 2.168 & 0.037252 & 0.018626 \tabularnewline
S & 0.327261431599046 & 0.225862 & 1.4489 & 0.156514 & 0.078257 \tabularnewline
D & -1.66026239739375 & 0.463823 & -3.5795 & 0.001061 & 0.00053 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=108248&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]3.58737153871768[/C][C]0.467805[/C][C]7.6685[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]L[/C][C]-0.0094560197051181[/C][C]0.011399[/C][C]-0.8295[/C][C]0.412587[/C][C]0.206293[/C][/ROW]
[ROW][C]Wb[/C][C]0.00508884300409277[/C][C]0.002756[/C][C]1.8462[/C][C]0.073579[/C][C]0.03679[/C][/ROW]
[ROW][C]Wbr[/C][C]-0.00390837673295982[/C][C]0.002151[/C][C]-1.8174[/C][C]0.07798[/C][C]0.03899[/C][/ROW]
[ROW][C]Tg[/C][C]-0.000786575888854624[/C][C]0.002026[/C][C]-0.3882[/C][C]0.700288[/C][C]0.350144[/C][/ROW]
[ROW][C]P[/C][C]0.810406161455018[/C][C]0.373808[/C][C]2.168[/C][C]0.037252[/C][C]0.018626[/C][/ROW]
[ROW][C]S[/C][C]0.327261431599046[/C][C]0.225862[/C][C]1.4489[/C][C]0.156514[/C][C]0.078257[/C][/ROW]
[ROW][C]D[/C][C]-1.66026239739375[/C][C]0.463823[/C][C]-3.5795[/C][C]0.001061[/C][C]0.00053[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=108248&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=108248&T=2

As an alternative you can also use a QR Code:  
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)3.587371538717680.4678057.668500
L-0.00945601970511810.011399-0.82950.4125870.206293
Wb0.005088843004092770.0027561.84620.0735790.03679
Wbr-0.003908376732959820.002151-1.81740.077980.03899
Tg-0.0007865758888546240.002026-0.38820.7002880.350144
P0.8104061614550180.3738082.1680.0372520.018626
S0.3272614315990460.2258621.44890.1565140.078257
D-1.660262397393750.463823-3.57950.0010610.00053






Multiple Linear Regression - Regression Statistics
Multiple R0.731211507658874
R-squared0.534670268932763
Adjusted R-squared0.438867089007155
F-TEST (value)5.58092402932702
F-TEST (DF numerator)7
F-TEST (DF denominator)34
p-value0.000242790446010499
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.04033754608211
Sum Squared Residuals36.7982751327971

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.731211507658874 \tabularnewline
R-squared & 0.534670268932763 \tabularnewline
Adjusted R-squared & 0.438867089007155 \tabularnewline
F-TEST (value) & 5.58092402932702 \tabularnewline
F-TEST (DF numerator) & 7 \tabularnewline
F-TEST (DF denominator) & 34 \tabularnewline
p-value & 0.000242790446010499 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 1.04033754608211 \tabularnewline
Sum Squared Residuals & 36.7982751327971 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=108248&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.731211507658874[/C][/ROW]
[ROW][C]R-squared[/C][C]0.534670268932763[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.438867089007155[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]5.58092402932702[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]7[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]34[/C][/ROW]
[ROW][C]p-value[/C][C]0.000242790446010499[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]1.04033754608211[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]36.7982751327971[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=108248&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=108248&T=3

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Regression Statistics
Multiple R0.731211507658874
R-squared0.534670268932763
Adjusted R-squared0.438867089007155
F-TEST (value)5.58092402932702
F-TEST (DF numerator)7
F-TEST (DF denominator)34
p-value0.000242790446010499
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.04033754608211
Sum Squared Residuals36.7982751327971






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
121.267206192368990.732793807631013
21.8-0.1344700977658541.93447009776585
30.70.4525655904391780.247434409560822
43.92.856526734240041.04347326575996
511.38215221827602-0.382152218276021
63.62.991364067191210.60863593280879
71.40.955997368396030.444002631603971
81.52.13182234320539-0.631822343205394
90.71.18275773995869-0.482757739958694
102.12.99511776457053-0.895117764570533
1101.96491393058276-1.96491393058276
124.12.440770859302081.65922914069792
131.22.33092610337855-1.13092610337855
140.50.3618881711906290.138111828809371
153.42.972565253250180.427434746749823
161.52.25009348125726-0.750093481257256
173.42.863628749388150.53637125061185
180.81.83642272362023-1.03642272362023
190.80.3664315569723190.433568443027681
201.41.61085685824459-0.210856858244591
2122.79757726125923-0.797577261259228
221.92.21950821050824-0.319508210508243
231.32.12881991170684-0.828819911706844
2422.13191560802199-0.131915608021995
255.63.805191312251591.79480868774841
263.13.696692889041-0.596692889041002
271.82.25849768605185-0.458497686051852
280.90.7736879279608180.126312072039182
291.82.04580599410783-0.245805994107826
301.91.424773606955510.475226393044487
310.90.7481713179893980.151828682010602
322.61.297710998795761.30228900120424
332.42.75614233041578-0.356142330415776
341.21.80224281024561-0.602242810245615
350.92.25592632856774-1.35592632856774
360.51.39688946150080-0.896889461500795
370.60.2675134436610740.332486556338926
382.32.118028684939760.181971315060242
390.51.10010610215389-0.60010610215389
402.63.28346086475779-0.683460864757789
410.60.5712750016822910.0287249983177086
426.63.840524639358222.75947536064177

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 2 & 1.26720619236899 & 0.732793807631013 \tabularnewline
2 & 1.8 & -0.134470097765854 & 1.93447009776585 \tabularnewline
3 & 0.7 & 0.452565590439178 & 0.247434409560822 \tabularnewline
4 & 3.9 & 2.85652673424004 & 1.04347326575996 \tabularnewline
5 & 1 & 1.38215221827602 & -0.382152218276021 \tabularnewline
6 & 3.6 & 2.99136406719121 & 0.60863593280879 \tabularnewline
7 & 1.4 & 0.95599736839603 & 0.444002631603971 \tabularnewline
8 & 1.5 & 2.13182234320539 & -0.631822343205394 \tabularnewline
9 & 0.7 & 1.18275773995869 & -0.482757739958694 \tabularnewline
10 & 2.1 & 2.99511776457053 & -0.895117764570533 \tabularnewline
11 & 0 & 1.96491393058276 & -1.96491393058276 \tabularnewline
12 & 4.1 & 2.44077085930208 & 1.65922914069792 \tabularnewline
13 & 1.2 & 2.33092610337855 & -1.13092610337855 \tabularnewline
14 & 0.5 & 0.361888171190629 & 0.138111828809371 \tabularnewline
15 & 3.4 & 2.97256525325018 & 0.427434746749823 \tabularnewline
16 & 1.5 & 2.25009348125726 & -0.750093481257256 \tabularnewline
17 & 3.4 & 2.86362874938815 & 0.53637125061185 \tabularnewline
18 & 0.8 & 1.83642272362023 & -1.03642272362023 \tabularnewline
19 & 0.8 & 0.366431556972319 & 0.433568443027681 \tabularnewline
20 & 1.4 & 1.61085685824459 & -0.210856858244591 \tabularnewline
21 & 2 & 2.79757726125923 & -0.797577261259228 \tabularnewline
22 & 1.9 & 2.21950821050824 & -0.319508210508243 \tabularnewline
23 & 1.3 & 2.12881991170684 & -0.828819911706844 \tabularnewline
24 & 2 & 2.13191560802199 & -0.131915608021995 \tabularnewline
25 & 5.6 & 3.80519131225159 & 1.79480868774841 \tabularnewline
26 & 3.1 & 3.696692889041 & -0.596692889041002 \tabularnewline
27 & 1.8 & 2.25849768605185 & -0.458497686051852 \tabularnewline
28 & 0.9 & 0.773687927960818 & 0.126312072039182 \tabularnewline
29 & 1.8 & 2.04580599410783 & -0.245805994107826 \tabularnewline
30 & 1.9 & 1.42477360695551 & 0.475226393044487 \tabularnewline
31 & 0.9 & 0.748171317989398 & 0.151828682010602 \tabularnewline
32 & 2.6 & 1.29771099879576 & 1.30228900120424 \tabularnewline
33 & 2.4 & 2.75614233041578 & -0.356142330415776 \tabularnewline
34 & 1.2 & 1.80224281024561 & -0.602242810245615 \tabularnewline
35 & 0.9 & 2.25592632856774 & -1.35592632856774 \tabularnewline
36 & 0.5 & 1.39688946150080 & -0.896889461500795 \tabularnewline
37 & 0.6 & 0.267513443661074 & 0.332486556338926 \tabularnewline
38 & 2.3 & 2.11802868493976 & 0.181971315060242 \tabularnewline
39 & 0.5 & 1.10010610215389 & -0.60010610215389 \tabularnewline
40 & 2.6 & 3.28346086475779 & -0.683460864757789 \tabularnewline
41 & 0.6 & 0.571275001682291 & 0.0287249983177086 \tabularnewline
42 & 6.6 & 3.84052463935822 & 2.75947536064177 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=108248&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]2[/C][C]1.26720619236899[/C][C]0.732793807631013[/C][/ROW]
[ROW][C]2[/C][C]1.8[/C][C]-0.134470097765854[/C][C]1.93447009776585[/C][/ROW]
[ROW][C]3[/C][C]0.7[/C][C]0.452565590439178[/C][C]0.247434409560822[/C][/ROW]
[ROW][C]4[/C][C]3.9[/C][C]2.85652673424004[/C][C]1.04347326575996[/C][/ROW]
[ROW][C]5[/C][C]1[/C][C]1.38215221827602[/C][C]-0.382152218276021[/C][/ROW]
[ROW][C]6[/C][C]3.6[/C][C]2.99136406719121[/C][C]0.60863593280879[/C][/ROW]
[ROW][C]7[/C][C]1.4[/C][C]0.95599736839603[/C][C]0.444002631603971[/C][/ROW]
[ROW][C]8[/C][C]1.5[/C][C]2.13182234320539[/C][C]-0.631822343205394[/C][/ROW]
[ROW][C]9[/C][C]0.7[/C][C]1.18275773995869[/C][C]-0.482757739958694[/C][/ROW]
[ROW][C]10[/C][C]2.1[/C][C]2.99511776457053[/C][C]-0.895117764570533[/C][/ROW]
[ROW][C]11[/C][C]0[/C][C]1.96491393058276[/C][C]-1.96491393058276[/C][/ROW]
[ROW][C]12[/C][C]4.1[/C][C]2.44077085930208[/C][C]1.65922914069792[/C][/ROW]
[ROW][C]13[/C][C]1.2[/C][C]2.33092610337855[/C][C]-1.13092610337855[/C][/ROW]
[ROW][C]14[/C][C]0.5[/C][C]0.361888171190629[/C][C]0.138111828809371[/C][/ROW]
[ROW][C]15[/C][C]3.4[/C][C]2.97256525325018[/C][C]0.427434746749823[/C][/ROW]
[ROW][C]16[/C][C]1.5[/C][C]2.25009348125726[/C][C]-0.750093481257256[/C][/ROW]
[ROW][C]17[/C][C]3.4[/C][C]2.86362874938815[/C][C]0.53637125061185[/C][/ROW]
[ROW][C]18[/C][C]0.8[/C][C]1.83642272362023[/C][C]-1.03642272362023[/C][/ROW]
[ROW][C]19[/C][C]0.8[/C][C]0.366431556972319[/C][C]0.433568443027681[/C][/ROW]
[ROW][C]20[/C][C]1.4[/C][C]1.61085685824459[/C][C]-0.210856858244591[/C][/ROW]
[ROW][C]21[/C][C]2[/C][C]2.79757726125923[/C][C]-0.797577261259228[/C][/ROW]
[ROW][C]22[/C][C]1.9[/C][C]2.21950821050824[/C][C]-0.319508210508243[/C][/ROW]
[ROW][C]23[/C][C]1.3[/C][C]2.12881991170684[/C][C]-0.828819911706844[/C][/ROW]
[ROW][C]24[/C][C]2[/C][C]2.13191560802199[/C][C]-0.131915608021995[/C][/ROW]
[ROW][C]25[/C][C]5.6[/C][C]3.80519131225159[/C][C]1.79480868774841[/C][/ROW]
[ROW][C]26[/C][C]3.1[/C][C]3.696692889041[/C][C]-0.596692889041002[/C][/ROW]
[ROW][C]27[/C][C]1.8[/C][C]2.25849768605185[/C][C]-0.458497686051852[/C][/ROW]
[ROW][C]28[/C][C]0.9[/C][C]0.773687927960818[/C][C]0.126312072039182[/C][/ROW]
[ROW][C]29[/C][C]1.8[/C][C]2.04580599410783[/C][C]-0.245805994107826[/C][/ROW]
[ROW][C]30[/C][C]1.9[/C][C]1.42477360695551[/C][C]0.475226393044487[/C][/ROW]
[ROW][C]31[/C][C]0.9[/C][C]0.748171317989398[/C][C]0.151828682010602[/C][/ROW]
[ROW][C]32[/C][C]2.6[/C][C]1.29771099879576[/C][C]1.30228900120424[/C][/ROW]
[ROW][C]33[/C][C]2.4[/C][C]2.75614233041578[/C][C]-0.356142330415776[/C][/ROW]
[ROW][C]34[/C][C]1.2[/C][C]1.80224281024561[/C][C]-0.602242810245615[/C][/ROW]
[ROW][C]35[/C][C]0.9[/C][C]2.25592632856774[/C][C]-1.35592632856774[/C][/ROW]
[ROW][C]36[/C][C]0.5[/C][C]1.39688946150080[/C][C]-0.896889461500795[/C][/ROW]
[ROW][C]37[/C][C]0.6[/C][C]0.267513443661074[/C][C]0.332486556338926[/C][/ROW]
[ROW][C]38[/C][C]2.3[/C][C]2.11802868493976[/C][C]0.181971315060242[/C][/ROW]
[ROW][C]39[/C][C]0.5[/C][C]1.10010610215389[/C][C]-0.60010610215389[/C][/ROW]
[ROW][C]40[/C][C]2.6[/C][C]3.28346086475779[/C][C]-0.683460864757789[/C][/ROW]
[ROW][C]41[/C][C]0.6[/C][C]0.571275001682291[/C][C]0.0287249983177086[/C][/ROW]
[ROW][C]42[/C][C]6.6[/C][C]3.84052463935822[/C][C]2.75947536064177[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=108248&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=108248&T=4

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
121.267206192368990.732793807631013
21.8-0.1344700977658541.93447009776585
30.70.4525655904391780.247434409560822
43.92.856526734240041.04347326575996
511.38215221827602-0.382152218276021
63.62.991364067191210.60863593280879
71.40.955997368396030.444002631603971
81.52.13182234320539-0.631822343205394
90.71.18275773995869-0.482757739958694
102.12.99511776457053-0.895117764570533
1101.96491393058276-1.96491393058276
124.12.440770859302081.65922914069792
131.22.33092610337855-1.13092610337855
140.50.3618881711906290.138111828809371
153.42.972565253250180.427434746749823
161.52.25009348125726-0.750093481257256
173.42.863628749388150.53637125061185
180.81.83642272362023-1.03642272362023
190.80.3664315569723190.433568443027681
201.41.61085685824459-0.210856858244591
2122.79757726125923-0.797577261259228
221.92.21950821050824-0.319508210508243
231.32.12881991170684-0.828819911706844
2422.13191560802199-0.131915608021995
255.63.805191312251591.79480868774841
263.13.696692889041-0.596692889041002
271.82.25849768605185-0.458497686051852
280.90.7736879279608180.126312072039182
291.82.04580599410783-0.245805994107826
301.91.424773606955510.475226393044487
310.90.7481713179893980.151828682010602
322.61.297710998795761.30228900120424
332.42.75614233041578-0.356142330415776
341.21.80224281024561-0.602242810245615
350.92.25592632856774-1.35592632856774
360.51.39688946150080-0.896889461500795
370.60.2675134436610740.332486556338926
382.32.118028684939760.181971315060242
390.51.10010610215389-0.60010610215389
402.63.28346086475779-0.683460864757789
410.60.5712750016822910.0287249983177086
426.63.840524639358222.75947536064177






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
110.8894151923564130.2211696152871750.110584807643587
120.9083101455154710.1833797089690580.091689854484529
130.9446634718210040.1106730563579920.0553365281789958
140.9006214733071660.1987570533856690.0993785266928344
150.8720159117490530.2559681765018940.127984088250947
160.8166090692275830.3667818615448330.183390930772417
170.7494669751696710.5010660496606580.250533024830329
180.7446485628252930.5107028743494130.255351437174706
190.679354200032760.641291599934480.32064579996724
200.5970033723979630.8059932552040730.402996627602037
210.5693729883444530.8612540233110950.430627011655547
220.456858336204630.913716672409260.54314166379537
230.5281853118566850.943629376286630.471814688143315
240.5828188139483540.8343623721032910.417181186051646
250.6804878988284630.6390242023430750.319512101171537
260.6492434570168030.7015130859663940.350756542983197
270.5523997845858840.8952004308282310.447600215414116
280.4348843354693860.8697686709387730.565115664530614
290.3769636357139930.7539272714279860.623036364286007
300.4245850175922880.8491700351845760.575414982407712
310.2909074267167650.581814853433530.709092573283235

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
11 & 0.889415192356413 & 0.221169615287175 & 0.110584807643587 \tabularnewline
12 & 0.908310145515471 & 0.183379708969058 & 0.091689854484529 \tabularnewline
13 & 0.944663471821004 & 0.110673056357992 & 0.0553365281789958 \tabularnewline
14 & 0.900621473307166 & 0.198757053385669 & 0.0993785266928344 \tabularnewline
15 & 0.872015911749053 & 0.255968176501894 & 0.127984088250947 \tabularnewline
16 & 0.816609069227583 & 0.366781861544833 & 0.183390930772417 \tabularnewline
17 & 0.749466975169671 & 0.501066049660658 & 0.250533024830329 \tabularnewline
18 & 0.744648562825293 & 0.510702874349413 & 0.255351437174706 \tabularnewline
19 & 0.67935420003276 & 0.64129159993448 & 0.32064579996724 \tabularnewline
20 & 0.597003372397963 & 0.805993255204073 & 0.402996627602037 \tabularnewline
21 & 0.569372988344453 & 0.861254023311095 & 0.430627011655547 \tabularnewline
22 & 0.45685833620463 & 0.91371667240926 & 0.54314166379537 \tabularnewline
23 & 0.528185311856685 & 0.94362937628663 & 0.471814688143315 \tabularnewline
24 & 0.582818813948354 & 0.834362372103291 & 0.417181186051646 \tabularnewline
25 & 0.680487898828463 & 0.639024202343075 & 0.319512101171537 \tabularnewline
26 & 0.649243457016803 & 0.701513085966394 & 0.350756542983197 \tabularnewline
27 & 0.552399784585884 & 0.895200430828231 & 0.447600215414116 \tabularnewline
28 & 0.434884335469386 & 0.869768670938773 & 0.565115664530614 \tabularnewline
29 & 0.376963635713993 & 0.753927271427986 & 0.623036364286007 \tabularnewline
30 & 0.424585017592288 & 0.849170035184576 & 0.575414982407712 \tabularnewline
31 & 0.290907426716765 & 0.58181485343353 & 0.709092573283235 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=108248&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]0.889415192356413[/C][C]0.221169615287175[/C][C]0.110584807643587[/C][/ROW]
[ROW][C]12[/C][C]0.908310145515471[/C][C]0.183379708969058[/C][C]0.091689854484529[/C][/ROW]
[ROW][C]13[/C][C]0.944663471821004[/C][C]0.110673056357992[/C][C]0.0553365281789958[/C][/ROW]
[ROW][C]14[/C][C]0.900621473307166[/C][C]0.198757053385669[/C][C]0.0993785266928344[/C][/ROW]
[ROW][C]15[/C][C]0.872015911749053[/C][C]0.255968176501894[/C][C]0.127984088250947[/C][/ROW]
[ROW][C]16[/C][C]0.816609069227583[/C][C]0.366781861544833[/C][C]0.183390930772417[/C][/ROW]
[ROW][C]17[/C][C]0.749466975169671[/C][C]0.501066049660658[/C][C]0.250533024830329[/C][/ROW]
[ROW][C]18[/C][C]0.744648562825293[/C][C]0.510702874349413[/C][C]0.255351437174706[/C][/ROW]
[ROW][C]19[/C][C]0.67935420003276[/C][C]0.64129159993448[/C][C]0.32064579996724[/C][/ROW]
[ROW][C]20[/C][C]0.597003372397963[/C][C]0.805993255204073[/C][C]0.402996627602037[/C][/ROW]
[ROW][C]21[/C][C]0.569372988344453[/C][C]0.861254023311095[/C][C]0.430627011655547[/C][/ROW]
[ROW][C]22[/C][C]0.45685833620463[/C][C]0.91371667240926[/C][C]0.54314166379537[/C][/ROW]
[ROW][C]23[/C][C]0.528185311856685[/C][C]0.94362937628663[/C][C]0.471814688143315[/C][/ROW]
[ROW][C]24[/C][C]0.582818813948354[/C][C]0.834362372103291[/C][C]0.417181186051646[/C][/ROW]
[ROW][C]25[/C][C]0.680487898828463[/C][C]0.639024202343075[/C][C]0.319512101171537[/C][/ROW]
[ROW][C]26[/C][C]0.649243457016803[/C][C]0.701513085966394[/C][C]0.350756542983197[/C][/ROW]
[ROW][C]27[/C][C]0.552399784585884[/C][C]0.895200430828231[/C][C]0.447600215414116[/C][/ROW]
[ROW][C]28[/C][C]0.434884335469386[/C][C]0.869768670938773[/C][C]0.565115664530614[/C][/ROW]
[ROW][C]29[/C][C]0.376963635713993[/C][C]0.753927271427986[/C][C]0.623036364286007[/C][/ROW]
[ROW][C]30[/C][C]0.424585017592288[/C][C]0.849170035184576[/C][C]0.575414982407712[/C][/ROW]
[ROW][C]31[/C][C]0.290907426716765[/C][C]0.58181485343353[/C][C]0.709092573283235[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=108248&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=108248&T=5

As an alternative you can also use a QR Code:  
QR Code


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
110.8894151923564130.2211696152871750.110584807643587
120.9083101455154710.1833797089690580.091689854484529
130.9446634718210040.1106730563579920.0553365281789958
140.9006214733071660.1987570533856690.0993785266928344
150.8720159117490530.2559681765018940.127984088250947
160.8166090692275830.3667818615448330.183390930772417
170.7494669751696710.5010660496606580.250533024830329
180.7446485628252930.5107028743494130.255351437174706
190.679354200032760.641291599934480.32064579996724
200.5970033723979630.8059932552040730.402996627602037
210.5693729883444530.8612540233110950.430627011655547
220.456858336204630.913716672409260.54314166379537
230.5281853118566850.943629376286630.471814688143315
240.5828188139483540.8343623721032910.417181186051646
250.6804878988284630.6390242023430750.319512101171537
260.6492434570168030.7015130859663940.350756542983197
270.5523997845858840.8952004308282310.447600215414116
280.4348843354693860.8697686709387730.565115664530614
290.3769636357139930.7539272714279860.623036364286007
300.4245850175922880.8491700351845760.575414982407712
310.2909074267167650.581814853433530.709092573283235






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=108248&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=108248&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=108248&T=6

As an alternative you can also use a QR Code:  
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


Figures (Output of Computation)

Figure 1
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Figure 2
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Figure 4
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Figure 5
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Figure 6
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Figure 7
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Figure 8
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Figure 9
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Figure 10
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Input Parameters & R Code

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')
}