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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, 25 Nov 2010 17:43:15 +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/Nov/25/t1290707153mmjhzjsyd443mc6.htm/, Retrieved Thu, 28 Mar 2024 12:25:04 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=101272, Retrieved Thu, 28 Mar 2024 12:25:04 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact136
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Q1 The Seatbeltlaw] [2007-11-14 19:27:43] [8cd6641b921d30ebe00b648d1481bba0]
- RMPD  [Multiple Regression] [Seatbelt] [2009-11-12 13:54:52] [b98453cac15ba1066b407e146608df68]
-    D      [Multiple Regression] [paper - lineaire ...] [2010-11-25 17:43:15] [9e558c3b35e49a9a9a626d1906801b69] [Current]
-   PD        [Multiple Regression] [paper - lineaire ...] [2010-11-25 19:59:15] [58c953249d32df2a8d41ff3671d3073f]
-   P           [Multiple Regression] [paper minitutoria...] [2010-11-26 08:14:23] [94f495cfd7e7946e5228cbd267a6841d]
-             [Multiple Regression] [paper minitutoria...] [2010-11-26 08:12:21] [94f495cfd7e7946e5228cbd267a6841d]
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Dataseries X:
5125	0
5366	0
5078	0
2775	0
2952	0
2784	0
2350	0
2413	0
2203	0
705	0
765	0
800	0
1161	0
1223	0
1188	0
1178	0
1225	0
1100	0
1087	0
1104	0
1046	0
571	0
591	0
536	0
347	0
390	0
339	0
76	0
68	0
68	0
4044	1
4976	1
2208	1
2721	1
1837	1
2255	1
549	1
669	1
959	1
1158	1
894	1
1074	1
841	1
1107	1
459	1
564	1
284	1
332	1
59	1
71	1




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 seconds
R Server'RServer@AstonUniversity' @ vre.aston.ac.uk

\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 & 5 seconds \tabularnewline
R Server & 'RServer@AstonUniversity' @ vre.aston.ac.uk \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=101272&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]5 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'RServer@AstonUniversity' @ vre.aston.ac.uk[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=101272&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=101272&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 time5 seconds
R Server'RServer@AstonUniversity' @ vre.aston.ac.uk







Multiple Linear Regression - Estimated Regression Equation
aantalrokers[t] = + 1553.8 -200.75rookverbod[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
aantalrokers[t] =  +  1553.8 -200.75rookverbod[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=101272&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]aantalrokers[t] =  +  1553.8 -200.75rookverbod[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=101272&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=101272&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
aantalrokers[t] = + 1553.8 -200.75rookverbod[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)1553.8258.974175.999800
rookverbod-200.75409.474116-0.49030.6261810.313091

\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) & 1553.8 & 258.97417 & 5.9998 & 0 & 0 \tabularnewline
rookverbod & -200.75 & 409.474116 & -0.4903 & 0.626181 & 0.313091 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=101272&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]1553.8[/C][C]258.97417[/C][C]5.9998[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]rookverbod[/C][C]-200.75[/C][C]409.474116[/C][C]-0.4903[/C][C]0.626181[/C][C]0.313091[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=101272&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=101272&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)1553.8258.974175.999800
rookverbod-200.75409.474116-0.49030.6261810.313091







Multiple Linear Regression - Regression Statistics
Multiple R0.07058685622796
R-squared0.0049825042721467
Adjusted R-squared-0.0157470268888502
F-TEST (value)0.240357788772444
F-TEST (DF numerator)1
F-TEST (DF denominator)48
p-value0.626181435302837
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1418.45994648833
Sum Squared Residuals96577373.75

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.07058685622796 \tabularnewline
R-squared & 0.0049825042721467 \tabularnewline
Adjusted R-squared & -0.0157470268888502 \tabularnewline
F-TEST (value) & 0.240357788772444 \tabularnewline
F-TEST (DF numerator) & 1 \tabularnewline
F-TEST (DF denominator) & 48 \tabularnewline
p-value & 0.626181435302837 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 1418.45994648833 \tabularnewline
Sum Squared Residuals & 96577373.75 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=101272&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.07058685622796[/C][/ROW]
[ROW][C]R-squared[/C][C]0.0049825042721467[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]-0.0157470268888502[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]0.240357788772444[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]1[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]48[/C][/ROW]
[ROW][C]p-value[/C][C]0.626181435302837[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]1418.45994648833[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]96577373.75[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=101272&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=101272&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 R0.07058685622796
R-squared0.0049825042721467
Adjusted R-squared-0.0157470268888502
F-TEST (value)0.240357788772444
F-TEST (DF numerator)1
F-TEST (DF denominator)48
p-value0.626181435302837
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1418.45994648833
Sum Squared Residuals96577373.75







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
151251553.83571.2
253661553.83812.2
350781553.83524.2
427751553.81221.2
529521553.81398.2
627841553.81230.2
723501553.8796.2
824131553.8859.2
922031553.8649.2
107051553.8-848.8
117651553.8-788.8
128001553.8-753.8
1311611553.8-392.8
1412231553.8-330.8
1511881553.8-365.8
1611781553.8-375.8
1712251553.8-328.8
1811001553.8-453.8
1910871553.8-466.8
2011041553.8-449.8
2110461553.8-507.8
225711553.8-982.8
235911553.8-962.8
245361553.8-1017.8
253471553.8-1206.8
263901553.8-1163.8
273391553.8-1214.8
28761553.8-1477.8
29681553.8-1485.8
30681553.8-1485.8
3140441353.052690.95
3249761353.053622.95
3322081353.05854.95
3427211353.051367.95
3518371353.05483.95
3622551353.05901.95
375491353.05-804.05
386691353.05-684.05
399591353.05-394.05
4011581353.05-195.05
418941353.05-459.05
4210741353.05-279.05
438411353.05-512.05
4411071353.05-246.05
454591353.05-894.05
465641353.05-789.05
472841353.05-1069.05
483321353.05-1021.05
49591353.05-1294.05
50711353.05-1282.05

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 5125 & 1553.8 & 3571.2 \tabularnewline
2 & 5366 & 1553.8 & 3812.2 \tabularnewline
3 & 5078 & 1553.8 & 3524.2 \tabularnewline
4 & 2775 & 1553.8 & 1221.2 \tabularnewline
5 & 2952 & 1553.8 & 1398.2 \tabularnewline
6 & 2784 & 1553.8 & 1230.2 \tabularnewline
7 & 2350 & 1553.8 & 796.2 \tabularnewline
8 & 2413 & 1553.8 & 859.2 \tabularnewline
9 & 2203 & 1553.8 & 649.2 \tabularnewline
10 & 705 & 1553.8 & -848.8 \tabularnewline
11 & 765 & 1553.8 & -788.8 \tabularnewline
12 & 800 & 1553.8 & -753.8 \tabularnewline
13 & 1161 & 1553.8 & -392.8 \tabularnewline
14 & 1223 & 1553.8 & -330.8 \tabularnewline
15 & 1188 & 1553.8 & -365.8 \tabularnewline
16 & 1178 & 1553.8 & -375.8 \tabularnewline
17 & 1225 & 1553.8 & -328.8 \tabularnewline
18 & 1100 & 1553.8 & -453.8 \tabularnewline
19 & 1087 & 1553.8 & -466.8 \tabularnewline
20 & 1104 & 1553.8 & -449.8 \tabularnewline
21 & 1046 & 1553.8 & -507.8 \tabularnewline
22 & 571 & 1553.8 & -982.8 \tabularnewline
23 & 591 & 1553.8 & -962.8 \tabularnewline
24 & 536 & 1553.8 & -1017.8 \tabularnewline
25 & 347 & 1553.8 & -1206.8 \tabularnewline
26 & 390 & 1553.8 & -1163.8 \tabularnewline
27 & 339 & 1553.8 & -1214.8 \tabularnewline
28 & 76 & 1553.8 & -1477.8 \tabularnewline
29 & 68 & 1553.8 & -1485.8 \tabularnewline
30 & 68 & 1553.8 & -1485.8 \tabularnewline
31 & 4044 & 1353.05 & 2690.95 \tabularnewline
32 & 4976 & 1353.05 & 3622.95 \tabularnewline
33 & 2208 & 1353.05 & 854.95 \tabularnewline
34 & 2721 & 1353.05 & 1367.95 \tabularnewline
35 & 1837 & 1353.05 & 483.95 \tabularnewline
36 & 2255 & 1353.05 & 901.95 \tabularnewline
37 & 549 & 1353.05 & -804.05 \tabularnewline
38 & 669 & 1353.05 & -684.05 \tabularnewline
39 & 959 & 1353.05 & -394.05 \tabularnewline
40 & 1158 & 1353.05 & -195.05 \tabularnewline
41 & 894 & 1353.05 & -459.05 \tabularnewline
42 & 1074 & 1353.05 & -279.05 \tabularnewline
43 & 841 & 1353.05 & -512.05 \tabularnewline
44 & 1107 & 1353.05 & -246.05 \tabularnewline
45 & 459 & 1353.05 & -894.05 \tabularnewline
46 & 564 & 1353.05 & -789.05 \tabularnewline
47 & 284 & 1353.05 & -1069.05 \tabularnewline
48 & 332 & 1353.05 & -1021.05 \tabularnewline
49 & 59 & 1353.05 & -1294.05 \tabularnewline
50 & 71 & 1353.05 & -1282.05 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=101272&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]5125[/C][C]1553.8[/C][C]3571.2[/C][/ROW]
[ROW][C]2[/C][C]5366[/C][C]1553.8[/C][C]3812.2[/C][/ROW]
[ROW][C]3[/C][C]5078[/C][C]1553.8[/C][C]3524.2[/C][/ROW]
[ROW][C]4[/C][C]2775[/C][C]1553.8[/C][C]1221.2[/C][/ROW]
[ROW][C]5[/C][C]2952[/C][C]1553.8[/C][C]1398.2[/C][/ROW]
[ROW][C]6[/C][C]2784[/C][C]1553.8[/C][C]1230.2[/C][/ROW]
[ROW][C]7[/C][C]2350[/C][C]1553.8[/C][C]796.2[/C][/ROW]
[ROW][C]8[/C][C]2413[/C][C]1553.8[/C][C]859.2[/C][/ROW]
[ROW][C]9[/C][C]2203[/C][C]1553.8[/C][C]649.2[/C][/ROW]
[ROW][C]10[/C][C]705[/C][C]1553.8[/C][C]-848.8[/C][/ROW]
[ROW][C]11[/C][C]765[/C][C]1553.8[/C][C]-788.8[/C][/ROW]
[ROW][C]12[/C][C]800[/C][C]1553.8[/C][C]-753.8[/C][/ROW]
[ROW][C]13[/C][C]1161[/C][C]1553.8[/C][C]-392.8[/C][/ROW]
[ROW][C]14[/C][C]1223[/C][C]1553.8[/C][C]-330.8[/C][/ROW]
[ROW][C]15[/C][C]1188[/C][C]1553.8[/C][C]-365.8[/C][/ROW]
[ROW][C]16[/C][C]1178[/C][C]1553.8[/C][C]-375.8[/C][/ROW]
[ROW][C]17[/C][C]1225[/C][C]1553.8[/C][C]-328.8[/C][/ROW]
[ROW][C]18[/C][C]1100[/C][C]1553.8[/C][C]-453.8[/C][/ROW]
[ROW][C]19[/C][C]1087[/C][C]1553.8[/C][C]-466.8[/C][/ROW]
[ROW][C]20[/C][C]1104[/C][C]1553.8[/C][C]-449.8[/C][/ROW]
[ROW][C]21[/C][C]1046[/C][C]1553.8[/C][C]-507.8[/C][/ROW]
[ROW][C]22[/C][C]571[/C][C]1553.8[/C][C]-982.8[/C][/ROW]
[ROW][C]23[/C][C]591[/C][C]1553.8[/C][C]-962.8[/C][/ROW]
[ROW][C]24[/C][C]536[/C][C]1553.8[/C][C]-1017.8[/C][/ROW]
[ROW][C]25[/C][C]347[/C][C]1553.8[/C][C]-1206.8[/C][/ROW]
[ROW][C]26[/C][C]390[/C][C]1553.8[/C][C]-1163.8[/C][/ROW]
[ROW][C]27[/C][C]339[/C][C]1553.8[/C][C]-1214.8[/C][/ROW]
[ROW][C]28[/C][C]76[/C][C]1553.8[/C][C]-1477.8[/C][/ROW]
[ROW][C]29[/C][C]68[/C][C]1553.8[/C][C]-1485.8[/C][/ROW]
[ROW][C]30[/C][C]68[/C][C]1553.8[/C][C]-1485.8[/C][/ROW]
[ROW][C]31[/C][C]4044[/C][C]1353.05[/C][C]2690.95[/C][/ROW]
[ROW][C]32[/C][C]4976[/C][C]1353.05[/C][C]3622.95[/C][/ROW]
[ROW][C]33[/C][C]2208[/C][C]1353.05[/C][C]854.95[/C][/ROW]
[ROW][C]34[/C][C]2721[/C][C]1353.05[/C][C]1367.95[/C][/ROW]
[ROW][C]35[/C][C]1837[/C][C]1353.05[/C][C]483.95[/C][/ROW]
[ROW][C]36[/C][C]2255[/C][C]1353.05[/C][C]901.95[/C][/ROW]
[ROW][C]37[/C][C]549[/C][C]1353.05[/C][C]-804.05[/C][/ROW]
[ROW][C]38[/C][C]669[/C][C]1353.05[/C][C]-684.05[/C][/ROW]
[ROW][C]39[/C][C]959[/C][C]1353.05[/C][C]-394.05[/C][/ROW]
[ROW][C]40[/C][C]1158[/C][C]1353.05[/C][C]-195.05[/C][/ROW]
[ROW][C]41[/C][C]894[/C][C]1353.05[/C][C]-459.05[/C][/ROW]
[ROW][C]42[/C][C]1074[/C][C]1353.05[/C][C]-279.05[/C][/ROW]
[ROW][C]43[/C][C]841[/C][C]1353.05[/C][C]-512.05[/C][/ROW]
[ROW][C]44[/C][C]1107[/C][C]1353.05[/C][C]-246.05[/C][/ROW]
[ROW][C]45[/C][C]459[/C][C]1353.05[/C][C]-894.05[/C][/ROW]
[ROW][C]46[/C][C]564[/C][C]1353.05[/C][C]-789.05[/C][/ROW]
[ROW][C]47[/C][C]284[/C][C]1353.05[/C][C]-1069.05[/C][/ROW]
[ROW][C]48[/C][C]332[/C][C]1353.05[/C][C]-1021.05[/C][/ROW]
[ROW][C]49[/C][C]59[/C][C]1353.05[/C][C]-1294.05[/C][/ROW]
[ROW][C]50[/C][C]71[/C][C]1353.05[/C][C]-1282.05[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=101272&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=101272&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
151251553.83571.2
253661553.83812.2
350781553.83524.2
427751553.81221.2
529521553.81398.2
627841553.81230.2
723501553.8796.2
824131553.8859.2
922031553.8649.2
107051553.8-848.8
117651553.8-788.8
128001553.8-753.8
1311611553.8-392.8
1412231553.8-330.8
1511881553.8-365.8
1611781553.8-375.8
1712251553.8-328.8
1811001553.8-453.8
1910871553.8-466.8
2011041553.8-449.8
2110461553.8-507.8
225711553.8-982.8
235911553.8-962.8
245361553.8-1017.8
253471553.8-1206.8
263901553.8-1163.8
273391553.8-1214.8
28761553.8-1477.8
29681553.8-1485.8
30681553.8-1485.8
3140441353.052690.95
3249761353.053622.95
3322081353.05854.95
3427211353.051367.95
3518371353.05483.95
3622551353.05901.95
375491353.05-804.05
386691353.05-684.05
399591353.05-394.05
4011581353.05-195.05
418941353.05-459.05
4210741353.05-279.05
438411353.05-512.05
4411071353.05-246.05
454591353.05-894.05
465641353.05-789.05
472841353.05-1069.05
483321353.05-1021.05
49591353.05-1294.05
50711353.05-1282.05







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
50.8773203157902750.2453593684194490.122679684209725
60.895546838663380.2089063226732410.104453161336621
70.9227476517709770.1545046964580460.0772523482290232
80.9317696909889570.1364606180220860.068230309011043
90.9413929256244350.1172141487511310.0586070743755653
100.9827212908480130.03455741830397440.0172787091519872
110.9907386748457070.01852265030858660.00926132515429331
120.9929066841783040.01418663164339170.00709331582169584
130.9918733204090220.01625335918195560.0081266795909778
140.9898322801988040.02033543960239170.0101677198011959
150.9869489923278070.02610201534438650.0130510076721932
160.9829207280841680.0341585438316650.0170792719158325
170.9773226761898850.04535464762022970.0226773238101148
180.9703613620429650.05927727591407070.0296386379570354
190.9613461857294760.0773076285410480.038653814270524
200.949933069215290.1001338615694190.0500669307847094
210.936156373583520.1276872528329590.0638436264164796
220.923326393508850.1533472129822990.0766736064911495
230.9062020901750420.1875958196499160.0937979098249582
240.8854277812195130.2291444375609750.114572218780487
250.8633256177201770.2733487645596470.136674382279823
260.8345384143778990.3309231712442020.165461585622101
270.8008499942523090.3983000114953830.199150005747691
280.7683569373621970.4632861252756060.231643062637803
290.7290062663987920.5419874672024170.270993733601208
300.6823514145498110.6352971709003780.317648585450189
310.8001016510468320.3997966979063360.199898348953168
320.9930672409615940.01386551807681230.00693275903840616
330.9954887207021860.009022558595627940.00451127929781397
340.9993327268396380.001334546320724230.000667273160362117
350.9995852434293850.0008295131412297080.000414756570614854
360.9999877515424862.44969150274369e-051.22484575137184e-05
370.999967685905026.4628189960567e-053.23140949802835e-05
380.9998999891910850.0002000216178303210.00010001080891516
390.999739574439640.0005208511207189390.000260425560359469
400.9995826376828360.0008347246343279460.000417362317163973
410.9988955944670870.002208811065825790.00110440553291289
420.9983133930160380.003373213967922990.00168660698396149
430.9959000649171390.008199870165722980.00409993508286149
440.9987370576440070.002525884711985080.00126294235599254
450.9935076197558060.0129847604883870.00649238024419349

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
5 & 0.877320315790275 & 0.245359368419449 & 0.122679684209725 \tabularnewline
6 & 0.89554683866338 & 0.208906322673241 & 0.104453161336621 \tabularnewline
7 & 0.922747651770977 & 0.154504696458046 & 0.0772523482290232 \tabularnewline
8 & 0.931769690988957 & 0.136460618022086 & 0.068230309011043 \tabularnewline
9 & 0.941392925624435 & 0.117214148751131 & 0.0586070743755653 \tabularnewline
10 & 0.982721290848013 & 0.0345574183039744 & 0.0172787091519872 \tabularnewline
11 & 0.990738674845707 & 0.0185226503085866 & 0.00926132515429331 \tabularnewline
12 & 0.992906684178304 & 0.0141866316433917 & 0.00709331582169584 \tabularnewline
13 & 0.991873320409022 & 0.0162533591819556 & 0.0081266795909778 \tabularnewline
14 & 0.989832280198804 & 0.0203354396023917 & 0.0101677198011959 \tabularnewline
15 & 0.986948992327807 & 0.0261020153443865 & 0.0130510076721932 \tabularnewline
16 & 0.982920728084168 & 0.034158543831665 & 0.0170792719158325 \tabularnewline
17 & 0.977322676189885 & 0.0453546476202297 & 0.0226773238101148 \tabularnewline
18 & 0.970361362042965 & 0.0592772759140707 & 0.0296386379570354 \tabularnewline
19 & 0.961346185729476 & 0.077307628541048 & 0.038653814270524 \tabularnewline
20 & 0.94993306921529 & 0.100133861569419 & 0.0500669307847094 \tabularnewline
21 & 0.93615637358352 & 0.127687252832959 & 0.0638436264164796 \tabularnewline
22 & 0.92332639350885 & 0.153347212982299 & 0.0766736064911495 \tabularnewline
23 & 0.906202090175042 & 0.187595819649916 & 0.0937979098249582 \tabularnewline
24 & 0.885427781219513 & 0.229144437560975 & 0.114572218780487 \tabularnewline
25 & 0.863325617720177 & 0.273348764559647 & 0.136674382279823 \tabularnewline
26 & 0.834538414377899 & 0.330923171244202 & 0.165461585622101 \tabularnewline
27 & 0.800849994252309 & 0.398300011495383 & 0.199150005747691 \tabularnewline
28 & 0.768356937362197 & 0.463286125275606 & 0.231643062637803 \tabularnewline
29 & 0.729006266398792 & 0.541987467202417 & 0.270993733601208 \tabularnewline
30 & 0.682351414549811 & 0.635297170900378 & 0.317648585450189 \tabularnewline
31 & 0.800101651046832 & 0.399796697906336 & 0.199898348953168 \tabularnewline
32 & 0.993067240961594 & 0.0138655180768123 & 0.00693275903840616 \tabularnewline
33 & 0.995488720702186 & 0.00902255859562794 & 0.00451127929781397 \tabularnewline
34 & 0.999332726839638 & 0.00133454632072423 & 0.000667273160362117 \tabularnewline
35 & 0.999585243429385 & 0.000829513141229708 & 0.000414756570614854 \tabularnewline
36 & 0.999987751542486 & 2.44969150274369e-05 & 1.22484575137184e-05 \tabularnewline
37 & 0.99996768590502 & 6.4628189960567e-05 & 3.23140949802835e-05 \tabularnewline
38 & 0.999899989191085 & 0.000200021617830321 & 0.00010001080891516 \tabularnewline
39 & 0.99973957443964 & 0.000520851120718939 & 0.000260425560359469 \tabularnewline
40 & 0.999582637682836 & 0.000834724634327946 & 0.000417362317163973 \tabularnewline
41 & 0.998895594467087 & 0.00220881106582579 & 0.00110440553291289 \tabularnewline
42 & 0.998313393016038 & 0.00337321396792299 & 0.00168660698396149 \tabularnewline
43 & 0.995900064917139 & 0.00819987016572298 & 0.00409993508286149 \tabularnewline
44 & 0.998737057644007 & 0.00252588471198508 & 0.00126294235599254 \tabularnewline
45 & 0.993507619755806 & 0.012984760488387 & 0.00649238024419349 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=101272&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]5[/C][C]0.877320315790275[/C][C]0.245359368419449[/C][C]0.122679684209725[/C][/ROW]
[ROW][C]6[/C][C]0.89554683866338[/C][C]0.208906322673241[/C][C]0.104453161336621[/C][/ROW]
[ROW][C]7[/C][C]0.922747651770977[/C][C]0.154504696458046[/C][C]0.0772523482290232[/C][/ROW]
[ROW][C]8[/C][C]0.931769690988957[/C][C]0.136460618022086[/C][C]0.068230309011043[/C][/ROW]
[ROW][C]9[/C][C]0.941392925624435[/C][C]0.117214148751131[/C][C]0.0586070743755653[/C][/ROW]
[ROW][C]10[/C][C]0.982721290848013[/C][C]0.0345574183039744[/C][C]0.0172787091519872[/C][/ROW]
[ROW][C]11[/C][C]0.990738674845707[/C][C]0.0185226503085866[/C][C]0.00926132515429331[/C][/ROW]
[ROW][C]12[/C][C]0.992906684178304[/C][C]0.0141866316433917[/C][C]0.00709331582169584[/C][/ROW]
[ROW][C]13[/C][C]0.991873320409022[/C][C]0.0162533591819556[/C][C]0.0081266795909778[/C][/ROW]
[ROW][C]14[/C][C]0.989832280198804[/C][C]0.0203354396023917[/C][C]0.0101677198011959[/C][/ROW]
[ROW][C]15[/C][C]0.986948992327807[/C][C]0.0261020153443865[/C][C]0.0130510076721932[/C][/ROW]
[ROW][C]16[/C][C]0.982920728084168[/C][C]0.034158543831665[/C][C]0.0170792719158325[/C][/ROW]
[ROW][C]17[/C][C]0.977322676189885[/C][C]0.0453546476202297[/C][C]0.0226773238101148[/C][/ROW]
[ROW][C]18[/C][C]0.970361362042965[/C][C]0.0592772759140707[/C][C]0.0296386379570354[/C][/ROW]
[ROW][C]19[/C][C]0.961346185729476[/C][C]0.077307628541048[/C][C]0.038653814270524[/C][/ROW]
[ROW][C]20[/C][C]0.94993306921529[/C][C]0.100133861569419[/C][C]0.0500669307847094[/C][/ROW]
[ROW][C]21[/C][C]0.93615637358352[/C][C]0.127687252832959[/C][C]0.0638436264164796[/C][/ROW]
[ROW][C]22[/C][C]0.92332639350885[/C][C]0.153347212982299[/C][C]0.0766736064911495[/C][/ROW]
[ROW][C]23[/C][C]0.906202090175042[/C][C]0.187595819649916[/C][C]0.0937979098249582[/C][/ROW]
[ROW][C]24[/C][C]0.885427781219513[/C][C]0.229144437560975[/C][C]0.114572218780487[/C][/ROW]
[ROW][C]25[/C][C]0.863325617720177[/C][C]0.273348764559647[/C][C]0.136674382279823[/C][/ROW]
[ROW][C]26[/C][C]0.834538414377899[/C][C]0.330923171244202[/C][C]0.165461585622101[/C][/ROW]
[ROW][C]27[/C][C]0.800849994252309[/C][C]0.398300011495383[/C][C]0.199150005747691[/C][/ROW]
[ROW][C]28[/C][C]0.768356937362197[/C][C]0.463286125275606[/C][C]0.231643062637803[/C][/ROW]
[ROW][C]29[/C][C]0.729006266398792[/C][C]0.541987467202417[/C][C]0.270993733601208[/C][/ROW]
[ROW][C]30[/C][C]0.682351414549811[/C][C]0.635297170900378[/C][C]0.317648585450189[/C][/ROW]
[ROW][C]31[/C][C]0.800101651046832[/C][C]0.399796697906336[/C][C]0.199898348953168[/C][/ROW]
[ROW][C]32[/C][C]0.993067240961594[/C][C]0.0138655180768123[/C][C]0.00693275903840616[/C][/ROW]
[ROW][C]33[/C][C]0.995488720702186[/C][C]0.00902255859562794[/C][C]0.00451127929781397[/C][/ROW]
[ROW][C]34[/C][C]0.999332726839638[/C][C]0.00133454632072423[/C][C]0.000667273160362117[/C][/ROW]
[ROW][C]35[/C][C]0.999585243429385[/C][C]0.000829513141229708[/C][C]0.000414756570614854[/C][/ROW]
[ROW][C]36[/C][C]0.999987751542486[/C][C]2.44969150274369e-05[/C][C]1.22484575137184e-05[/C][/ROW]
[ROW][C]37[/C][C]0.99996768590502[/C][C]6.4628189960567e-05[/C][C]3.23140949802835e-05[/C][/ROW]
[ROW][C]38[/C][C]0.999899989191085[/C][C]0.000200021617830321[/C][C]0.00010001080891516[/C][/ROW]
[ROW][C]39[/C][C]0.99973957443964[/C][C]0.000520851120718939[/C][C]0.000260425560359469[/C][/ROW]
[ROW][C]40[/C][C]0.999582637682836[/C][C]0.000834724634327946[/C][C]0.000417362317163973[/C][/ROW]
[ROW][C]41[/C][C]0.998895594467087[/C][C]0.00220881106582579[/C][C]0.00110440553291289[/C][/ROW]
[ROW][C]42[/C][C]0.998313393016038[/C][C]0.00337321396792299[/C][C]0.00168660698396149[/C][/ROW]
[ROW][C]43[/C][C]0.995900064917139[/C][C]0.00819987016572298[/C][C]0.00409993508286149[/C][/ROW]
[ROW][C]44[/C][C]0.998737057644007[/C][C]0.00252588471198508[/C][C]0.00126294235599254[/C][/ROW]
[ROW][C]45[/C][C]0.993507619755806[/C][C]0.012984760488387[/C][C]0.00649238024419349[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=101272&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=101272&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
50.8773203157902750.2453593684194490.122679684209725
60.895546838663380.2089063226732410.104453161336621
70.9227476517709770.1545046964580460.0772523482290232
80.9317696909889570.1364606180220860.068230309011043
90.9413929256244350.1172141487511310.0586070743755653
100.9827212908480130.03455741830397440.0172787091519872
110.9907386748457070.01852265030858660.00926132515429331
120.9929066841783040.01418663164339170.00709331582169584
130.9918733204090220.01625335918195560.0081266795909778
140.9898322801988040.02033543960239170.0101677198011959
150.9869489923278070.02610201534438650.0130510076721932
160.9829207280841680.0341585438316650.0170792719158325
170.9773226761898850.04535464762022970.0226773238101148
180.9703613620429650.05927727591407070.0296386379570354
190.9613461857294760.0773076285410480.038653814270524
200.949933069215290.1001338615694190.0500669307847094
210.936156373583520.1276872528329590.0638436264164796
220.923326393508850.1533472129822990.0766736064911495
230.9062020901750420.1875958196499160.0937979098249582
240.8854277812195130.2291444375609750.114572218780487
250.8633256177201770.2733487645596470.136674382279823
260.8345384143778990.3309231712442020.165461585622101
270.8008499942523090.3983000114953830.199150005747691
280.7683569373621970.4632861252756060.231643062637803
290.7290062663987920.5419874672024170.270993733601208
300.6823514145498110.6352971709003780.317648585450189
310.8001016510468320.3997966979063360.199898348953168
320.9930672409615940.01386551807681230.00693275903840616
330.9954887207021860.009022558595627940.00451127929781397
340.9993327268396380.001334546320724230.000667273160362117
350.9995852434293850.0008295131412297080.000414756570614854
360.9999877515424862.44969150274369e-051.22484575137184e-05
370.999967685905026.4628189960567e-053.23140949802835e-05
380.9998999891910850.0002000216178303210.00010001080891516
390.999739574439640.0005208511207189390.000260425560359469
400.9995826376828360.0008347246343279460.000417362317163973
410.9988955944670870.002208811065825790.00110440553291289
420.9983133930160380.003373213967922990.00168660698396149
430.9959000649171390.008199870165722980.00409993508286149
440.9987370576440070.002525884711985080.00126294235599254
450.9935076197558060.0129847604883870.00649238024419349







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level120.292682926829268NOK
5% type I error level220.536585365853659NOK
10% type I error level240.585365853658537NOK

\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 & 12 & 0.292682926829268 & NOK \tabularnewline
5% type I error level & 22 & 0.536585365853659 & NOK \tabularnewline
10% type I error level & 24 & 0.585365853658537 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=101272&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]12[/C][C]0.292682926829268[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]22[/C][C]0.536585365853659[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]24[/C][C]0.585365853658537[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=101272&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=101272&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 level120.292682926829268NOK
5% type I error level220.536585365853659NOK
10% type I error level240.585365853658537NOK



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')
}