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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 computationSun, 19 Dec 2010 13:09:36 +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/19/t12927640439tjt8exdomg5i81.htm/, Retrieved Sun, 05 May 2024 07:15:16 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=112351, Retrieved Sun, 05 May 2024 07:15:16 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact178

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [ws sleep] [2010-12-12 12:39:51] [df61ce38492c371f14c407a12b3bb2eb]
- RM D  [Kendall tau Correlation Matrix] [ws sleep] [2010-12-13 12:38:57] [df61ce38492c371f14c407a12b3bb2eb]
- RMPD      [Multiple Regression] [] [2010-12-19 13:09:36] [40b262140b988d7b8204c4955f8b7651] [Current]
-    D        [Multiple Regression] [] [2010-12-21 12:54:44] [cc61d4f8286f3f36f43e751ed98b6d78]
-    D        [Multiple Regression] [] [2010-12-21 13:02:33] [cc61d4f8286f3f36f43e751ed98b6d78]
-    D        [Multiple Regression] [] [2010-12-21 13:02:33] [cc61d4f8286f3f36f43e751ed98b6d78]
-    D        [Multiple Regression] [] [2010-12-21 13:38:39] [1c63f3c303537b65dfa698074d619a3e]
-    D        [Multiple Regression] [Regression SWS] [2010-12-21 19:41:39] [8e42c8cdf50f15ce85eb45a67cf771d0]
- RM D        [Kendall tau Correlation Matrix] [Multiple regressi...] [2010-12-21 20:34:45] [8e42c8cdf50f15ce85eb45a67cf771d0]
- RM D          [Multiple Regression] [Multiple regressi...] [2010-12-21 20:57:09] [8e42c8cdf50f15ce85eb45a67cf771d0]
-             [Multiple Regression] [Regression SWS] [2010-12-21 21:14:25] [8e42c8cdf50f15ce85eb45a67cf771d0]
-    D        [Multiple Regression] [] [2010-12-21 23:35:07] [64a7ae6044525e7ca71ecb546c042c9e]
-    D        [Multiple Regression] [Multiple Regressi...] [2010-12-22 09:59:33] [f1bd7399181c649098ca7b814ee0e027]
-    D          [Multiple Regression] [Multiple Regressi...] [2010-12-22 10:22:20] [f1bd7399181c649098ca7b814ee0e027]
-    D        [Multiple Regression] [Regression SWS] [2010-12-22 10:20:05] [caa3ae6143f673f211f626a62938a034]
-    D        [Multiple Regression] [Regression PS] [2010-12-22 10:25:26] [caa3ae6143f673f211f626a62938a034]
-    D        [Multiple Regression] [] [2010-12-22 18:59:57] [f1052bedf858e5044a431fba108f61db]
- R  D        [Multiple Regression] [opdracht science_...] [2010-12-22 19:32:28] [46e2473aa7b3b1358cef648d2cdd04a9]
- R  D        [Multiple Regression] [opdracht science_...] [2010-12-22 19:56:22] [46e2473aa7b3b1358cef648d2cdd04a9]
-    D        [Multiple Regression] [] [2010-12-22 20:19:08] [f1052bedf858e5044a431fba108f61db]
-    D        [Multiple Regression] [SWS] [2010-12-22 21:25:38] [2ae6beac29e6e5c076a37b2886f2a670]
-    D          [Multiple Regression] [PS] [2010-12-22 21:30:16] [2ae6beac29e6e5c076a37b2886f2a670]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
6,3	0	3
14,3	0,544068044	1
9,1	1,02325246	4
15,8	-1,638272164	1
10,9	0,51851394	1
8,3	1,717337583	1
11	-0,37161107	4
3,2	2,667452953	5
2,1	3,406028945	4
7,4	0,626853415	1
9,5	-0,698970004	2
3,3	1,441852176	5
5,7	-0,124938737	2
7,4	0,017033339	4
11	-0,920818754	2
6,6	-0,105130343	2
2,1	2,716837723	5
17,9	-2	1
12,8	0,544068044	1
6,1	1,792391689	1
6,3	-1,124938737	1
11,9	-1,638272164	3
13,8	0,230448921	1
15,2	-0,318758763	2
10	1	4
11,9	0,209515015	2
6,5	2,283301229	4
7,5	0,397940009	5
10,6	-0,552841969	3
8,4	0,832508913	2
4,9	0,556302501	3
4,7	1,929418926	1
3,2	1,744292983	5
10,4	-0,995678626	3
5,2	2,204119983	4
11	-0,045757491	2
4,9	0,301029996	3
13,2	-0,982966661	2
9,7	0,622214023	4

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24

\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 & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=112351&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]'Sir Ronald Aylmer Fisher' @ 193.190.124.24[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=112351&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=112351&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 time5 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24






Multiple Linear Regression - Estimated Regression Equation
SWS[t] = + 11.6991087210001 -1.81485814734191`log(wb)`[t] -0.80621691930904D[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
SWS[t] =  +  11.6991087210001 -1.81485814734191`log(wb)`[t] -0.80621691930904D[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=112351&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]SWS[t] =  +  11.6991087210001 -1.81485814734191`log(wb)`[t] -0.80621691930904D[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=112351&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=112351&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
SWS[t] = + 11.6991087210001 -1.81485814734191`log(wb)`[t] -0.80621691930904D[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)11.69910872100010.94109512.431400
`log(wb)`-1.814858147341910.37295-4.86622.3e-051.1e-05
D-0.806216919309040.336956-2.39270.0220680.011034

\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) & 11.6991087210001 & 0.941095 & 12.4314 & 0 & 0 \tabularnewline
`log(wb)` & -1.81485814734191 & 0.37295 & -4.8662 & 2.3e-05 & 1.1e-05 \tabularnewline
D & -0.80621691930904 & 0.336956 & -2.3927 & 0.022068 & 0.011034 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=112351&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]11.6991087210001[/C][C]0.941095[/C][C]12.4314[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]`log(wb)`[/C][C]-1.81485814734191[/C][C]0.37295[/C][C]-4.8662[/C][C]2.3e-05[/C][C]1.1e-05[/C][/ROW]
[ROW][C]D[/C][C]-0.80621691930904[/C][C]0.336956[/C][C]-2.3927[/C][C]0.022068[/C][C]0.011034[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=112351&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=112351&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)11.69910872100010.94109512.431400
`log(wb)`-1.814858147341910.37295-4.86622.3e-051.1e-05
D-0.806216919309040.336956-2.39270.0220680.011034






Multiple Linear Regression - Regression Statistics
Multiple R0.757704457897525
R-squared0.574116045517782
Adjusted R-squared0.550455825824325
F-TEST (value)24.2650344314664
F-TEST (DF numerator)2
F-TEST (DF denominator)36
p-value2.12443282854302e-07
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.66067288469349
Sum Squared Residuals254.850487176355

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.757704457897525 \tabularnewline
R-squared & 0.574116045517782 \tabularnewline
Adjusted R-squared & 0.550455825824325 \tabularnewline
F-TEST (value) & 24.2650344314664 \tabularnewline
F-TEST (DF numerator) & 2 \tabularnewline
F-TEST (DF denominator) & 36 \tabularnewline
p-value & 2.12443282854302e-07 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.66067288469349 \tabularnewline
Sum Squared Residuals & 254.850487176355 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=112351&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.757704457897525[/C][/ROW]
[ROW][C]R-squared[/C][C]0.574116045517782[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.550455825824325[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]24.2650344314664[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]2[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]36[/C][/ROW]
[ROW][C]p-value[/C][C]2.12443282854302e-07[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]2.66067288469349[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]254.850487176355[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=112351&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=112351&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 R0.757704457897525
R-squared0.574116045517782
Adjusted R-squared0.550455825824325
F-TEST (value)24.2650344314664
F-TEST (DF numerator)2
F-TEST (DF denominator)36
p-value2.12443282854302e-07
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.66067288469349
Sum Squared Residuals254.850487176355






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
16.39.28045796307299-2.98045796307299
214.39.905485479329284.39451452067072
39.16.617182979945292.48281702005471
415.813.86612338608991.93387661391007
510.99.95186255317170.94813744682829
68.37.776167697447050.523832302552955
7119.148662421795891.85133757820411
83.22.826975400051610.373024599948394
92.12.29278166284831-0.192781662848311
107.49.75524177428921-2.35524177428921
119.511.3552062888890-1.85520628888904
123.35.05126695557864-1.75126695557864
135.710.3134209671451-4.61342096714508
147.48.44332794970336-1.04332794970336
151111.7578303003042-0.757830300304155
166.610.2774715419084-3.67747154190843
172.12.7373490478625-0.6373490478625
1817.914.52260809637493.37739190362511
1912.89.905485479329292.89451452067071
206.17.63995514168148-1.53995514168148
216.312.9344960337960-6.63449603379604
2211.912.2536895474719-0.353689547471852
2313.810.47465969986813.32534030013194
2415.210.66517682044924.53482317955079
25106.659382896422033.34061710357797
2611.99.706434850418812.19356514958119
276.54.330373205477492.16962679452251
287.56.945819456967940.554180543032061
2910.610.28378771470520.316212285294819
308.48.57578929888921-0.175789298889214
314.98.27084783674645-3.37084783674645
324.77.39127014420428-2.69127014420428
333.24.50237979290602-1.30237979290602
3410.411.0874734296033-0.687473429603286
355.24.474075934897270.725924065102726
361110.16971823772530.8302817622747
374.98.73413122223808-3.83413122223808
3813.211.87061993566341.32938006433665
399.77.3450108547322.35498914526799

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 6.3 & 9.28045796307299 & -2.98045796307299 \tabularnewline
2 & 14.3 & 9.90548547932928 & 4.39451452067072 \tabularnewline
3 & 9.1 & 6.61718297994529 & 2.48281702005471 \tabularnewline
4 & 15.8 & 13.8661233860899 & 1.93387661391007 \tabularnewline
5 & 10.9 & 9.9518625531717 & 0.94813744682829 \tabularnewline
6 & 8.3 & 7.77616769744705 & 0.523832302552955 \tabularnewline
7 & 11 & 9.14866242179589 & 1.85133757820411 \tabularnewline
8 & 3.2 & 2.82697540005161 & 0.373024599948394 \tabularnewline
9 & 2.1 & 2.29278166284831 & -0.192781662848311 \tabularnewline
10 & 7.4 & 9.75524177428921 & -2.35524177428921 \tabularnewline
11 & 9.5 & 11.3552062888890 & -1.85520628888904 \tabularnewline
12 & 3.3 & 5.05126695557864 & -1.75126695557864 \tabularnewline
13 & 5.7 & 10.3134209671451 & -4.61342096714508 \tabularnewline
14 & 7.4 & 8.44332794970336 & -1.04332794970336 \tabularnewline
15 & 11 & 11.7578303003042 & -0.757830300304155 \tabularnewline
16 & 6.6 & 10.2774715419084 & -3.67747154190843 \tabularnewline
17 & 2.1 & 2.7373490478625 & -0.6373490478625 \tabularnewline
18 & 17.9 & 14.5226080963749 & 3.37739190362511 \tabularnewline
19 & 12.8 & 9.90548547932929 & 2.89451452067071 \tabularnewline
20 & 6.1 & 7.63995514168148 & -1.53995514168148 \tabularnewline
21 & 6.3 & 12.9344960337960 & -6.63449603379604 \tabularnewline
22 & 11.9 & 12.2536895474719 & -0.353689547471852 \tabularnewline
23 & 13.8 & 10.4746596998681 & 3.32534030013194 \tabularnewline
24 & 15.2 & 10.6651768204492 & 4.53482317955079 \tabularnewline
25 & 10 & 6.65938289642203 & 3.34061710357797 \tabularnewline
26 & 11.9 & 9.70643485041881 & 2.19356514958119 \tabularnewline
27 & 6.5 & 4.33037320547749 & 2.16962679452251 \tabularnewline
28 & 7.5 & 6.94581945696794 & 0.554180543032061 \tabularnewline
29 & 10.6 & 10.2837877147052 & 0.316212285294819 \tabularnewline
30 & 8.4 & 8.57578929888921 & -0.175789298889214 \tabularnewline
31 & 4.9 & 8.27084783674645 & -3.37084783674645 \tabularnewline
32 & 4.7 & 7.39127014420428 & -2.69127014420428 \tabularnewline
33 & 3.2 & 4.50237979290602 & -1.30237979290602 \tabularnewline
34 & 10.4 & 11.0874734296033 & -0.687473429603286 \tabularnewline
35 & 5.2 & 4.47407593489727 & 0.725924065102726 \tabularnewline
36 & 11 & 10.1697182377253 & 0.8302817622747 \tabularnewline
37 & 4.9 & 8.73413122223808 & -3.83413122223808 \tabularnewline
38 & 13.2 & 11.8706199356634 & 1.32938006433665 \tabularnewline
39 & 9.7 & 7.345010854732 & 2.35498914526799 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=112351&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]6.3[/C][C]9.28045796307299[/C][C]-2.98045796307299[/C][/ROW]
[ROW][C]2[/C][C]14.3[/C][C]9.90548547932928[/C][C]4.39451452067072[/C][/ROW]
[ROW][C]3[/C][C]9.1[/C][C]6.61718297994529[/C][C]2.48281702005471[/C][/ROW]
[ROW][C]4[/C][C]15.8[/C][C]13.8661233860899[/C][C]1.93387661391007[/C][/ROW]
[ROW][C]5[/C][C]10.9[/C][C]9.9518625531717[/C][C]0.94813744682829[/C][/ROW]
[ROW][C]6[/C][C]8.3[/C][C]7.77616769744705[/C][C]0.523832302552955[/C][/ROW]
[ROW][C]7[/C][C]11[/C][C]9.14866242179589[/C][C]1.85133757820411[/C][/ROW]
[ROW][C]8[/C][C]3.2[/C][C]2.82697540005161[/C][C]0.373024599948394[/C][/ROW]
[ROW][C]9[/C][C]2.1[/C][C]2.29278166284831[/C][C]-0.192781662848311[/C][/ROW]
[ROW][C]10[/C][C]7.4[/C][C]9.75524177428921[/C][C]-2.35524177428921[/C][/ROW]
[ROW][C]11[/C][C]9.5[/C][C]11.3552062888890[/C][C]-1.85520628888904[/C][/ROW]
[ROW][C]12[/C][C]3.3[/C][C]5.05126695557864[/C][C]-1.75126695557864[/C][/ROW]
[ROW][C]13[/C][C]5.7[/C][C]10.3134209671451[/C][C]-4.61342096714508[/C][/ROW]
[ROW][C]14[/C][C]7.4[/C][C]8.44332794970336[/C][C]-1.04332794970336[/C][/ROW]
[ROW][C]15[/C][C]11[/C][C]11.7578303003042[/C][C]-0.757830300304155[/C][/ROW]
[ROW][C]16[/C][C]6.6[/C][C]10.2774715419084[/C][C]-3.67747154190843[/C][/ROW]
[ROW][C]17[/C][C]2.1[/C][C]2.7373490478625[/C][C]-0.6373490478625[/C][/ROW]
[ROW][C]18[/C][C]17.9[/C][C]14.5226080963749[/C][C]3.37739190362511[/C][/ROW]
[ROW][C]19[/C][C]12.8[/C][C]9.90548547932929[/C][C]2.89451452067071[/C][/ROW]
[ROW][C]20[/C][C]6.1[/C][C]7.63995514168148[/C][C]-1.53995514168148[/C][/ROW]
[ROW][C]21[/C][C]6.3[/C][C]12.9344960337960[/C][C]-6.63449603379604[/C][/ROW]
[ROW][C]22[/C][C]11.9[/C][C]12.2536895474719[/C][C]-0.353689547471852[/C][/ROW]
[ROW][C]23[/C][C]13.8[/C][C]10.4746596998681[/C][C]3.32534030013194[/C][/ROW]
[ROW][C]24[/C][C]15.2[/C][C]10.6651768204492[/C][C]4.53482317955079[/C][/ROW]
[ROW][C]25[/C][C]10[/C][C]6.65938289642203[/C][C]3.34061710357797[/C][/ROW]
[ROW][C]26[/C][C]11.9[/C][C]9.70643485041881[/C][C]2.19356514958119[/C][/ROW]
[ROW][C]27[/C][C]6.5[/C][C]4.33037320547749[/C][C]2.16962679452251[/C][/ROW]
[ROW][C]28[/C][C]7.5[/C][C]6.94581945696794[/C][C]0.554180543032061[/C][/ROW]
[ROW][C]29[/C][C]10.6[/C][C]10.2837877147052[/C][C]0.316212285294819[/C][/ROW]
[ROW][C]30[/C][C]8.4[/C][C]8.57578929888921[/C][C]-0.175789298889214[/C][/ROW]
[ROW][C]31[/C][C]4.9[/C][C]8.27084783674645[/C][C]-3.37084783674645[/C][/ROW]
[ROW][C]32[/C][C]4.7[/C][C]7.39127014420428[/C][C]-2.69127014420428[/C][/ROW]
[ROW][C]33[/C][C]3.2[/C][C]4.50237979290602[/C][C]-1.30237979290602[/C][/ROW]
[ROW][C]34[/C][C]10.4[/C][C]11.0874734296033[/C][C]-0.687473429603286[/C][/ROW]
[ROW][C]35[/C][C]5.2[/C][C]4.47407593489727[/C][C]0.725924065102726[/C][/ROW]
[ROW][C]36[/C][C]11[/C][C]10.1697182377253[/C][C]0.8302817622747[/C][/ROW]
[ROW][C]37[/C][C]4.9[/C][C]8.73413122223808[/C][C]-3.83413122223808[/C][/ROW]
[ROW][C]38[/C][C]13.2[/C][C]11.8706199356634[/C][C]1.32938006433665[/C][/ROW]
[ROW][C]39[/C][C]9.7[/C][C]7.345010854732[/C][C]2.35498914526799[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=112351&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=112351&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
16.39.28045796307299-2.98045796307299
214.39.905485479329284.39451452067072
39.16.617182979945292.48281702005471
415.813.86612338608991.93387661391007
510.99.95186255317170.94813744682829
68.37.776167697447050.523832302552955
7119.148662421795891.85133757820411
83.22.826975400051610.373024599948394
92.12.29278166284831-0.192781662848311
107.49.75524177428921-2.35524177428921
119.511.3552062888890-1.85520628888904
123.35.05126695557864-1.75126695557864
135.710.3134209671451-4.61342096714508
147.48.44332794970336-1.04332794970336
151111.7578303003042-0.757830300304155
166.610.2774715419084-3.67747154190843
172.12.7373490478625-0.6373490478625
1817.914.52260809637493.37739190362511
1912.89.905485479329292.89451452067071
206.17.63995514168148-1.53995514168148
216.312.9344960337960-6.63449603379604
2211.912.2536895474719-0.353689547471852
2313.810.47465969986813.32534030013194
2415.210.66517682044924.53482317955079
25106.659382896422033.34061710357797
2611.99.706434850418812.19356514958119
276.54.330373205477492.16962679452251
287.56.945819456967940.554180543032061
2910.610.28378771470520.316212285294819
308.48.57578929888921-0.175789298889214
314.98.27084783674645-3.37084783674645
324.77.39127014420428-2.69127014420428
333.24.50237979290602-1.30237979290602
3410.411.0874734296033-0.687473429603286
355.24.474075934897270.725924065102726
361110.16971823772530.8302817622747
374.98.73413122223808-3.83413122223808
3813.211.87061993566341.32938006433665
399.77.3450108547322.35498914526799






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
60.7121873172712780.5756253654574430.287812682728722
70.5960311729845030.8079376540309950.403968827015497
80.4434690838527910.8869381677055820.556530916147209
90.3149135138370110.6298270276740220.685086486162989
100.3836927797385020.7673855594770050.616307220261498
110.3639318316109670.7278636632219350.636068168389033
120.3027255600901530.6054511201803060.697274439909847
130.5064160308951240.9871679382097510.493583969104876
140.407595650588820.815191301177640.59240434941118
150.312033066007290.624066132014580.68796693399271
160.3728203543123070.7456407086246140.627179645687693
170.2839980627324270.5679961254648540.716001937267573
180.3466173015582190.6932346031164370.653382698441781
190.3554755041632580.7109510083265160.644524495836742
200.2956957853587040.5913915707174080.704304214641296
210.739281585714120.5214368285717610.260718414285881
220.6659482843573990.6681034312852020.334051715642601
230.6974278132594010.6051443734811990.302572186740599
240.8412803651757210.3174392696485570.158719634824279
250.871134828854870.2577303422902580.128865171145129
260.8745770497694590.2508459004610830.125422950230541
270.8811256575186620.2377486849626770.118874342481338
280.8094343216708010.3811313566583980.190565678329199
290.7125472433169350.5749055133661290.287452756683065
300.6065690340720320.7868619318559360.393430965927968
310.6324571558734520.7350856882530960.367542844126548
320.5420138591350830.9159722817298330.457986140864917
330.4010892663909410.8021785327818830.598910733609059

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
6 & 0.712187317271278 & 0.575625365457443 & 0.287812682728722 \tabularnewline
7 & 0.596031172984503 & 0.807937654030995 & 0.403968827015497 \tabularnewline
8 & 0.443469083852791 & 0.886938167705582 & 0.556530916147209 \tabularnewline
9 & 0.314913513837011 & 0.629827027674022 & 0.685086486162989 \tabularnewline
10 & 0.383692779738502 & 0.767385559477005 & 0.616307220261498 \tabularnewline
11 & 0.363931831610967 & 0.727863663221935 & 0.636068168389033 \tabularnewline
12 & 0.302725560090153 & 0.605451120180306 & 0.697274439909847 \tabularnewline
13 & 0.506416030895124 & 0.987167938209751 & 0.493583969104876 \tabularnewline
14 & 0.40759565058882 & 0.81519130117764 & 0.59240434941118 \tabularnewline
15 & 0.31203306600729 & 0.62406613201458 & 0.68796693399271 \tabularnewline
16 & 0.372820354312307 & 0.745640708624614 & 0.627179645687693 \tabularnewline
17 & 0.283998062732427 & 0.567996125464854 & 0.716001937267573 \tabularnewline
18 & 0.346617301558219 & 0.693234603116437 & 0.653382698441781 \tabularnewline
19 & 0.355475504163258 & 0.710951008326516 & 0.644524495836742 \tabularnewline
20 & 0.295695785358704 & 0.591391570717408 & 0.704304214641296 \tabularnewline
21 & 0.73928158571412 & 0.521436828571761 & 0.260718414285881 \tabularnewline
22 & 0.665948284357399 & 0.668103431285202 & 0.334051715642601 \tabularnewline
23 & 0.697427813259401 & 0.605144373481199 & 0.302572186740599 \tabularnewline
24 & 0.841280365175721 & 0.317439269648557 & 0.158719634824279 \tabularnewline
25 & 0.87113482885487 & 0.257730342290258 & 0.128865171145129 \tabularnewline
26 & 0.874577049769459 & 0.250845900461083 & 0.125422950230541 \tabularnewline
27 & 0.881125657518662 & 0.237748684962677 & 0.118874342481338 \tabularnewline
28 & 0.809434321670801 & 0.381131356658398 & 0.190565678329199 \tabularnewline
29 & 0.712547243316935 & 0.574905513366129 & 0.287452756683065 \tabularnewline
30 & 0.606569034072032 & 0.786861931855936 & 0.393430965927968 \tabularnewline
31 & 0.632457155873452 & 0.735085688253096 & 0.367542844126548 \tabularnewline
32 & 0.542013859135083 & 0.915972281729833 & 0.457986140864917 \tabularnewline
33 & 0.401089266390941 & 0.802178532781883 & 0.598910733609059 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=112351&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]6[/C][C]0.712187317271278[/C][C]0.575625365457443[/C][C]0.287812682728722[/C][/ROW]
[ROW][C]7[/C][C]0.596031172984503[/C][C]0.807937654030995[/C][C]0.403968827015497[/C][/ROW]
[ROW][C]8[/C][C]0.443469083852791[/C][C]0.886938167705582[/C][C]0.556530916147209[/C][/ROW]
[ROW][C]9[/C][C]0.314913513837011[/C][C]0.629827027674022[/C][C]0.685086486162989[/C][/ROW]
[ROW][C]10[/C][C]0.383692779738502[/C][C]0.767385559477005[/C][C]0.616307220261498[/C][/ROW]
[ROW][C]11[/C][C]0.363931831610967[/C][C]0.727863663221935[/C][C]0.636068168389033[/C][/ROW]
[ROW][C]12[/C][C]0.302725560090153[/C][C]0.605451120180306[/C][C]0.697274439909847[/C][/ROW]
[ROW][C]13[/C][C]0.506416030895124[/C][C]0.987167938209751[/C][C]0.493583969104876[/C][/ROW]
[ROW][C]14[/C][C]0.40759565058882[/C][C]0.81519130117764[/C][C]0.59240434941118[/C][/ROW]
[ROW][C]15[/C][C]0.31203306600729[/C][C]0.62406613201458[/C][C]0.68796693399271[/C][/ROW]
[ROW][C]16[/C][C]0.372820354312307[/C][C]0.745640708624614[/C][C]0.627179645687693[/C][/ROW]
[ROW][C]17[/C][C]0.283998062732427[/C][C]0.567996125464854[/C][C]0.716001937267573[/C][/ROW]
[ROW][C]18[/C][C]0.346617301558219[/C][C]0.693234603116437[/C][C]0.653382698441781[/C][/ROW]
[ROW][C]19[/C][C]0.355475504163258[/C][C]0.710951008326516[/C][C]0.644524495836742[/C][/ROW]
[ROW][C]20[/C][C]0.295695785358704[/C][C]0.591391570717408[/C][C]0.704304214641296[/C][/ROW]
[ROW][C]21[/C][C]0.73928158571412[/C][C]0.521436828571761[/C][C]0.260718414285881[/C][/ROW]
[ROW][C]22[/C][C]0.665948284357399[/C][C]0.668103431285202[/C][C]0.334051715642601[/C][/ROW]
[ROW][C]23[/C][C]0.697427813259401[/C][C]0.605144373481199[/C][C]0.302572186740599[/C][/ROW]
[ROW][C]24[/C][C]0.841280365175721[/C][C]0.317439269648557[/C][C]0.158719634824279[/C][/ROW]
[ROW][C]25[/C][C]0.87113482885487[/C][C]0.257730342290258[/C][C]0.128865171145129[/C][/ROW]
[ROW][C]26[/C][C]0.874577049769459[/C][C]0.250845900461083[/C][C]0.125422950230541[/C][/ROW]
[ROW][C]27[/C][C]0.881125657518662[/C][C]0.237748684962677[/C][C]0.118874342481338[/C][/ROW]
[ROW][C]28[/C][C]0.809434321670801[/C][C]0.381131356658398[/C][C]0.190565678329199[/C][/ROW]
[ROW][C]29[/C][C]0.712547243316935[/C][C]0.574905513366129[/C][C]0.287452756683065[/C][/ROW]
[ROW][C]30[/C][C]0.606569034072032[/C][C]0.786861931855936[/C][C]0.393430965927968[/C][/ROW]
[ROW][C]31[/C][C]0.632457155873452[/C][C]0.735085688253096[/C][C]0.367542844126548[/C][/ROW]
[ROW][C]32[/C][C]0.542013859135083[/C][C]0.915972281729833[/C][C]0.457986140864917[/C][/ROW]
[ROW][C]33[/C][C]0.401089266390941[/C][C]0.802178532781883[/C][C]0.598910733609059[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=112351&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=112351&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
60.7121873172712780.5756253654574430.287812682728722
70.5960311729845030.8079376540309950.403968827015497
80.4434690838527910.8869381677055820.556530916147209
90.3149135138370110.6298270276740220.685086486162989
100.3836927797385020.7673855594770050.616307220261498
110.3639318316109670.7278636632219350.636068168389033
120.3027255600901530.6054511201803060.697274439909847
130.5064160308951240.9871679382097510.493583969104876
140.407595650588820.815191301177640.59240434941118
150.312033066007290.624066132014580.68796693399271
160.3728203543123070.7456407086246140.627179645687693
170.2839980627324270.5679961254648540.716001937267573
180.3466173015582190.6932346031164370.653382698441781
190.3554755041632580.7109510083265160.644524495836742
200.2956957853587040.5913915707174080.704304214641296
210.739281585714120.5214368285717610.260718414285881
220.6659482843573990.6681034312852020.334051715642601
230.6974278132594010.6051443734811990.302572186740599
240.8412803651757210.3174392696485570.158719634824279
250.871134828854870.2577303422902580.128865171145129
260.8745770497694590.2508459004610830.125422950230541
270.8811256575186620.2377486849626770.118874342481338
280.8094343216708010.3811313566583980.190565678329199
290.7125472433169350.5749055133661290.287452756683065
300.6065690340720320.7868619318559360.393430965927968
310.6324571558734520.7350856882530960.367542844126548
320.5420138591350830.9159722817298330.457986140864917
330.4010892663909410.8021785327818830.598910733609059






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=112351&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=112351&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=112351&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 3
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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')
}