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Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationThu, 19 Nov 2009 02:48:41 -0700
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2009/Nov/19/t1258624171n1m1l1oplu8ky3v.htm/, Retrieved Thu, 28 Mar 2024 17:59:23 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=57673, Retrieved Thu, 28 Mar 2024 17:59:23 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact169
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [Indicator voor he...] [2009-11-19 09:48:41] [41dcf2419e4beff0486cef71832b5d35] [Current]
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Dataseries X:
23	25.7
19	24.7
18	24.2
19	23.6
19	24.4
22	22.5
23	19.4
20	18.1
14	18.1
14	20.7
14	19.1
15	18.3
11	16.9
17	17.9
16	20.2
20	21.2
24	23.8
23	24
20	26.6
21	25.3
19	27.6
23	24.7
23	26.6
23	24.4
23	24.6
27	26
26	24.8
17	24
24	22.7
26	23
24	24.1
27	24
27	22.7
26	22.6
24	23.1
23	24.4
23	23
24	22
17	21.3
21	21.5
19	21.3
22	23.2
22	21.8
18	23.3
16	21
14	22.4
12	20.4
14	19.9
16	21.3
8	18.9
3	15.6
0	12.5
5	7.8
1	5.5
1	4
3	3.3
6	3.7
7	3.1
8	5
14	6.3




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 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 & 3 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57673&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]3 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57673&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57673&T=0

As an alternative you can also use a QR Code:  

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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







Multiple Linear Regression - Estimated Regression Equation
Y[t] = + 0.601780378749309 + 0.921662359123831X[t] -1.95485098721075M1[t] -1.78618604356121M2[t] -4.15945563935701M3[t] -4.15115848233528M4[t] -0.835027561225937M5[t] + 0.0967708880586427M6[t] -0.279264426744397M7[t] -0.129032730277341M8[t] -1.36313350563505M9[t] -1.03686649436496M10[t] -1.76589922464229M11[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Y[t] =  +  0.601780378749309 +  0.921662359123831X[t] -1.95485098721075M1[t] -1.78618604356121M2[t] -4.15945563935701M3[t] -4.15115848233528M4[t] -0.835027561225937M5[t] +  0.0967708880586427M6[t] -0.279264426744397M7[t] -0.129032730277341M8[t] -1.36313350563505M9[t] -1.03686649436496M10[t] -1.76589922464229M11[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57673&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Y[t] =  +  0.601780378749309 +  0.921662359123831X[t] -1.95485098721075M1[t] -1.78618604356121M2[t] -4.15945563935701M3[t] -4.15115848233528M4[t] -0.835027561225937M5[t] +  0.0967708880586427M6[t] -0.279264426744397M7[t] -0.129032730277341M8[t] -1.36313350563505M9[t] -1.03686649436496M10[t] -1.76589922464229M11[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57673&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57673&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
Y[t] = + 0.601780378749309 + 0.921662359123831X[t] -1.95485098721075M1[t] -1.78618604356121M2[t] -4.15945563935701M3[t] -4.15115848233528M4[t] -0.835027561225937M5[t] + 0.0967708880586427M6[t] -0.279264426744397M7[t] -0.129032730277341M8[t] -1.36313350563505M9[t] -1.03686649436496M10[t] -1.76589922464229M11[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)0.6017803787493092.5843390.23290.8168840.408442
X0.9216623591238310.08916610.336500
M1-1.954850987210752.815365-0.69440.490880.24544
M2-1.786186043561212.811476-0.63530.5283010.264151
M3-4.159455639357012.805894-1.48240.1449090.072454
M4-4.151158482335282.801721-1.48160.1451080.072554
M5-0.8350275612259372.799145-0.29830.7667770.383389
M60.09677088805864272.7979590.03460.9725560.486278
M7-0.2792644267443972.796978-0.09980.9208920.460446
M8-0.1290327302773412.796622-0.04610.9633950.481698
M9-1.363133505635052.796596-0.48740.6282220.314111
M10-1.036866494364962.796596-0.37080.7124820.356241
M11-1.765899224642292.79664-0.63140.5308150.265407

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Ordinary Least Squares \tabularnewline
Variable & Parameter & S.D. & T-STATH0: parameter = 0 & 2-tail p-value & 1-tail p-value \tabularnewline
(Intercept) & 0.601780378749309 & 2.584339 & 0.2329 & 0.816884 & 0.408442 \tabularnewline
X & 0.921662359123831 & 0.089166 & 10.3365 & 0 & 0 \tabularnewline
M1 & -1.95485098721075 & 2.815365 & -0.6944 & 0.49088 & 0.24544 \tabularnewline
M2 & -1.78618604356121 & 2.811476 & -0.6353 & 0.528301 & 0.264151 \tabularnewline
M3 & -4.15945563935701 & 2.805894 & -1.4824 & 0.144909 & 0.072454 \tabularnewline
M4 & -4.15115848233528 & 2.801721 & -1.4816 & 0.145108 & 0.072554 \tabularnewline
M5 & -0.835027561225937 & 2.799145 & -0.2983 & 0.766777 & 0.383389 \tabularnewline
M6 & 0.0967708880586427 & 2.797959 & 0.0346 & 0.972556 & 0.486278 \tabularnewline
M7 & -0.279264426744397 & 2.796978 & -0.0998 & 0.920892 & 0.460446 \tabularnewline
M8 & -0.129032730277341 & 2.796622 & -0.0461 & 0.963395 & 0.481698 \tabularnewline
M9 & -1.36313350563505 & 2.796596 & -0.4874 & 0.628222 & 0.314111 \tabularnewline
M10 & -1.03686649436496 & 2.796596 & -0.3708 & 0.712482 & 0.356241 \tabularnewline
M11 & -1.76589922464229 & 2.79664 & -0.6314 & 0.530815 & 0.265407 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57673&T=2

[TABLE]
[ROW][C]Multiple Linear Regression - Ordinary Least Squares[/C][/ROW]
[ROW][C]Variable[/C][C]Parameter[/C][C]S.D.[/C][C]T-STATH0: parameter = 0[/C][C]2-tail p-value[/C][C]1-tail p-value[/C][/ROW]
[ROW][C](Intercept)[/C][C]0.601780378749309[/C][C]2.584339[/C][C]0.2329[/C][C]0.816884[/C][C]0.408442[/C][/ROW]
[ROW][C]X[/C][C]0.921662359123831[/C][C]0.089166[/C][C]10.3365[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M1[/C][C]-1.95485098721075[/C][C]2.815365[/C][C]-0.6944[/C][C]0.49088[/C][C]0.24544[/C][/ROW]
[ROW][C]M2[/C][C]-1.78618604356121[/C][C]2.811476[/C][C]-0.6353[/C][C]0.528301[/C][C]0.264151[/C][/ROW]
[ROW][C]M3[/C][C]-4.15945563935701[/C][C]2.805894[/C][C]-1.4824[/C][C]0.144909[/C][C]0.072454[/C][/ROW]
[ROW][C]M4[/C][C]-4.15115848233528[/C][C]2.801721[/C][C]-1.4816[/C][C]0.145108[/C][C]0.072554[/C][/ROW]
[ROW][C]M5[/C][C]-0.835027561225937[/C][C]2.799145[/C][C]-0.2983[/C][C]0.766777[/C][C]0.383389[/C][/ROW]
[ROW][C]M6[/C][C]0.0967708880586427[/C][C]2.797959[/C][C]0.0346[/C][C]0.972556[/C][C]0.486278[/C][/ROW]
[ROW][C]M7[/C][C]-0.279264426744397[/C][C]2.796978[/C][C]-0.0998[/C][C]0.920892[/C][C]0.460446[/C][/ROW]
[ROW][C]M8[/C][C]-0.129032730277341[/C][C]2.796622[/C][C]-0.0461[/C][C]0.963395[/C][C]0.481698[/C][/ROW]
[ROW][C]M9[/C][C]-1.36313350563505[/C][C]2.796596[/C][C]-0.4874[/C][C]0.628222[/C][C]0.314111[/C][/ROW]
[ROW][C]M10[/C][C]-1.03686649436496[/C][C]2.796596[/C][C]-0.3708[/C][C]0.712482[/C][C]0.356241[/C][/ROW]
[ROW][C]M11[/C][C]-1.76589922464229[/C][C]2.79664[/C][C]-0.6314[/C][C]0.530815[/C][C]0.265407[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57673&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57673&T=2

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)0.6017803787493092.5843390.23290.8168840.408442
X0.9216623591238310.08916610.336500
M1-1.954850987210752.815365-0.69440.490880.24544
M2-1.786186043561212.811476-0.63530.5283010.264151
M3-4.159455639357012.805894-1.48240.1449090.072454
M4-4.151158482335282.801721-1.48160.1451080.072554
M5-0.8350275612259372.799145-0.29830.7667770.383389
M60.09677088805864272.7979590.03460.9725560.486278
M7-0.2792644267443972.796978-0.09980.9208920.460446
M8-0.1290327302773412.796622-0.04610.9633950.481698
M9-1.363133505635052.796596-0.48740.6282220.314111
M10-1.036866494364962.796596-0.37080.7124820.356241
M11-1.765899224642292.79664-0.63140.5308150.265407







Multiple Linear Regression - Regression Statistics
Multiple R0.838610587508146
R-squared0.703267717480758
Adjusted R-squared0.627506283646058
F-TEST (value)9.28266113620792
F-TEST (DF numerator)12
F-TEST (DF denominator)47
p-value8.0980888661486e-09
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation4.4218030807938
Sum Squared Residuals918.960096809924

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.838610587508146 \tabularnewline
R-squared & 0.703267717480758 \tabularnewline
Adjusted R-squared & 0.627506283646058 \tabularnewline
F-TEST (value) & 9.28266113620792 \tabularnewline
F-TEST (DF numerator) & 12 \tabularnewline
F-TEST (DF denominator) & 47 \tabularnewline
p-value & 8.0980888661486e-09 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 4.4218030807938 \tabularnewline
Sum Squared Residuals & 918.960096809924 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57673&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.838610587508146[/C][/ROW]
[ROW][C]R-squared[/C][C]0.703267717480758[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.627506283646058[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]9.28266113620792[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]12[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]47[/C][/ROW]
[ROW][C]p-value[/C][C]8.0980888661486e-09[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]4.4218030807938[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]918.960096809924[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57673&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57673&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 R0.838610587508146
R-squared0.703267717480758
Adjusted R-squared0.627506283646058
F-TEST (value)9.28266113620792
F-TEST (DF numerator)12
F-TEST (DF denominator)47
p-value8.0980888661486e-09
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation4.4218030807938
Sum Squared Residuals918.960096809924







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
12322.33365202102100.66634797897896
21921.5806546055467-2.58065460554671
31818.746553830189-0.746553830189013
41918.20185357173640.798146428263554
51922.2553143801449-3.25531438014486
62221.43595434709420.564045652905844
72318.20276571900724.79723428099276
82017.15483634861332.84516365138668
91415.9207355732556-1.92073557325561
101418.6433247182477-4.64332471824766
111416.4396322133722-2.43963221337219
121517.4682015507154-2.46820155071542
131114.2230232607313-3.22302326073131
141715.31335056350471.68664943649532
151615.05990439369370.940095606306308
162015.98986390983934.01013609016075
172421.70231696467062.29768303532944
182322.81844788577990.181552114220096
192024.8387347046988-4.83873470469883
202123.7908053343049-2.7908053343049
211924.676527984932-5.676527984932
222322.3299741547430.670025845257014
232323.3520999068009-0.352099906800930
242323.0903419413708-0.090341941370793
252321.31982342598481.68017657401519
262722.77881567240774.22118432759229
272619.29955124566336.70044875433668
281718.570518515386-1.57051851538598
292420.68848836963433.31151163036566
302621.89678552665614.10321447334392
312422.53457880688931.46542119311075
322722.59264426744394.40735573255608
332720.16038242522526.83961757477477
342620.39448320058295.60551679941706
352420.12628164986753.87371835013248
362323.0903419413708-0.090341941370793
372319.84516365138673.15483634861332
382419.09216623591244.90783376408761
391716.07373298872990.926267011270093
402116.26636261757644.7336373824236
411919.3981610668610-0.398161066860982
422222.0811179984808-0.0811179984808388
432220.41475538090441.58524461909556
441821.9474806160572-3.94748061605724
451618.5935564147147-2.59355641471472
461420.2101507287582-6.21015072875817
471217.6377932802332-5.63779328023318
481418.9428613253136-4.94286132531355
491618.2783376408762-2.27833764087617
50816.2350129226285-8.2350129226285
51310.8202575417241-7.82025754172407
5207.97140138546192-7.97140138546192
5356.95571921868926-1.95571921868926
5415.76769424198903-4.76769424198903
5514.00916538850024-3.00916538850024
5633.51423343358061-0.514233433580615
5762.648797601872443.35120239812756
5872.422067197668234.57793280233177
5983.444192949726184.55580705027382
60146.408253241229457.59174675877055

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 23 & 22.3336520210210 & 0.66634797897896 \tabularnewline
2 & 19 & 21.5806546055467 & -2.58065460554671 \tabularnewline
3 & 18 & 18.746553830189 & -0.746553830189013 \tabularnewline
4 & 19 & 18.2018535717364 & 0.798146428263554 \tabularnewline
5 & 19 & 22.2553143801449 & -3.25531438014486 \tabularnewline
6 & 22 & 21.4359543470942 & 0.564045652905844 \tabularnewline
7 & 23 & 18.2027657190072 & 4.79723428099276 \tabularnewline
8 & 20 & 17.1548363486133 & 2.84516365138668 \tabularnewline
9 & 14 & 15.9207355732556 & -1.92073557325561 \tabularnewline
10 & 14 & 18.6433247182477 & -4.64332471824766 \tabularnewline
11 & 14 & 16.4396322133722 & -2.43963221337219 \tabularnewline
12 & 15 & 17.4682015507154 & -2.46820155071542 \tabularnewline
13 & 11 & 14.2230232607313 & -3.22302326073131 \tabularnewline
14 & 17 & 15.3133505635047 & 1.68664943649532 \tabularnewline
15 & 16 & 15.0599043936937 & 0.940095606306308 \tabularnewline
16 & 20 & 15.9898639098393 & 4.01013609016075 \tabularnewline
17 & 24 & 21.7023169646706 & 2.29768303532944 \tabularnewline
18 & 23 & 22.8184478857799 & 0.181552114220096 \tabularnewline
19 & 20 & 24.8387347046988 & -4.83873470469883 \tabularnewline
20 & 21 & 23.7908053343049 & -2.7908053343049 \tabularnewline
21 & 19 & 24.676527984932 & -5.676527984932 \tabularnewline
22 & 23 & 22.329974154743 & 0.670025845257014 \tabularnewline
23 & 23 & 23.3520999068009 & -0.352099906800930 \tabularnewline
24 & 23 & 23.0903419413708 & -0.090341941370793 \tabularnewline
25 & 23 & 21.3198234259848 & 1.68017657401519 \tabularnewline
26 & 27 & 22.7788156724077 & 4.22118432759229 \tabularnewline
27 & 26 & 19.2995512456633 & 6.70044875433668 \tabularnewline
28 & 17 & 18.570518515386 & -1.57051851538598 \tabularnewline
29 & 24 & 20.6884883696343 & 3.31151163036566 \tabularnewline
30 & 26 & 21.8967855266561 & 4.10321447334392 \tabularnewline
31 & 24 & 22.5345788068893 & 1.46542119311075 \tabularnewline
32 & 27 & 22.5926442674439 & 4.40735573255608 \tabularnewline
33 & 27 & 20.1603824252252 & 6.83961757477477 \tabularnewline
34 & 26 & 20.3944832005829 & 5.60551679941706 \tabularnewline
35 & 24 & 20.1262816498675 & 3.87371835013248 \tabularnewline
36 & 23 & 23.0903419413708 & -0.090341941370793 \tabularnewline
37 & 23 & 19.8451636513867 & 3.15483634861332 \tabularnewline
38 & 24 & 19.0921662359124 & 4.90783376408761 \tabularnewline
39 & 17 & 16.0737329887299 & 0.926267011270093 \tabularnewline
40 & 21 & 16.2663626175764 & 4.7336373824236 \tabularnewline
41 & 19 & 19.3981610668610 & -0.398161066860982 \tabularnewline
42 & 22 & 22.0811179984808 & -0.0811179984808388 \tabularnewline
43 & 22 & 20.4147553809044 & 1.58524461909556 \tabularnewline
44 & 18 & 21.9474806160572 & -3.94748061605724 \tabularnewline
45 & 16 & 18.5935564147147 & -2.59355641471472 \tabularnewline
46 & 14 & 20.2101507287582 & -6.21015072875817 \tabularnewline
47 & 12 & 17.6377932802332 & -5.63779328023318 \tabularnewline
48 & 14 & 18.9428613253136 & -4.94286132531355 \tabularnewline
49 & 16 & 18.2783376408762 & -2.27833764087617 \tabularnewline
50 & 8 & 16.2350129226285 & -8.2350129226285 \tabularnewline
51 & 3 & 10.8202575417241 & -7.82025754172407 \tabularnewline
52 & 0 & 7.97140138546192 & -7.97140138546192 \tabularnewline
53 & 5 & 6.95571921868926 & -1.95571921868926 \tabularnewline
54 & 1 & 5.76769424198903 & -4.76769424198903 \tabularnewline
55 & 1 & 4.00916538850024 & -3.00916538850024 \tabularnewline
56 & 3 & 3.51423343358061 & -0.514233433580615 \tabularnewline
57 & 6 & 2.64879760187244 & 3.35120239812756 \tabularnewline
58 & 7 & 2.42206719766823 & 4.57793280233177 \tabularnewline
59 & 8 & 3.44419294972618 & 4.55580705027382 \tabularnewline
60 & 14 & 6.40825324122945 & 7.59174675877055 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57673&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]23[/C][C]22.3336520210210[/C][C]0.66634797897896[/C][/ROW]
[ROW][C]2[/C][C]19[/C][C]21.5806546055467[/C][C]-2.58065460554671[/C][/ROW]
[ROW][C]3[/C][C]18[/C][C]18.746553830189[/C][C]-0.746553830189013[/C][/ROW]
[ROW][C]4[/C][C]19[/C][C]18.2018535717364[/C][C]0.798146428263554[/C][/ROW]
[ROW][C]5[/C][C]19[/C][C]22.2553143801449[/C][C]-3.25531438014486[/C][/ROW]
[ROW][C]6[/C][C]22[/C][C]21.4359543470942[/C][C]0.564045652905844[/C][/ROW]
[ROW][C]7[/C][C]23[/C][C]18.2027657190072[/C][C]4.79723428099276[/C][/ROW]
[ROW][C]8[/C][C]20[/C][C]17.1548363486133[/C][C]2.84516365138668[/C][/ROW]
[ROW][C]9[/C][C]14[/C][C]15.9207355732556[/C][C]-1.92073557325561[/C][/ROW]
[ROW][C]10[/C][C]14[/C][C]18.6433247182477[/C][C]-4.64332471824766[/C][/ROW]
[ROW][C]11[/C][C]14[/C][C]16.4396322133722[/C][C]-2.43963221337219[/C][/ROW]
[ROW][C]12[/C][C]15[/C][C]17.4682015507154[/C][C]-2.46820155071542[/C][/ROW]
[ROW][C]13[/C][C]11[/C][C]14.2230232607313[/C][C]-3.22302326073131[/C][/ROW]
[ROW][C]14[/C][C]17[/C][C]15.3133505635047[/C][C]1.68664943649532[/C][/ROW]
[ROW][C]15[/C][C]16[/C][C]15.0599043936937[/C][C]0.940095606306308[/C][/ROW]
[ROW][C]16[/C][C]20[/C][C]15.9898639098393[/C][C]4.01013609016075[/C][/ROW]
[ROW][C]17[/C][C]24[/C][C]21.7023169646706[/C][C]2.29768303532944[/C][/ROW]
[ROW][C]18[/C][C]23[/C][C]22.8184478857799[/C][C]0.181552114220096[/C][/ROW]
[ROW][C]19[/C][C]20[/C][C]24.8387347046988[/C][C]-4.83873470469883[/C][/ROW]
[ROW][C]20[/C][C]21[/C][C]23.7908053343049[/C][C]-2.7908053343049[/C][/ROW]
[ROW][C]21[/C][C]19[/C][C]24.676527984932[/C][C]-5.676527984932[/C][/ROW]
[ROW][C]22[/C][C]23[/C][C]22.329974154743[/C][C]0.670025845257014[/C][/ROW]
[ROW][C]23[/C][C]23[/C][C]23.3520999068009[/C][C]-0.352099906800930[/C][/ROW]
[ROW][C]24[/C][C]23[/C][C]23.0903419413708[/C][C]-0.090341941370793[/C][/ROW]
[ROW][C]25[/C][C]23[/C][C]21.3198234259848[/C][C]1.68017657401519[/C][/ROW]
[ROW][C]26[/C][C]27[/C][C]22.7788156724077[/C][C]4.22118432759229[/C][/ROW]
[ROW][C]27[/C][C]26[/C][C]19.2995512456633[/C][C]6.70044875433668[/C][/ROW]
[ROW][C]28[/C][C]17[/C][C]18.570518515386[/C][C]-1.57051851538598[/C][/ROW]
[ROW][C]29[/C][C]24[/C][C]20.6884883696343[/C][C]3.31151163036566[/C][/ROW]
[ROW][C]30[/C][C]26[/C][C]21.8967855266561[/C][C]4.10321447334392[/C][/ROW]
[ROW][C]31[/C][C]24[/C][C]22.5345788068893[/C][C]1.46542119311075[/C][/ROW]
[ROW][C]32[/C][C]27[/C][C]22.5926442674439[/C][C]4.40735573255608[/C][/ROW]
[ROW][C]33[/C][C]27[/C][C]20.1603824252252[/C][C]6.83961757477477[/C][/ROW]
[ROW][C]34[/C][C]26[/C][C]20.3944832005829[/C][C]5.60551679941706[/C][/ROW]
[ROW][C]35[/C][C]24[/C][C]20.1262816498675[/C][C]3.87371835013248[/C][/ROW]
[ROW][C]36[/C][C]23[/C][C]23.0903419413708[/C][C]-0.090341941370793[/C][/ROW]
[ROW][C]37[/C][C]23[/C][C]19.8451636513867[/C][C]3.15483634861332[/C][/ROW]
[ROW][C]38[/C][C]24[/C][C]19.0921662359124[/C][C]4.90783376408761[/C][/ROW]
[ROW][C]39[/C][C]17[/C][C]16.0737329887299[/C][C]0.926267011270093[/C][/ROW]
[ROW][C]40[/C][C]21[/C][C]16.2663626175764[/C][C]4.7336373824236[/C][/ROW]
[ROW][C]41[/C][C]19[/C][C]19.3981610668610[/C][C]-0.398161066860982[/C][/ROW]
[ROW][C]42[/C][C]22[/C][C]22.0811179984808[/C][C]-0.0811179984808388[/C][/ROW]
[ROW][C]43[/C][C]22[/C][C]20.4147553809044[/C][C]1.58524461909556[/C][/ROW]
[ROW][C]44[/C][C]18[/C][C]21.9474806160572[/C][C]-3.94748061605724[/C][/ROW]
[ROW][C]45[/C][C]16[/C][C]18.5935564147147[/C][C]-2.59355641471472[/C][/ROW]
[ROW][C]46[/C][C]14[/C][C]20.2101507287582[/C][C]-6.21015072875817[/C][/ROW]
[ROW][C]47[/C][C]12[/C][C]17.6377932802332[/C][C]-5.63779328023318[/C][/ROW]
[ROW][C]48[/C][C]14[/C][C]18.9428613253136[/C][C]-4.94286132531355[/C][/ROW]
[ROW][C]49[/C][C]16[/C][C]18.2783376408762[/C][C]-2.27833764087617[/C][/ROW]
[ROW][C]50[/C][C]8[/C][C]16.2350129226285[/C][C]-8.2350129226285[/C][/ROW]
[ROW][C]51[/C][C]3[/C][C]10.8202575417241[/C][C]-7.82025754172407[/C][/ROW]
[ROW][C]52[/C][C]0[/C][C]7.97140138546192[/C][C]-7.97140138546192[/C][/ROW]
[ROW][C]53[/C][C]5[/C][C]6.95571921868926[/C][C]-1.95571921868926[/C][/ROW]
[ROW][C]54[/C][C]1[/C][C]5.76769424198903[/C][C]-4.76769424198903[/C][/ROW]
[ROW][C]55[/C][C]1[/C][C]4.00916538850024[/C][C]-3.00916538850024[/C][/ROW]
[ROW][C]56[/C][C]3[/C][C]3.51423343358061[/C][C]-0.514233433580615[/C][/ROW]
[ROW][C]57[/C][C]6[/C][C]2.64879760187244[/C][C]3.35120239812756[/C][/ROW]
[ROW][C]58[/C][C]7[/C][C]2.42206719766823[/C][C]4.57793280233177[/C][/ROW]
[ROW][C]59[/C][C]8[/C][C]3.44419294972618[/C][C]4.55580705027382[/C][/ROW]
[ROW][C]60[/C][C]14[/C][C]6.40825324122945[/C][C]7.59174675877055[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57673&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57673&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
12322.33365202102100.66634797897896
21921.5806546055467-2.58065460554671
31818.746553830189-0.746553830189013
41918.20185357173640.798146428263554
51922.2553143801449-3.25531438014486
62221.43595434709420.564045652905844
72318.20276571900724.79723428099276
82017.15483634861332.84516365138668
91415.9207355732556-1.92073557325561
101418.6433247182477-4.64332471824766
111416.4396322133722-2.43963221337219
121517.4682015507154-2.46820155071542
131114.2230232607313-3.22302326073131
141715.31335056350471.68664943649532
151615.05990439369370.940095606306308
162015.98986390983934.01013609016075
172421.70231696467062.29768303532944
182322.81844788577990.181552114220096
192024.8387347046988-4.83873470469883
202123.7908053343049-2.7908053343049
211924.676527984932-5.676527984932
222322.3299741547430.670025845257014
232323.3520999068009-0.352099906800930
242323.0903419413708-0.090341941370793
252321.31982342598481.68017657401519
262722.77881567240774.22118432759229
272619.29955124566336.70044875433668
281718.570518515386-1.57051851538598
292420.68848836963433.31151163036566
302621.89678552665614.10321447334392
312422.53457880688931.46542119311075
322722.59264426744394.40735573255608
332720.16038242522526.83961757477477
342620.39448320058295.60551679941706
352420.12628164986753.87371835013248
362323.0903419413708-0.090341941370793
372319.84516365138673.15483634861332
382419.09216623591244.90783376408761
391716.07373298872990.926267011270093
402116.26636261757644.7336373824236
411919.3981610668610-0.398161066860982
422222.0811179984808-0.0811179984808388
432220.41475538090441.58524461909556
441821.9474806160572-3.94748061605724
451618.5935564147147-2.59355641471472
461420.2101507287582-6.21015072875817
471217.6377932802332-5.63779328023318
481418.9428613253136-4.94286132531355
491618.2783376408762-2.27833764087617
50816.2350129226285-8.2350129226285
51310.8202575417241-7.82025754172407
5207.97140138546192-7.97140138546192
5356.95571921868926-1.95571921868926
5415.76769424198903-4.76769424198903
5514.00916538850024-3.00916538850024
5633.51423343358061-0.514233433580615
5762.648797601872443.35120239812756
5872.422067197668234.57793280233177
5983.444192949726184.55580705027382
60146.408253241229457.59174675877055







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
160.1803272724691910.3606545449383830.819672727530809
170.1734506449782360.3469012899564720.826549355021764
180.0842964208896240.1685928417792480.915703579110376
190.1433284271351420.2866568542702830.856671572864859
200.08719204134284890.1743840826856980.912807958657151
210.05676269139878180.1135253827975640.943237308601218
220.07054750927616830.1410950185523370.929452490723832
230.05415686927637680.1083137385527540.945843130723623
240.03759552134267980.07519104268535950.96240447865732
250.02636529682594990.05273059365189990.97363470317405
260.02803284217255410.05606568434510810.971967157827446
270.05030104593425240.1006020918685050.949698954065748
280.03571891048723450.07143782097446890.964281089512766
290.0264330946817910.0528661893635820.97356690531821
300.02201658191825190.04403316383650380.977983418081748
310.01254288352697830.02508576705395670.987457116473022
320.01220049688891140.02440099377782290.987799503111089
330.03694048065567520.07388096131135040.963059519344325
340.05019235117100160.1003847023420030.949807648828998
350.04541826828657940.09083653657315880.95458173171342
360.02630173284960610.05260346569921220.973698267150394
370.01923006513812660.03846013027625320.980769934861873
380.03727643743265660.07455287486531320.962723562567343
390.0403655413640920.0807310827281840.959634458635908
400.1339650966066060.2679301932132120.866034903393394
410.09962302398316720.1992460479663340.900376976016833
420.1607337468502790.3214674937005570.839266253149721
430.4935246234656330.9870492469312670.506475376534367
440.6807715933372390.6384568133255230.319228406662761

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
16 & 0.180327272469191 & 0.360654544938383 & 0.819672727530809 \tabularnewline
17 & 0.173450644978236 & 0.346901289956472 & 0.826549355021764 \tabularnewline
18 & 0.084296420889624 & 0.168592841779248 & 0.915703579110376 \tabularnewline
19 & 0.143328427135142 & 0.286656854270283 & 0.856671572864859 \tabularnewline
20 & 0.0871920413428489 & 0.174384082685698 & 0.912807958657151 \tabularnewline
21 & 0.0567626913987818 & 0.113525382797564 & 0.943237308601218 \tabularnewline
22 & 0.0705475092761683 & 0.141095018552337 & 0.929452490723832 \tabularnewline
23 & 0.0541568692763768 & 0.108313738552754 & 0.945843130723623 \tabularnewline
24 & 0.0375955213426798 & 0.0751910426853595 & 0.96240447865732 \tabularnewline
25 & 0.0263652968259499 & 0.0527305936518999 & 0.97363470317405 \tabularnewline
26 & 0.0280328421725541 & 0.0560656843451081 & 0.971967157827446 \tabularnewline
27 & 0.0503010459342524 & 0.100602091868505 & 0.949698954065748 \tabularnewline
28 & 0.0357189104872345 & 0.0714378209744689 & 0.964281089512766 \tabularnewline
29 & 0.026433094681791 & 0.052866189363582 & 0.97356690531821 \tabularnewline
30 & 0.0220165819182519 & 0.0440331638365038 & 0.977983418081748 \tabularnewline
31 & 0.0125428835269783 & 0.0250857670539567 & 0.987457116473022 \tabularnewline
32 & 0.0122004968889114 & 0.0244009937778229 & 0.987799503111089 \tabularnewline
33 & 0.0369404806556752 & 0.0738809613113504 & 0.963059519344325 \tabularnewline
34 & 0.0501923511710016 & 0.100384702342003 & 0.949807648828998 \tabularnewline
35 & 0.0454182682865794 & 0.0908365365731588 & 0.95458173171342 \tabularnewline
36 & 0.0263017328496061 & 0.0526034656992122 & 0.973698267150394 \tabularnewline
37 & 0.0192300651381266 & 0.0384601302762532 & 0.980769934861873 \tabularnewline
38 & 0.0372764374326566 & 0.0745528748653132 & 0.962723562567343 \tabularnewline
39 & 0.040365541364092 & 0.080731082728184 & 0.959634458635908 \tabularnewline
40 & 0.133965096606606 & 0.267930193213212 & 0.866034903393394 \tabularnewline
41 & 0.0996230239831672 & 0.199246047966334 & 0.900376976016833 \tabularnewline
42 & 0.160733746850279 & 0.321467493700557 & 0.839266253149721 \tabularnewline
43 & 0.493524623465633 & 0.987049246931267 & 0.506475376534367 \tabularnewline
44 & 0.680771593337239 & 0.638456813325523 & 0.319228406662761 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57673&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]16[/C][C]0.180327272469191[/C][C]0.360654544938383[/C][C]0.819672727530809[/C][/ROW]
[ROW][C]17[/C][C]0.173450644978236[/C][C]0.346901289956472[/C][C]0.826549355021764[/C][/ROW]
[ROW][C]18[/C][C]0.084296420889624[/C][C]0.168592841779248[/C][C]0.915703579110376[/C][/ROW]
[ROW][C]19[/C][C]0.143328427135142[/C][C]0.286656854270283[/C][C]0.856671572864859[/C][/ROW]
[ROW][C]20[/C][C]0.0871920413428489[/C][C]0.174384082685698[/C][C]0.912807958657151[/C][/ROW]
[ROW][C]21[/C][C]0.0567626913987818[/C][C]0.113525382797564[/C][C]0.943237308601218[/C][/ROW]
[ROW][C]22[/C][C]0.0705475092761683[/C][C]0.141095018552337[/C][C]0.929452490723832[/C][/ROW]
[ROW][C]23[/C][C]0.0541568692763768[/C][C]0.108313738552754[/C][C]0.945843130723623[/C][/ROW]
[ROW][C]24[/C][C]0.0375955213426798[/C][C]0.0751910426853595[/C][C]0.96240447865732[/C][/ROW]
[ROW][C]25[/C][C]0.0263652968259499[/C][C]0.0527305936518999[/C][C]0.97363470317405[/C][/ROW]
[ROW][C]26[/C][C]0.0280328421725541[/C][C]0.0560656843451081[/C][C]0.971967157827446[/C][/ROW]
[ROW][C]27[/C][C]0.0503010459342524[/C][C]0.100602091868505[/C][C]0.949698954065748[/C][/ROW]
[ROW][C]28[/C][C]0.0357189104872345[/C][C]0.0714378209744689[/C][C]0.964281089512766[/C][/ROW]
[ROW][C]29[/C][C]0.026433094681791[/C][C]0.052866189363582[/C][C]0.97356690531821[/C][/ROW]
[ROW][C]30[/C][C]0.0220165819182519[/C][C]0.0440331638365038[/C][C]0.977983418081748[/C][/ROW]
[ROW][C]31[/C][C]0.0125428835269783[/C][C]0.0250857670539567[/C][C]0.987457116473022[/C][/ROW]
[ROW][C]32[/C][C]0.0122004968889114[/C][C]0.0244009937778229[/C][C]0.987799503111089[/C][/ROW]
[ROW][C]33[/C][C]0.0369404806556752[/C][C]0.0738809613113504[/C][C]0.963059519344325[/C][/ROW]
[ROW][C]34[/C][C]0.0501923511710016[/C][C]0.100384702342003[/C][C]0.949807648828998[/C][/ROW]
[ROW][C]35[/C][C]0.0454182682865794[/C][C]0.0908365365731588[/C][C]0.95458173171342[/C][/ROW]
[ROW][C]36[/C][C]0.0263017328496061[/C][C]0.0526034656992122[/C][C]0.973698267150394[/C][/ROW]
[ROW][C]37[/C][C]0.0192300651381266[/C][C]0.0384601302762532[/C][C]0.980769934861873[/C][/ROW]
[ROW][C]38[/C][C]0.0372764374326566[/C][C]0.0745528748653132[/C][C]0.962723562567343[/C][/ROW]
[ROW][C]39[/C][C]0.040365541364092[/C][C]0.080731082728184[/C][C]0.959634458635908[/C][/ROW]
[ROW][C]40[/C][C]0.133965096606606[/C][C]0.267930193213212[/C][C]0.866034903393394[/C][/ROW]
[ROW][C]41[/C][C]0.0996230239831672[/C][C]0.199246047966334[/C][C]0.900376976016833[/C][/ROW]
[ROW][C]42[/C][C]0.160733746850279[/C][C]0.321467493700557[/C][C]0.839266253149721[/C][/ROW]
[ROW][C]43[/C][C]0.493524623465633[/C][C]0.987049246931267[/C][C]0.506475376534367[/C][/ROW]
[ROW][C]44[/C][C]0.680771593337239[/C][C]0.638456813325523[/C][C]0.319228406662761[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57673&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57673&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
160.1803272724691910.3606545449383830.819672727530809
170.1734506449782360.3469012899564720.826549355021764
180.0842964208896240.1685928417792480.915703579110376
190.1433284271351420.2866568542702830.856671572864859
200.08719204134284890.1743840826856980.912807958657151
210.05676269139878180.1135253827975640.943237308601218
220.07054750927616830.1410950185523370.929452490723832
230.05415686927637680.1083137385527540.945843130723623
240.03759552134267980.07519104268535950.96240447865732
250.02636529682594990.05273059365189990.97363470317405
260.02803284217255410.05606568434510810.971967157827446
270.05030104593425240.1006020918685050.949698954065748
280.03571891048723450.07143782097446890.964281089512766
290.0264330946817910.0528661893635820.97356690531821
300.02201658191825190.04403316383650380.977983418081748
310.01254288352697830.02508576705395670.987457116473022
320.01220049688891140.02440099377782290.987799503111089
330.03694048065567520.07388096131135040.963059519344325
340.05019235117100160.1003847023420030.949807648828998
350.04541826828657940.09083653657315880.95458173171342
360.02630173284960610.05260346569921220.973698267150394
370.01923006513812660.03846013027625320.980769934861873
380.03727643743265660.07455287486531320.962723562567343
390.0403655413640920.0807310827281840.959634458635908
400.1339650966066060.2679301932132120.866034903393394
410.09962302398316720.1992460479663340.900376976016833
420.1607337468502790.3214674937005570.839266253149721
430.4935246234656330.9870492469312670.506475376534367
440.6807715933372390.6384568133255230.319228406662761







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level40.137931034482759NOK
10% type I error level140.482758620689655NOK

\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 & 4 & 0.137931034482759 & NOK \tabularnewline
10% type I error level & 14 & 0.482758620689655 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57673&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]4[/C][C]0.137931034482759[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]14[/C][C]0.482758620689655[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57673&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57673&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 level00OK
5% type I error level40.137931034482759NOK
10% type I error level140.482758620689655NOK



Parameters (Session):
par1 = 1 ; par2 = Include Monthly Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 1 ; par2 = Include Monthly 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')
}