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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 computationMon, 13 Dec 2010 20:43:53 +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/13/t1292273010555rtu39tw275dq.htm/, Retrieved Tue, 07 May 2024 02:59:23 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=109184, Retrieved Tue, 07 May 2024 02:59:23 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact138

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Kendall tau Correlation Matrix] [] [2010-12-05 17:44:33] [b98453cac15ba1066b407e146608df68]
F   PD  [Kendall tau Correlation Matrix] [Workshop 10 - Ken...] [2010-12-10 12:29:09] [6f0e7a2d1a07390e3505a2db8288f975]
-    D    [Kendall tau Correlation Matrix] [Bonus - Pearson C...] [2010-12-13 19:37:37] [6f0e7a2d1a07390e3505a2db8288f975]
- RM D        [Multiple Regression] [Bonus - Multiple ...] [2010-12-13 20:43:53] [708f372e2a7a3c78ea31b4de2d1213f8] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
2,00	4,50	1000,00	6600,00	42,00	3,00	1,00	3,00
1,80	69,00	2547000,00	4603000,00	624,00	3,00	5,00	4,00
0,70	27,00	10550,00	179500,00	180,00	4,00	4,00	4,00
3,90	19,00	23,00	300,00	35,00	1,00	1,00	1,00
1,00	30,40	160000,00	169000,00	392,00	4,00	5,00	4,00
3,60	28,00	3300,00	25600,00	63,00	1,00	2,00	1,00
1,40	50,00	52160,00	440000,00	230,00	1,00	1,00	1,00
1,50	7,00	425,00	6400,00	112,00	5,00	4,00	4,00
0,70	30,00	465000,00	423000,00	281,00	5,00	5,00	5,00
2,10	3,50	75,00	1200,00	42,00	1,00	1,00	1,00
0,00	50,00	3000,00	25000,00	28,00	2,00	2,00	2,00
4,10	6,00	785,00	3500,00	42,00	2,00	2,00	2,00
1,20	10,40	200,00	5000,00	120,00	2,00	2,00	2,00
0,50	20,00	27660,00	115000,00	148,00	5,00	5,00	5,00
3,40	3,90	120,00	1000,00	16,00	3,00	1,00	2,00
1,50	41,00	85000,00	325000,00	310,00	1,00	3,00	1,00
3,40	9,00	101,00	4000,00	28,00	5,00	1,00	3,00
0,80	7,60	1040,00	5500,00	68,00	5,00	3,00	4,00
0,80	46,00	521000,00	655000,00	336,00	5,00	5,00	5,00
1,40	2,60	5,00	140,00	21,50	5,00	2,00	4,00
2,00	24,00	10,00	250,00	50,00	1,00	1,00	1,00
1,90	100,00	62000,00	1320000,00	267,00	1,00	1,00	1,00
1,30	3,20	23,00	400,00	19,00	4,00	1,00	3,00
2,00	2,00	48,00	330,00	30,00	4,00	1,00	3,00
5,60	5,00	1700,00	6300,00	12,00	2,00	1,00	1,00
3,10	6,50	3500,00	10800,00	120,00	2,00	1,00	1,00
1,80	12,00	480,00	15500,00	140,00	2,00	2,00	2,00
0,90	20,20	10000,00	115000,00	170,00	4,00	4,00	4,00
1,80	13,00	1620,00	11400,00	17,00	2,00	1,00	2,00
1,90	27,00	192000,00	180000,00	115,00	4,00	4,00	4,00
0,90	18,00	2500,00	12100,00	31,00	5,00	5,00	5,00
2,60	4,70	280,00	1900,00	21,00	3,00	1,00	3,00
2,40	9,80	4235,00	50400,00	52,00	1,00	1,00	1,00
1,20	29,00	6800,00	179000,00	164,00	2,00	3,00	2,00
0,90	7,00	750,00	12300,00	225,00	2,00	2,00	2,00
0,50	6,00	3600,00	21000,00	225,00	3,00	2,00	3,00
0,60	20,00	55500,00	175000,00	151,00	5,00	5,00	5,00
2,30	4,50	900,00	2600,00	60,00	2,00	1,00	2,00
0,50	7,50	2000,00	12300,00	200,00	3,00	1,00	3,00
2,60	2,30	104,00	2500,00	46,00	3,00	2,00	2,00
0,60	24,00	4190,00	58000,00	210,00	4,00	3,00	4,00
6,60	3,00	3500,00	3900,00	14,00	2,00	1,00	1,00

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'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=109184&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=109184&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=109184&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'RServer@AstonUniversity' @ vre.aston.ac.uk






Multiple Linear Regression - Estimated Regression Equation
PS[t] = + 3.77763738061614 -0.0133949634614039L[t] + 1.37031210793507e-06Wb[t] + 2.96123778267419e-07Wbr[t] -0.00500701775504269Tg[t] + 0.900361471653855P[t] + 0.360021324193483S[t] -1.73845778307009D[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
PS[t] =  +  3.77763738061614 -0.0133949634614039L[t] +  1.37031210793507e-06Wb[t] +  2.96123778267419e-07Wbr[t] -0.00500701775504269Tg[t] +  0.900361471653855P[t] +  0.360021324193483S[t] -1.73845778307009D[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=109184&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]PS[t] =  +  3.77763738061614 -0.0133949634614039L[t] +  1.37031210793507e-06Wb[t] +  2.96123778267419e-07Wbr[t] -0.00500701775504269Tg[t] +  0.900361471653855P[t] +  0.360021324193483S[t] -1.73845778307009D[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=109184&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=109184&T=1

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


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

Multiple Linear Regression - Estimated Regression Equation
PS[t] = + 3.77763738061614 -0.0133949634614039L[t] + 1.37031210793507e-06Wb[t] + 2.96123778267419e-07Wbr[t] -0.00500701775504269Tg[t] + 0.900361471653855P[t] + 0.360021324193483S[t] -1.73845778307009D[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)3.777637380616140.4132689.140900
L-0.01339496346140390.014311-0.9360.3558690.177935
Wb1.37031210793507e-062e-060.74830.4594340.229717
Wbr2.96123778267419e-071e-060.26940.7892270.394614
Tg-0.005007017755042690.002158-2.31970.0264970.013248
P0.9003614716538550.3379062.66450.0117040.005852
S0.3600213241934830.2117011.70060.0981470.049074
D-1.738457783070090.419569-4.14340.0002140.000107

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Ordinary Least Squares \tabularnewline
Variable & Parameter & S.D. & T-STATH0: parameter = 0 & 2-tail p-value & 1-tail p-value \tabularnewline
(Intercept) & 3.77763738061614 & 0.413268 & 9.1409 & 0 & 0 \tabularnewline
L & -0.0133949634614039 & 0.014311 & -0.936 & 0.355869 & 0.177935 \tabularnewline
Wb & 1.37031210793507e-06 & 2e-06 & 0.7483 & 0.459434 & 0.229717 \tabularnewline
Wbr & 2.96123778267419e-07 & 1e-06 & 0.2694 & 0.789227 & 0.394614 \tabularnewline
Tg & -0.00500701775504269 & 0.002158 & -2.3197 & 0.026497 & 0.013248 \tabularnewline
P & 0.900361471653855 & 0.337906 & 2.6645 & 0.011704 & 0.005852 \tabularnewline
S & 0.360021324193483 & 0.211701 & 1.7006 & 0.098147 & 0.049074 \tabularnewline
D & -1.73845778307009 & 0.419569 & -4.1434 & 0.000214 & 0.000107 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=109184&T=2

[TABLE]
[ROW][C]Multiple Linear Regression - Ordinary Least Squares[/C][/ROW]
[ROW][C]Variable[/C][C]Parameter[/C][C]S.D.[/C][C]T-STATH0: parameter = 0[/C][C]2-tail p-value[/C][C]1-tail p-value[/C][/ROW]
[ROW][C](Intercept)[/C][C]3.77763738061614[/C][C]0.413268[/C][C]9.1409[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]L[/C][C]-0.0133949634614039[/C][C]0.014311[/C][C]-0.936[/C][C]0.355869[/C][C]0.177935[/C][/ROW]
[ROW][C]Wb[/C][C]1.37031210793507e-06[/C][C]2e-06[/C][C]0.7483[/C][C]0.459434[/C][C]0.229717[/C][/ROW]
[ROW][C]Wbr[/C][C]2.96123778267419e-07[/C][C]1e-06[/C][C]0.2694[/C][C]0.789227[/C][C]0.394614[/C][/ROW]
[ROW][C]Tg[/C][C]-0.00500701775504269[/C][C]0.002158[/C][C]-2.3197[/C][C]0.026497[/C][C]0.013248[/C][/ROW]
[ROW][C]P[/C][C]0.900361471653855[/C][C]0.337906[/C][C]2.6645[/C][C]0.011704[/C][C]0.005852[/C][/ROW]
[ROW][C]S[/C][C]0.360021324193483[/C][C]0.211701[/C][C]1.7006[/C][C]0.098147[/C][C]0.049074[/C][/ROW]
[ROW][C]D[/C][C]-1.73845778307009[/C][C]0.419569[/C][C]-4.1434[/C][C]0.000214[/C][C]0.000107[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=109184&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=109184&T=2

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


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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)3.777637380616140.4132689.140900
L-0.01339496346140390.014311-0.9360.3558690.177935
Wb1.37031210793507e-062e-060.74830.4594340.229717
Wbr2.96123778267419e-071e-060.26940.7892270.394614
Tg-0.005007017755042690.002158-2.31970.0264970.013248
P0.9003614716538550.3379062.66450.0117040.005852
S0.3600213241934830.2117011.70060.0981470.049074
D-1.738457783070090.419569-4.14340.0002140.000107






Multiple Linear Regression - Regression Statistics
Multiple R0.787651358957768
R-squared0.620394663268018
Adjusted R-squared0.54224062335261
F-TEST (value)7.9381010110228
F-TEST (DF numerator)7
F-TEST (DF denominator)34
p-value1.06575639513551e-05
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.939636820152983
Sum Squared Residuals30.0191900287651

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.787651358957768 \tabularnewline
R-squared & 0.620394663268018 \tabularnewline
Adjusted R-squared & 0.54224062335261 \tabularnewline
F-TEST (value) & 7.9381010110228 \tabularnewline
F-TEST (DF numerator) & 7 \tabularnewline
F-TEST (DF denominator) & 34 \tabularnewline
p-value & 1.06575639513551e-05 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.939636820152983 \tabularnewline
Sum Squared Residuals & 30.0191900287651 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=109184&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.787651358957768[/C][/ROW]
[ROW][C]R-squared[/C][C]0.620394663268018[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.54224062335261[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]7.9381010110228[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]7[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]34[/C][/ROW]
[ROW][C]p-value[/C][C]1.06575639513551e-05[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.939636820152983[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]30.0191900287651[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=109184&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=109184&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 R0.787651358957768
R-squared0.620394663268018
Adjusted R-squared0.54224062335261
F-TEST (value)7.9381010110228
F-TEST (DF numerator)7
F-TEST (DF denominator)34
p-value1.06575639513551e-05
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.939636820152983
Sum Squared Residuals30.0191900287651






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
121.35612241831730.643877581682701
21.82.12960841655679-0.329608416556788
30.70.6700212332972540.0299787667027454
43.92.869932820512181.03006717948782
510.1246957625120020.875304237487998
63.62.98118542077970.618814579220295
71.41.67997007865093-0.27997007865093
81.52.11362574541116-0.613625745411159
90.70.3408970398901850.359102960109815
102.13.0428433975087-0.942843397508697
1102.02305676673973-2.02305676673973
124.12.532935007919081.56706499208092
131.22.08309233687984-0.883092336879835
140.50.4502816149342150.0497183850657847
153.43.229935473282070.170064526717932
161.52.13095279291095-0.630952792910953
173.43.164664442210770.235335557789233
180.81.96645245490865-1.16645245490865
190.8-0.003370157417221580.803370157417222
201.41.90322677714845-0.503226777148446
2122.72782011663321-0.727820116633206
221.91.099035044661560.800964955338435
231.32.38588398848225-1.08588398848226
2422.34689427846869-0.346894278468691
255.64.077059945066291.52294005493371
263.13.52000870112606-0.420008701126056
271.81.96501302730277-0.165013027302766
280.90.7913235070276160.108676492972385
291.82.20780797183028-0.407807971830278
301.91.244268581248980.65573141875102
310.90.997944429777653-0.097944429777653
322.61.456212392005351.14378760799465
332.42.92865473841119-0.528654738411193
341.22.03422815680023-0.83422815680023
350.91.60581372360984-0.705813723609844
360.50.787594042033552-0.287594042033552
370.60.4911774774500440.108822522549956
382.32.102770883818910.19722911618109
390.50.527886940280408-0.0278869402804076
402.63.46160046703619-0.861600467036191
410.60.1552800427707440.444719957229256
426.64.095591701205452.50440829879455

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 2 & 1.3561224183173 & 0.643877581682701 \tabularnewline
2 & 1.8 & 2.12960841655679 & -0.329608416556788 \tabularnewline
3 & 0.7 & 0.670021233297254 & 0.0299787667027454 \tabularnewline
4 & 3.9 & 2.86993282051218 & 1.03006717948782 \tabularnewline
5 & 1 & 0.124695762512002 & 0.875304237487998 \tabularnewline
6 & 3.6 & 2.9811854207797 & 0.618814579220295 \tabularnewline
7 & 1.4 & 1.67997007865093 & -0.27997007865093 \tabularnewline
8 & 1.5 & 2.11362574541116 & -0.613625745411159 \tabularnewline
9 & 0.7 & 0.340897039890185 & 0.359102960109815 \tabularnewline
10 & 2.1 & 3.0428433975087 & -0.942843397508697 \tabularnewline
11 & 0 & 2.02305676673973 & -2.02305676673973 \tabularnewline
12 & 4.1 & 2.53293500791908 & 1.56706499208092 \tabularnewline
13 & 1.2 & 2.08309233687984 & -0.883092336879835 \tabularnewline
14 & 0.5 & 0.450281614934215 & 0.0497183850657847 \tabularnewline
15 & 3.4 & 3.22993547328207 & 0.170064526717932 \tabularnewline
16 & 1.5 & 2.13095279291095 & -0.630952792910953 \tabularnewline
17 & 3.4 & 3.16466444221077 & 0.235335557789233 \tabularnewline
18 & 0.8 & 1.96645245490865 & -1.16645245490865 \tabularnewline
19 & 0.8 & -0.00337015741722158 & 0.803370157417222 \tabularnewline
20 & 1.4 & 1.90322677714845 & -0.503226777148446 \tabularnewline
21 & 2 & 2.72782011663321 & -0.727820116633206 \tabularnewline
22 & 1.9 & 1.09903504466156 & 0.800964955338435 \tabularnewline
23 & 1.3 & 2.38588398848225 & -1.08588398848226 \tabularnewline
24 & 2 & 2.34689427846869 & -0.346894278468691 \tabularnewline
25 & 5.6 & 4.07705994506629 & 1.52294005493371 \tabularnewline
26 & 3.1 & 3.52000870112606 & -0.420008701126056 \tabularnewline
27 & 1.8 & 1.96501302730277 & -0.165013027302766 \tabularnewline
28 & 0.9 & 0.791323507027616 & 0.108676492972385 \tabularnewline
29 & 1.8 & 2.20780797183028 & -0.407807971830278 \tabularnewline
30 & 1.9 & 1.24426858124898 & 0.65573141875102 \tabularnewline
31 & 0.9 & 0.997944429777653 & -0.097944429777653 \tabularnewline
32 & 2.6 & 1.45621239200535 & 1.14378760799465 \tabularnewline
33 & 2.4 & 2.92865473841119 & -0.528654738411193 \tabularnewline
34 & 1.2 & 2.03422815680023 & -0.83422815680023 \tabularnewline
35 & 0.9 & 1.60581372360984 & -0.705813723609844 \tabularnewline
36 & 0.5 & 0.787594042033552 & -0.287594042033552 \tabularnewline
37 & 0.6 & 0.491177477450044 & 0.108822522549956 \tabularnewline
38 & 2.3 & 2.10277088381891 & 0.19722911618109 \tabularnewline
39 & 0.5 & 0.527886940280408 & -0.0278869402804076 \tabularnewline
40 & 2.6 & 3.46160046703619 & -0.861600467036191 \tabularnewline
41 & 0.6 & 0.155280042770744 & 0.444719957229256 \tabularnewline
42 & 6.6 & 4.09559170120545 & 2.50440829879455 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=109184&T=4

[TABLE]
[ROW][C]Multiple Linear Regression - Actuals, Interpolation, and Residuals[/C][/ROW]
[ROW][C]Time or Index[/C][C]Actuals[/C][C]InterpolationForecast[/C][C]ResidualsPrediction Error[/C][/ROW]
[ROW][C]1[/C][C]2[/C][C]1.3561224183173[/C][C]0.643877581682701[/C][/ROW]
[ROW][C]2[/C][C]1.8[/C][C]2.12960841655679[/C][C]-0.329608416556788[/C][/ROW]
[ROW][C]3[/C][C]0.7[/C][C]0.670021233297254[/C][C]0.0299787667027454[/C][/ROW]
[ROW][C]4[/C][C]3.9[/C][C]2.86993282051218[/C][C]1.03006717948782[/C][/ROW]
[ROW][C]5[/C][C]1[/C][C]0.124695762512002[/C][C]0.875304237487998[/C][/ROW]
[ROW][C]6[/C][C]3.6[/C][C]2.9811854207797[/C][C]0.618814579220295[/C][/ROW]
[ROW][C]7[/C][C]1.4[/C][C]1.67997007865093[/C][C]-0.27997007865093[/C][/ROW]
[ROW][C]8[/C][C]1.5[/C][C]2.11362574541116[/C][C]-0.613625745411159[/C][/ROW]
[ROW][C]9[/C][C]0.7[/C][C]0.340897039890185[/C][C]0.359102960109815[/C][/ROW]
[ROW][C]10[/C][C]2.1[/C][C]3.0428433975087[/C][C]-0.942843397508697[/C][/ROW]
[ROW][C]11[/C][C]0[/C][C]2.02305676673973[/C][C]-2.02305676673973[/C][/ROW]
[ROW][C]12[/C][C]4.1[/C][C]2.53293500791908[/C][C]1.56706499208092[/C][/ROW]
[ROW][C]13[/C][C]1.2[/C][C]2.08309233687984[/C][C]-0.883092336879835[/C][/ROW]
[ROW][C]14[/C][C]0.5[/C][C]0.450281614934215[/C][C]0.0497183850657847[/C][/ROW]
[ROW][C]15[/C][C]3.4[/C][C]3.22993547328207[/C][C]0.170064526717932[/C][/ROW]
[ROW][C]16[/C][C]1.5[/C][C]2.13095279291095[/C][C]-0.630952792910953[/C][/ROW]
[ROW][C]17[/C][C]3.4[/C][C]3.16466444221077[/C][C]0.235335557789233[/C][/ROW]
[ROW][C]18[/C][C]0.8[/C][C]1.96645245490865[/C][C]-1.16645245490865[/C][/ROW]
[ROW][C]19[/C][C]0.8[/C][C]-0.00337015741722158[/C][C]0.803370157417222[/C][/ROW]
[ROW][C]20[/C][C]1.4[/C][C]1.90322677714845[/C][C]-0.503226777148446[/C][/ROW]
[ROW][C]21[/C][C]2[/C][C]2.72782011663321[/C][C]-0.727820116633206[/C][/ROW]
[ROW][C]22[/C][C]1.9[/C][C]1.09903504466156[/C][C]0.800964955338435[/C][/ROW]
[ROW][C]23[/C][C]1.3[/C][C]2.38588398848225[/C][C]-1.08588398848226[/C][/ROW]
[ROW][C]24[/C][C]2[/C][C]2.34689427846869[/C][C]-0.346894278468691[/C][/ROW]
[ROW][C]25[/C][C]5.6[/C][C]4.07705994506629[/C][C]1.52294005493371[/C][/ROW]
[ROW][C]26[/C][C]3.1[/C][C]3.52000870112606[/C][C]-0.420008701126056[/C][/ROW]
[ROW][C]27[/C][C]1.8[/C][C]1.96501302730277[/C][C]-0.165013027302766[/C][/ROW]
[ROW][C]28[/C][C]0.9[/C][C]0.791323507027616[/C][C]0.108676492972385[/C][/ROW]
[ROW][C]29[/C][C]1.8[/C][C]2.20780797183028[/C][C]-0.407807971830278[/C][/ROW]
[ROW][C]30[/C][C]1.9[/C][C]1.24426858124898[/C][C]0.65573141875102[/C][/ROW]
[ROW][C]31[/C][C]0.9[/C][C]0.997944429777653[/C][C]-0.097944429777653[/C][/ROW]
[ROW][C]32[/C][C]2.6[/C][C]1.45621239200535[/C][C]1.14378760799465[/C][/ROW]
[ROW][C]33[/C][C]2.4[/C][C]2.92865473841119[/C][C]-0.528654738411193[/C][/ROW]
[ROW][C]34[/C][C]1.2[/C][C]2.03422815680023[/C][C]-0.83422815680023[/C][/ROW]
[ROW][C]35[/C][C]0.9[/C][C]1.60581372360984[/C][C]-0.705813723609844[/C][/ROW]
[ROW][C]36[/C][C]0.5[/C][C]0.787594042033552[/C][C]-0.287594042033552[/C][/ROW]
[ROW][C]37[/C][C]0.6[/C][C]0.491177477450044[/C][C]0.108822522549956[/C][/ROW]
[ROW][C]38[/C][C]2.3[/C][C]2.10277088381891[/C][C]0.19722911618109[/C][/ROW]
[ROW][C]39[/C][C]0.5[/C][C]0.527886940280408[/C][C]-0.0278869402804076[/C][/ROW]
[ROW][C]40[/C][C]2.6[/C][C]3.46160046703619[/C][C]-0.861600467036191[/C][/ROW]
[ROW][C]41[/C][C]0.6[/C][C]0.155280042770744[/C][C]0.444719957229256[/C][/ROW]
[ROW][C]42[/C][C]6.6[/C][C]4.09559170120545[/C][C]2.50440829879455[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=109184&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=109184&T=4

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


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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
121.35612241831730.643877581682701
21.82.12960841655679-0.329608416556788
30.70.6700212332972540.0299787667027454
43.92.869932820512181.03006717948782
510.1246957625120020.875304237487998
63.62.98118542077970.618814579220295
71.41.67997007865093-0.27997007865093
81.52.11362574541116-0.613625745411159
90.70.3408970398901850.359102960109815
102.13.0428433975087-0.942843397508697
1102.02305676673973-2.02305676673973
124.12.532935007919081.56706499208092
131.22.08309233687984-0.883092336879835
140.50.4502816149342150.0497183850657847
153.43.229935473282070.170064526717932
161.52.13095279291095-0.630952792910953
173.43.164664442210770.235335557789233
180.81.96645245490865-1.16645245490865
190.8-0.003370157417221580.803370157417222
201.41.90322677714845-0.503226777148446
2122.72782011663321-0.727820116633206
221.91.099035044661560.800964955338435
231.32.38588398848225-1.08588398848226
2422.34689427846869-0.346894278468691
255.64.077059945066291.52294005493371
263.13.52000870112606-0.420008701126056
271.81.96501302730277-0.165013027302766
280.90.7913235070276160.108676492972385
291.82.20780797183028-0.407807971830278
301.91.244268581248980.65573141875102
310.90.997944429777653-0.097944429777653
322.61.456212392005351.14378760799465
332.42.92865473841119-0.528654738411193
341.22.03422815680023-0.83422815680023
350.91.60581372360984-0.705813723609844
360.50.787594042033552-0.287594042033552
370.60.4911774774500440.108822522549956
382.32.102770883818910.19722911618109
390.50.527886940280408-0.0278869402804076
402.63.46160046703619-0.861600467036191
410.60.1552800427707440.444719957229256
426.64.095591701205452.50440829879455






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
110.8677923197015680.2644153605968630.132207680298432
120.8743541160205610.2512917679588780.125645883979439
130.9136694840510280.1726610318979450.0863305159489724
140.8511536516042070.2976926967915870.148846348395793
150.786353142054450.4272937158911010.213646857945551
160.711591202030520.576817595938960.28840879796948
170.6409433464995440.7181133070009120.359056653500456
180.6473278135972360.7053443728055280.352672186402764
190.5731301283344340.8537397433311320.426869871665566
200.491023383375740.982046766751480.50897661662426
210.4228143679877390.8456287359754780.577185632012261
220.4386158032124610.8772316064249220.561384196787539
230.5092969931540590.9814060136918820.490703006845941
240.5638430084620080.8723139830759850.436156991537993
250.6389035248003840.7221929503992310.361096475199616
260.5924902868090260.8150194263819480.407509713190974
270.4819250697068860.9638501394137720.518074930293114
280.3672351061571130.7344702123142270.632764893842887
290.3148463565720550.6296927131441110.685153643427945
300.3465896702553310.6931793405106610.65341032974467
310.2249404333045690.4498808666091390.775059566695431

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
11 & 0.867792319701568 & 0.264415360596863 & 0.132207680298432 \tabularnewline
12 & 0.874354116020561 & 0.251291767958878 & 0.125645883979439 \tabularnewline
13 & 0.913669484051028 & 0.172661031897945 & 0.0863305159489724 \tabularnewline
14 & 0.851153651604207 & 0.297692696791587 & 0.148846348395793 \tabularnewline
15 & 0.78635314205445 & 0.427293715891101 & 0.213646857945551 \tabularnewline
16 & 0.71159120203052 & 0.57681759593896 & 0.28840879796948 \tabularnewline
17 & 0.640943346499544 & 0.718113307000912 & 0.359056653500456 \tabularnewline
18 & 0.647327813597236 & 0.705344372805528 & 0.352672186402764 \tabularnewline
19 & 0.573130128334434 & 0.853739743331132 & 0.426869871665566 \tabularnewline
20 & 0.49102338337574 & 0.98204676675148 & 0.50897661662426 \tabularnewline
21 & 0.422814367987739 & 0.845628735975478 & 0.577185632012261 \tabularnewline
22 & 0.438615803212461 & 0.877231606424922 & 0.561384196787539 \tabularnewline
23 & 0.509296993154059 & 0.981406013691882 & 0.490703006845941 \tabularnewline
24 & 0.563843008462008 & 0.872313983075985 & 0.436156991537993 \tabularnewline
25 & 0.638903524800384 & 0.722192950399231 & 0.361096475199616 \tabularnewline
26 & 0.592490286809026 & 0.815019426381948 & 0.407509713190974 \tabularnewline
27 & 0.481925069706886 & 0.963850139413772 & 0.518074930293114 \tabularnewline
28 & 0.367235106157113 & 0.734470212314227 & 0.632764893842887 \tabularnewline
29 & 0.314846356572055 & 0.629692713144111 & 0.685153643427945 \tabularnewline
30 & 0.346589670255331 & 0.693179340510661 & 0.65341032974467 \tabularnewline
31 & 0.224940433304569 & 0.449880866609139 & 0.775059566695431 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=109184&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]11[/C][C]0.867792319701568[/C][C]0.264415360596863[/C][C]0.132207680298432[/C][/ROW]
[ROW][C]12[/C][C]0.874354116020561[/C][C]0.251291767958878[/C][C]0.125645883979439[/C][/ROW]
[ROW][C]13[/C][C]0.913669484051028[/C][C]0.172661031897945[/C][C]0.0863305159489724[/C][/ROW]
[ROW][C]14[/C][C]0.851153651604207[/C][C]0.297692696791587[/C][C]0.148846348395793[/C][/ROW]
[ROW][C]15[/C][C]0.78635314205445[/C][C]0.427293715891101[/C][C]0.213646857945551[/C][/ROW]
[ROW][C]16[/C][C]0.71159120203052[/C][C]0.57681759593896[/C][C]0.28840879796948[/C][/ROW]
[ROW][C]17[/C][C]0.640943346499544[/C][C]0.718113307000912[/C][C]0.359056653500456[/C][/ROW]
[ROW][C]18[/C][C]0.647327813597236[/C][C]0.705344372805528[/C][C]0.352672186402764[/C][/ROW]
[ROW][C]19[/C][C]0.573130128334434[/C][C]0.853739743331132[/C][C]0.426869871665566[/C][/ROW]
[ROW][C]20[/C][C]0.49102338337574[/C][C]0.98204676675148[/C][C]0.50897661662426[/C][/ROW]
[ROW][C]21[/C][C]0.422814367987739[/C][C]0.845628735975478[/C][C]0.577185632012261[/C][/ROW]
[ROW][C]22[/C][C]0.438615803212461[/C][C]0.877231606424922[/C][C]0.561384196787539[/C][/ROW]
[ROW][C]23[/C][C]0.509296993154059[/C][C]0.981406013691882[/C][C]0.490703006845941[/C][/ROW]
[ROW][C]24[/C][C]0.563843008462008[/C][C]0.872313983075985[/C][C]0.436156991537993[/C][/ROW]
[ROW][C]25[/C][C]0.638903524800384[/C][C]0.722192950399231[/C][C]0.361096475199616[/C][/ROW]
[ROW][C]26[/C][C]0.592490286809026[/C][C]0.815019426381948[/C][C]0.407509713190974[/C][/ROW]
[ROW][C]27[/C][C]0.481925069706886[/C][C]0.963850139413772[/C][C]0.518074930293114[/C][/ROW]
[ROW][C]28[/C][C]0.367235106157113[/C][C]0.734470212314227[/C][C]0.632764893842887[/C][/ROW]
[ROW][C]29[/C][C]0.314846356572055[/C][C]0.629692713144111[/C][C]0.685153643427945[/C][/ROW]
[ROW][C]30[/C][C]0.346589670255331[/C][C]0.693179340510661[/C][C]0.65341032974467[/C][/ROW]
[ROW][C]31[/C][C]0.224940433304569[/C][C]0.449880866609139[/C][C]0.775059566695431[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=109184&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=109184&T=5

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


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
110.8677923197015680.2644153605968630.132207680298432
120.8743541160205610.2512917679588780.125645883979439
130.9136694840510280.1726610318979450.0863305159489724
140.8511536516042070.2976926967915870.148846348395793
150.786353142054450.4272937158911010.213646857945551
160.711591202030520.576817595938960.28840879796948
170.6409433464995440.7181133070009120.359056653500456
180.6473278135972360.7053443728055280.352672186402764
190.5731301283344340.8537397433311320.426869871665566
200.491023383375740.982046766751480.50897661662426
210.4228143679877390.8456287359754780.577185632012261
220.4386158032124610.8772316064249220.561384196787539
230.5092969931540590.9814060136918820.490703006845941
240.5638430084620080.8723139830759850.436156991537993
250.6389035248003840.7221929503992310.361096475199616
260.5924902868090260.8150194263819480.407509713190974
270.4819250697068860.9638501394137720.518074930293114
280.3672351061571130.7344702123142270.632764893842887
290.3148463565720550.6296927131441110.685153643427945
300.3465896702553310.6931793405106610.65341032974467
310.2249404333045690.4498808666091390.775059566695431






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=109184&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=109184&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=109184&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 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 = pearson ;
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')
}