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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationTue, 30 Nov 2010 14:44:52 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Nov/30/t1291128244h6wnxpxpjupj08u.htm/, Retrieved Thu, 31 Oct 2024 23:25:21 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=103550, Retrieved Thu, 31 Oct 2024 23:25:21 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact212
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Univariate Explorative Data Analysis] [Run Sequence gebo...] [2008-12-12 13:32:37] [76963dc1903f0f612b6153510a3818cf]
- R  D  [Univariate Explorative Data Analysis] [Run Sequence gebo...] [2008-12-17 12:14:40] [76963dc1903f0f612b6153510a3818cf]
-         [Univariate Explorative Data Analysis] [Run Sequence Plot...] [2008-12-22 18:19:51] [1ce0d16c8f4225c977b42c8fa93bc163]
- RMP       [Univariate Data Series] [Identifying Integ...] [2009-11-22 12:08:06] [b98453cac15ba1066b407e146608df68]
-   PD        [Univariate Data Series] [WS 8: basisreeks] [2009-11-27 12:55:11] [b97b96148b0223bc16666763988dc147]
- RMPD            [Multiple Regression] [multiple regressi...] [2010-11-30 14:44:52] [9be3691a9b6ce074cb51fd18377fce28] [Current]
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Dataseries X:
149
134
123
116
117
111
105
102
95
93
124
130
124
115
106
105
105
101
95
93
84
87
116
120
117
109
105
107
109
109
108
107
99
103
131
137
135
124
118
121
121
118
113
107
100
102
130
136
133
120




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time7 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 & 7 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=103550&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]7 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=103550&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=103550&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 time7 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







Multiple Linear Regression - Estimated Regression Equation
-25[t] = + 130.75 + 0.849999999999999M1[t] -10.3500000000000M2[t] -17.75M3[t] -18.5M4[t] -17.7500000000000M5[t] -21M6[t] -25.5M7[t] -28.5M8[t] -36.2500M9[t] -34.5M10[t] -5.50000000000001M11[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
-25[t] =  +  130.75 +  0.849999999999999M1[t] -10.3500000000000M2[t] -17.75M3[t] -18.5M4[t] -17.7500000000000M5[t] -21M6[t] -25.5M7[t] -28.5M8[t] -36.2500M9[t] -34.5M10[t] -5.50000000000001M11[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=103550&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]-25[t] =  +  130.75 +  0.849999999999999M1[t] -10.3500000000000M2[t] -17.75M3[t] -18.5M4[t] -17.7500000000000M5[t] -21M6[t] -25.5M7[t] -28.5M8[t] -36.2500M9[t] -34.5M10[t] -5.50000000000001M11[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=103550&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=103550&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
-25[t] = + 130.75 + 0.849999999999999M1[t] -10.3500000000000M2[t] -17.75M3[t] -18.5M4[t] -17.7500000000000M5[t] -21M6[t] -25.5M7[t] -28.5M8[t] -36.2500M9[t] -34.5M10[t] -5.50000000000001M11[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)130.754.1554131.46500
M10.8499999999999995.5750680.15250.8796270.439814
M2-10.35000000000005.575068-1.85650.0711510.035575
M3-17.755.876638-3.02040.0044960.002248
M4-18.55.876638-3.14810.0031940.001597
M5-17.75000000000005.876638-3.02040.0044960.002248
M6-215.876638-3.57350.0009780.000489
M7-25.55.876638-4.33920.0001025.1e-05
M8-28.55.876638-4.84972.1e-051.1e-05
M9-36.25005.876638-6.168500
M10-34.55.876638-5.87071e-060
M11-5.500000000000015.876638-0.93590.355230.177615

\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) & 130.75 & 4.15541 & 31.465 & 0 & 0 \tabularnewline
M1 & 0.849999999999999 & 5.575068 & 0.1525 & 0.879627 & 0.439814 \tabularnewline
M2 & -10.3500000000000 & 5.575068 & -1.8565 & 0.071151 & 0.035575 \tabularnewline
M3 & -17.75 & 5.876638 & -3.0204 & 0.004496 & 0.002248 \tabularnewline
M4 & -18.5 & 5.876638 & -3.1481 & 0.003194 & 0.001597 \tabularnewline
M5 & -17.7500000000000 & 5.876638 & -3.0204 & 0.004496 & 0.002248 \tabularnewline
M6 & -21 & 5.876638 & -3.5735 & 0.000978 & 0.000489 \tabularnewline
M7 & -25.5 & 5.876638 & -4.3392 & 0.000102 & 5.1e-05 \tabularnewline
M8 & -28.5 & 5.876638 & -4.8497 & 2.1e-05 & 1.1e-05 \tabularnewline
M9 & -36.2500 & 5.876638 & -6.1685 & 0 & 0 \tabularnewline
M10 & -34.5 & 5.876638 & -5.8707 & 1e-06 & 0 \tabularnewline
M11 & -5.50000000000001 & 5.876638 & -0.9359 & 0.35523 & 0.177615 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=103550&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]130.75[/C][C]4.15541[/C][C]31.465[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M1[/C][C]0.849999999999999[/C][C]5.575068[/C][C]0.1525[/C][C]0.879627[/C][C]0.439814[/C][/ROW]
[ROW][C]M2[/C][C]-10.3500000000000[/C][C]5.575068[/C][C]-1.8565[/C][C]0.071151[/C][C]0.035575[/C][/ROW]
[ROW][C]M3[/C][C]-17.75[/C][C]5.876638[/C][C]-3.0204[/C][C]0.004496[/C][C]0.002248[/C][/ROW]
[ROW][C]M4[/C][C]-18.5[/C][C]5.876638[/C][C]-3.1481[/C][C]0.003194[/C][C]0.001597[/C][/ROW]
[ROW][C]M5[/C][C]-17.7500000000000[/C][C]5.876638[/C][C]-3.0204[/C][C]0.004496[/C][C]0.002248[/C][/ROW]
[ROW][C]M6[/C][C]-21[/C][C]5.876638[/C][C]-3.5735[/C][C]0.000978[/C][C]0.000489[/C][/ROW]
[ROW][C]M7[/C][C]-25.5[/C][C]5.876638[/C][C]-4.3392[/C][C]0.000102[/C][C]5.1e-05[/C][/ROW]
[ROW][C]M8[/C][C]-28.5[/C][C]5.876638[/C][C]-4.8497[/C][C]2.1e-05[/C][C]1.1e-05[/C][/ROW]
[ROW][C]M9[/C][C]-36.2500[/C][C]5.876638[/C][C]-6.1685[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M10[/C][C]-34.5[/C][C]5.876638[/C][C]-5.8707[/C][C]1e-06[/C][C]0[/C][/ROW]
[ROW][C]M11[/C][C]-5.50000000000001[/C][C]5.876638[/C][C]-0.9359[/C][C]0.35523[/C][C]0.177615[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=103550&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=103550&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)130.754.1554131.46500
M10.8499999999999995.5750680.15250.8796270.439814
M2-10.35000000000005.575068-1.85650.0711510.035575
M3-17.755.876638-3.02040.0044960.002248
M4-18.55.876638-3.14810.0031940.001597
M5-17.75000000000005.876638-3.02040.0044960.002248
M6-215.876638-3.57350.0009780.000489
M7-25.55.876638-4.33920.0001025.1e-05
M8-28.55.876638-4.84972.1e-051.1e-05
M9-36.25005.876638-6.168500
M10-34.55.876638-5.87071e-060
M11-5.500000000000015.876638-0.93590.355230.177615







Multiple Linear Regression - Regression Statistics
Multiple R0.854381746061913
R-squared0.729968168003803
Adjusted R-squared0.651801058741746
F-TEST (value)9.3385590806047
F-TEST (DF numerator)11
F-TEST (DF denominator)38
p-value8.22244993203824e-08
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation8.31082046744515
Sum Squared Residuals2624.65000000000

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.854381746061913 \tabularnewline
R-squared & 0.729968168003803 \tabularnewline
Adjusted R-squared & 0.651801058741746 \tabularnewline
F-TEST (value) & 9.3385590806047 \tabularnewline
F-TEST (DF numerator) & 11 \tabularnewline
F-TEST (DF denominator) & 38 \tabularnewline
p-value & 8.22244993203824e-08 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 8.31082046744515 \tabularnewline
Sum Squared Residuals & 2624.65000000000 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=103550&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.854381746061913[/C][/ROW]
[ROW][C]R-squared[/C][C]0.729968168003803[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.651801058741746[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]9.3385590806047[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]11[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]38[/C][/ROW]
[ROW][C]p-value[/C][C]8.22244993203824e-08[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]8.31082046744515[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]2624.65000000000[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=103550&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=103550&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 R0.854381746061913
R-squared0.729968168003803
Adjusted R-squared0.651801058741746
F-TEST (value)9.3385590806047
F-TEST (DF numerator)11
F-TEST (DF denominator)38
p-value8.22244993203824e-08
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation8.31082046744515
Sum Squared Residuals2624.65000000000







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1149131.617.4000000000000
2134120.413.6000000000000
312311310
4116112.253.74999999999998
51171133.99999999999997
6111109.751.25000000000003
7105105.25-0.249999999999993
8102102.25-0.249999999999989
99594.50.499999999999975
109396.25-3.24999999999998
11124125.25-1.25000000000001
12130130.75-0.750000000000004
13124131.6-7.59999999999999
14115120.4-5.39999999999999
15106113-7
16105112.25-7.24999999999999
17105113-8
18101109.75-8.75000000000001
1995105.25-10.25
2093102.25-9.25
218494.5-10.5000000000000
228796.25-9.25
23116125.25-9.25
24120130.75-10.7500000000000
25117131.6-14.6
26109120.4-11.4
27105113-8
28107112.25-5.24999999999999
29109113-3.99999999999999
30109109.75-0.750000000000009
31108105.252.75
32107102.254.75
339994.54.50000000000001
3410396.256.74999999999999
35131125.255.75
36137130.756.25
37135131.63.40000000000001
38124120.43.60000000000001
391181135
40121112.258.75
411211138.00000000000001
42118109.758.25
43113105.257.75
44107102.254.75
4510094.55.50000000000001
4610296.255.74999999999999
47130125.254.75
48136130.755.25
49133131.61.40000000000001
50120120.4-0.399999999999992

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 149 & 131.6 & 17.4000000000000 \tabularnewline
2 & 134 & 120.4 & 13.6000000000000 \tabularnewline
3 & 123 & 113 & 10 \tabularnewline
4 & 116 & 112.25 & 3.74999999999998 \tabularnewline
5 & 117 & 113 & 3.99999999999997 \tabularnewline
6 & 111 & 109.75 & 1.25000000000003 \tabularnewline
7 & 105 & 105.25 & -0.249999999999993 \tabularnewline
8 & 102 & 102.25 & -0.249999999999989 \tabularnewline
9 & 95 & 94.5 & 0.499999999999975 \tabularnewline
10 & 93 & 96.25 & -3.24999999999998 \tabularnewline
11 & 124 & 125.25 & -1.25000000000001 \tabularnewline
12 & 130 & 130.75 & -0.750000000000004 \tabularnewline
13 & 124 & 131.6 & -7.59999999999999 \tabularnewline
14 & 115 & 120.4 & -5.39999999999999 \tabularnewline
15 & 106 & 113 & -7 \tabularnewline
16 & 105 & 112.25 & -7.24999999999999 \tabularnewline
17 & 105 & 113 & -8 \tabularnewline
18 & 101 & 109.75 & -8.75000000000001 \tabularnewline
19 & 95 & 105.25 & -10.25 \tabularnewline
20 & 93 & 102.25 & -9.25 \tabularnewline
21 & 84 & 94.5 & -10.5000000000000 \tabularnewline
22 & 87 & 96.25 & -9.25 \tabularnewline
23 & 116 & 125.25 & -9.25 \tabularnewline
24 & 120 & 130.75 & -10.7500000000000 \tabularnewline
25 & 117 & 131.6 & -14.6 \tabularnewline
26 & 109 & 120.4 & -11.4 \tabularnewline
27 & 105 & 113 & -8 \tabularnewline
28 & 107 & 112.25 & -5.24999999999999 \tabularnewline
29 & 109 & 113 & -3.99999999999999 \tabularnewline
30 & 109 & 109.75 & -0.750000000000009 \tabularnewline
31 & 108 & 105.25 & 2.75 \tabularnewline
32 & 107 & 102.25 & 4.75 \tabularnewline
33 & 99 & 94.5 & 4.50000000000001 \tabularnewline
34 & 103 & 96.25 & 6.74999999999999 \tabularnewline
35 & 131 & 125.25 & 5.75 \tabularnewline
36 & 137 & 130.75 & 6.25 \tabularnewline
37 & 135 & 131.6 & 3.40000000000001 \tabularnewline
38 & 124 & 120.4 & 3.60000000000001 \tabularnewline
39 & 118 & 113 & 5 \tabularnewline
40 & 121 & 112.25 & 8.75 \tabularnewline
41 & 121 & 113 & 8.00000000000001 \tabularnewline
42 & 118 & 109.75 & 8.25 \tabularnewline
43 & 113 & 105.25 & 7.75 \tabularnewline
44 & 107 & 102.25 & 4.75 \tabularnewline
45 & 100 & 94.5 & 5.50000000000001 \tabularnewline
46 & 102 & 96.25 & 5.74999999999999 \tabularnewline
47 & 130 & 125.25 & 4.75 \tabularnewline
48 & 136 & 130.75 & 5.25 \tabularnewline
49 & 133 & 131.6 & 1.40000000000001 \tabularnewline
50 & 120 & 120.4 & -0.399999999999992 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=103550&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]149[/C][C]131.6[/C][C]17.4000000000000[/C][/ROW]
[ROW][C]2[/C][C]134[/C][C]120.4[/C][C]13.6000000000000[/C][/ROW]
[ROW][C]3[/C][C]123[/C][C]113[/C][C]10[/C][/ROW]
[ROW][C]4[/C][C]116[/C][C]112.25[/C][C]3.74999999999998[/C][/ROW]
[ROW][C]5[/C][C]117[/C][C]113[/C][C]3.99999999999997[/C][/ROW]
[ROW][C]6[/C][C]111[/C][C]109.75[/C][C]1.25000000000003[/C][/ROW]
[ROW][C]7[/C][C]105[/C][C]105.25[/C][C]-0.249999999999993[/C][/ROW]
[ROW][C]8[/C][C]102[/C][C]102.25[/C][C]-0.249999999999989[/C][/ROW]
[ROW][C]9[/C][C]95[/C][C]94.5[/C][C]0.499999999999975[/C][/ROW]
[ROW][C]10[/C][C]93[/C][C]96.25[/C][C]-3.24999999999998[/C][/ROW]
[ROW][C]11[/C][C]124[/C][C]125.25[/C][C]-1.25000000000001[/C][/ROW]
[ROW][C]12[/C][C]130[/C][C]130.75[/C][C]-0.750000000000004[/C][/ROW]
[ROW][C]13[/C][C]124[/C][C]131.6[/C][C]-7.59999999999999[/C][/ROW]
[ROW][C]14[/C][C]115[/C][C]120.4[/C][C]-5.39999999999999[/C][/ROW]
[ROW][C]15[/C][C]106[/C][C]113[/C][C]-7[/C][/ROW]
[ROW][C]16[/C][C]105[/C][C]112.25[/C][C]-7.24999999999999[/C][/ROW]
[ROW][C]17[/C][C]105[/C][C]113[/C][C]-8[/C][/ROW]
[ROW][C]18[/C][C]101[/C][C]109.75[/C][C]-8.75000000000001[/C][/ROW]
[ROW][C]19[/C][C]95[/C][C]105.25[/C][C]-10.25[/C][/ROW]
[ROW][C]20[/C][C]93[/C][C]102.25[/C][C]-9.25[/C][/ROW]
[ROW][C]21[/C][C]84[/C][C]94.5[/C][C]-10.5000000000000[/C][/ROW]
[ROW][C]22[/C][C]87[/C][C]96.25[/C][C]-9.25[/C][/ROW]
[ROW][C]23[/C][C]116[/C][C]125.25[/C][C]-9.25[/C][/ROW]
[ROW][C]24[/C][C]120[/C][C]130.75[/C][C]-10.7500000000000[/C][/ROW]
[ROW][C]25[/C][C]117[/C][C]131.6[/C][C]-14.6[/C][/ROW]
[ROW][C]26[/C][C]109[/C][C]120.4[/C][C]-11.4[/C][/ROW]
[ROW][C]27[/C][C]105[/C][C]113[/C][C]-8[/C][/ROW]
[ROW][C]28[/C][C]107[/C][C]112.25[/C][C]-5.24999999999999[/C][/ROW]
[ROW][C]29[/C][C]109[/C][C]113[/C][C]-3.99999999999999[/C][/ROW]
[ROW][C]30[/C][C]109[/C][C]109.75[/C][C]-0.750000000000009[/C][/ROW]
[ROW][C]31[/C][C]108[/C][C]105.25[/C][C]2.75[/C][/ROW]
[ROW][C]32[/C][C]107[/C][C]102.25[/C][C]4.75[/C][/ROW]
[ROW][C]33[/C][C]99[/C][C]94.5[/C][C]4.50000000000001[/C][/ROW]
[ROW][C]34[/C][C]103[/C][C]96.25[/C][C]6.74999999999999[/C][/ROW]
[ROW][C]35[/C][C]131[/C][C]125.25[/C][C]5.75[/C][/ROW]
[ROW][C]36[/C][C]137[/C][C]130.75[/C][C]6.25[/C][/ROW]
[ROW][C]37[/C][C]135[/C][C]131.6[/C][C]3.40000000000001[/C][/ROW]
[ROW][C]38[/C][C]124[/C][C]120.4[/C][C]3.60000000000001[/C][/ROW]
[ROW][C]39[/C][C]118[/C][C]113[/C][C]5[/C][/ROW]
[ROW][C]40[/C][C]121[/C][C]112.25[/C][C]8.75[/C][/ROW]
[ROW][C]41[/C][C]121[/C][C]113[/C][C]8.00000000000001[/C][/ROW]
[ROW][C]42[/C][C]118[/C][C]109.75[/C][C]8.25[/C][/ROW]
[ROW][C]43[/C][C]113[/C][C]105.25[/C][C]7.75[/C][/ROW]
[ROW][C]44[/C][C]107[/C][C]102.25[/C][C]4.75[/C][/ROW]
[ROW][C]45[/C][C]100[/C][C]94.5[/C][C]5.50000000000001[/C][/ROW]
[ROW][C]46[/C][C]102[/C][C]96.25[/C][C]5.74999999999999[/C][/ROW]
[ROW][C]47[/C][C]130[/C][C]125.25[/C][C]4.75[/C][/ROW]
[ROW][C]48[/C][C]136[/C][C]130.75[/C][C]5.25[/C][/ROW]
[ROW][C]49[/C][C]133[/C][C]131.6[/C][C]1.40000000000001[/C][/ROW]
[ROW][C]50[/C][C]120[/C][C]120.4[/C][C]-0.399999999999992[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=103550&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=103550&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
1149131.617.4000000000000
2134120.413.6000000000000
312311310
4116112.253.74999999999998
51171133.99999999999997
6111109.751.25000000000003
7105105.25-0.249999999999993
8102102.25-0.249999999999989
99594.50.499999999999975
109396.25-3.24999999999998
11124125.25-1.25000000000001
12130130.75-0.750000000000004
13124131.6-7.59999999999999
14115120.4-5.39999999999999
15106113-7
16105112.25-7.24999999999999
17105113-8
18101109.75-8.75000000000001
1995105.25-10.25
2093102.25-9.25
218494.5-10.5000000000000
228796.25-9.25
23116125.25-9.25
24120130.75-10.7500000000000
25117131.6-14.6
26109120.4-11.4
27105113-8
28107112.25-5.24999999999999
29109113-3.99999999999999
30109109.75-0.750000000000009
31108105.252.75
32107102.254.75
339994.54.50000000000001
3410396.256.74999999999999
35131125.255.75
36137130.756.25
37135131.63.40000000000001
38124120.43.60000000000001
391181135
40121112.258.75
411211138.00000000000001
42118109.758.25
43113105.257.75
44107102.254.75
4510094.55.50000000000001
4610296.255.74999999999999
47130125.254.75
48136130.755.25
49133131.61.40000000000001
50120120.4-0.399999999999992







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
150.9335804892952750.1328390214094490.0664195107047246
160.9002036854802560.1995926290394880.0997963145197438
170.870813745890310.2583725082193790.129186254109690
180.8399520479152670.3200959041694650.160047952084733
190.8285777181623070.3428445636753850.171422281837693
200.8112806359231860.3774387281536290.188719364076814
210.8232589497845690.3534821004308620.176741050215431
220.8266793881637970.3466412236724050.173320611836202
230.8391287942003050.3217424115993890.160871205799695
240.8916145115686410.2167709768627180.108385488431359
250.973394838888690.05321032222262030.0266051611113102
260.989275390472920.02144921905416100.0107246095270805
270.9939546537302760.01209069253944880.00604534626972441
280.998295254763850.003409490472298170.00170474523614908
290.999736658761290.0005266824774193520.000263341238709676
300.9999744874131315.10251737383911e-052.55125868691955e-05
310.9999884121678782.31756642438758e-051.15878321219379e-05
320.999923131791950.0001537364161009477.68682080504737e-05
330.9995658308990650.0008683382018699050.000434169100934952
340.9977533134528280.004493373094343980.00224668654717199
350.9882441920412980.02351161591740330.0117558079587016

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
15 & 0.933580489295275 & 0.132839021409449 & 0.0664195107047246 \tabularnewline
16 & 0.900203685480256 & 0.199592629039488 & 0.0997963145197438 \tabularnewline
17 & 0.87081374589031 & 0.258372508219379 & 0.129186254109690 \tabularnewline
18 & 0.839952047915267 & 0.320095904169465 & 0.160047952084733 \tabularnewline
19 & 0.828577718162307 & 0.342844563675385 & 0.171422281837693 \tabularnewline
20 & 0.811280635923186 & 0.377438728153629 & 0.188719364076814 \tabularnewline
21 & 0.823258949784569 & 0.353482100430862 & 0.176741050215431 \tabularnewline
22 & 0.826679388163797 & 0.346641223672405 & 0.173320611836202 \tabularnewline
23 & 0.839128794200305 & 0.321742411599389 & 0.160871205799695 \tabularnewline
24 & 0.891614511568641 & 0.216770976862718 & 0.108385488431359 \tabularnewline
25 & 0.97339483888869 & 0.0532103222226203 & 0.0266051611113102 \tabularnewline
26 & 0.98927539047292 & 0.0214492190541610 & 0.0107246095270805 \tabularnewline
27 & 0.993954653730276 & 0.0120906925394488 & 0.00604534626972441 \tabularnewline
28 & 0.99829525476385 & 0.00340949047229817 & 0.00170474523614908 \tabularnewline
29 & 0.99973665876129 & 0.000526682477419352 & 0.000263341238709676 \tabularnewline
30 & 0.999974487413131 & 5.10251737383911e-05 & 2.55125868691955e-05 \tabularnewline
31 & 0.999988412167878 & 2.31756642438758e-05 & 1.15878321219379e-05 \tabularnewline
32 & 0.99992313179195 & 0.000153736416100947 & 7.68682080504737e-05 \tabularnewline
33 & 0.999565830899065 & 0.000868338201869905 & 0.000434169100934952 \tabularnewline
34 & 0.997753313452828 & 0.00449337309434398 & 0.00224668654717199 \tabularnewline
35 & 0.988244192041298 & 0.0235116159174033 & 0.0117558079587016 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=103550&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]15[/C][C]0.933580489295275[/C][C]0.132839021409449[/C][C]0.0664195107047246[/C][/ROW]
[ROW][C]16[/C][C]0.900203685480256[/C][C]0.199592629039488[/C][C]0.0997963145197438[/C][/ROW]
[ROW][C]17[/C][C]0.87081374589031[/C][C]0.258372508219379[/C][C]0.129186254109690[/C][/ROW]
[ROW][C]18[/C][C]0.839952047915267[/C][C]0.320095904169465[/C][C]0.160047952084733[/C][/ROW]
[ROW][C]19[/C][C]0.828577718162307[/C][C]0.342844563675385[/C][C]0.171422281837693[/C][/ROW]
[ROW][C]20[/C][C]0.811280635923186[/C][C]0.377438728153629[/C][C]0.188719364076814[/C][/ROW]
[ROW][C]21[/C][C]0.823258949784569[/C][C]0.353482100430862[/C][C]0.176741050215431[/C][/ROW]
[ROW][C]22[/C][C]0.826679388163797[/C][C]0.346641223672405[/C][C]0.173320611836202[/C][/ROW]
[ROW][C]23[/C][C]0.839128794200305[/C][C]0.321742411599389[/C][C]0.160871205799695[/C][/ROW]
[ROW][C]24[/C][C]0.891614511568641[/C][C]0.216770976862718[/C][C]0.108385488431359[/C][/ROW]
[ROW][C]25[/C][C]0.97339483888869[/C][C]0.0532103222226203[/C][C]0.0266051611113102[/C][/ROW]
[ROW][C]26[/C][C]0.98927539047292[/C][C]0.0214492190541610[/C][C]0.0107246095270805[/C][/ROW]
[ROW][C]27[/C][C]0.993954653730276[/C][C]0.0120906925394488[/C][C]0.00604534626972441[/C][/ROW]
[ROW][C]28[/C][C]0.99829525476385[/C][C]0.00340949047229817[/C][C]0.00170474523614908[/C][/ROW]
[ROW][C]29[/C][C]0.99973665876129[/C][C]0.000526682477419352[/C][C]0.000263341238709676[/C][/ROW]
[ROW][C]30[/C][C]0.999974487413131[/C][C]5.10251737383911e-05[/C][C]2.55125868691955e-05[/C][/ROW]
[ROW][C]31[/C][C]0.999988412167878[/C][C]2.31756642438758e-05[/C][C]1.15878321219379e-05[/C][/ROW]
[ROW][C]32[/C][C]0.99992313179195[/C][C]0.000153736416100947[/C][C]7.68682080504737e-05[/C][/ROW]
[ROW][C]33[/C][C]0.999565830899065[/C][C]0.000868338201869905[/C][C]0.000434169100934952[/C][/ROW]
[ROW][C]34[/C][C]0.997753313452828[/C][C]0.00449337309434398[/C][C]0.00224668654717199[/C][/ROW]
[ROW][C]35[/C][C]0.988244192041298[/C][C]0.0235116159174033[/C][C]0.0117558079587016[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=103550&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=103550&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
150.9335804892952750.1328390214094490.0664195107047246
160.9002036854802560.1995926290394880.0997963145197438
170.870813745890310.2583725082193790.129186254109690
180.8399520479152670.3200959041694650.160047952084733
190.8285777181623070.3428445636753850.171422281837693
200.8112806359231860.3774387281536290.188719364076814
210.8232589497845690.3534821004308620.176741050215431
220.8266793881637970.3466412236724050.173320611836202
230.8391287942003050.3217424115993890.160871205799695
240.8916145115686410.2167709768627180.108385488431359
250.973394838888690.05321032222262030.0266051611113102
260.989275390472920.02144921905416100.0107246095270805
270.9939546537302760.01209069253944880.00604534626972441
280.998295254763850.003409490472298170.00170474523614908
290.999736658761290.0005266824774193520.000263341238709676
300.9999744874131315.10251737383911e-052.55125868691955e-05
310.9999884121678782.31756642438758e-051.15878321219379e-05
320.999923131791950.0001537364161009477.68682080504737e-05
330.9995658308990650.0008683382018699050.000434169100934952
340.9977533134528280.004493373094343980.00224668654717199
350.9882441920412980.02351161591740330.0117558079587016







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level70.333333333333333NOK
5% type I error level100.476190476190476NOK
10% type I error level110.523809523809524NOK

\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 & 7 & 0.333333333333333 & NOK \tabularnewline
5% type I error level & 10 & 0.476190476190476 & NOK \tabularnewline
10% type I error level & 11 & 0.523809523809524 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=103550&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]7[/C][C]0.333333333333333[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]10[/C][C]0.476190476190476[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]11[/C][C]0.523809523809524[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=103550&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=103550&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 level70.333333333333333NOK
5% type I error level100.476190476190476NOK
10% type I error level110.523809523809524NOK



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')
}