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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 computationFri, 20 Nov 2009 13:33:59 -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/20/t1258749614jjoig99hku5h3lb.htm/, Retrieved Thu, 28 Mar 2024 23:56:57 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=58458, Retrieved Thu, 28 Mar 2024 23:56:57 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact107
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [Shwws7_v1] [2009-11-20 20:33:59] [93b66894f6318f3da4fcda772f2ffa6f] [Current]
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Dataseries X:
102,1	100,35
102,86	100,35
102,99	100,36
103,73	100,39
105,02	100,34
104,43	100,34
104,63	100,35
104,93	100,43
105,87	100,47
105,66	100,67
106,76	100,75
106	100,78
107,22	100,79
107,33	100,67
107,11	100,64
108,86	100,64
107,72	100,76
107,88	100,79
108,38	100,79
107,72	100,9
108,41	100,98
109,9	101,11
111,45	101,18
112,18	101,22
113,34	101,23
113,46	101,09
114,06	101,26
115,54	101,28
116,39	101,43
115,94	101,53
116,97	101,54
115,94	101,54
115,91	101,79
116,43	102,18
116,26	102,37
116,35	102,46
117,9	102,46
117,7	102,03
117,53	102,26
117,86	102,33
117,65	102,44
116,51	102,5
115,93	102,52
115,31	102,66
115	102,72




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 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 & 4 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58458&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]4 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=58458&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58458&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 time4 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







Multiple Linear Regression - Estimated Regression Equation
vmtot[t] = -496.182843436178 + 5.99546433915312ktot[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
vmtot[t] =  -496.182843436178 +  5.99546433915312ktot[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58458&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]vmtot[t] =  -496.182843436178 +  5.99546433915312ktot[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58458&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58458&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
vmtot[t] = -496.182843436178 + 5.99546433915312ktot[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-496.18284343617844.176154-11.231900
ktot5.995464339153120.43615913.746100

\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) & -496.182843436178 & 44.176154 & -11.2319 & 0 & 0 \tabularnewline
ktot & 5.99546433915312 & 0.436159 & 13.7461 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58458&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]-496.182843436178[/C][C]44.176154[/C][C]-11.2319[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]ktot[/C][C]5.99546433915312[/C][C]0.436159[/C][C]13.7461[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58458&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58458&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)-496.18284343617844.176154-11.231900
ktot5.995464339153120.43615913.746100







Multiple Linear Regression - Regression Statistics
Multiple R0.902562117697146
R-squared0.814618376301957
Adjusted R-squared0.81030717575084
F-TEST (value)188.953950678737
F-TEST (DF numerator)1
F-TEST (DF denominator)43
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.26710358170506
Sum Squared Residuals221.009621957737

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.902562117697146 \tabularnewline
R-squared & 0.814618376301957 \tabularnewline
Adjusted R-squared & 0.81030717575084 \tabularnewline
F-TEST (value) & 188.953950678737 \tabularnewline
F-TEST (DF numerator) & 1 \tabularnewline
F-TEST (DF denominator) & 43 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.26710358170506 \tabularnewline
Sum Squared Residuals & 221.009621957737 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58458&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.902562117697146[/C][/ROW]
[ROW][C]R-squared[/C][C]0.814618376301957[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.81030717575084[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]188.953950678737[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]1[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]43[/C][/ROW]
[ROW][C]p-value[/C][C]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]2.26710358170506[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]221.009621957737[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58458&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58458&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 R0.902562117697146
R-squared0.814618376301957
Adjusted R-squared0.81030717575084
F-TEST (value)188.953950678737
F-TEST (DF numerator)1
F-TEST (DF denominator)43
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.26710358170506
Sum Squared Residuals221.009621957737







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1102.1105.462002997838-3.36200299783781
2102.86105.462002997838-2.60200299783777
3102.99105.521957641229-2.53195764122933
4103.73105.701821571404-1.97182157140392
5105.02105.402048354446-0.382048354446288
6104.43105.402048354446-0.972048354446277
7104.63105.462002997838-0.832002997837764
8104.93105.94164014497-1.01164014497008
9105.87106.181458718536-0.311458718536158
10105.66107.380551586367-1.72055158636681
11106.76107.860188733499-1.10018873349904
12106108.040052663674-2.04005266367364
13107.22108.100007307065-0.880007307065206
14107.33107.380551586367-0.0505515863668045
15107.11107.200687656192-0.0906876561922033
16108.86107.2006876561921.65931234380780
17107.72107.920143376891-0.200143376890605
18107.88108.100007307065-0.220007307065209
19108.38108.1000073070650.279992692934791
20107.72108.759508384372-1.03950838437205
21108.41109.239145531504-0.829145531504287
22109.9110.018555895594-0.118555895594157
23111.45110.4382383993351.01176160066508
24112.18110.6780569729011.50194302709900
25113.34110.7380116162932.60198838370744
26113.46109.8986466088113.56135339118887
27114.06110.9178755464673.14212445353284
28115.54111.0377848332504.50221516674980
29116.39111.9371044841234.4528955158768
30115.94112.5366509180383.40334908196151
31116.97112.596605561434.37339443856995
32115.94112.596605561433.34339443856995
33115.91114.0954716462181.81452835378167
34116.43116.433702738488-0.00370273848803870
35116.26117.572840962927-1.31284096292712
36116.35118.112432753451-1.76243275345085
37117.9118.112432753451-0.212432753450835
38117.7115.5343830876152.16561691238496
39117.53116.9133398856200.616660114379716
40117.86117.3330223893610.526977610639037
41117.65117.992523466668-0.342523466667796
42116.51118.352251327017-1.84225132701700
43115.93118.4721606138-2.54216061380003
44115.31119.311525621281-4.00152562128148
45115119.671253481631-4.67125348163068

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 102.1 & 105.462002997838 & -3.36200299783781 \tabularnewline
2 & 102.86 & 105.462002997838 & -2.60200299783777 \tabularnewline
3 & 102.99 & 105.521957641229 & -2.53195764122933 \tabularnewline
4 & 103.73 & 105.701821571404 & -1.97182157140392 \tabularnewline
5 & 105.02 & 105.402048354446 & -0.382048354446288 \tabularnewline
6 & 104.43 & 105.402048354446 & -0.972048354446277 \tabularnewline
7 & 104.63 & 105.462002997838 & -0.832002997837764 \tabularnewline
8 & 104.93 & 105.94164014497 & -1.01164014497008 \tabularnewline
9 & 105.87 & 106.181458718536 & -0.311458718536158 \tabularnewline
10 & 105.66 & 107.380551586367 & -1.72055158636681 \tabularnewline
11 & 106.76 & 107.860188733499 & -1.10018873349904 \tabularnewline
12 & 106 & 108.040052663674 & -2.04005266367364 \tabularnewline
13 & 107.22 & 108.100007307065 & -0.880007307065206 \tabularnewline
14 & 107.33 & 107.380551586367 & -0.0505515863668045 \tabularnewline
15 & 107.11 & 107.200687656192 & -0.0906876561922033 \tabularnewline
16 & 108.86 & 107.200687656192 & 1.65931234380780 \tabularnewline
17 & 107.72 & 107.920143376891 & -0.200143376890605 \tabularnewline
18 & 107.88 & 108.100007307065 & -0.220007307065209 \tabularnewline
19 & 108.38 & 108.100007307065 & 0.279992692934791 \tabularnewline
20 & 107.72 & 108.759508384372 & -1.03950838437205 \tabularnewline
21 & 108.41 & 109.239145531504 & -0.829145531504287 \tabularnewline
22 & 109.9 & 110.018555895594 & -0.118555895594157 \tabularnewline
23 & 111.45 & 110.438238399335 & 1.01176160066508 \tabularnewline
24 & 112.18 & 110.678056972901 & 1.50194302709900 \tabularnewline
25 & 113.34 & 110.738011616293 & 2.60198838370744 \tabularnewline
26 & 113.46 & 109.898646608811 & 3.56135339118887 \tabularnewline
27 & 114.06 & 110.917875546467 & 3.14212445353284 \tabularnewline
28 & 115.54 & 111.037784833250 & 4.50221516674980 \tabularnewline
29 & 116.39 & 111.937104484123 & 4.4528955158768 \tabularnewline
30 & 115.94 & 112.536650918038 & 3.40334908196151 \tabularnewline
31 & 116.97 & 112.59660556143 & 4.37339443856995 \tabularnewline
32 & 115.94 & 112.59660556143 & 3.34339443856995 \tabularnewline
33 & 115.91 & 114.095471646218 & 1.81452835378167 \tabularnewline
34 & 116.43 & 116.433702738488 & -0.00370273848803870 \tabularnewline
35 & 116.26 & 117.572840962927 & -1.31284096292712 \tabularnewline
36 & 116.35 & 118.112432753451 & -1.76243275345085 \tabularnewline
37 & 117.9 & 118.112432753451 & -0.212432753450835 \tabularnewline
38 & 117.7 & 115.534383087615 & 2.16561691238496 \tabularnewline
39 & 117.53 & 116.913339885620 & 0.616660114379716 \tabularnewline
40 & 117.86 & 117.333022389361 & 0.526977610639037 \tabularnewline
41 & 117.65 & 117.992523466668 & -0.342523466667796 \tabularnewline
42 & 116.51 & 118.352251327017 & -1.84225132701700 \tabularnewline
43 & 115.93 & 118.4721606138 & -2.54216061380003 \tabularnewline
44 & 115.31 & 119.311525621281 & -4.00152562128148 \tabularnewline
45 & 115 & 119.671253481631 & -4.67125348163068 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58458&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]102.1[/C][C]105.462002997838[/C][C]-3.36200299783781[/C][/ROW]
[ROW][C]2[/C][C]102.86[/C][C]105.462002997838[/C][C]-2.60200299783777[/C][/ROW]
[ROW][C]3[/C][C]102.99[/C][C]105.521957641229[/C][C]-2.53195764122933[/C][/ROW]
[ROW][C]4[/C][C]103.73[/C][C]105.701821571404[/C][C]-1.97182157140392[/C][/ROW]
[ROW][C]5[/C][C]105.02[/C][C]105.402048354446[/C][C]-0.382048354446288[/C][/ROW]
[ROW][C]6[/C][C]104.43[/C][C]105.402048354446[/C][C]-0.972048354446277[/C][/ROW]
[ROW][C]7[/C][C]104.63[/C][C]105.462002997838[/C][C]-0.832002997837764[/C][/ROW]
[ROW][C]8[/C][C]104.93[/C][C]105.94164014497[/C][C]-1.01164014497008[/C][/ROW]
[ROW][C]9[/C][C]105.87[/C][C]106.181458718536[/C][C]-0.311458718536158[/C][/ROW]
[ROW][C]10[/C][C]105.66[/C][C]107.380551586367[/C][C]-1.72055158636681[/C][/ROW]
[ROW][C]11[/C][C]106.76[/C][C]107.860188733499[/C][C]-1.10018873349904[/C][/ROW]
[ROW][C]12[/C][C]106[/C][C]108.040052663674[/C][C]-2.04005266367364[/C][/ROW]
[ROW][C]13[/C][C]107.22[/C][C]108.100007307065[/C][C]-0.880007307065206[/C][/ROW]
[ROW][C]14[/C][C]107.33[/C][C]107.380551586367[/C][C]-0.0505515863668045[/C][/ROW]
[ROW][C]15[/C][C]107.11[/C][C]107.200687656192[/C][C]-0.0906876561922033[/C][/ROW]
[ROW][C]16[/C][C]108.86[/C][C]107.200687656192[/C][C]1.65931234380780[/C][/ROW]
[ROW][C]17[/C][C]107.72[/C][C]107.920143376891[/C][C]-0.200143376890605[/C][/ROW]
[ROW][C]18[/C][C]107.88[/C][C]108.100007307065[/C][C]-0.220007307065209[/C][/ROW]
[ROW][C]19[/C][C]108.38[/C][C]108.100007307065[/C][C]0.279992692934791[/C][/ROW]
[ROW][C]20[/C][C]107.72[/C][C]108.759508384372[/C][C]-1.03950838437205[/C][/ROW]
[ROW][C]21[/C][C]108.41[/C][C]109.239145531504[/C][C]-0.829145531504287[/C][/ROW]
[ROW][C]22[/C][C]109.9[/C][C]110.018555895594[/C][C]-0.118555895594157[/C][/ROW]
[ROW][C]23[/C][C]111.45[/C][C]110.438238399335[/C][C]1.01176160066508[/C][/ROW]
[ROW][C]24[/C][C]112.18[/C][C]110.678056972901[/C][C]1.50194302709900[/C][/ROW]
[ROW][C]25[/C][C]113.34[/C][C]110.738011616293[/C][C]2.60198838370744[/C][/ROW]
[ROW][C]26[/C][C]113.46[/C][C]109.898646608811[/C][C]3.56135339118887[/C][/ROW]
[ROW][C]27[/C][C]114.06[/C][C]110.917875546467[/C][C]3.14212445353284[/C][/ROW]
[ROW][C]28[/C][C]115.54[/C][C]111.037784833250[/C][C]4.50221516674980[/C][/ROW]
[ROW][C]29[/C][C]116.39[/C][C]111.937104484123[/C][C]4.4528955158768[/C][/ROW]
[ROW][C]30[/C][C]115.94[/C][C]112.536650918038[/C][C]3.40334908196151[/C][/ROW]
[ROW][C]31[/C][C]116.97[/C][C]112.59660556143[/C][C]4.37339443856995[/C][/ROW]
[ROW][C]32[/C][C]115.94[/C][C]112.59660556143[/C][C]3.34339443856995[/C][/ROW]
[ROW][C]33[/C][C]115.91[/C][C]114.095471646218[/C][C]1.81452835378167[/C][/ROW]
[ROW][C]34[/C][C]116.43[/C][C]116.433702738488[/C][C]-0.00370273848803870[/C][/ROW]
[ROW][C]35[/C][C]116.26[/C][C]117.572840962927[/C][C]-1.31284096292712[/C][/ROW]
[ROW][C]36[/C][C]116.35[/C][C]118.112432753451[/C][C]-1.76243275345085[/C][/ROW]
[ROW][C]37[/C][C]117.9[/C][C]118.112432753451[/C][C]-0.212432753450835[/C][/ROW]
[ROW][C]38[/C][C]117.7[/C][C]115.534383087615[/C][C]2.16561691238496[/C][/ROW]
[ROW][C]39[/C][C]117.53[/C][C]116.913339885620[/C][C]0.616660114379716[/C][/ROW]
[ROW][C]40[/C][C]117.86[/C][C]117.333022389361[/C][C]0.526977610639037[/C][/ROW]
[ROW][C]41[/C][C]117.65[/C][C]117.992523466668[/C][C]-0.342523466667796[/C][/ROW]
[ROW][C]42[/C][C]116.51[/C][C]118.352251327017[/C][C]-1.84225132701700[/C][/ROW]
[ROW][C]43[/C][C]115.93[/C][C]118.4721606138[/C][C]-2.54216061380003[/C][/ROW]
[ROW][C]44[/C][C]115.31[/C][C]119.311525621281[/C][C]-4.00152562128148[/C][/ROW]
[ROW][C]45[/C][C]115[/C][C]119.671253481631[/C][C]-4.67125348163068[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58458&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58458&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
1102.1105.462002997838-3.36200299783781
2102.86105.462002997838-2.60200299783777
3102.99105.521957641229-2.53195764122933
4103.73105.701821571404-1.97182157140392
5105.02105.402048354446-0.382048354446288
6104.43105.402048354446-0.972048354446277
7104.63105.462002997838-0.832002997837764
8104.93105.94164014497-1.01164014497008
9105.87106.181458718536-0.311458718536158
10105.66107.380551586367-1.72055158636681
11106.76107.860188733499-1.10018873349904
12106108.040052663674-2.04005266367364
13107.22108.100007307065-0.880007307065206
14107.33107.380551586367-0.0505515863668045
15107.11107.200687656192-0.0906876561922033
16108.86107.2006876561921.65931234380780
17107.72107.920143376891-0.200143376890605
18107.88108.100007307065-0.220007307065209
19108.38108.1000073070650.279992692934791
20107.72108.759508384372-1.03950838437205
21108.41109.239145531504-0.829145531504287
22109.9110.018555895594-0.118555895594157
23111.45110.4382383993351.01176160066508
24112.18110.6780569729011.50194302709900
25113.34110.7380116162932.60198838370744
26113.46109.8986466088113.56135339118887
27114.06110.9178755464673.14212445353284
28115.54111.0377848332504.50221516674980
29116.39111.9371044841234.4528955158768
30115.94112.5366509180383.40334908196151
31116.97112.596605561434.37339443856995
32115.94112.596605561433.34339443856995
33115.91114.0954716462181.81452835378167
34116.43116.433702738488-0.00370273848803870
35116.26117.572840962927-1.31284096292712
36116.35118.112432753451-1.76243275345085
37117.9118.112432753451-0.212432753450835
38117.7115.5343830876152.16561691238496
39117.53116.9133398856200.616660114379716
40117.86117.3330223893610.526977610639037
41117.65117.992523466668-0.342523466667796
42116.51118.352251327017-1.84225132701700
43115.93118.4721606138-2.54216061380003
44115.31119.311525621281-4.00152562128148
45115119.671253481631-4.67125348163068







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
50.1993187311742570.3986374623485140.800681268825743
60.1155941086255760.2311882172511510.884405891374424
70.07116248119383390.1423249623876680.928837518806166
80.04994679478743060.09989358957486130.95005320521257
90.02674140759304450.0534828151860890.973258592406955
100.02066384744070090.04132769488140170.9793361525593
110.01020975378693190.02041950757386390.989790246213068
120.007312742701783720.01462548540356740.992687257298216
130.004177249898586550.00835449979717310.995822750101413
140.00385005450575960.00770010901151920.99614994549424
150.003287227657232890.006574455314465780.996712772342767
160.01251915847461190.02503831694922370.987480841525388
170.00879916193831470.01759832387662940.991200838061685
180.00675391623238720.01350783246477440.993246083767613
190.005841429139558760.01168285827911750.994158570860441
200.01222296050373820.02444592100747650.987777039496262
210.03585311457838870.07170622915677730.964146885421611
220.08920258680432950.1784051736086590.91079741319567
230.1628785629761810.3257571259523610.83712143702382
240.2900010155001950.5800020310003910.709998984499805
250.4347822507163650.869564501432730.565217749283635
260.7392006900268640.5215986199462720.260799309973136
270.8639696525100370.2720606949799270.136030347489963
280.9034627230105480.1930745539789040.0965372769894518
290.8767528643722350.2464942712555300.123247135627765
300.8445645473216780.3108709053566450.155435452678323
310.7826640201820120.4346719596359760.217335979817988
320.7822432884708260.4355134230583480.217756711529174
330.9506480841714790.09870383165704170.0493519158285208
340.9900237394515910.01995252109681710.00997626054840856
350.9949276257273660.01014474854526880.00507237427263441
360.9930936857676080.01381262846478470.00690631423239236
370.995290478812870.009419042374259780.00470952118712989
380.9976715448221110.004656910355777220.00232845517788861
390.9967652298272140.006469540345571630.00323477017278581
400.9836665242631670.03266695147366620.0163334757368331

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
5 & 0.199318731174257 & 0.398637462348514 & 0.800681268825743 \tabularnewline
6 & 0.115594108625576 & 0.231188217251151 & 0.884405891374424 \tabularnewline
7 & 0.0711624811938339 & 0.142324962387668 & 0.928837518806166 \tabularnewline
8 & 0.0499467947874306 & 0.0998935895748613 & 0.95005320521257 \tabularnewline
9 & 0.0267414075930445 & 0.053482815186089 & 0.973258592406955 \tabularnewline
10 & 0.0206638474407009 & 0.0413276948814017 & 0.9793361525593 \tabularnewline
11 & 0.0102097537869319 & 0.0204195075738639 & 0.989790246213068 \tabularnewline
12 & 0.00731274270178372 & 0.0146254854035674 & 0.992687257298216 \tabularnewline
13 & 0.00417724989858655 & 0.0083544997971731 & 0.995822750101413 \tabularnewline
14 & 0.0038500545057596 & 0.0077001090115192 & 0.99614994549424 \tabularnewline
15 & 0.00328722765723289 & 0.00657445531446578 & 0.996712772342767 \tabularnewline
16 & 0.0125191584746119 & 0.0250383169492237 & 0.987480841525388 \tabularnewline
17 & 0.0087991619383147 & 0.0175983238766294 & 0.991200838061685 \tabularnewline
18 & 0.0067539162323872 & 0.0135078324647744 & 0.993246083767613 \tabularnewline
19 & 0.00584142913955876 & 0.0116828582791175 & 0.994158570860441 \tabularnewline
20 & 0.0122229605037382 & 0.0244459210074765 & 0.987777039496262 \tabularnewline
21 & 0.0358531145783887 & 0.0717062291567773 & 0.964146885421611 \tabularnewline
22 & 0.0892025868043295 & 0.178405173608659 & 0.91079741319567 \tabularnewline
23 & 0.162878562976181 & 0.325757125952361 & 0.83712143702382 \tabularnewline
24 & 0.290001015500195 & 0.580002031000391 & 0.709998984499805 \tabularnewline
25 & 0.434782250716365 & 0.86956450143273 & 0.565217749283635 \tabularnewline
26 & 0.739200690026864 & 0.521598619946272 & 0.260799309973136 \tabularnewline
27 & 0.863969652510037 & 0.272060694979927 & 0.136030347489963 \tabularnewline
28 & 0.903462723010548 & 0.193074553978904 & 0.0965372769894518 \tabularnewline
29 & 0.876752864372235 & 0.246494271255530 & 0.123247135627765 \tabularnewline
30 & 0.844564547321678 & 0.310870905356645 & 0.155435452678323 \tabularnewline
31 & 0.782664020182012 & 0.434671959635976 & 0.217335979817988 \tabularnewline
32 & 0.782243288470826 & 0.435513423058348 & 0.217756711529174 \tabularnewline
33 & 0.950648084171479 & 0.0987038316570417 & 0.0493519158285208 \tabularnewline
34 & 0.990023739451591 & 0.0199525210968171 & 0.00997626054840856 \tabularnewline
35 & 0.994927625727366 & 0.0101447485452688 & 0.00507237427263441 \tabularnewline
36 & 0.993093685767608 & 0.0138126284647847 & 0.00690631423239236 \tabularnewline
37 & 0.99529047881287 & 0.00941904237425978 & 0.00470952118712989 \tabularnewline
38 & 0.997671544822111 & 0.00465691035577722 & 0.00232845517788861 \tabularnewline
39 & 0.996765229827214 & 0.00646954034557163 & 0.00323477017278581 \tabularnewline
40 & 0.983666524263167 & 0.0326669514736662 & 0.0163334757368331 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58458&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]5[/C][C]0.199318731174257[/C][C]0.398637462348514[/C][C]0.800681268825743[/C][/ROW]
[ROW][C]6[/C][C]0.115594108625576[/C][C]0.231188217251151[/C][C]0.884405891374424[/C][/ROW]
[ROW][C]7[/C][C]0.0711624811938339[/C][C]0.142324962387668[/C][C]0.928837518806166[/C][/ROW]
[ROW][C]8[/C][C]0.0499467947874306[/C][C]0.0998935895748613[/C][C]0.95005320521257[/C][/ROW]
[ROW][C]9[/C][C]0.0267414075930445[/C][C]0.053482815186089[/C][C]0.973258592406955[/C][/ROW]
[ROW][C]10[/C][C]0.0206638474407009[/C][C]0.0413276948814017[/C][C]0.9793361525593[/C][/ROW]
[ROW][C]11[/C][C]0.0102097537869319[/C][C]0.0204195075738639[/C][C]0.989790246213068[/C][/ROW]
[ROW][C]12[/C][C]0.00731274270178372[/C][C]0.0146254854035674[/C][C]0.992687257298216[/C][/ROW]
[ROW][C]13[/C][C]0.00417724989858655[/C][C]0.0083544997971731[/C][C]0.995822750101413[/C][/ROW]
[ROW][C]14[/C][C]0.0038500545057596[/C][C]0.0077001090115192[/C][C]0.99614994549424[/C][/ROW]
[ROW][C]15[/C][C]0.00328722765723289[/C][C]0.00657445531446578[/C][C]0.996712772342767[/C][/ROW]
[ROW][C]16[/C][C]0.0125191584746119[/C][C]0.0250383169492237[/C][C]0.987480841525388[/C][/ROW]
[ROW][C]17[/C][C]0.0087991619383147[/C][C]0.0175983238766294[/C][C]0.991200838061685[/C][/ROW]
[ROW][C]18[/C][C]0.0067539162323872[/C][C]0.0135078324647744[/C][C]0.993246083767613[/C][/ROW]
[ROW][C]19[/C][C]0.00584142913955876[/C][C]0.0116828582791175[/C][C]0.994158570860441[/C][/ROW]
[ROW][C]20[/C][C]0.0122229605037382[/C][C]0.0244459210074765[/C][C]0.987777039496262[/C][/ROW]
[ROW][C]21[/C][C]0.0358531145783887[/C][C]0.0717062291567773[/C][C]0.964146885421611[/C][/ROW]
[ROW][C]22[/C][C]0.0892025868043295[/C][C]0.178405173608659[/C][C]0.91079741319567[/C][/ROW]
[ROW][C]23[/C][C]0.162878562976181[/C][C]0.325757125952361[/C][C]0.83712143702382[/C][/ROW]
[ROW][C]24[/C][C]0.290001015500195[/C][C]0.580002031000391[/C][C]0.709998984499805[/C][/ROW]
[ROW][C]25[/C][C]0.434782250716365[/C][C]0.86956450143273[/C][C]0.565217749283635[/C][/ROW]
[ROW][C]26[/C][C]0.739200690026864[/C][C]0.521598619946272[/C][C]0.260799309973136[/C][/ROW]
[ROW][C]27[/C][C]0.863969652510037[/C][C]0.272060694979927[/C][C]0.136030347489963[/C][/ROW]
[ROW][C]28[/C][C]0.903462723010548[/C][C]0.193074553978904[/C][C]0.0965372769894518[/C][/ROW]
[ROW][C]29[/C][C]0.876752864372235[/C][C]0.246494271255530[/C][C]0.123247135627765[/C][/ROW]
[ROW][C]30[/C][C]0.844564547321678[/C][C]0.310870905356645[/C][C]0.155435452678323[/C][/ROW]
[ROW][C]31[/C][C]0.782664020182012[/C][C]0.434671959635976[/C][C]0.217335979817988[/C][/ROW]
[ROW][C]32[/C][C]0.782243288470826[/C][C]0.435513423058348[/C][C]0.217756711529174[/C][/ROW]
[ROW][C]33[/C][C]0.950648084171479[/C][C]0.0987038316570417[/C][C]0.0493519158285208[/C][/ROW]
[ROW][C]34[/C][C]0.990023739451591[/C][C]0.0199525210968171[/C][C]0.00997626054840856[/C][/ROW]
[ROW][C]35[/C][C]0.994927625727366[/C][C]0.0101447485452688[/C][C]0.00507237427263441[/C][/ROW]
[ROW][C]36[/C][C]0.993093685767608[/C][C]0.0138126284647847[/C][C]0.00690631423239236[/C][/ROW]
[ROW][C]37[/C][C]0.99529047881287[/C][C]0.00941904237425978[/C][C]0.00470952118712989[/C][/ROW]
[ROW][C]38[/C][C]0.997671544822111[/C][C]0.00465691035577722[/C][C]0.00232845517788861[/C][/ROW]
[ROW][C]39[/C][C]0.996765229827214[/C][C]0.00646954034557163[/C][C]0.00323477017278581[/C][/ROW]
[ROW][C]40[/C][C]0.983666524263167[/C][C]0.0326669514736662[/C][C]0.0163334757368331[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58458&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58458&T=5

As an alternative you can also use a QR Code:  

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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
50.1993187311742570.3986374623485140.800681268825743
60.1155941086255760.2311882172511510.884405891374424
70.07116248119383390.1423249623876680.928837518806166
80.04994679478743060.09989358957486130.95005320521257
90.02674140759304450.0534828151860890.973258592406955
100.02066384744070090.04132769488140170.9793361525593
110.01020975378693190.02041950757386390.989790246213068
120.007312742701783720.01462548540356740.992687257298216
130.004177249898586550.00835449979717310.995822750101413
140.00385005450575960.00770010901151920.99614994549424
150.003287227657232890.006574455314465780.996712772342767
160.01251915847461190.02503831694922370.987480841525388
170.00879916193831470.01759832387662940.991200838061685
180.00675391623238720.01350783246477440.993246083767613
190.005841429139558760.01168285827911750.994158570860441
200.01222296050373820.02444592100747650.987777039496262
210.03585311457838870.07170622915677730.964146885421611
220.08920258680432950.1784051736086590.91079741319567
230.1628785629761810.3257571259523610.83712143702382
240.2900010155001950.5800020310003910.709998984499805
250.4347822507163650.869564501432730.565217749283635
260.7392006900268640.5215986199462720.260799309973136
270.8639696525100370.2720606949799270.136030347489963
280.9034627230105480.1930745539789040.0965372769894518
290.8767528643722350.2464942712555300.123247135627765
300.8445645473216780.3108709053566450.155435452678323
310.7826640201820120.4346719596359760.217335979817988
320.7822432884708260.4355134230583480.217756711529174
330.9506480841714790.09870383165704170.0493519158285208
340.9900237394515910.01995252109681710.00997626054840856
350.9949276257273660.01014474854526880.00507237427263441
360.9930936857676080.01381262846478470.00690631423239236
370.995290478812870.009419042374259780.00470952118712989
380.9976715448221110.004656910355777220.00232845517788861
390.9967652298272140.006469540345571630.00323477017278581
400.9836665242631670.03266695147366620.0163334757368331







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level60.166666666666667NOK
5% type I error level180.5NOK
10% type I error level220.611111111111111NOK

\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 & 6 & 0.166666666666667 & NOK \tabularnewline
5% type I error level & 18 & 0.5 & NOK \tabularnewline
10% type I error level & 22 & 0.611111111111111 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58458&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]6[/C][C]0.166666666666667[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]18[/C][C]0.5[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]22[/C][C]0.611111111111111[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58458&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58458&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 level60.166666666666667NOK
5% type I error level180.5NOK
10% type I error level220.611111111111111NOK



Parameters (Session):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
R code (references can be found in the software module):
library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,mysum$coefficients[i,1])
a<-table.element(a, round(mysum$coefficients[i,2],6))
a<-table.element(a, round(mysum$coefficients[i,3],4))
a<-table.element(a, round(mysum$coefficients[i,4],6))
a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, sqrt(mysum$r.squared))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, mysum$r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, mysum$adj.r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, mysum$fstatistic[1])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[3])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a, mysum$sigma)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, sum(myerror*myerror))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,x[i])
a<-table.element(a,x[i]-mysum$resid[i])
a<-table.element(a,mysum$resid[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
if (n > n25) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-values',header=TRUE)
a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'breakpoint index',header=TRUE)
a<-table.element(a,'greater',header=TRUE)
a<-table.element(a,'2-sided',header=TRUE)
a<-table.element(a,'less',header=TRUE)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
a<-table.element(a,mypoint,header=TRUE)
a<-table.element(a,gqarr[mypoint-kp3+1,1])
a<-table.element(a,gqarr[mypoint-kp3+1,2])
a<-table.element(a,gqarr[mypoint-kp3+1,3])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,numsignificant1)
a<-table.element(a,numsignificant1/numgqtests)
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,numsignificant5)
a<-table.element(a,numsignificant5/numgqtests)
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,numsignificant10)
a<-table.element(a,numsignificant10/numgqtests)
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}