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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 computationSat, 20 Dec 2008 07:03:20 -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/2008/Dec/20/t1229782120hboop9mx1g62ohz.htm/, Retrieved Thu, 31 Oct 2024 23:31:34 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=35376, Retrieved Thu, 31 Oct 2024 23:31:34 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact308
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [WS 6 Q3 G6 eigen ...] [2007-11-15 11:12:24] [22f18fc6a98517db16300404be421f9a]
- R  D  [Multiple Regression] [Multiple Linear R...] [2008-12-13 12:42:32] [f5709eefd05c649ca6dad46019ffd879]
-    D      [Multiple Regression] [Investeringsgoede...] [2008-12-20 14:03:20] [28deb8481dba3cc87d2d53a86e0e0d0b] [Current]
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Dataseries X:
97.7	0
101.5	0
119.6	0
108.1	0
117.8	0
125.5	0
89.2	0
92.3	0
104.6	0
122.8	0
96.0	0
94.6	0
93.3	0
101.1	0
114.2	0
104.7	0
113.3	0
118.2	0
83.6	0
73.9	0
99.5	0
97.7	0
103.0	0
106.3	0
92.2	0
101.8	0
122.8	0
111.8	0
106.3	0
121.5	0
81.9	0
85.4	0
110.9	0
117.3	0
106.3	0
105.5	0
101.3	0
105.9	0
126.3	0
111.9	0
108.9	0
127.2	0
94.2	0
85.7	0
116.2	0
107.2	0
110.6	0
112.0	0
104.5	0
112.0	0
132.8	0
110.8	0
128.7	0
136.8	0
94.9	0
88.8	0
123.2	0
125.3	0
122.7	0
125.7	0
116.3	0
118.7	0
142.0	0
127.9	0
131.9	0
152.3	0
110.8	1
99.1	1
135.0	1
133.2	1
131.0	1
133.9	1
119.9	1
136.9	1
148.9	1
145.1	1
142.4	1
159.6	1
120.7	1
109.0	1
142.0	1




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=35376&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=35376&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=35376&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
Y[t] = + 109.771212121212 + 21.3954545454545X[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Y[t] =  +  109.771212121212 +  21.3954545454545X[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=35376&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Y[t] =  +  109.771212121212 +  21.3954545454545X[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=35376&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=35376&T=1

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Estimated Regression Equation
Y[t] = + 109.771212121212 + 21.3954545454545X[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)109.7712121212121.91698557.262400
X21.39545454545454.454674.80297e-064e-06

\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) & 109.771212121212 & 1.916985 & 57.2624 & 0 & 0 \tabularnewline
X & 21.3954545454545 & 4.45467 & 4.8029 & 7e-06 & 4e-06 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=35376&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]109.771212121212[/C][C]1.916985[/C][C]57.2624[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]X[/C][C]21.3954545454545[/C][C]4.45467[/C][C]4.8029[/C][C]7e-06[/C][C]4e-06[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=35376&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=35376&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)109.7712121212121.91698557.262400
X21.39545454545454.454674.80297e-064e-06







Multiple Linear Regression - Regression Statistics
Multiple R0.475401918013747
R-squared0.226006983651150
Adjusted R-squared0.216209603697367
F-TEST (value)23.0681044031455
F-TEST (DF numerator)1
F-TEST (DF denominator)79
p-value7.29512625641249e-06
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation15.5736586420235
Sum Squared Residuals19160.5686363636

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.475401918013747 \tabularnewline
R-squared & 0.226006983651150 \tabularnewline
Adjusted R-squared & 0.216209603697367 \tabularnewline
F-TEST (value) & 23.0681044031455 \tabularnewline
F-TEST (DF numerator) & 1 \tabularnewline
F-TEST (DF denominator) & 79 \tabularnewline
p-value & 7.29512625641249e-06 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 15.5736586420235 \tabularnewline
Sum Squared Residuals & 19160.5686363636 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=35376&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.475401918013747[/C][/ROW]
[ROW][C]R-squared[/C][C]0.226006983651150[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.216209603697367[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]23.0681044031455[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]1[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]79[/C][/ROW]
[ROW][C]p-value[/C][C]7.29512625641249e-06[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]15.5736586420235[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]19160.5686363636[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=35376&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=35376&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 R0.475401918013747
R-squared0.226006983651150
Adjusted R-squared0.216209603697367
F-TEST (value)23.0681044031455
F-TEST (DF numerator)1
F-TEST (DF denominator)79
p-value7.29512625641249e-06
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation15.5736586420235
Sum Squared Residuals19160.5686363636







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
197.7109.771212121212-12.0712121212122
2101.5109.771212121212-8.27121212121214
3119.6109.7712121212129.82878787878788
4108.1109.771212121212-1.67121212121212
5117.8109.7712121212128.02878787878788
6125.5109.77121212121215.7287878787879
789.2109.771212121212-20.5712121212121
892.3109.771212121212-17.4712121212121
9104.6109.771212121212-5.17121212121213
10122.8109.77121212121213.0287878787879
1196109.771212121212-13.7712121212121
1294.6109.771212121212-15.1712121212121
1393.3109.771212121212-16.4712121212121
14101.1109.771212121212-8.67121212121213
15114.2109.7712121212124.42878787878788
16104.7109.771212121212-5.07121212121212
17113.3109.7712121212123.52878787878788
18118.2109.7712121212128.42878787878788
1983.6109.771212121212-26.1712121212121
2073.9109.771212121212-35.8712121212121
2199.5109.771212121212-10.2712121212121
2297.7109.771212121212-12.0712121212121
23103109.771212121212-6.77121212121212
24106.3109.771212121212-3.47121212121212
2592.2109.771212121212-17.5712121212121
26101.8109.771212121212-7.97121212121212
27122.8109.77121212121213.0287878787879
28111.8109.7712121212122.02878787878788
29106.3109.771212121212-3.47121212121212
30121.5109.77121212121211.7287878787879
3181.9109.771212121212-27.8712121212121
3285.4109.771212121212-24.3712121212121
33110.9109.7712121212121.12878787878789
34117.3109.7712121212127.52878787878788
35106.3109.771212121212-3.47121212121212
36105.5109.771212121212-4.27121212121212
37101.3109.771212121212-8.47121212121212
38105.9109.771212121212-3.87121212121211
39126.3109.77121212121216.5287878787879
40111.9109.7712121212122.12878787878789
41108.9109.771212121212-0.871212121212114
42127.2109.77121212121217.4287878787879
4394.2109.771212121212-15.5712121212121
4485.7109.771212121212-24.0712121212121
45116.2109.7712121212126.42878787878788
46107.2109.771212121212-2.57121212121212
47110.6109.7712121212120.828787878787875
48112109.7712121212122.22878787878788
49104.5109.771212121212-5.27121212121212
50112109.7712121212122.22878787878788
51132.8109.77121212121223.0287878787879
52110.8109.7712121212121.02878787878788
53128.7109.77121212121218.9287878787879
54136.8109.77121212121227.0287878787879
5594.9109.771212121212-14.8712121212121
5688.8109.771212121212-20.9712121212121
57123.2109.77121212121213.4287878787879
58125.3109.77121212121215.5287878787879
59122.7109.77121212121212.9287878787879
60125.7109.77121212121215.9287878787879
61116.3109.7712121212126.52878787878788
62118.7109.7712121212128.92878787878788
63142109.77121212121232.2287878787879
64127.9109.77121212121218.1287878787879
65131.9109.77121212121222.1287878787879
66152.3109.77121212121242.5287878787879
67110.8131.166666666667-20.3666666666667
6899.1131.166666666667-32.0666666666667
69135131.1666666666673.83333333333334
70133.2131.1666666666672.03333333333332
71131131.166666666667-0.166666666666665
72133.9131.1666666666672.73333333333334
73119.9131.166666666667-11.2666666666667
74136.9131.1666666666675.73333333333334
75148.9131.16666666666717.7333333333333
76145.1131.16666666666713.9333333333333
77142.4131.16666666666711.2333333333333
78159.6131.16666666666728.4333333333333
79120.7131.166666666667-10.4666666666667
80109131.166666666667-22.1666666666667
81142131.16666666666710.8333333333333

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 97.7 & 109.771212121212 & -12.0712121212122 \tabularnewline
2 & 101.5 & 109.771212121212 & -8.27121212121214 \tabularnewline
3 & 119.6 & 109.771212121212 & 9.82878787878788 \tabularnewline
4 & 108.1 & 109.771212121212 & -1.67121212121212 \tabularnewline
5 & 117.8 & 109.771212121212 & 8.02878787878788 \tabularnewline
6 & 125.5 & 109.771212121212 & 15.7287878787879 \tabularnewline
7 & 89.2 & 109.771212121212 & -20.5712121212121 \tabularnewline
8 & 92.3 & 109.771212121212 & -17.4712121212121 \tabularnewline
9 & 104.6 & 109.771212121212 & -5.17121212121213 \tabularnewline
10 & 122.8 & 109.771212121212 & 13.0287878787879 \tabularnewline
11 & 96 & 109.771212121212 & -13.7712121212121 \tabularnewline
12 & 94.6 & 109.771212121212 & -15.1712121212121 \tabularnewline
13 & 93.3 & 109.771212121212 & -16.4712121212121 \tabularnewline
14 & 101.1 & 109.771212121212 & -8.67121212121213 \tabularnewline
15 & 114.2 & 109.771212121212 & 4.42878787878788 \tabularnewline
16 & 104.7 & 109.771212121212 & -5.07121212121212 \tabularnewline
17 & 113.3 & 109.771212121212 & 3.52878787878788 \tabularnewline
18 & 118.2 & 109.771212121212 & 8.42878787878788 \tabularnewline
19 & 83.6 & 109.771212121212 & -26.1712121212121 \tabularnewline
20 & 73.9 & 109.771212121212 & -35.8712121212121 \tabularnewline
21 & 99.5 & 109.771212121212 & -10.2712121212121 \tabularnewline
22 & 97.7 & 109.771212121212 & -12.0712121212121 \tabularnewline
23 & 103 & 109.771212121212 & -6.77121212121212 \tabularnewline
24 & 106.3 & 109.771212121212 & -3.47121212121212 \tabularnewline
25 & 92.2 & 109.771212121212 & -17.5712121212121 \tabularnewline
26 & 101.8 & 109.771212121212 & -7.97121212121212 \tabularnewline
27 & 122.8 & 109.771212121212 & 13.0287878787879 \tabularnewline
28 & 111.8 & 109.771212121212 & 2.02878787878788 \tabularnewline
29 & 106.3 & 109.771212121212 & -3.47121212121212 \tabularnewline
30 & 121.5 & 109.771212121212 & 11.7287878787879 \tabularnewline
31 & 81.9 & 109.771212121212 & -27.8712121212121 \tabularnewline
32 & 85.4 & 109.771212121212 & -24.3712121212121 \tabularnewline
33 & 110.9 & 109.771212121212 & 1.12878787878789 \tabularnewline
34 & 117.3 & 109.771212121212 & 7.52878787878788 \tabularnewline
35 & 106.3 & 109.771212121212 & -3.47121212121212 \tabularnewline
36 & 105.5 & 109.771212121212 & -4.27121212121212 \tabularnewline
37 & 101.3 & 109.771212121212 & -8.47121212121212 \tabularnewline
38 & 105.9 & 109.771212121212 & -3.87121212121211 \tabularnewline
39 & 126.3 & 109.771212121212 & 16.5287878787879 \tabularnewline
40 & 111.9 & 109.771212121212 & 2.12878787878789 \tabularnewline
41 & 108.9 & 109.771212121212 & -0.871212121212114 \tabularnewline
42 & 127.2 & 109.771212121212 & 17.4287878787879 \tabularnewline
43 & 94.2 & 109.771212121212 & -15.5712121212121 \tabularnewline
44 & 85.7 & 109.771212121212 & -24.0712121212121 \tabularnewline
45 & 116.2 & 109.771212121212 & 6.42878787878788 \tabularnewline
46 & 107.2 & 109.771212121212 & -2.57121212121212 \tabularnewline
47 & 110.6 & 109.771212121212 & 0.828787878787875 \tabularnewline
48 & 112 & 109.771212121212 & 2.22878787878788 \tabularnewline
49 & 104.5 & 109.771212121212 & -5.27121212121212 \tabularnewline
50 & 112 & 109.771212121212 & 2.22878787878788 \tabularnewline
51 & 132.8 & 109.771212121212 & 23.0287878787879 \tabularnewline
52 & 110.8 & 109.771212121212 & 1.02878787878788 \tabularnewline
53 & 128.7 & 109.771212121212 & 18.9287878787879 \tabularnewline
54 & 136.8 & 109.771212121212 & 27.0287878787879 \tabularnewline
55 & 94.9 & 109.771212121212 & -14.8712121212121 \tabularnewline
56 & 88.8 & 109.771212121212 & -20.9712121212121 \tabularnewline
57 & 123.2 & 109.771212121212 & 13.4287878787879 \tabularnewline
58 & 125.3 & 109.771212121212 & 15.5287878787879 \tabularnewline
59 & 122.7 & 109.771212121212 & 12.9287878787879 \tabularnewline
60 & 125.7 & 109.771212121212 & 15.9287878787879 \tabularnewline
61 & 116.3 & 109.771212121212 & 6.52878787878788 \tabularnewline
62 & 118.7 & 109.771212121212 & 8.92878787878788 \tabularnewline
63 & 142 & 109.771212121212 & 32.2287878787879 \tabularnewline
64 & 127.9 & 109.771212121212 & 18.1287878787879 \tabularnewline
65 & 131.9 & 109.771212121212 & 22.1287878787879 \tabularnewline
66 & 152.3 & 109.771212121212 & 42.5287878787879 \tabularnewline
67 & 110.8 & 131.166666666667 & -20.3666666666667 \tabularnewline
68 & 99.1 & 131.166666666667 & -32.0666666666667 \tabularnewline
69 & 135 & 131.166666666667 & 3.83333333333334 \tabularnewline
70 & 133.2 & 131.166666666667 & 2.03333333333332 \tabularnewline
71 & 131 & 131.166666666667 & -0.166666666666665 \tabularnewline
72 & 133.9 & 131.166666666667 & 2.73333333333334 \tabularnewline
73 & 119.9 & 131.166666666667 & -11.2666666666667 \tabularnewline
74 & 136.9 & 131.166666666667 & 5.73333333333334 \tabularnewline
75 & 148.9 & 131.166666666667 & 17.7333333333333 \tabularnewline
76 & 145.1 & 131.166666666667 & 13.9333333333333 \tabularnewline
77 & 142.4 & 131.166666666667 & 11.2333333333333 \tabularnewline
78 & 159.6 & 131.166666666667 & 28.4333333333333 \tabularnewline
79 & 120.7 & 131.166666666667 & -10.4666666666667 \tabularnewline
80 & 109 & 131.166666666667 & -22.1666666666667 \tabularnewline
81 & 142 & 131.166666666667 & 10.8333333333333 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=35376&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]97.7[/C][C]109.771212121212[/C][C]-12.0712121212122[/C][/ROW]
[ROW][C]2[/C][C]101.5[/C][C]109.771212121212[/C][C]-8.27121212121214[/C][/ROW]
[ROW][C]3[/C][C]119.6[/C][C]109.771212121212[/C][C]9.82878787878788[/C][/ROW]
[ROW][C]4[/C][C]108.1[/C][C]109.771212121212[/C][C]-1.67121212121212[/C][/ROW]
[ROW][C]5[/C][C]117.8[/C][C]109.771212121212[/C][C]8.02878787878788[/C][/ROW]
[ROW][C]6[/C][C]125.5[/C][C]109.771212121212[/C][C]15.7287878787879[/C][/ROW]
[ROW][C]7[/C][C]89.2[/C][C]109.771212121212[/C][C]-20.5712121212121[/C][/ROW]
[ROW][C]8[/C][C]92.3[/C][C]109.771212121212[/C][C]-17.4712121212121[/C][/ROW]
[ROW][C]9[/C][C]104.6[/C][C]109.771212121212[/C][C]-5.17121212121213[/C][/ROW]
[ROW][C]10[/C][C]122.8[/C][C]109.771212121212[/C][C]13.0287878787879[/C][/ROW]
[ROW][C]11[/C][C]96[/C][C]109.771212121212[/C][C]-13.7712121212121[/C][/ROW]
[ROW][C]12[/C][C]94.6[/C][C]109.771212121212[/C][C]-15.1712121212121[/C][/ROW]
[ROW][C]13[/C][C]93.3[/C][C]109.771212121212[/C][C]-16.4712121212121[/C][/ROW]
[ROW][C]14[/C][C]101.1[/C][C]109.771212121212[/C][C]-8.67121212121213[/C][/ROW]
[ROW][C]15[/C][C]114.2[/C][C]109.771212121212[/C][C]4.42878787878788[/C][/ROW]
[ROW][C]16[/C][C]104.7[/C][C]109.771212121212[/C][C]-5.07121212121212[/C][/ROW]
[ROW][C]17[/C][C]113.3[/C][C]109.771212121212[/C][C]3.52878787878788[/C][/ROW]
[ROW][C]18[/C][C]118.2[/C][C]109.771212121212[/C][C]8.42878787878788[/C][/ROW]
[ROW][C]19[/C][C]83.6[/C][C]109.771212121212[/C][C]-26.1712121212121[/C][/ROW]
[ROW][C]20[/C][C]73.9[/C][C]109.771212121212[/C][C]-35.8712121212121[/C][/ROW]
[ROW][C]21[/C][C]99.5[/C][C]109.771212121212[/C][C]-10.2712121212121[/C][/ROW]
[ROW][C]22[/C][C]97.7[/C][C]109.771212121212[/C][C]-12.0712121212121[/C][/ROW]
[ROW][C]23[/C][C]103[/C][C]109.771212121212[/C][C]-6.77121212121212[/C][/ROW]
[ROW][C]24[/C][C]106.3[/C][C]109.771212121212[/C][C]-3.47121212121212[/C][/ROW]
[ROW][C]25[/C][C]92.2[/C][C]109.771212121212[/C][C]-17.5712121212121[/C][/ROW]
[ROW][C]26[/C][C]101.8[/C][C]109.771212121212[/C][C]-7.97121212121212[/C][/ROW]
[ROW][C]27[/C][C]122.8[/C][C]109.771212121212[/C][C]13.0287878787879[/C][/ROW]
[ROW][C]28[/C][C]111.8[/C][C]109.771212121212[/C][C]2.02878787878788[/C][/ROW]
[ROW][C]29[/C][C]106.3[/C][C]109.771212121212[/C][C]-3.47121212121212[/C][/ROW]
[ROW][C]30[/C][C]121.5[/C][C]109.771212121212[/C][C]11.7287878787879[/C][/ROW]
[ROW][C]31[/C][C]81.9[/C][C]109.771212121212[/C][C]-27.8712121212121[/C][/ROW]
[ROW][C]32[/C][C]85.4[/C][C]109.771212121212[/C][C]-24.3712121212121[/C][/ROW]
[ROW][C]33[/C][C]110.9[/C][C]109.771212121212[/C][C]1.12878787878789[/C][/ROW]
[ROW][C]34[/C][C]117.3[/C][C]109.771212121212[/C][C]7.52878787878788[/C][/ROW]
[ROW][C]35[/C][C]106.3[/C][C]109.771212121212[/C][C]-3.47121212121212[/C][/ROW]
[ROW][C]36[/C][C]105.5[/C][C]109.771212121212[/C][C]-4.27121212121212[/C][/ROW]
[ROW][C]37[/C][C]101.3[/C][C]109.771212121212[/C][C]-8.47121212121212[/C][/ROW]
[ROW][C]38[/C][C]105.9[/C][C]109.771212121212[/C][C]-3.87121212121211[/C][/ROW]
[ROW][C]39[/C][C]126.3[/C][C]109.771212121212[/C][C]16.5287878787879[/C][/ROW]
[ROW][C]40[/C][C]111.9[/C][C]109.771212121212[/C][C]2.12878787878789[/C][/ROW]
[ROW][C]41[/C][C]108.9[/C][C]109.771212121212[/C][C]-0.871212121212114[/C][/ROW]
[ROW][C]42[/C][C]127.2[/C][C]109.771212121212[/C][C]17.4287878787879[/C][/ROW]
[ROW][C]43[/C][C]94.2[/C][C]109.771212121212[/C][C]-15.5712121212121[/C][/ROW]
[ROW][C]44[/C][C]85.7[/C][C]109.771212121212[/C][C]-24.0712121212121[/C][/ROW]
[ROW][C]45[/C][C]116.2[/C][C]109.771212121212[/C][C]6.42878787878788[/C][/ROW]
[ROW][C]46[/C][C]107.2[/C][C]109.771212121212[/C][C]-2.57121212121212[/C][/ROW]
[ROW][C]47[/C][C]110.6[/C][C]109.771212121212[/C][C]0.828787878787875[/C][/ROW]
[ROW][C]48[/C][C]112[/C][C]109.771212121212[/C][C]2.22878787878788[/C][/ROW]
[ROW][C]49[/C][C]104.5[/C][C]109.771212121212[/C][C]-5.27121212121212[/C][/ROW]
[ROW][C]50[/C][C]112[/C][C]109.771212121212[/C][C]2.22878787878788[/C][/ROW]
[ROW][C]51[/C][C]132.8[/C][C]109.771212121212[/C][C]23.0287878787879[/C][/ROW]
[ROW][C]52[/C][C]110.8[/C][C]109.771212121212[/C][C]1.02878787878788[/C][/ROW]
[ROW][C]53[/C][C]128.7[/C][C]109.771212121212[/C][C]18.9287878787879[/C][/ROW]
[ROW][C]54[/C][C]136.8[/C][C]109.771212121212[/C][C]27.0287878787879[/C][/ROW]
[ROW][C]55[/C][C]94.9[/C][C]109.771212121212[/C][C]-14.8712121212121[/C][/ROW]
[ROW][C]56[/C][C]88.8[/C][C]109.771212121212[/C][C]-20.9712121212121[/C][/ROW]
[ROW][C]57[/C][C]123.2[/C][C]109.771212121212[/C][C]13.4287878787879[/C][/ROW]
[ROW][C]58[/C][C]125.3[/C][C]109.771212121212[/C][C]15.5287878787879[/C][/ROW]
[ROW][C]59[/C][C]122.7[/C][C]109.771212121212[/C][C]12.9287878787879[/C][/ROW]
[ROW][C]60[/C][C]125.7[/C][C]109.771212121212[/C][C]15.9287878787879[/C][/ROW]
[ROW][C]61[/C][C]116.3[/C][C]109.771212121212[/C][C]6.52878787878788[/C][/ROW]
[ROW][C]62[/C][C]118.7[/C][C]109.771212121212[/C][C]8.92878787878788[/C][/ROW]
[ROW][C]63[/C][C]142[/C][C]109.771212121212[/C][C]32.2287878787879[/C][/ROW]
[ROW][C]64[/C][C]127.9[/C][C]109.771212121212[/C][C]18.1287878787879[/C][/ROW]
[ROW][C]65[/C][C]131.9[/C][C]109.771212121212[/C][C]22.1287878787879[/C][/ROW]
[ROW][C]66[/C][C]152.3[/C][C]109.771212121212[/C][C]42.5287878787879[/C][/ROW]
[ROW][C]67[/C][C]110.8[/C][C]131.166666666667[/C][C]-20.3666666666667[/C][/ROW]
[ROW][C]68[/C][C]99.1[/C][C]131.166666666667[/C][C]-32.0666666666667[/C][/ROW]
[ROW][C]69[/C][C]135[/C][C]131.166666666667[/C][C]3.83333333333334[/C][/ROW]
[ROW][C]70[/C][C]133.2[/C][C]131.166666666667[/C][C]2.03333333333332[/C][/ROW]
[ROW][C]71[/C][C]131[/C][C]131.166666666667[/C][C]-0.166666666666665[/C][/ROW]
[ROW][C]72[/C][C]133.9[/C][C]131.166666666667[/C][C]2.73333333333334[/C][/ROW]
[ROW][C]73[/C][C]119.9[/C][C]131.166666666667[/C][C]-11.2666666666667[/C][/ROW]
[ROW][C]74[/C][C]136.9[/C][C]131.166666666667[/C][C]5.73333333333334[/C][/ROW]
[ROW][C]75[/C][C]148.9[/C][C]131.166666666667[/C][C]17.7333333333333[/C][/ROW]
[ROW][C]76[/C][C]145.1[/C][C]131.166666666667[/C][C]13.9333333333333[/C][/ROW]
[ROW][C]77[/C][C]142.4[/C][C]131.166666666667[/C][C]11.2333333333333[/C][/ROW]
[ROW][C]78[/C][C]159.6[/C][C]131.166666666667[/C][C]28.4333333333333[/C][/ROW]
[ROW][C]79[/C][C]120.7[/C][C]131.166666666667[/C][C]-10.4666666666667[/C][/ROW]
[ROW][C]80[/C][C]109[/C][C]131.166666666667[/C][C]-22.1666666666667[/C][/ROW]
[ROW][C]81[/C][C]142[/C][C]131.166666666667[/C][C]10.8333333333333[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=35376&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=35376&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
197.7109.771212121212-12.0712121212122
2101.5109.771212121212-8.27121212121214
3119.6109.7712121212129.82878787878788
4108.1109.771212121212-1.67121212121212
5117.8109.7712121212128.02878787878788
6125.5109.77121212121215.7287878787879
789.2109.771212121212-20.5712121212121
892.3109.771212121212-17.4712121212121
9104.6109.771212121212-5.17121212121213
10122.8109.77121212121213.0287878787879
1196109.771212121212-13.7712121212121
1294.6109.771212121212-15.1712121212121
1393.3109.771212121212-16.4712121212121
14101.1109.771212121212-8.67121212121213
15114.2109.7712121212124.42878787878788
16104.7109.771212121212-5.07121212121212
17113.3109.7712121212123.52878787878788
18118.2109.7712121212128.42878787878788
1983.6109.771212121212-26.1712121212121
2073.9109.771212121212-35.8712121212121
2199.5109.771212121212-10.2712121212121
2297.7109.771212121212-12.0712121212121
23103109.771212121212-6.77121212121212
24106.3109.771212121212-3.47121212121212
2592.2109.771212121212-17.5712121212121
26101.8109.771212121212-7.97121212121212
27122.8109.77121212121213.0287878787879
28111.8109.7712121212122.02878787878788
29106.3109.771212121212-3.47121212121212
30121.5109.77121212121211.7287878787879
3181.9109.771212121212-27.8712121212121
3285.4109.771212121212-24.3712121212121
33110.9109.7712121212121.12878787878789
34117.3109.7712121212127.52878787878788
35106.3109.771212121212-3.47121212121212
36105.5109.771212121212-4.27121212121212
37101.3109.771212121212-8.47121212121212
38105.9109.771212121212-3.87121212121211
39126.3109.77121212121216.5287878787879
40111.9109.7712121212122.12878787878789
41108.9109.771212121212-0.871212121212114
42127.2109.77121212121217.4287878787879
4394.2109.771212121212-15.5712121212121
4485.7109.771212121212-24.0712121212121
45116.2109.7712121212126.42878787878788
46107.2109.771212121212-2.57121212121212
47110.6109.7712121212120.828787878787875
48112109.7712121212122.22878787878788
49104.5109.771212121212-5.27121212121212
50112109.7712121212122.22878787878788
51132.8109.77121212121223.0287878787879
52110.8109.7712121212121.02878787878788
53128.7109.77121212121218.9287878787879
54136.8109.77121212121227.0287878787879
5594.9109.771212121212-14.8712121212121
5688.8109.771212121212-20.9712121212121
57123.2109.77121212121213.4287878787879
58125.3109.77121212121215.5287878787879
59122.7109.77121212121212.9287878787879
60125.7109.77121212121215.9287878787879
61116.3109.7712121212126.52878787878788
62118.7109.7712121212128.92878787878788
63142109.77121212121232.2287878787879
64127.9109.77121212121218.1287878787879
65131.9109.77121212121222.1287878787879
66152.3109.77121212121242.5287878787879
67110.8131.166666666667-20.3666666666667
6899.1131.166666666667-32.0666666666667
69135131.1666666666673.83333333333334
70133.2131.1666666666672.03333333333332
71131131.166666666667-0.166666666666665
72133.9131.1666666666672.73333333333334
73119.9131.166666666667-11.2666666666667
74136.9131.1666666666675.73333333333334
75148.9131.16666666666717.7333333333333
76145.1131.16666666666713.9333333333333
77142.4131.16666666666711.2333333333333
78159.6131.16666666666728.4333333333333
79120.7131.166666666667-10.4666666666667
80109131.166666666667-22.1666666666667
81142131.16666666666710.8333333333333







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
50.3109954953415770.6219909906831540.689004504658423
60.331818007385190.663636014770380.66818199261481
70.4636633710427770.9273267420855540.536336628957223
80.4582295266970440.9164590533940880.541770473302956
90.3401976884199330.6803953768398670.659802311580067
100.346260504437140.692521008874280.65373949556286
110.3058725048380240.6117450096760490.694127495161976
120.2754182074287690.5508364148575380.724581792571231
130.2532910435883940.5065820871767880.746708956411606
140.1886827347714760.3773654695429520.811317265228524
150.1514889074542040.3029778149084090.848511092545796
160.1047196866080840.2094393732161690.895280313391916
170.07829864316444550.1565972863288910.921701356835555
180.06909954363150110.1381990872630020.930900456368499
190.1263749976437520.2527499952875040.873625002356248
200.3425553459636050.685110691927210.657444654036395
210.287915413932670.575830827865340.71208458606733
220.2455801140978900.4911602281957810.75441988590211
230.1951507759824270.3903015519648530.804849224017573
240.1513810553478120.3027621106956230.848618944652188
250.1483013752761320.2966027505522640.851698624723868
260.1159914458788840.2319828917577670.884008554121116
270.1353908673502900.2707817347005800.86460913264971
280.1082727523290140.2165455046580280.891727247670986
290.08145748435895830.1629149687179170.918542515641042
300.08511071858611910.1702214371722380.914889281413881
310.1563575464970770.3127150929941550.843642453502923
320.2209644268450740.4419288536901480.779035573154926
330.1842297058897220.3684594117794450.815770294110278
340.1666826010163470.3333652020326940.833317398983653
350.1346881537738040.2693763075476080.865311846226196
360.108249884844980.216499769689960.89175011515502
370.0919473928639280.1838947857278560.908052607136072
380.07304147802137850.1460829560427570.926958521978621
390.08896326873840450.1779265374768090.911036731261595
400.06997888660490690.1399577732098140.930021113395093
410.05390460100027870.1078092020005570.946095398999721
420.0649833987295960.1299667974591920.935016601270404
430.0740103608222180.1480207216444360.925989639177782
440.143613327424580.287226654849160.85638667257542
450.1217365977842410.2434731955684830.878263402215758
460.1035102518465570.2070205036931140.896489748153443
470.08524685715503770.1704937143100750.914753142844962
480.06950592560629890.1390118512125980.930494074393701
490.06446723968092750.1289344793618550.935532760319073
500.05331872241609840.1066374448321970.946681277583902
510.07292764532550150.1458552906510030.927072354674499
520.06100475774981750.1220095154996350.938995242250182
530.06393787213801670.1278757442760330.936062127861983
540.09429313680478510.1885862736095700.905706863195215
550.1413338507591210.2826677015182420.858666149240879
560.3439188304917390.6878376609834790.65608116950826
570.3143322558266990.6286645116533970.685667744173301
580.2868739907215130.5737479814430260.713126009278487
590.257906234272690.515812468545380.74209376572731
600.2313036157425420.4626072314850840.768696384257458
610.2300034287234850.4600068574469710.769996571276515
620.2401762795796660.4803525591593320.759823720420334
630.2606633623348230.5213267246696470.739336637665177
640.2477931986829740.4955863973659480.752206801317026
650.2572566858605560.5145133717211110.742743314139444
660.2903807644935890.5807615289871780.70961923550641
670.3165632746332290.6331265492664570.683436725366771
680.6106977771787780.7786044456424440.389302222821222
690.5450291782800980.9099416434398030.454970821719901
700.4575248415381380.9150496830762770.542475158461862
710.367315087944220.734630175888440.63268491205578
720.2752635871649850.5505271743299690.724736412835015
730.2695474600978580.5390949201957150.730452539902142
740.1816993010331820.3633986020663640.818300698966818
750.1418870843468210.2837741686936430.858112915653179
760.09032617295135040.1806523459027010.90967382704865

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
5 & 0.310995495341577 & 0.621990990683154 & 0.689004504658423 \tabularnewline
6 & 0.33181800738519 & 0.66363601477038 & 0.66818199261481 \tabularnewline
7 & 0.463663371042777 & 0.927326742085554 & 0.536336628957223 \tabularnewline
8 & 0.458229526697044 & 0.916459053394088 & 0.541770473302956 \tabularnewline
9 & 0.340197688419933 & 0.680395376839867 & 0.659802311580067 \tabularnewline
10 & 0.34626050443714 & 0.69252100887428 & 0.65373949556286 \tabularnewline
11 & 0.305872504838024 & 0.611745009676049 & 0.694127495161976 \tabularnewline
12 & 0.275418207428769 & 0.550836414857538 & 0.724581792571231 \tabularnewline
13 & 0.253291043588394 & 0.506582087176788 & 0.746708956411606 \tabularnewline
14 & 0.188682734771476 & 0.377365469542952 & 0.811317265228524 \tabularnewline
15 & 0.151488907454204 & 0.302977814908409 & 0.848511092545796 \tabularnewline
16 & 0.104719686608084 & 0.209439373216169 & 0.895280313391916 \tabularnewline
17 & 0.0782986431644455 & 0.156597286328891 & 0.921701356835555 \tabularnewline
18 & 0.0690995436315011 & 0.138199087263002 & 0.930900456368499 \tabularnewline
19 & 0.126374997643752 & 0.252749995287504 & 0.873625002356248 \tabularnewline
20 & 0.342555345963605 & 0.68511069192721 & 0.657444654036395 \tabularnewline
21 & 0.28791541393267 & 0.57583082786534 & 0.71208458606733 \tabularnewline
22 & 0.245580114097890 & 0.491160228195781 & 0.75441988590211 \tabularnewline
23 & 0.195150775982427 & 0.390301551964853 & 0.804849224017573 \tabularnewline
24 & 0.151381055347812 & 0.302762110695623 & 0.848618944652188 \tabularnewline
25 & 0.148301375276132 & 0.296602750552264 & 0.851698624723868 \tabularnewline
26 & 0.115991445878884 & 0.231982891757767 & 0.884008554121116 \tabularnewline
27 & 0.135390867350290 & 0.270781734700580 & 0.86460913264971 \tabularnewline
28 & 0.108272752329014 & 0.216545504658028 & 0.891727247670986 \tabularnewline
29 & 0.0814574843589583 & 0.162914968717917 & 0.918542515641042 \tabularnewline
30 & 0.0851107185861191 & 0.170221437172238 & 0.914889281413881 \tabularnewline
31 & 0.156357546497077 & 0.312715092994155 & 0.843642453502923 \tabularnewline
32 & 0.220964426845074 & 0.441928853690148 & 0.779035573154926 \tabularnewline
33 & 0.184229705889722 & 0.368459411779445 & 0.815770294110278 \tabularnewline
34 & 0.166682601016347 & 0.333365202032694 & 0.833317398983653 \tabularnewline
35 & 0.134688153773804 & 0.269376307547608 & 0.865311846226196 \tabularnewline
36 & 0.10824988484498 & 0.21649976968996 & 0.89175011515502 \tabularnewline
37 & 0.091947392863928 & 0.183894785727856 & 0.908052607136072 \tabularnewline
38 & 0.0730414780213785 & 0.146082956042757 & 0.926958521978621 \tabularnewline
39 & 0.0889632687384045 & 0.177926537476809 & 0.911036731261595 \tabularnewline
40 & 0.0699788866049069 & 0.139957773209814 & 0.930021113395093 \tabularnewline
41 & 0.0539046010002787 & 0.107809202000557 & 0.946095398999721 \tabularnewline
42 & 0.064983398729596 & 0.129966797459192 & 0.935016601270404 \tabularnewline
43 & 0.074010360822218 & 0.148020721644436 & 0.925989639177782 \tabularnewline
44 & 0.14361332742458 & 0.28722665484916 & 0.85638667257542 \tabularnewline
45 & 0.121736597784241 & 0.243473195568483 & 0.878263402215758 \tabularnewline
46 & 0.103510251846557 & 0.207020503693114 & 0.896489748153443 \tabularnewline
47 & 0.0852468571550377 & 0.170493714310075 & 0.914753142844962 \tabularnewline
48 & 0.0695059256062989 & 0.139011851212598 & 0.930494074393701 \tabularnewline
49 & 0.0644672396809275 & 0.128934479361855 & 0.935532760319073 \tabularnewline
50 & 0.0533187224160984 & 0.106637444832197 & 0.946681277583902 \tabularnewline
51 & 0.0729276453255015 & 0.145855290651003 & 0.927072354674499 \tabularnewline
52 & 0.0610047577498175 & 0.122009515499635 & 0.938995242250182 \tabularnewline
53 & 0.0639378721380167 & 0.127875744276033 & 0.936062127861983 \tabularnewline
54 & 0.0942931368047851 & 0.188586273609570 & 0.905706863195215 \tabularnewline
55 & 0.141333850759121 & 0.282667701518242 & 0.858666149240879 \tabularnewline
56 & 0.343918830491739 & 0.687837660983479 & 0.65608116950826 \tabularnewline
57 & 0.314332255826699 & 0.628664511653397 & 0.685667744173301 \tabularnewline
58 & 0.286873990721513 & 0.573747981443026 & 0.713126009278487 \tabularnewline
59 & 0.25790623427269 & 0.51581246854538 & 0.74209376572731 \tabularnewline
60 & 0.231303615742542 & 0.462607231485084 & 0.768696384257458 \tabularnewline
61 & 0.230003428723485 & 0.460006857446971 & 0.769996571276515 \tabularnewline
62 & 0.240176279579666 & 0.480352559159332 & 0.759823720420334 \tabularnewline
63 & 0.260663362334823 & 0.521326724669647 & 0.739336637665177 \tabularnewline
64 & 0.247793198682974 & 0.495586397365948 & 0.752206801317026 \tabularnewline
65 & 0.257256685860556 & 0.514513371721111 & 0.742743314139444 \tabularnewline
66 & 0.290380764493589 & 0.580761528987178 & 0.70961923550641 \tabularnewline
67 & 0.316563274633229 & 0.633126549266457 & 0.683436725366771 \tabularnewline
68 & 0.610697777178778 & 0.778604445642444 & 0.389302222821222 \tabularnewline
69 & 0.545029178280098 & 0.909941643439803 & 0.454970821719901 \tabularnewline
70 & 0.457524841538138 & 0.915049683076277 & 0.542475158461862 \tabularnewline
71 & 0.36731508794422 & 0.73463017588844 & 0.63268491205578 \tabularnewline
72 & 0.275263587164985 & 0.550527174329969 & 0.724736412835015 \tabularnewline
73 & 0.269547460097858 & 0.539094920195715 & 0.730452539902142 \tabularnewline
74 & 0.181699301033182 & 0.363398602066364 & 0.818300698966818 \tabularnewline
75 & 0.141887084346821 & 0.283774168693643 & 0.858112915653179 \tabularnewline
76 & 0.0903261729513504 & 0.180652345902701 & 0.90967382704865 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=35376&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]0.310995495341577[/C][C]0.621990990683154[/C][C]0.689004504658423[/C][/ROW]
[ROW][C]6[/C][C]0.33181800738519[/C][C]0.66363601477038[/C][C]0.66818199261481[/C][/ROW]
[ROW][C]7[/C][C]0.463663371042777[/C][C]0.927326742085554[/C][C]0.536336628957223[/C][/ROW]
[ROW][C]8[/C][C]0.458229526697044[/C][C]0.916459053394088[/C][C]0.541770473302956[/C][/ROW]
[ROW][C]9[/C][C]0.340197688419933[/C][C]0.680395376839867[/C][C]0.659802311580067[/C][/ROW]
[ROW][C]10[/C][C]0.34626050443714[/C][C]0.69252100887428[/C][C]0.65373949556286[/C][/ROW]
[ROW][C]11[/C][C]0.305872504838024[/C][C]0.611745009676049[/C][C]0.694127495161976[/C][/ROW]
[ROW][C]12[/C][C]0.275418207428769[/C][C]0.550836414857538[/C][C]0.724581792571231[/C][/ROW]
[ROW][C]13[/C][C]0.253291043588394[/C][C]0.506582087176788[/C][C]0.746708956411606[/C][/ROW]
[ROW][C]14[/C][C]0.188682734771476[/C][C]0.377365469542952[/C][C]0.811317265228524[/C][/ROW]
[ROW][C]15[/C][C]0.151488907454204[/C][C]0.302977814908409[/C][C]0.848511092545796[/C][/ROW]
[ROW][C]16[/C][C]0.104719686608084[/C][C]0.209439373216169[/C][C]0.895280313391916[/C][/ROW]
[ROW][C]17[/C][C]0.0782986431644455[/C][C]0.156597286328891[/C][C]0.921701356835555[/C][/ROW]
[ROW][C]18[/C][C]0.0690995436315011[/C][C]0.138199087263002[/C][C]0.930900456368499[/C][/ROW]
[ROW][C]19[/C][C]0.126374997643752[/C][C]0.252749995287504[/C][C]0.873625002356248[/C][/ROW]
[ROW][C]20[/C][C]0.342555345963605[/C][C]0.68511069192721[/C][C]0.657444654036395[/C][/ROW]
[ROW][C]21[/C][C]0.28791541393267[/C][C]0.57583082786534[/C][C]0.71208458606733[/C][/ROW]
[ROW][C]22[/C][C]0.245580114097890[/C][C]0.491160228195781[/C][C]0.75441988590211[/C][/ROW]
[ROW][C]23[/C][C]0.195150775982427[/C][C]0.390301551964853[/C][C]0.804849224017573[/C][/ROW]
[ROW][C]24[/C][C]0.151381055347812[/C][C]0.302762110695623[/C][C]0.848618944652188[/C][/ROW]
[ROW][C]25[/C][C]0.148301375276132[/C][C]0.296602750552264[/C][C]0.851698624723868[/C][/ROW]
[ROW][C]26[/C][C]0.115991445878884[/C][C]0.231982891757767[/C][C]0.884008554121116[/C][/ROW]
[ROW][C]27[/C][C]0.135390867350290[/C][C]0.270781734700580[/C][C]0.86460913264971[/C][/ROW]
[ROW][C]28[/C][C]0.108272752329014[/C][C]0.216545504658028[/C][C]0.891727247670986[/C][/ROW]
[ROW][C]29[/C][C]0.0814574843589583[/C][C]0.162914968717917[/C][C]0.918542515641042[/C][/ROW]
[ROW][C]30[/C][C]0.0851107185861191[/C][C]0.170221437172238[/C][C]0.914889281413881[/C][/ROW]
[ROW][C]31[/C][C]0.156357546497077[/C][C]0.312715092994155[/C][C]0.843642453502923[/C][/ROW]
[ROW][C]32[/C][C]0.220964426845074[/C][C]0.441928853690148[/C][C]0.779035573154926[/C][/ROW]
[ROW][C]33[/C][C]0.184229705889722[/C][C]0.368459411779445[/C][C]0.815770294110278[/C][/ROW]
[ROW][C]34[/C][C]0.166682601016347[/C][C]0.333365202032694[/C][C]0.833317398983653[/C][/ROW]
[ROW][C]35[/C][C]0.134688153773804[/C][C]0.269376307547608[/C][C]0.865311846226196[/C][/ROW]
[ROW][C]36[/C][C]0.10824988484498[/C][C]0.21649976968996[/C][C]0.89175011515502[/C][/ROW]
[ROW][C]37[/C][C]0.091947392863928[/C][C]0.183894785727856[/C][C]0.908052607136072[/C][/ROW]
[ROW][C]38[/C][C]0.0730414780213785[/C][C]0.146082956042757[/C][C]0.926958521978621[/C][/ROW]
[ROW][C]39[/C][C]0.0889632687384045[/C][C]0.177926537476809[/C][C]0.911036731261595[/C][/ROW]
[ROW][C]40[/C][C]0.0699788866049069[/C][C]0.139957773209814[/C][C]0.930021113395093[/C][/ROW]
[ROW][C]41[/C][C]0.0539046010002787[/C][C]0.107809202000557[/C][C]0.946095398999721[/C][/ROW]
[ROW][C]42[/C][C]0.064983398729596[/C][C]0.129966797459192[/C][C]0.935016601270404[/C][/ROW]
[ROW][C]43[/C][C]0.074010360822218[/C][C]0.148020721644436[/C][C]0.925989639177782[/C][/ROW]
[ROW][C]44[/C][C]0.14361332742458[/C][C]0.28722665484916[/C][C]0.85638667257542[/C][/ROW]
[ROW][C]45[/C][C]0.121736597784241[/C][C]0.243473195568483[/C][C]0.878263402215758[/C][/ROW]
[ROW][C]46[/C][C]0.103510251846557[/C][C]0.207020503693114[/C][C]0.896489748153443[/C][/ROW]
[ROW][C]47[/C][C]0.0852468571550377[/C][C]0.170493714310075[/C][C]0.914753142844962[/C][/ROW]
[ROW][C]48[/C][C]0.0695059256062989[/C][C]0.139011851212598[/C][C]0.930494074393701[/C][/ROW]
[ROW][C]49[/C][C]0.0644672396809275[/C][C]0.128934479361855[/C][C]0.935532760319073[/C][/ROW]
[ROW][C]50[/C][C]0.0533187224160984[/C][C]0.106637444832197[/C][C]0.946681277583902[/C][/ROW]
[ROW][C]51[/C][C]0.0729276453255015[/C][C]0.145855290651003[/C][C]0.927072354674499[/C][/ROW]
[ROW][C]52[/C][C]0.0610047577498175[/C][C]0.122009515499635[/C][C]0.938995242250182[/C][/ROW]
[ROW][C]53[/C][C]0.0639378721380167[/C][C]0.127875744276033[/C][C]0.936062127861983[/C][/ROW]
[ROW][C]54[/C][C]0.0942931368047851[/C][C]0.188586273609570[/C][C]0.905706863195215[/C][/ROW]
[ROW][C]55[/C][C]0.141333850759121[/C][C]0.282667701518242[/C][C]0.858666149240879[/C][/ROW]
[ROW][C]56[/C][C]0.343918830491739[/C][C]0.687837660983479[/C][C]0.65608116950826[/C][/ROW]
[ROW][C]57[/C][C]0.314332255826699[/C][C]0.628664511653397[/C][C]0.685667744173301[/C][/ROW]
[ROW][C]58[/C][C]0.286873990721513[/C][C]0.573747981443026[/C][C]0.713126009278487[/C][/ROW]
[ROW][C]59[/C][C]0.25790623427269[/C][C]0.51581246854538[/C][C]0.74209376572731[/C][/ROW]
[ROW][C]60[/C][C]0.231303615742542[/C][C]0.462607231485084[/C][C]0.768696384257458[/C][/ROW]
[ROW][C]61[/C][C]0.230003428723485[/C][C]0.460006857446971[/C][C]0.769996571276515[/C][/ROW]
[ROW][C]62[/C][C]0.240176279579666[/C][C]0.480352559159332[/C][C]0.759823720420334[/C][/ROW]
[ROW][C]63[/C][C]0.260663362334823[/C][C]0.521326724669647[/C][C]0.739336637665177[/C][/ROW]
[ROW][C]64[/C][C]0.247793198682974[/C][C]0.495586397365948[/C][C]0.752206801317026[/C][/ROW]
[ROW][C]65[/C][C]0.257256685860556[/C][C]0.514513371721111[/C][C]0.742743314139444[/C][/ROW]
[ROW][C]66[/C][C]0.290380764493589[/C][C]0.580761528987178[/C][C]0.70961923550641[/C][/ROW]
[ROW][C]67[/C][C]0.316563274633229[/C][C]0.633126549266457[/C][C]0.683436725366771[/C][/ROW]
[ROW][C]68[/C][C]0.610697777178778[/C][C]0.778604445642444[/C][C]0.389302222821222[/C][/ROW]
[ROW][C]69[/C][C]0.545029178280098[/C][C]0.909941643439803[/C][C]0.454970821719901[/C][/ROW]
[ROW][C]70[/C][C]0.457524841538138[/C][C]0.915049683076277[/C][C]0.542475158461862[/C][/ROW]
[ROW][C]71[/C][C]0.36731508794422[/C][C]0.73463017588844[/C][C]0.63268491205578[/C][/ROW]
[ROW][C]72[/C][C]0.275263587164985[/C][C]0.550527174329969[/C][C]0.724736412835015[/C][/ROW]
[ROW][C]73[/C][C]0.269547460097858[/C][C]0.539094920195715[/C][C]0.730452539902142[/C][/ROW]
[ROW][C]74[/C][C]0.181699301033182[/C][C]0.363398602066364[/C][C]0.818300698966818[/C][/ROW]
[ROW][C]75[/C][C]0.141887084346821[/C][C]0.283774168693643[/C][C]0.858112915653179[/C][/ROW]
[ROW][C]76[/C][C]0.0903261729513504[/C][C]0.180652345902701[/C][C]0.90967382704865[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=35376&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=35376&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
50.3109954953415770.6219909906831540.689004504658423
60.331818007385190.663636014770380.66818199261481
70.4636633710427770.9273267420855540.536336628957223
80.4582295266970440.9164590533940880.541770473302956
90.3401976884199330.6803953768398670.659802311580067
100.346260504437140.692521008874280.65373949556286
110.3058725048380240.6117450096760490.694127495161976
120.2754182074287690.5508364148575380.724581792571231
130.2532910435883940.5065820871767880.746708956411606
140.1886827347714760.3773654695429520.811317265228524
150.1514889074542040.3029778149084090.848511092545796
160.1047196866080840.2094393732161690.895280313391916
170.07829864316444550.1565972863288910.921701356835555
180.06909954363150110.1381990872630020.930900456368499
190.1263749976437520.2527499952875040.873625002356248
200.3425553459636050.685110691927210.657444654036395
210.287915413932670.575830827865340.71208458606733
220.2455801140978900.4911602281957810.75441988590211
230.1951507759824270.3903015519648530.804849224017573
240.1513810553478120.3027621106956230.848618944652188
250.1483013752761320.2966027505522640.851698624723868
260.1159914458788840.2319828917577670.884008554121116
270.1353908673502900.2707817347005800.86460913264971
280.1082727523290140.2165455046580280.891727247670986
290.08145748435895830.1629149687179170.918542515641042
300.08511071858611910.1702214371722380.914889281413881
310.1563575464970770.3127150929941550.843642453502923
320.2209644268450740.4419288536901480.779035573154926
330.1842297058897220.3684594117794450.815770294110278
340.1666826010163470.3333652020326940.833317398983653
350.1346881537738040.2693763075476080.865311846226196
360.108249884844980.216499769689960.89175011515502
370.0919473928639280.1838947857278560.908052607136072
380.07304147802137850.1460829560427570.926958521978621
390.08896326873840450.1779265374768090.911036731261595
400.06997888660490690.1399577732098140.930021113395093
410.05390460100027870.1078092020005570.946095398999721
420.0649833987295960.1299667974591920.935016601270404
430.0740103608222180.1480207216444360.925989639177782
440.143613327424580.287226654849160.85638667257542
450.1217365977842410.2434731955684830.878263402215758
460.1035102518465570.2070205036931140.896489748153443
470.08524685715503770.1704937143100750.914753142844962
480.06950592560629890.1390118512125980.930494074393701
490.06446723968092750.1289344793618550.935532760319073
500.05331872241609840.1066374448321970.946681277583902
510.07292764532550150.1458552906510030.927072354674499
520.06100475774981750.1220095154996350.938995242250182
530.06393787213801670.1278757442760330.936062127861983
540.09429313680478510.1885862736095700.905706863195215
550.1413338507591210.2826677015182420.858666149240879
560.3439188304917390.6878376609834790.65608116950826
570.3143322558266990.6286645116533970.685667744173301
580.2868739907215130.5737479814430260.713126009278487
590.257906234272690.515812468545380.74209376572731
600.2313036157425420.4626072314850840.768696384257458
610.2300034287234850.4600068574469710.769996571276515
620.2401762795796660.4803525591593320.759823720420334
630.2606633623348230.5213267246696470.739336637665177
640.2477931986829740.4955863973659480.752206801317026
650.2572566858605560.5145133717211110.742743314139444
660.2903807644935890.5807615289871780.70961923550641
670.3165632746332290.6331265492664570.683436725366771
680.6106977771787780.7786044456424440.389302222821222
690.5450291782800980.9099416434398030.454970821719901
700.4575248415381380.9150496830762770.542475158461862
710.367315087944220.734630175888440.63268491205578
720.2752635871649850.5505271743299690.724736412835015
730.2695474600978580.5390949201957150.730452539902142
740.1816993010331820.3633986020663640.818300698966818
750.1418870843468210.2837741686936430.858112915653179
760.09032617295135040.1806523459027010.90967382704865







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=35376&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=35376&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=35376&T=6

As an alternative you can also use a QR Code:  

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

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



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')
}