Free Statistics

of Irreproducible Research!

Author's title
Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationTue, 24 Nov 2009 02:26:23 -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/24/t125905495179dszmn1vp72v6s.htm/, Retrieved Sat, 25 May 2024 04:26:50 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=58964, Retrieved Sat, 25 May 2024 04:26:50 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact211
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Q1 The Seatbeltlaw] [2007-11-14 19:27:43] [8cd6641b921d30ebe00b648d1481bba0]
- RMPD  [Multiple Regression] [Seatbelt] [2009-11-12 13:54:52] [b98453cac15ba1066b407e146608df68]
- R  D    [Multiple Regression] [] [2009-11-20 17:19:40] [8f79fe502d085bc4aad43092067387d5]
-    D        [Multiple Regression] [review] [2009-11-24 09:26:23] [94ba0ef70f5b330d175ff4daa1c9cd40] [Current]
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Dataseries X:
3,2	0
1,9	0
0	0
0,6	0
0,2	0
0,9	0
2,4	0
4,7	0
9,4	0
12,5	0
15,8	0
18,2	0
16,8	1
17,3	1
19,3	1
17,9	1
20,2	1
18,7	1
20,1	1
18,2	1
18,4	1
18,2	1
18,9	1
19,9	1
21,3	1
20	1
19,5	1
19,6	1
20,9	1
21	1
19,9	1
19,6	1
20,9	1
21,7	1
22,9	1
21,5	1
21,3	1
23,5	1
21,6	1
24,5	1
22,2	1
23,5	1
20,9	1
20,7	1
18,1	1
17,1	1
14,8	1
13,8	1
15,2	1
16	1
17,6	1
15	1
15	1
16,3	1
19,4	1
21,3	1
20,5	1
21,1	1
21,6	1
22,6	1

 Summary of computational transaction Raw Input view raw input (R code) Raw Output view raw output of R engine Computing time 5 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 & 5 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58964&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]5 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58964&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58964&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 Input view raw input (R code) Raw Output view raw output of R engine Computing time 5 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135

 Multiple Linear Regression - Estimated Regression Equation Y[t] = + 5.81666666666664 + 13.6895833333334X[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Y[t] =  +  5.81666666666664 +  13.6895833333334X[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58964&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Y[t] =  +  5.81666666666664 +  13.6895833333334X[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58964&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58964&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] = + 5.81666666666664 + 13.6895833333334X[t] + e[t]

 Multiple Linear Regression - Ordinary Least Squares Variable Parameter S.D. T-STATH0: parameter = 0 2-tail p-value 1-tail p-value (Intercept) 5.81666666666664 1.043263 5.5755 1e-06 0 X 13.6895833333334 1.166403 11.7366 0 0

\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) & 5.81666666666664 & 1.043263 & 5.5755 & 1e-06 & 0 \tabularnewline
X & 13.6895833333334 & 1.166403 & 11.7366 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58964&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]5.81666666666664[/C][C]1.043263[/C][C]5.5755[/C][C]1e-06[/C][C]0[/C][/ROW]
[ROW][C]X[/C][C]13.6895833333334[/C][C]1.166403[/C][C]11.7366[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58964&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58964&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 Variable Parameter S.D. T-STATH0: parameter = 0 2-tail p-value 1-tail p-value (Intercept) 5.81666666666664 1.043263 5.5755 1e-06 0 X 13.6895833333334 1.166403 11.7366 0 0

 Multiple Linear Regression - Regression Statistics Multiple R 0.838867982627093 R-squared 0.703699492276849 Adjusted R-squared 0.698590862833346 F-TEST (value) 137.747217734073 F-TEST (DF numerator) 1 F-TEST (DF denominator) 58 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 3.61396904664594 Sum Squared Residuals 757.524791666667

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.838867982627093 \tabularnewline
R-squared & 0.703699492276849 \tabularnewline
F-TEST (value) & 137.747217734073 \tabularnewline
F-TEST (DF numerator) & 1 \tabularnewline
F-TEST (DF denominator) & 58 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 3.61396904664594 \tabularnewline
Sum Squared Residuals & 757.524791666667 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58964&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.838867982627093[/C][/ROW]
[ROW][C]R-squared[/C][C]0.703699492276849[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]137.747217734073[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]1[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]58[/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]3.61396904664594[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]757.524791666667[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58964&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58964&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 R 0.838867982627093 R-squared 0.703699492276849 Adjusted R-squared 0.698590862833346 F-TEST (value) 137.747217734073 F-TEST (DF numerator) 1 F-TEST (DF denominator) 58 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 3.61396904664594 Sum Squared Residuals 757.524791666667

 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals InterpolationForecast ResidualsPrediction Error 1 3.2 5.81666666666671 -2.61666666666671 2 1.9 5.8166666666667 -3.9166666666667 3 0 5.81666666666666 -5.81666666666666 4 0.6 5.81666666666666 -5.21666666666666 5 0.2 5.81666666666666 -5.61666666666666 6 0.9 5.81666666666666 -4.91666666666666 7 2.4 5.81666666666666 -3.41666666666666 8 4.7 5.81666666666666 -1.11666666666666 9 9.4 5.81666666666666 3.58333333333334 10 12.5 5.81666666666666 6.68333333333334 11 15.8 5.81666666666666 9.98333333333334 12 18.2 5.81666666666666 12.3833333333333 13 16.8 19.50625 -2.70625 14 17.3 19.50625 -2.20625 15 19.3 19.50625 -0.206249999999999 16 17.9 19.50625 -1.60625 17 20.2 19.50625 0.69375 18 18.7 19.50625 -0.80625 19 20.1 19.50625 0.593750000000002 20 18.2 19.50625 -1.30625 21 18.4 19.50625 -1.10625000000000 22 18.2 19.50625 -1.30625 23 18.9 19.50625 -0.606250000000001 24 19.9 19.50625 0.393749999999999 25 21.3 19.50625 1.79375 26 20 19.50625 0.49375 27 19.5 19.50625 -0.00624999999999976 28 19.6 19.50625 0.0937500000000017 29 20.9 19.50625 1.39375 30 21 19.50625 1.49375 31 19.9 19.50625 0.393749999999999 32 19.6 19.50625 0.0937500000000017 33 20.9 19.50625 1.39375 34 21.7 19.50625 2.19375 35 22.9 19.50625 3.39375 36 21.5 19.50625 1.99375 37 21.3 19.50625 1.79375 38 23.5 19.50625 3.99375 39 21.6 19.50625 2.09375 40 24.5 19.50625 4.99375 41 22.2 19.50625 2.69375 42 23.5 19.50625 3.99375 43 20.9 19.50625 1.39375 44 20.7 19.50625 1.19375 45 18.1 19.50625 -1.40625000000000 46 17.1 19.50625 -2.40625 47 14.8 19.50625 -4.70625 48 13.8 19.50625 -5.70625 49 15.2 19.50625 -4.30625 50 16 19.50625 -3.50625 51 17.6 19.50625 -1.90625 52 15 19.50625 -4.50625 53 15 19.50625 -4.50625 54 16.3 19.50625 -3.20625 55 19.4 19.50625 -0.106250000000001 56 21.3 19.50625 1.79375 57 20.5 19.50625 0.99375 58 21.1 19.50625 1.59375000000000 59 21.6 19.50625 2.09375 60 22.6 19.50625 3.09375

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 3.2 & 5.81666666666671 & -2.61666666666671 \tabularnewline
2 & 1.9 & 5.8166666666667 & -3.9166666666667 \tabularnewline
3 & 0 & 5.81666666666666 & -5.81666666666666 \tabularnewline
4 & 0.6 & 5.81666666666666 & -5.21666666666666 \tabularnewline
5 & 0.2 & 5.81666666666666 & -5.61666666666666 \tabularnewline
6 & 0.9 & 5.81666666666666 & -4.91666666666666 \tabularnewline
7 & 2.4 & 5.81666666666666 & -3.41666666666666 \tabularnewline
8 & 4.7 & 5.81666666666666 & -1.11666666666666 \tabularnewline
9 & 9.4 & 5.81666666666666 & 3.58333333333334 \tabularnewline
10 & 12.5 & 5.81666666666666 & 6.68333333333334 \tabularnewline
11 & 15.8 & 5.81666666666666 & 9.98333333333334 \tabularnewline
12 & 18.2 & 5.81666666666666 & 12.3833333333333 \tabularnewline
13 & 16.8 & 19.50625 & -2.70625 \tabularnewline
14 & 17.3 & 19.50625 & -2.20625 \tabularnewline
15 & 19.3 & 19.50625 & -0.206249999999999 \tabularnewline
16 & 17.9 & 19.50625 & -1.60625 \tabularnewline
17 & 20.2 & 19.50625 & 0.69375 \tabularnewline
18 & 18.7 & 19.50625 & -0.80625 \tabularnewline
19 & 20.1 & 19.50625 & 0.593750000000002 \tabularnewline
20 & 18.2 & 19.50625 & -1.30625 \tabularnewline
21 & 18.4 & 19.50625 & -1.10625000000000 \tabularnewline
22 & 18.2 & 19.50625 & -1.30625 \tabularnewline
23 & 18.9 & 19.50625 & -0.606250000000001 \tabularnewline
24 & 19.9 & 19.50625 & 0.393749999999999 \tabularnewline
25 & 21.3 & 19.50625 & 1.79375 \tabularnewline
26 & 20 & 19.50625 & 0.49375 \tabularnewline
27 & 19.5 & 19.50625 & -0.00624999999999976 \tabularnewline
28 & 19.6 & 19.50625 & 0.0937500000000017 \tabularnewline
29 & 20.9 & 19.50625 & 1.39375 \tabularnewline
30 & 21 & 19.50625 & 1.49375 \tabularnewline
31 & 19.9 & 19.50625 & 0.393749999999999 \tabularnewline
32 & 19.6 & 19.50625 & 0.0937500000000017 \tabularnewline
33 & 20.9 & 19.50625 & 1.39375 \tabularnewline
34 & 21.7 & 19.50625 & 2.19375 \tabularnewline
35 & 22.9 & 19.50625 & 3.39375 \tabularnewline
36 & 21.5 & 19.50625 & 1.99375 \tabularnewline
37 & 21.3 & 19.50625 & 1.79375 \tabularnewline
38 & 23.5 & 19.50625 & 3.99375 \tabularnewline
39 & 21.6 & 19.50625 & 2.09375 \tabularnewline
40 & 24.5 & 19.50625 & 4.99375 \tabularnewline
41 & 22.2 & 19.50625 & 2.69375 \tabularnewline
42 & 23.5 & 19.50625 & 3.99375 \tabularnewline
43 & 20.9 & 19.50625 & 1.39375 \tabularnewline
44 & 20.7 & 19.50625 & 1.19375 \tabularnewline
45 & 18.1 & 19.50625 & -1.40625000000000 \tabularnewline
46 & 17.1 & 19.50625 & -2.40625 \tabularnewline
47 & 14.8 & 19.50625 & -4.70625 \tabularnewline
48 & 13.8 & 19.50625 & -5.70625 \tabularnewline
49 & 15.2 & 19.50625 & -4.30625 \tabularnewline
50 & 16 & 19.50625 & -3.50625 \tabularnewline
51 & 17.6 & 19.50625 & -1.90625 \tabularnewline
52 & 15 & 19.50625 & -4.50625 \tabularnewline
53 & 15 & 19.50625 & -4.50625 \tabularnewline
54 & 16.3 & 19.50625 & -3.20625 \tabularnewline
55 & 19.4 & 19.50625 & -0.106250000000001 \tabularnewline
56 & 21.3 & 19.50625 & 1.79375 \tabularnewline
57 & 20.5 & 19.50625 & 0.99375 \tabularnewline
58 & 21.1 & 19.50625 & 1.59375000000000 \tabularnewline
59 & 21.6 & 19.50625 & 2.09375 \tabularnewline
60 & 22.6 & 19.50625 & 3.09375 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58964&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]3.2[/C][C]5.81666666666671[/C][C]-2.61666666666671[/C][/ROW]
[ROW][C]2[/C][C]1.9[/C][C]5.8166666666667[/C][C]-3.9166666666667[/C][/ROW]
[ROW][C]3[/C][C]0[/C][C]5.81666666666666[/C][C]-5.81666666666666[/C][/ROW]
[ROW][C]4[/C][C]0.6[/C][C]5.81666666666666[/C][C]-5.21666666666666[/C][/ROW]
[ROW][C]5[/C][C]0.2[/C][C]5.81666666666666[/C][C]-5.61666666666666[/C][/ROW]
[ROW][C]6[/C][C]0.9[/C][C]5.81666666666666[/C][C]-4.91666666666666[/C][/ROW]
[ROW][C]7[/C][C]2.4[/C][C]5.81666666666666[/C][C]-3.41666666666666[/C][/ROW]
[ROW][C]8[/C][C]4.7[/C][C]5.81666666666666[/C][C]-1.11666666666666[/C][/ROW]
[ROW][C]9[/C][C]9.4[/C][C]5.81666666666666[/C][C]3.58333333333334[/C][/ROW]
[ROW][C]10[/C][C]12.5[/C][C]5.81666666666666[/C][C]6.68333333333334[/C][/ROW]
[ROW][C]11[/C][C]15.8[/C][C]5.81666666666666[/C][C]9.98333333333334[/C][/ROW]
[ROW][C]12[/C][C]18.2[/C][C]5.81666666666666[/C][C]12.3833333333333[/C][/ROW]
[ROW][C]13[/C][C]16.8[/C][C]19.50625[/C][C]-2.70625[/C][/ROW]
[ROW][C]14[/C][C]17.3[/C][C]19.50625[/C][C]-2.20625[/C][/ROW]
[ROW][C]15[/C][C]19.3[/C][C]19.50625[/C][C]-0.206249999999999[/C][/ROW]
[ROW][C]16[/C][C]17.9[/C][C]19.50625[/C][C]-1.60625[/C][/ROW]
[ROW][C]17[/C][C]20.2[/C][C]19.50625[/C][C]0.69375[/C][/ROW]
[ROW][C]18[/C][C]18.7[/C][C]19.50625[/C][C]-0.80625[/C][/ROW]
[ROW][C]19[/C][C]20.1[/C][C]19.50625[/C][C]0.593750000000002[/C][/ROW]
[ROW][C]20[/C][C]18.2[/C][C]19.50625[/C][C]-1.30625[/C][/ROW]
[ROW][C]21[/C][C]18.4[/C][C]19.50625[/C][C]-1.10625000000000[/C][/ROW]
[ROW][C]22[/C][C]18.2[/C][C]19.50625[/C][C]-1.30625[/C][/ROW]
[ROW][C]23[/C][C]18.9[/C][C]19.50625[/C][C]-0.606250000000001[/C][/ROW]
[ROW][C]24[/C][C]19.9[/C][C]19.50625[/C][C]0.393749999999999[/C][/ROW]
[ROW][C]25[/C][C]21.3[/C][C]19.50625[/C][C]1.79375[/C][/ROW]
[ROW][C]26[/C][C]20[/C][C]19.50625[/C][C]0.49375[/C][/ROW]
[ROW][C]27[/C][C]19.5[/C][C]19.50625[/C][C]-0.00624999999999976[/C][/ROW]
[ROW][C]28[/C][C]19.6[/C][C]19.50625[/C][C]0.0937500000000017[/C][/ROW]
[ROW][C]29[/C][C]20.9[/C][C]19.50625[/C][C]1.39375[/C][/ROW]
[ROW][C]30[/C][C]21[/C][C]19.50625[/C][C]1.49375[/C][/ROW]
[ROW][C]31[/C][C]19.9[/C][C]19.50625[/C][C]0.393749999999999[/C][/ROW]
[ROW][C]32[/C][C]19.6[/C][C]19.50625[/C][C]0.0937500000000017[/C][/ROW]
[ROW][C]33[/C][C]20.9[/C][C]19.50625[/C][C]1.39375[/C][/ROW]
[ROW][C]34[/C][C]21.7[/C][C]19.50625[/C][C]2.19375[/C][/ROW]
[ROW][C]35[/C][C]22.9[/C][C]19.50625[/C][C]3.39375[/C][/ROW]
[ROW][C]36[/C][C]21.5[/C][C]19.50625[/C][C]1.99375[/C][/ROW]
[ROW][C]37[/C][C]21.3[/C][C]19.50625[/C][C]1.79375[/C][/ROW]
[ROW][C]38[/C][C]23.5[/C][C]19.50625[/C][C]3.99375[/C][/ROW]
[ROW][C]39[/C][C]21.6[/C][C]19.50625[/C][C]2.09375[/C][/ROW]
[ROW][C]40[/C][C]24.5[/C][C]19.50625[/C][C]4.99375[/C][/ROW]
[ROW][C]41[/C][C]22.2[/C][C]19.50625[/C][C]2.69375[/C][/ROW]
[ROW][C]42[/C][C]23.5[/C][C]19.50625[/C][C]3.99375[/C][/ROW]
[ROW][C]43[/C][C]20.9[/C][C]19.50625[/C][C]1.39375[/C][/ROW]
[ROW][C]44[/C][C]20.7[/C][C]19.50625[/C][C]1.19375[/C][/ROW]
[ROW][C]45[/C][C]18.1[/C][C]19.50625[/C][C]-1.40625000000000[/C][/ROW]
[ROW][C]46[/C][C]17.1[/C][C]19.50625[/C][C]-2.40625[/C][/ROW]
[ROW][C]47[/C][C]14.8[/C][C]19.50625[/C][C]-4.70625[/C][/ROW]
[ROW][C]48[/C][C]13.8[/C][C]19.50625[/C][C]-5.70625[/C][/ROW]
[ROW][C]49[/C][C]15.2[/C][C]19.50625[/C][C]-4.30625[/C][/ROW]
[ROW][C]50[/C][C]16[/C][C]19.50625[/C][C]-3.50625[/C][/ROW]
[ROW][C]51[/C][C]17.6[/C][C]19.50625[/C][C]-1.90625[/C][/ROW]
[ROW][C]52[/C][C]15[/C][C]19.50625[/C][C]-4.50625[/C][/ROW]
[ROW][C]53[/C][C]15[/C][C]19.50625[/C][C]-4.50625[/C][/ROW]
[ROW][C]54[/C][C]16.3[/C][C]19.50625[/C][C]-3.20625[/C][/ROW]
[ROW][C]55[/C][C]19.4[/C][C]19.50625[/C][C]-0.106250000000001[/C][/ROW]
[ROW][C]56[/C][C]21.3[/C][C]19.50625[/C][C]1.79375[/C][/ROW]
[ROW][C]57[/C][C]20.5[/C][C]19.50625[/C][C]0.99375[/C][/ROW]
[ROW][C]58[/C][C]21.1[/C][C]19.50625[/C][C]1.59375000000000[/C][/ROW]
[ROW][C]59[/C][C]21.6[/C][C]19.50625[/C][C]2.09375[/C][/ROW]
[ROW][C]60[/C][C]22.6[/C][C]19.50625[/C][C]3.09375[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58964&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58964&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 Index Actuals InterpolationForecast ResidualsPrediction Error 1 3.2 5.81666666666671 -2.61666666666671 2 1.9 5.8166666666667 -3.9166666666667 3 0 5.81666666666666 -5.81666666666666 4 0.6 5.81666666666666 -5.21666666666666 5 0.2 5.81666666666666 -5.61666666666666 6 0.9 5.81666666666666 -4.91666666666666 7 2.4 5.81666666666666 -3.41666666666666 8 4.7 5.81666666666666 -1.11666666666666 9 9.4 5.81666666666666 3.58333333333334 10 12.5 5.81666666666666 6.68333333333334 11 15.8 5.81666666666666 9.98333333333334 12 18.2 5.81666666666666 12.3833333333333 13 16.8 19.50625 -2.70625 14 17.3 19.50625 -2.20625 15 19.3 19.50625 -0.206249999999999 16 17.9 19.50625 -1.60625 17 20.2 19.50625 0.69375 18 18.7 19.50625 -0.80625 19 20.1 19.50625 0.593750000000002 20 18.2 19.50625 -1.30625 21 18.4 19.50625 -1.10625000000000 22 18.2 19.50625 -1.30625 23 18.9 19.50625 -0.606250000000001 24 19.9 19.50625 0.393749999999999 25 21.3 19.50625 1.79375 26 20 19.50625 0.49375 27 19.5 19.50625 -0.00624999999999976 28 19.6 19.50625 0.0937500000000017 29 20.9 19.50625 1.39375 30 21 19.50625 1.49375 31 19.9 19.50625 0.393749999999999 32 19.6 19.50625 0.0937500000000017 33 20.9 19.50625 1.39375 34 21.7 19.50625 2.19375 35 22.9 19.50625 3.39375 36 21.5 19.50625 1.99375 37 21.3 19.50625 1.79375 38 23.5 19.50625 3.99375 39 21.6 19.50625 2.09375 40 24.5 19.50625 4.99375 41 22.2 19.50625 2.69375 42 23.5 19.50625 3.99375 43 20.9 19.50625 1.39375 44 20.7 19.50625 1.19375 45 18.1 19.50625 -1.40625000000000 46 17.1 19.50625 -2.40625 47 14.8 19.50625 -4.70625 48 13.8 19.50625 -5.70625 49 15.2 19.50625 -4.30625 50 16 19.50625 -3.50625 51 17.6 19.50625 -1.90625 52 15 19.50625 -4.50625 53 15 19.50625 -4.50625 54 16.3 19.50625 -3.20625 55 19.4 19.50625 -0.106250000000001 56 21.3 19.50625 1.79375 57 20.5 19.50625 0.99375 58 21.1 19.50625 1.59375000000000 59 21.6 19.50625 2.09375 60 22.6 19.50625 3.09375

 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.123150511609714 0.246301023219428 0.876849488390286 6 0.0607720626191958 0.121544125238392 0.939227937380804 7 0.0468095311278893 0.0936190622557786 0.95319046887211 8 0.16595332683969 0.33190665367938 0.83404667316031 9 0.840565676979509 0.318868646040983 0.159434323020491 10 0.994658913575692 0.0106821728486168 0.00534108642430838 11 0.999935870279595 0.000128259440810105 6.41297204050523e-05 12 0.999998957410045 2.0851799097921e-06 1.04258995489605e-06 13 0.999997853739072 4.29252185671588e-06 2.14626092835794e-06 14 0.999995380202273 9.23959545498073e-06 4.61979772749037e-06 15 0.999989151627274 2.16967454527043e-05 1.08483727263521e-05 16 0.99997648242797 4.70351440596933e-05 2.35175720298467e-05 17 0.999950939524722 9.81209505559624e-05 4.90604752779812e-05 18 0.999893894988669 0.000212210022662064 0.000106105011331032 19 0.999785142135737 0.00042971572852514 0.00021485786426257 20 0.99958853462418 0.000822930751640584 0.000411465375820292 21 0.9992248840589 0.00155023188219967 0.000775115941099837 22 0.998619900890076 0.0027601982198477 0.00138009910992385 23 0.997499867086882 0.00500026582623705 0.00250013291311852 24 0.995636392467387 0.00872721506522592 0.00436360753261296 25 0.993551895057171 0.0128962098856571 0.00644810494282857 26 0.989296854609021 0.0214062907819580 0.0107031453909790 27 0.982573705044473 0.0348525899110531 0.0174262949555265 28 0.972537194843779 0.0549256103124421 0.0274628051562210 29 0.96057623512777 0.0788475297444597 0.0394237648722299 30 0.94499826472776 0.110003470544480 0.0550017352722401 31 0.92034635515082 0.15930728969836 0.07965364484918 32 0.887624602549118 0.224750794901764 0.112375397450882 33 0.852556212465746 0.294887575068508 0.147443787534254 34 0.821808416355114 0.356383167289772 0.178191583644886 35 0.81557080968427 0.368858380631461 0.184429190315731 36 0.777407066153003 0.445185867693993 0.222592933846996 37 0.73164099938193 0.536718001236139 0.268359000618070 38 0.752725467546986 0.494549064906028 0.247274532453014 39 0.715006401732449 0.569987196535102 0.284993598267551 40 0.800244456904326 0.399511086191349 0.199755543095674 41 0.795241526256726 0.409516947486549 0.204758473743275 42 0.851539750509942 0.296920498980117 0.148460249490058 43 0.828197532163548 0.343604935672903 0.171802467836452 44 0.80151495184863 0.396970096302739 0.198485048151370 45 0.736437190854881 0.527125618290238 0.263562809145119 46 0.669160922956759 0.661678154086482 0.330839077043241 47 0.676518038013102 0.646963923973797 0.323481961986898 48 0.753027737804964 0.493944524390072 0.246972262195036 49 0.759913271547688 0.480173456904624 0.240086728452312 50 0.737644145915463 0.524711708169073 0.262355854084537 51 0.65361523832442 0.692769523351161 0.346384761675581 52 0.728247526775111 0.543504946449778 0.271752473224889 53 0.87532539099499 0.249349218010021 0.124674609005010 54 0.981158307257745 0.0376833854845093 0.0188416927422547 55 0.981734628503734 0.0365307429925325 0.0182653714962663

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
5 & 0.123150511609714 & 0.246301023219428 & 0.876849488390286 \tabularnewline
6 & 0.0607720626191958 & 0.121544125238392 & 0.939227937380804 \tabularnewline
7 & 0.0468095311278893 & 0.0936190622557786 & 0.95319046887211 \tabularnewline
8 & 0.16595332683969 & 0.33190665367938 & 0.83404667316031 \tabularnewline
9 & 0.840565676979509 & 0.318868646040983 & 0.159434323020491 \tabularnewline
10 & 0.994658913575692 & 0.0106821728486168 & 0.00534108642430838 \tabularnewline
11 & 0.999935870279595 & 0.000128259440810105 & 6.41297204050523e-05 \tabularnewline
12 & 0.999998957410045 & 2.0851799097921e-06 & 1.04258995489605e-06 \tabularnewline
13 & 0.999997853739072 & 4.29252185671588e-06 & 2.14626092835794e-06 \tabularnewline
14 & 0.999995380202273 & 9.23959545498073e-06 & 4.61979772749037e-06 \tabularnewline
15 & 0.999989151627274 & 2.16967454527043e-05 & 1.08483727263521e-05 \tabularnewline
16 & 0.99997648242797 & 4.70351440596933e-05 & 2.35175720298467e-05 \tabularnewline
17 & 0.999950939524722 & 9.81209505559624e-05 & 4.90604752779812e-05 \tabularnewline
18 & 0.999893894988669 & 0.000212210022662064 & 0.000106105011331032 \tabularnewline
19 & 0.999785142135737 & 0.00042971572852514 & 0.00021485786426257 \tabularnewline
20 & 0.99958853462418 & 0.000822930751640584 & 0.000411465375820292 \tabularnewline
21 & 0.9992248840589 & 0.00155023188219967 & 0.000775115941099837 \tabularnewline
22 & 0.998619900890076 & 0.0027601982198477 & 0.00138009910992385 \tabularnewline
23 & 0.997499867086882 & 0.00500026582623705 & 0.00250013291311852 \tabularnewline
24 & 0.995636392467387 & 0.00872721506522592 & 0.00436360753261296 \tabularnewline
25 & 0.993551895057171 & 0.0128962098856571 & 0.00644810494282857 \tabularnewline
26 & 0.989296854609021 & 0.0214062907819580 & 0.0107031453909790 \tabularnewline
27 & 0.982573705044473 & 0.0348525899110531 & 0.0174262949555265 \tabularnewline
28 & 0.972537194843779 & 0.0549256103124421 & 0.0274628051562210 \tabularnewline
29 & 0.96057623512777 & 0.0788475297444597 & 0.0394237648722299 \tabularnewline
30 & 0.94499826472776 & 0.110003470544480 & 0.0550017352722401 \tabularnewline
31 & 0.92034635515082 & 0.15930728969836 & 0.07965364484918 \tabularnewline
32 & 0.887624602549118 & 0.224750794901764 & 0.112375397450882 \tabularnewline
33 & 0.852556212465746 & 0.294887575068508 & 0.147443787534254 \tabularnewline
34 & 0.821808416355114 & 0.356383167289772 & 0.178191583644886 \tabularnewline
35 & 0.81557080968427 & 0.368858380631461 & 0.184429190315731 \tabularnewline
36 & 0.777407066153003 & 0.445185867693993 & 0.222592933846996 \tabularnewline
37 & 0.73164099938193 & 0.536718001236139 & 0.268359000618070 \tabularnewline
38 & 0.752725467546986 & 0.494549064906028 & 0.247274532453014 \tabularnewline
39 & 0.715006401732449 & 0.569987196535102 & 0.284993598267551 \tabularnewline
40 & 0.800244456904326 & 0.399511086191349 & 0.199755543095674 \tabularnewline
41 & 0.795241526256726 & 0.409516947486549 & 0.204758473743275 \tabularnewline
42 & 0.851539750509942 & 0.296920498980117 & 0.148460249490058 \tabularnewline
43 & 0.828197532163548 & 0.343604935672903 & 0.171802467836452 \tabularnewline
44 & 0.80151495184863 & 0.396970096302739 & 0.198485048151370 \tabularnewline
45 & 0.736437190854881 & 0.527125618290238 & 0.263562809145119 \tabularnewline
46 & 0.669160922956759 & 0.661678154086482 & 0.330839077043241 \tabularnewline
47 & 0.676518038013102 & 0.646963923973797 & 0.323481961986898 \tabularnewline
48 & 0.753027737804964 & 0.493944524390072 & 0.246972262195036 \tabularnewline
49 & 0.759913271547688 & 0.480173456904624 & 0.240086728452312 \tabularnewline
50 & 0.737644145915463 & 0.524711708169073 & 0.262355854084537 \tabularnewline
51 & 0.65361523832442 & 0.692769523351161 & 0.346384761675581 \tabularnewline
52 & 0.728247526775111 & 0.543504946449778 & 0.271752473224889 \tabularnewline
53 & 0.87532539099499 & 0.249349218010021 & 0.124674609005010 \tabularnewline
54 & 0.981158307257745 & 0.0376833854845093 & 0.0188416927422547 \tabularnewline
55 & 0.981734628503734 & 0.0365307429925325 & 0.0182653714962663 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58964&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]0.123150511609714[/C][C]0.246301023219428[/C][C]0.876849488390286[/C][/ROW]
[ROW][C]6[/C][C]0.0607720626191958[/C][C]0.121544125238392[/C][C]0.939227937380804[/C][/ROW]
[ROW][C]7[/C][C]0.0468095311278893[/C][C]0.0936190622557786[/C][C]0.95319046887211[/C][/ROW]
[ROW][C]8[/C][C]0.16595332683969[/C][C]0.33190665367938[/C][C]0.83404667316031[/C][/ROW]
[ROW][C]9[/C][C]0.840565676979509[/C][C]0.318868646040983[/C][C]0.159434323020491[/C][/ROW]
[ROW][C]10[/C][C]0.994658913575692[/C][C]0.0106821728486168[/C][C]0.00534108642430838[/C][/ROW]
[ROW][C]11[/C][C]0.999935870279595[/C][C]0.000128259440810105[/C][C]6.41297204050523e-05[/C][/ROW]
[ROW][C]12[/C][C]0.999998957410045[/C][C]2.0851799097921e-06[/C][C]1.04258995489605e-06[/C][/ROW]
[ROW][C]13[/C][C]0.999997853739072[/C][C]4.29252185671588e-06[/C][C]2.14626092835794e-06[/C][/ROW]
[ROW][C]14[/C][C]0.999995380202273[/C][C]9.23959545498073e-06[/C][C]4.61979772749037e-06[/C][/ROW]
[ROW][C]15[/C][C]0.999989151627274[/C][C]2.16967454527043e-05[/C][C]1.08483727263521e-05[/C][/ROW]
[ROW][C]16[/C][C]0.99997648242797[/C][C]4.70351440596933e-05[/C][C]2.35175720298467e-05[/C][/ROW]
[ROW][C]17[/C][C]0.999950939524722[/C][C]9.81209505559624e-05[/C][C]4.90604752779812e-05[/C][/ROW]
[ROW][C]18[/C][C]0.999893894988669[/C][C]0.000212210022662064[/C][C]0.000106105011331032[/C][/ROW]
[ROW][C]19[/C][C]0.999785142135737[/C][C]0.00042971572852514[/C][C]0.00021485786426257[/C][/ROW]
[ROW][C]20[/C][C]0.99958853462418[/C][C]0.000822930751640584[/C][C]0.000411465375820292[/C][/ROW]
[ROW][C]21[/C][C]0.9992248840589[/C][C]0.00155023188219967[/C][C]0.000775115941099837[/C][/ROW]
[ROW][C]22[/C][C]0.998619900890076[/C][C]0.0027601982198477[/C][C]0.00138009910992385[/C][/ROW]
[ROW][C]23[/C][C]0.997499867086882[/C][C]0.00500026582623705[/C][C]0.00250013291311852[/C][/ROW]
[ROW][C]24[/C][C]0.995636392467387[/C][C]0.00872721506522592[/C][C]0.00436360753261296[/C][/ROW]
[ROW][C]25[/C][C]0.993551895057171[/C][C]0.0128962098856571[/C][C]0.00644810494282857[/C][/ROW]
[ROW][C]26[/C][C]0.989296854609021[/C][C]0.0214062907819580[/C][C]0.0107031453909790[/C][/ROW]
[ROW][C]27[/C][C]0.982573705044473[/C][C]0.0348525899110531[/C][C]0.0174262949555265[/C][/ROW]
[ROW][C]28[/C][C]0.972537194843779[/C][C]0.0549256103124421[/C][C]0.0274628051562210[/C][/ROW]
[ROW][C]29[/C][C]0.96057623512777[/C][C]0.0788475297444597[/C][C]0.0394237648722299[/C][/ROW]
[ROW][C]30[/C][C]0.94499826472776[/C][C]0.110003470544480[/C][C]0.0550017352722401[/C][/ROW]
[ROW][C]31[/C][C]0.92034635515082[/C][C]0.15930728969836[/C][C]0.07965364484918[/C][/ROW]
[ROW][C]32[/C][C]0.887624602549118[/C][C]0.224750794901764[/C][C]0.112375397450882[/C][/ROW]
[ROW][C]33[/C][C]0.852556212465746[/C][C]0.294887575068508[/C][C]0.147443787534254[/C][/ROW]
[ROW][C]34[/C][C]0.821808416355114[/C][C]0.356383167289772[/C][C]0.178191583644886[/C][/ROW]
[ROW][C]35[/C][C]0.81557080968427[/C][C]0.368858380631461[/C][C]0.184429190315731[/C][/ROW]
[ROW][C]36[/C][C]0.777407066153003[/C][C]0.445185867693993[/C][C]0.222592933846996[/C][/ROW]
[ROW][C]37[/C][C]0.73164099938193[/C][C]0.536718001236139[/C][C]0.268359000618070[/C][/ROW]
[ROW][C]38[/C][C]0.752725467546986[/C][C]0.494549064906028[/C][C]0.247274532453014[/C][/ROW]
[ROW][C]39[/C][C]0.715006401732449[/C][C]0.569987196535102[/C][C]0.284993598267551[/C][/ROW]
[ROW][C]40[/C][C]0.800244456904326[/C][C]0.399511086191349[/C][C]0.199755543095674[/C][/ROW]
[ROW][C]41[/C][C]0.795241526256726[/C][C]0.409516947486549[/C][C]0.204758473743275[/C][/ROW]
[ROW][C]42[/C][C]0.851539750509942[/C][C]0.296920498980117[/C][C]0.148460249490058[/C][/ROW]
[ROW][C]43[/C][C]0.828197532163548[/C][C]0.343604935672903[/C][C]0.171802467836452[/C][/ROW]
[ROW][C]44[/C][C]0.80151495184863[/C][C]0.396970096302739[/C][C]0.198485048151370[/C][/ROW]
[ROW][C]45[/C][C]0.736437190854881[/C][C]0.527125618290238[/C][C]0.263562809145119[/C][/ROW]
[ROW][C]46[/C][C]0.669160922956759[/C][C]0.661678154086482[/C][C]0.330839077043241[/C][/ROW]
[ROW][C]47[/C][C]0.676518038013102[/C][C]0.646963923973797[/C][C]0.323481961986898[/C][/ROW]
[ROW][C]48[/C][C]0.753027737804964[/C][C]0.493944524390072[/C][C]0.246972262195036[/C][/ROW]
[ROW][C]49[/C][C]0.759913271547688[/C][C]0.480173456904624[/C][C]0.240086728452312[/C][/ROW]
[ROW][C]50[/C][C]0.737644145915463[/C][C]0.524711708169073[/C][C]0.262355854084537[/C][/ROW]
[ROW][C]51[/C][C]0.65361523832442[/C][C]0.692769523351161[/C][C]0.346384761675581[/C][/ROW]
[ROW][C]52[/C][C]0.728247526775111[/C][C]0.543504946449778[/C][C]0.271752473224889[/C][/ROW]
[ROW][C]53[/C][C]0.87532539099499[/C][C]0.249349218010021[/C][C]0.124674609005010[/C][/ROW]
[ROW][C]54[/C][C]0.981158307257745[/C][C]0.0376833854845093[/C][C]0.0188416927422547[/C][/ROW]
[ROW][C]55[/C][C]0.981734628503734[/C][C]0.0365307429925325[/C][C]0.0182653714962663[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58964&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58964&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-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.123150511609714 0.246301023219428 0.876849488390286 6 0.0607720626191958 0.121544125238392 0.939227937380804 7 0.0468095311278893 0.0936190622557786 0.95319046887211 8 0.16595332683969 0.33190665367938 0.83404667316031 9 0.840565676979509 0.318868646040983 0.159434323020491 10 0.994658913575692 0.0106821728486168 0.00534108642430838 11 0.999935870279595 0.000128259440810105 6.41297204050523e-05 12 0.999998957410045 2.0851799097921e-06 1.04258995489605e-06 13 0.999997853739072 4.29252185671588e-06 2.14626092835794e-06 14 0.999995380202273 9.23959545498073e-06 4.61979772749037e-06 15 0.999989151627274 2.16967454527043e-05 1.08483727263521e-05 16 0.99997648242797 4.70351440596933e-05 2.35175720298467e-05 17 0.999950939524722 9.81209505559624e-05 4.90604752779812e-05 18 0.999893894988669 0.000212210022662064 0.000106105011331032 19 0.999785142135737 0.00042971572852514 0.00021485786426257 20 0.99958853462418 0.000822930751640584 0.000411465375820292 21 0.9992248840589 0.00155023188219967 0.000775115941099837 22 0.998619900890076 0.0027601982198477 0.00138009910992385 23 0.997499867086882 0.00500026582623705 0.00250013291311852 24 0.995636392467387 0.00872721506522592 0.00436360753261296 25 0.993551895057171 0.0128962098856571 0.00644810494282857 26 0.989296854609021 0.0214062907819580 0.0107031453909790 27 0.982573705044473 0.0348525899110531 0.0174262949555265 28 0.972537194843779 0.0549256103124421 0.0274628051562210 29 0.96057623512777 0.0788475297444597 0.0394237648722299 30 0.94499826472776 0.110003470544480 0.0550017352722401 31 0.92034635515082 0.15930728969836 0.07965364484918 32 0.887624602549118 0.224750794901764 0.112375397450882 33 0.852556212465746 0.294887575068508 0.147443787534254 34 0.821808416355114 0.356383167289772 0.178191583644886 35 0.81557080968427 0.368858380631461 0.184429190315731 36 0.777407066153003 0.445185867693993 0.222592933846996 37 0.73164099938193 0.536718001236139 0.268359000618070 38 0.752725467546986 0.494549064906028 0.247274532453014 39 0.715006401732449 0.569987196535102 0.284993598267551 40 0.800244456904326 0.399511086191349 0.199755543095674 41 0.795241526256726 0.409516947486549 0.204758473743275 42 0.851539750509942 0.296920498980117 0.148460249490058 43 0.828197532163548 0.343604935672903 0.171802467836452 44 0.80151495184863 0.396970096302739 0.198485048151370 45 0.736437190854881 0.527125618290238 0.263562809145119 46 0.669160922956759 0.661678154086482 0.330839077043241 47 0.676518038013102 0.646963923973797 0.323481961986898 48 0.753027737804964 0.493944524390072 0.246972262195036 49 0.759913271547688 0.480173456904624 0.240086728452312 50 0.737644145915463 0.524711708169073 0.262355854084537 51 0.65361523832442 0.692769523351161 0.346384761675581 52 0.728247526775111 0.543504946449778 0.271752473224889 53 0.87532539099499 0.249349218010021 0.124674609005010 54 0.981158307257745 0.0376833854845093 0.0188416927422547 55 0.981734628503734 0.0365307429925325 0.0182653714962663

 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 14 0.274509803921569 NOK 5% type I error level 20 0.392156862745098 NOK 10% type I error level 23 0.450980392156863 NOK

\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 & 14 & 0.274509803921569 & NOK \tabularnewline
5% type I error level & 20 & 0.392156862745098 & NOK \tabularnewline
10% type I error level & 23 & 0.450980392156863 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58964&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]14[/C][C]0.274509803921569[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]20[/C][C]0.392156862745098[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]23[/C][C]0.450980392156863[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58964&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58964&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 tests OK/NOK 1% type I error level 14 0.274509803921569 NOK 5% type I error level 20 0.392156862745098 NOK 10% type I error level 23 0.450980392156863 NOK

library(lattice)library(lmtest)n25 <- 25 #minimum number of obs. for Goldfeld-Quandt testpar1 <- 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 <- x1if (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'}xk <- length(x[1,])df <- as.data.frame(x)(mylm <- lm(df))(mysum <- summary(mylm))if (n > n25) {kp3 <- k + 3nmkm3 <- n - k - 3gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))numgqtests <- 0numsignificant1 <- 0numsignificant5 <- 0numsignificant10 <- 0for (mypoint in kp3:nmkm3) {j <- 0numgqtests <- numgqtests + 1for (myalt in c('greater', 'two.sided', 'less')) {j <- j + 1gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value}if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1}gqarr}bitmap(file='test0.png')plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')points(x[,1]-mysum$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)dumdum1 <- dum[2:length(myerror),]dum1z <- as.data.frame(dum1)zplot(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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')}}myeq <- paste(myeq, ' + e[t]')a<-table.row.start(a)a<-table.element(a, myeq)a<-table.row.end(a)a<-table.end(a)table.save(a,file='mytable1.tab')a<-table.start()a<-table.row.start(a)a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)a<-table.row.end(a)a<-table.row.start(a)a<-table.element(a,'Variable',header=TRUE)a<-table.element(a,'Parameter',header=TRUE)a<-table.element(a,'S.D.',header=TRUE)a<-table.element(a,'T-STATH0: parameter = 0',header=TRUE)a<-table.element(a,'2-tail p-value',header=TRUE)a<-table.element(a,'1-tail p-value',header=TRUE)a<-table.row.end(a)for (i in 1:k){a<-table.row.start(a)a<-table.element(a,rownames(mysum$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$sigma)a<-table.row.end(a)a<-table.row.start(a)a<-table.element(a, 'Sum Squared Residuals',1,TRUE)a<-table.element(a, sum(myerror*myerror))a<-table.row.end(a)a<-table.end(a)table.save(a,file='mytable3.tab')a<-table.start()a<-table.row.start(a)a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)a<-table.row.end(a)a<-table.row.start(a)a<-table.element(a, 'Time or Index', 1, TRUE)a<-table.element(a, 'Actuals', 1, TRUE)a<-table.element(a, 'InterpolationForecast', 1, TRUE)a<-table.element(a, 'ResidualsPrediction Error', 1, TRUE)a<-table.row.end(a)for (i in 1:n) {a<-table.row.start(a)a<-table.element(a,i, 1, TRUE)a<-table.element(a,x[i])a<-table.element(a,x[i]-mysum$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')}