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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 computationSat, 27 Nov 2010 17:21:01 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Nov/27/t1290878533vh4csyet83471bo.htm/, Retrieved Thu, 31 Oct 2024 23:51:15 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=102415, Retrieved Thu, 31 Oct 2024 23:51:15 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact175
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]
-    D      [Multiple Regression] [] [2010-11-27 17:21:01] [558c060a42ec367ec2c020fab85c25c7] [Current]
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Dataseries X:
47.54	0
45.31	0
46.9	0
47.16	0
48.24	0
52.7	0
51.72	0
51.5	0
52.45	0
53	0
48.36	0
46.63	0
45.92	0
45.53	0
42.17	0
43.66	0
45.32	0
47.43	0
47.76	0
49.49	0
50.69	0
49.8	0
52.13	0
53.94	0
60.75	0
59.19	0
57.58	0
59.16	0
64.74	0
67.04	0
75.53	0
78.91	0
78.4	0
70.07	0
66.8	0
61.02	0
52.38	0
42.37	0
39.83	0
38.79	0
37.33	0
39.4	0
39.45	0
43.24	0
42.33	0
45.5	0
43.44	0
43.88	0
45.61	0
45.12	0
47.56	1
47.04	1
51.07	1
54.72	1
55.37	1
55.39	1
53.13	1
53.71	1
54.59	1
54.61	1




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'RServer@AstonUniversity' @ vre.aston.ac.uk
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.

\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 & 6 seconds \tabularnewline
R Server & 'RServer@AstonUniversity' @ vre.aston.ac.uk \tabularnewline
R Framework error message & 
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
\tabularnewline \hline \end{tabular} %Source: https://freestatistics.org/blog/index.php?pk=102415&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]6 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'RServer@AstonUniversity' @ vre.aston.ac.uk[/C][/ROW]
[ROW][C]R Framework error message[/C][C]
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=102415&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=102415&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 time6 seconds
R Server'RServer@AstonUniversity' @ vre.aston.ac.uk
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.







Multiple Linear Regression - Estimated Regression Equation
Y[t] = + 51.2642 + 1.4548`X `[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Y[t] =  +  51.2642 +  1.4548`X
`[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=102415&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Y[t] =  +  51.2642 +  1.4548`X
`[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=102415&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=102415&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] = + 51.2642 + 1.4548`X `[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)51.26421.33469238.40900
`X `1.45483.2693150.4450.6579860.328993

\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) & 51.2642 & 1.334692 & 38.409 & 0 & 0 \tabularnewline
`X
` & 1.4548 & 3.269315 & 0.445 & 0.657986 & 0.328993 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=102415&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]51.2642[/C][C]1.334692[/C][C]38.409[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]`X
`[/C][C]1.4548[/C][C]3.269315[/C][C]0.445[/C][C]0.657986[/C][C]0.328993[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=102415&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=102415&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)51.26421.33469238.40900
`X `1.45483.2693150.4450.6579860.328993







Multiple Linear Regression - Regression Statistics
Multiple R0.0583300545013329
R-squared0.00340239525812847
Adjusted R-squared-0.0137803220650072
F-TEST (value)0.19801264224648
F-TEST (DF numerator)1
F-TEST (DF denominator)58
p-value0.657986088726541
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation9.43770090720156
Sum Squared Residuals5166.071508

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.0583300545013329 \tabularnewline
R-squared & 0.00340239525812847 \tabularnewline
Adjusted R-squared & -0.0137803220650072 \tabularnewline
F-TEST (value) & 0.19801264224648 \tabularnewline
F-TEST (DF numerator) & 1 \tabularnewline
F-TEST (DF denominator) & 58 \tabularnewline
p-value & 0.657986088726541 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 9.43770090720156 \tabularnewline
Sum Squared Residuals & 5166.071508 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=102415&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.0583300545013329[/C][/ROW]
[ROW][C]R-squared[/C][C]0.00340239525812847[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]-0.0137803220650072[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]0.19801264224648[/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]0.657986088726541[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]9.43770090720156[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]5166.071508[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=102415&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=102415&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 R0.0583300545013329
R-squared0.00340239525812847
Adjusted R-squared-0.0137803220650072
F-TEST (value)0.19801264224648
F-TEST (DF numerator)1
F-TEST (DF denominator)58
p-value0.657986088726541
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation9.43770090720156
Sum Squared Residuals5166.071508







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
147.5451.2642000000001-3.72420000000009
245.3151.2642-5.95419999999999
346.951.2642-4.3642
447.1651.2642-4.1042
548.2451.2642-3.0242
652.751.26421.4358
751.7251.26420.455800000000001
851.551.26420.235800000000002
952.4551.26421.1858
105351.26421.7358
1148.3651.2642-2.9042
1246.6351.2642-4.63419999999999
1345.9251.2642-5.3442
1445.5351.2642-5.7342
1542.1751.2642-9.0942
1643.6651.2642-7.6042
1745.3251.2642-5.9442
1847.4351.2642-3.8342
1947.7651.2642-3.5042
2049.4951.2642-1.7742
2150.6951.2642-0.5742
2249.851.2642-1.4642
2352.1351.26420.865800000000004
2453.9451.26422.6758
2560.7551.26429.4858
2659.1951.26427.9258
2757.5851.26426.3158
2859.1651.26427.8958
2964.7451.264213.4758
3067.0451.264215.7758
3175.5351.264224.2658
3278.9151.264227.6458
3378.451.264227.1358
3470.0751.264218.8058
3566.851.264215.5358
3661.0251.26429.7558
3752.3851.26421.1158
3842.3751.2642-8.8942
3939.8351.2642-11.4342
4038.7951.2642-12.4742
4137.3351.2642-13.9342
4239.451.2642-11.8642
4339.4551.2642-11.8142
4443.2451.2642-8.0242
4542.3351.2642-8.9342
4645.551.2642-5.7642
4743.4451.2642-7.8242
4843.8851.2642-7.3842
4945.6151.2642-5.6542
5045.1251.2642-6.1442
5147.5652.719-5.159
5247.0452.719-5.679
5351.0752.719-1.649
5454.7252.7192.001
5555.3752.7192.651
5655.3952.7192.671
5753.1352.7190.411000000000001
5853.7152.7190.991
5954.5952.7191.871
6054.6152.7191.891

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 47.54 & 51.2642000000001 & -3.72420000000009 \tabularnewline
2 & 45.31 & 51.2642 & -5.95419999999999 \tabularnewline
3 & 46.9 & 51.2642 & -4.3642 \tabularnewline
4 & 47.16 & 51.2642 & -4.1042 \tabularnewline
5 & 48.24 & 51.2642 & -3.0242 \tabularnewline
6 & 52.7 & 51.2642 & 1.4358 \tabularnewline
7 & 51.72 & 51.2642 & 0.455800000000001 \tabularnewline
8 & 51.5 & 51.2642 & 0.235800000000002 \tabularnewline
9 & 52.45 & 51.2642 & 1.1858 \tabularnewline
10 & 53 & 51.2642 & 1.7358 \tabularnewline
11 & 48.36 & 51.2642 & -2.9042 \tabularnewline
12 & 46.63 & 51.2642 & -4.63419999999999 \tabularnewline
13 & 45.92 & 51.2642 & -5.3442 \tabularnewline
14 & 45.53 & 51.2642 & -5.7342 \tabularnewline
15 & 42.17 & 51.2642 & -9.0942 \tabularnewline
16 & 43.66 & 51.2642 & -7.6042 \tabularnewline
17 & 45.32 & 51.2642 & -5.9442 \tabularnewline
18 & 47.43 & 51.2642 & -3.8342 \tabularnewline
19 & 47.76 & 51.2642 & -3.5042 \tabularnewline
20 & 49.49 & 51.2642 & -1.7742 \tabularnewline
21 & 50.69 & 51.2642 & -0.5742 \tabularnewline
22 & 49.8 & 51.2642 & -1.4642 \tabularnewline
23 & 52.13 & 51.2642 & 0.865800000000004 \tabularnewline
24 & 53.94 & 51.2642 & 2.6758 \tabularnewline
25 & 60.75 & 51.2642 & 9.4858 \tabularnewline
26 & 59.19 & 51.2642 & 7.9258 \tabularnewline
27 & 57.58 & 51.2642 & 6.3158 \tabularnewline
28 & 59.16 & 51.2642 & 7.8958 \tabularnewline
29 & 64.74 & 51.2642 & 13.4758 \tabularnewline
30 & 67.04 & 51.2642 & 15.7758 \tabularnewline
31 & 75.53 & 51.2642 & 24.2658 \tabularnewline
32 & 78.91 & 51.2642 & 27.6458 \tabularnewline
33 & 78.4 & 51.2642 & 27.1358 \tabularnewline
34 & 70.07 & 51.2642 & 18.8058 \tabularnewline
35 & 66.8 & 51.2642 & 15.5358 \tabularnewline
36 & 61.02 & 51.2642 & 9.7558 \tabularnewline
37 & 52.38 & 51.2642 & 1.1158 \tabularnewline
38 & 42.37 & 51.2642 & -8.8942 \tabularnewline
39 & 39.83 & 51.2642 & -11.4342 \tabularnewline
40 & 38.79 & 51.2642 & -12.4742 \tabularnewline
41 & 37.33 & 51.2642 & -13.9342 \tabularnewline
42 & 39.4 & 51.2642 & -11.8642 \tabularnewline
43 & 39.45 & 51.2642 & -11.8142 \tabularnewline
44 & 43.24 & 51.2642 & -8.0242 \tabularnewline
45 & 42.33 & 51.2642 & -8.9342 \tabularnewline
46 & 45.5 & 51.2642 & -5.7642 \tabularnewline
47 & 43.44 & 51.2642 & -7.8242 \tabularnewline
48 & 43.88 & 51.2642 & -7.3842 \tabularnewline
49 & 45.61 & 51.2642 & -5.6542 \tabularnewline
50 & 45.12 & 51.2642 & -6.1442 \tabularnewline
51 & 47.56 & 52.719 & -5.159 \tabularnewline
52 & 47.04 & 52.719 & -5.679 \tabularnewline
53 & 51.07 & 52.719 & -1.649 \tabularnewline
54 & 54.72 & 52.719 & 2.001 \tabularnewline
55 & 55.37 & 52.719 & 2.651 \tabularnewline
56 & 55.39 & 52.719 & 2.671 \tabularnewline
57 & 53.13 & 52.719 & 0.411000000000001 \tabularnewline
58 & 53.71 & 52.719 & 0.991 \tabularnewline
59 & 54.59 & 52.719 & 1.871 \tabularnewline
60 & 54.61 & 52.719 & 1.891 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=102415&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]47.54[/C][C]51.2642000000001[/C][C]-3.72420000000009[/C][/ROW]
[ROW][C]2[/C][C]45.31[/C][C]51.2642[/C][C]-5.95419999999999[/C][/ROW]
[ROW][C]3[/C][C]46.9[/C][C]51.2642[/C][C]-4.3642[/C][/ROW]
[ROW][C]4[/C][C]47.16[/C][C]51.2642[/C][C]-4.1042[/C][/ROW]
[ROW][C]5[/C][C]48.24[/C][C]51.2642[/C][C]-3.0242[/C][/ROW]
[ROW][C]6[/C][C]52.7[/C][C]51.2642[/C][C]1.4358[/C][/ROW]
[ROW][C]7[/C][C]51.72[/C][C]51.2642[/C][C]0.455800000000001[/C][/ROW]
[ROW][C]8[/C][C]51.5[/C][C]51.2642[/C][C]0.235800000000002[/C][/ROW]
[ROW][C]9[/C][C]52.45[/C][C]51.2642[/C][C]1.1858[/C][/ROW]
[ROW][C]10[/C][C]53[/C][C]51.2642[/C][C]1.7358[/C][/ROW]
[ROW][C]11[/C][C]48.36[/C][C]51.2642[/C][C]-2.9042[/C][/ROW]
[ROW][C]12[/C][C]46.63[/C][C]51.2642[/C][C]-4.63419999999999[/C][/ROW]
[ROW][C]13[/C][C]45.92[/C][C]51.2642[/C][C]-5.3442[/C][/ROW]
[ROW][C]14[/C][C]45.53[/C][C]51.2642[/C][C]-5.7342[/C][/ROW]
[ROW][C]15[/C][C]42.17[/C][C]51.2642[/C][C]-9.0942[/C][/ROW]
[ROW][C]16[/C][C]43.66[/C][C]51.2642[/C][C]-7.6042[/C][/ROW]
[ROW][C]17[/C][C]45.32[/C][C]51.2642[/C][C]-5.9442[/C][/ROW]
[ROW][C]18[/C][C]47.43[/C][C]51.2642[/C][C]-3.8342[/C][/ROW]
[ROW][C]19[/C][C]47.76[/C][C]51.2642[/C][C]-3.5042[/C][/ROW]
[ROW][C]20[/C][C]49.49[/C][C]51.2642[/C][C]-1.7742[/C][/ROW]
[ROW][C]21[/C][C]50.69[/C][C]51.2642[/C][C]-0.5742[/C][/ROW]
[ROW][C]22[/C][C]49.8[/C][C]51.2642[/C][C]-1.4642[/C][/ROW]
[ROW][C]23[/C][C]52.13[/C][C]51.2642[/C][C]0.865800000000004[/C][/ROW]
[ROW][C]24[/C][C]53.94[/C][C]51.2642[/C][C]2.6758[/C][/ROW]
[ROW][C]25[/C][C]60.75[/C][C]51.2642[/C][C]9.4858[/C][/ROW]
[ROW][C]26[/C][C]59.19[/C][C]51.2642[/C][C]7.9258[/C][/ROW]
[ROW][C]27[/C][C]57.58[/C][C]51.2642[/C][C]6.3158[/C][/ROW]
[ROW][C]28[/C][C]59.16[/C][C]51.2642[/C][C]7.8958[/C][/ROW]
[ROW][C]29[/C][C]64.74[/C][C]51.2642[/C][C]13.4758[/C][/ROW]
[ROW][C]30[/C][C]67.04[/C][C]51.2642[/C][C]15.7758[/C][/ROW]
[ROW][C]31[/C][C]75.53[/C][C]51.2642[/C][C]24.2658[/C][/ROW]
[ROW][C]32[/C][C]78.91[/C][C]51.2642[/C][C]27.6458[/C][/ROW]
[ROW][C]33[/C][C]78.4[/C][C]51.2642[/C][C]27.1358[/C][/ROW]
[ROW][C]34[/C][C]70.07[/C][C]51.2642[/C][C]18.8058[/C][/ROW]
[ROW][C]35[/C][C]66.8[/C][C]51.2642[/C][C]15.5358[/C][/ROW]
[ROW][C]36[/C][C]61.02[/C][C]51.2642[/C][C]9.7558[/C][/ROW]
[ROW][C]37[/C][C]52.38[/C][C]51.2642[/C][C]1.1158[/C][/ROW]
[ROW][C]38[/C][C]42.37[/C][C]51.2642[/C][C]-8.8942[/C][/ROW]
[ROW][C]39[/C][C]39.83[/C][C]51.2642[/C][C]-11.4342[/C][/ROW]
[ROW][C]40[/C][C]38.79[/C][C]51.2642[/C][C]-12.4742[/C][/ROW]
[ROW][C]41[/C][C]37.33[/C][C]51.2642[/C][C]-13.9342[/C][/ROW]
[ROW][C]42[/C][C]39.4[/C][C]51.2642[/C][C]-11.8642[/C][/ROW]
[ROW][C]43[/C][C]39.45[/C][C]51.2642[/C][C]-11.8142[/C][/ROW]
[ROW][C]44[/C][C]43.24[/C][C]51.2642[/C][C]-8.0242[/C][/ROW]
[ROW][C]45[/C][C]42.33[/C][C]51.2642[/C][C]-8.9342[/C][/ROW]
[ROW][C]46[/C][C]45.5[/C][C]51.2642[/C][C]-5.7642[/C][/ROW]
[ROW][C]47[/C][C]43.44[/C][C]51.2642[/C][C]-7.8242[/C][/ROW]
[ROW][C]48[/C][C]43.88[/C][C]51.2642[/C][C]-7.3842[/C][/ROW]
[ROW][C]49[/C][C]45.61[/C][C]51.2642[/C][C]-5.6542[/C][/ROW]
[ROW][C]50[/C][C]45.12[/C][C]51.2642[/C][C]-6.1442[/C][/ROW]
[ROW][C]51[/C][C]47.56[/C][C]52.719[/C][C]-5.159[/C][/ROW]
[ROW][C]52[/C][C]47.04[/C][C]52.719[/C][C]-5.679[/C][/ROW]
[ROW][C]53[/C][C]51.07[/C][C]52.719[/C][C]-1.649[/C][/ROW]
[ROW][C]54[/C][C]54.72[/C][C]52.719[/C][C]2.001[/C][/ROW]
[ROW][C]55[/C][C]55.37[/C][C]52.719[/C][C]2.651[/C][/ROW]
[ROW][C]56[/C][C]55.39[/C][C]52.719[/C][C]2.671[/C][/ROW]
[ROW][C]57[/C][C]53.13[/C][C]52.719[/C][C]0.411000000000001[/C][/ROW]
[ROW][C]58[/C][C]53.71[/C][C]52.719[/C][C]0.991[/C][/ROW]
[ROW][C]59[/C][C]54.59[/C][C]52.719[/C][C]1.871[/C][/ROW]
[ROW][C]60[/C][C]54.61[/C][C]52.719[/C][C]1.891[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=102415&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=102415&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
147.5451.2642000000001-3.72420000000009
245.3151.2642-5.95419999999999
346.951.2642-4.3642
447.1651.2642-4.1042
548.2451.2642-3.0242
652.751.26421.4358
751.7251.26420.455800000000001
851.551.26420.235800000000002
952.4551.26421.1858
105351.26421.7358
1148.3651.2642-2.9042
1246.6351.2642-4.63419999999999
1345.9251.2642-5.3442
1445.5351.2642-5.7342
1542.1751.2642-9.0942
1643.6651.2642-7.6042
1745.3251.2642-5.9442
1847.4351.2642-3.8342
1947.7651.2642-3.5042
2049.4951.2642-1.7742
2150.6951.2642-0.5742
2249.851.2642-1.4642
2352.1351.26420.865800000000004
2453.9451.26422.6758
2560.7551.26429.4858
2659.1951.26427.9258
2757.5851.26426.3158
2859.1651.26427.8958
2964.7451.264213.4758
3067.0451.264215.7758
3175.5351.264224.2658
3278.9151.264227.6458
3378.451.264227.1358
3470.0751.264218.8058
3566.851.264215.5358
3661.0251.26429.7558
3752.3851.26421.1158
3842.3751.2642-8.8942
3939.8351.2642-11.4342
4038.7951.2642-12.4742
4137.3351.2642-13.9342
4239.451.2642-11.8642
4339.4551.2642-11.8142
4443.2451.2642-8.0242
4542.3351.2642-8.9342
4645.551.2642-5.7642
4743.4451.2642-7.8242
4843.8851.2642-7.3842
4945.6151.2642-5.6542
5045.1251.2642-6.1442
5147.5652.719-5.159
5247.0452.719-5.679
5351.0752.719-1.649
5454.7252.7192.001
5555.3752.7192.651
5655.3952.7192.671
5753.1352.7190.411000000000001
5853.7152.7190.991
5954.5952.7191.871
6054.6152.7191.891







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
50.002913940528744830.005827881057489670.997086059471255
60.01212551736746740.02425103473493480.987874482632533
70.006143323908198510.0122866478163970.993856676091802
80.002475267213945970.004950534427891950.997524732786054
90.001202507769958110.002405015539916220.998797492230042
100.0006143970823107710.001228794164621540.99938560291769
110.0001801641003683690.0003603282007367380.999819835899632
127.34488299279166e-050.0001468976598558330.999926551170072
133.50670782228352e-057.01341564456703e-050.999964932921777
141.76238949664762e-053.52477899329525e-050.999982376105033
153.51391228209298e-057.02782456418595e-050.99996486087718
162.58075748922336e-055.16151497844673e-050.999974192425108
171.10583733199668e-052.21167466399335e-050.99998894162668
183.46575979911539e-066.93151959823079e-060.9999965342402
191.03910903717346e-062.07821807434693e-060.999998960890963
203.29527734096344e-076.59055468192688e-070.999999670472266
211.25338542425687e-072.50677084851374e-070.999999874661458
223.88051874549753e-087.76103749099507e-080.999999961194812
232.07336826433361e-084.14673652866721e-080.999999979266317
242.08104302735882e-084.16208605471764e-080.99999997918957
257.06431014990399e-071.4128620299808e-060.999999293568985
262.44006662829363e-064.88013325658725e-060.999997559933372
273.12901114481542e-066.25802228963085e-060.999996870988855
285.43945157941561e-061.08789031588312e-050.99999456054842
294.86301616789662e-059.72603233579325e-050.99995136983832
300.0004280028922662060.0008560057845324110.999571997107734
310.01855458477689690.03710916955379380.981445415223103
320.2704190200911980.5408380401823970.729580979908802
330.8371980285330070.3256039429339860.162801971466993
340.9798863734777060.04022725304458770.0201136265222939
350.9995703667082920.0008592665834165650.000429633291708283
360.999998346906393.30618721767932e-061.65309360883966e-06
370.9999999069279451.86144109922944e-079.30720549614718e-08
380.99999977350674.52986597253314e-072.26493298626657e-07
390.9999996103700447.79259911292286e-073.89629955646143e-07
400.9999995293991869.41201628945368e-074.70600814472684e-07
410.9999997816638824.36672236179212e-072.18336118089606e-07
420.9999997488605725.0227885596095e-072.51139427980475e-07
430.9999997790628874.41874225261978e-072.20937112630989e-07
440.9999992077894451.58442111040375e-067.92210555201875e-07
450.9999978393729234.32125415419547e-062.16062707709773e-06
460.9999919187936421.61624127161642e-058.08120635808211e-06
470.999972758814345.44823713198923e-052.72411856599461e-05
480.999909073256540.0001818534869186659.09267434593327e-05
490.9996789586693760.0006420826612478530.000321041330623927
500.998921001392790.002157997214420140.00107899860721007
510.99905354652350.00189290695299830.000946453476499152
520.9999367656780350.0001264686439292246.3234321964612e-05
530.9999805270019193.89459961628984e-051.94729980814492e-05
540.9997818325338330.0004363349323346360.000218167466167318
550.998484971103450.003030057793098980.00151502889654949

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
5 & 0.00291394052874483 & 0.00582788105748967 & 0.997086059471255 \tabularnewline
6 & 0.0121255173674674 & 0.0242510347349348 & 0.987874482632533 \tabularnewline
7 & 0.00614332390819851 & 0.012286647816397 & 0.993856676091802 \tabularnewline
8 & 0.00247526721394597 & 0.00495053442789195 & 0.997524732786054 \tabularnewline
9 & 0.00120250776995811 & 0.00240501553991622 & 0.998797492230042 \tabularnewline
10 & 0.000614397082310771 & 0.00122879416462154 & 0.99938560291769 \tabularnewline
11 & 0.000180164100368369 & 0.000360328200736738 & 0.999819835899632 \tabularnewline
12 & 7.34488299279166e-05 & 0.000146897659855833 & 0.999926551170072 \tabularnewline
13 & 3.50670782228352e-05 & 7.01341564456703e-05 & 0.999964932921777 \tabularnewline
14 & 1.76238949664762e-05 & 3.52477899329525e-05 & 0.999982376105033 \tabularnewline
15 & 3.51391228209298e-05 & 7.02782456418595e-05 & 0.99996486087718 \tabularnewline
16 & 2.58075748922336e-05 & 5.16151497844673e-05 & 0.999974192425108 \tabularnewline
17 & 1.10583733199668e-05 & 2.21167466399335e-05 & 0.99998894162668 \tabularnewline
18 & 3.46575979911539e-06 & 6.93151959823079e-06 & 0.9999965342402 \tabularnewline
19 & 1.03910903717346e-06 & 2.07821807434693e-06 & 0.999998960890963 \tabularnewline
20 & 3.29527734096344e-07 & 6.59055468192688e-07 & 0.999999670472266 \tabularnewline
21 & 1.25338542425687e-07 & 2.50677084851374e-07 & 0.999999874661458 \tabularnewline
22 & 3.88051874549753e-08 & 7.76103749099507e-08 & 0.999999961194812 \tabularnewline
23 & 2.07336826433361e-08 & 4.14673652866721e-08 & 0.999999979266317 \tabularnewline
24 & 2.08104302735882e-08 & 4.16208605471764e-08 & 0.99999997918957 \tabularnewline
25 & 7.06431014990399e-07 & 1.4128620299808e-06 & 0.999999293568985 \tabularnewline
26 & 2.44006662829363e-06 & 4.88013325658725e-06 & 0.999997559933372 \tabularnewline
27 & 3.12901114481542e-06 & 6.25802228963085e-06 & 0.999996870988855 \tabularnewline
28 & 5.43945157941561e-06 & 1.08789031588312e-05 & 0.99999456054842 \tabularnewline
29 & 4.86301616789662e-05 & 9.72603233579325e-05 & 0.99995136983832 \tabularnewline
30 & 0.000428002892266206 & 0.000856005784532411 & 0.999571997107734 \tabularnewline
31 & 0.0185545847768969 & 0.0371091695537938 & 0.981445415223103 \tabularnewline
32 & 0.270419020091198 & 0.540838040182397 & 0.729580979908802 \tabularnewline
33 & 0.837198028533007 & 0.325603942933986 & 0.162801971466993 \tabularnewline
34 & 0.979886373477706 & 0.0402272530445877 & 0.0201136265222939 \tabularnewline
35 & 0.999570366708292 & 0.000859266583416565 & 0.000429633291708283 \tabularnewline
36 & 0.99999834690639 & 3.30618721767932e-06 & 1.65309360883966e-06 \tabularnewline
37 & 0.999999906927945 & 1.86144109922944e-07 & 9.30720549614718e-08 \tabularnewline
38 & 0.9999997735067 & 4.52986597253314e-07 & 2.26493298626657e-07 \tabularnewline
39 & 0.999999610370044 & 7.79259911292286e-07 & 3.89629955646143e-07 \tabularnewline
40 & 0.999999529399186 & 9.41201628945368e-07 & 4.70600814472684e-07 \tabularnewline
41 & 0.999999781663882 & 4.36672236179212e-07 & 2.18336118089606e-07 \tabularnewline
42 & 0.999999748860572 & 5.0227885596095e-07 & 2.51139427980475e-07 \tabularnewline
43 & 0.999999779062887 & 4.41874225261978e-07 & 2.20937112630989e-07 \tabularnewline
44 & 0.999999207789445 & 1.58442111040375e-06 & 7.92210555201875e-07 \tabularnewline
45 & 0.999997839372923 & 4.32125415419547e-06 & 2.16062707709773e-06 \tabularnewline
46 & 0.999991918793642 & 1.61624127161642e-05 & 8.08120635808211e-06 \tabularnewline
47 & 0.99997275881434 & 5.44823713198923e-05 & 2.72411856599461e-05 \tabularnewline
48 & 0.99990907325654 & 0.000181853486918665 & 9.09267434593327e-05 \tabularnewline
49 & 0.999678958669376 & 0.000642082661247853 & 0.000321041330623927 \tabularnewline
50 & 0.99892100139279 & 0.00215799721442014 & 0.00107899860721007 \tabularnewline
51 & 0.9990535465235 & 0.0018929069529983 & 0.000946453476499152 \tabularnewline
52 & 0.999936765678035 & 0.000126468643929224 & 6.3234321964612e-05 \tabularnewline
53 & 0.999980527001919 & 3.89459961628984e-05 & 1.94729980814492e-05 \tabularnewline
54 & 0.999781832533833 & 0.000436334932334636 & 0.000218167466167318 \tabularnewline
55 & 0.99848497110345 & 0.00303005779309898 & 0.00151502889654949 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=102415&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]0.00291394052874483[/C][C]0.00582788105748967[/C][C]0.997086059471255[/C][/ROW]
[ROW][C]6[/C][C]0.0121255173674674[/C][C]0.0242510347349348[/C][C]0.987874482632533[/C][/ROW]
[ROW][C]7[/C][C]0.00614332390819851[/C][C]0.012286647816397[/C][C]0.993856676091802[/C][/ROW]
[ROW][C]8[/C][C]0.00247526721394597[/C][C]0.00495053442789195[/C][C]0.997524732786054[/C][/ROW]
[ROW][C]9[/C][C]0.00120250776995811[/C][C]0.00240501553991622[/C][C]0.998797492230042[/C][/ROW]
[ROW][C]10[/C][C]0.000614397082310771[/C][C]0.00122879416462154[/C][C]0.99938560291769[/C][/ROW]
[ROW][C]11[/C][C]0.000180164100368369[/C][C]0.000360328200736738[/C][C]0.999819835899632[/C][/ROW]
[ROW][C]12[/C][C]7.34488299279166e-05[/C][C]0.000146897659855833[/C][C]0.999926551170072[/C][/ROW]
[ROW][C]13[/C][C]3.50670782228352e-05[/C][C]7.01341564456703e-05[/C][C]0.999964932921777[/C][/ROW]
[ROW][C]14[/C][C]1.76238949664762e-05[/C][C]3.52477899329525e-05[/C][C]0.999982376105033[/C][/ROW]
[ROW][C]15[/C][C]3.51391228209298e-05[/C][C]7.02782456418595e-05[/C][C]0.99996486087718[/C][/ROW]
[ROW][C]16[/C][C]2.58075748922336e-05[/C][C]5.16151497844673e-05[/C][C]0.999974192425108[/C][/ROW]
[ROW][C]17[/C][C]1.10583733199668e-05[/C][C]2.21167466399335e-05[/C][C]0.99998894162668[/C][/ROW]
[ROW][C]18[/C][C]3.46575979911539e-06[/C][C]6.93151959823079e-06[/C][C]0.9999965342402[/C][/ROW]
[ROW][C]19[/C][C]1.03910903717346e-06[/C][C]2.07821807434693e-06[/C][C]0.999998960890963[/C][/ROW]
[ROW][C]20[/C][C]3.29527734096344e-07[/C][C]6.59055468192688e-07[/C][C]0.999999670472266[/C][/ROW]
[ROW][C]21[/C][C]1.25338542425687e-07[/C][C]2.50677084851374e-07[/C][C]0.999999874661458[/C][/ROW]
[ROW][C]22[/C][C]3.88051874549753e-08[/C][C]7.76103749099507e-08[/C][C]0.999999961194812[/C][/ROW]
[ROW][C]23[/C][C]2.07336826433361e-08[/C][C]4.14673652866721e-08[/C][C]0.999999979266317[/C][/ROW]
[ROW][C]24[/C][C]2.08104302735882e-08[/C][C]4.16208605471764e-08[/C][C]0.99999997918957[/C][/ROW]
[ROW][C]25[/C][C]7.06431014990399e-07[/C][C]1.4128620299808e-06[/C][C]0.999999293568985[/C][/ROW]
[ROW][C]26[/C][C]2.44006662829363e-06[/C][C]4.88013325658725e-06[/C][C]0.999997559933372[/C][/ROW]
[ROW][C]27[/C][C]3.12901114481542e-06[/C][C]6.25802228963085e-06[/C][C]0.999996870988855[/C][/ROW]
[ROW][C]28[/C][C]5.43945157941561e-06[/C][C]1.08789031588312e-05[/C][C]0.99999456054842[/C][/ROW]
[ROW][C]29[/C][C]4.86301616789662e-05[/C][C]9.72603233579325e-05[/C][C]0.99995136983832[/C][/ROW]
[ROW][C]30[/C][C]0.000428002892266206[/C][C]0.000856005784532411[/C][C]0.999571997107734[/C][/ROW]
[ROW][C]31[/C][C]0.0185545847768969[/C][C]0.0371091695537938[/C][C]0.981445415223103[/C][/ROW]
[ROW][C]32[/C][C]0.270419020091198[/C][C]0.540838040182397[/C][C]0.729580979908802[/C][/ROW]
[ROW][C]33[/C][C]0.837198028533007[/C][C]0.325603942933986[/C][C]0.162801971466993[/C][/ROW]
[ROW][C]34[/C][C]0.979886373477706[/C][C]0.0402272530445877[/C][C]0.0201136265222939[/C][/ROW]
[ROW][C]35[/C][C]0.999570366708292[/C][C]0.000859266583416565[/C][C]0.000429633291708283[/C][/ROW]
[ROW][C]36[/C][C]0.99999834690639[/C][C]3.30618721767932e-06[/C][C]1.65309360883966e-06[/C][/ROW]
[ROW][C]37[/C][C]0.999999906927945[/C][C]1.86144109922944e-07[/C][C]9.30720549614718e-08[/C][/ROW]
[ROW][C]38[/C][C]0.9999997735067[/C][C]4.52986597253314e-07[/C][C]2.26493298626657e-07[/C][/ROW]
[ROW][C]39[/C][C]0.999999610370044[/C][C]7.79259911292286e-07[/C][C]3.89629955646143e-07[/C][/ROW]
[ROW][C]40[/C][C]0.999999529399186[/C][C]9.41201628945368e-07[/C][C]4.70600814472684e-07[/C][/ROW]
[ROW][C]41[/C][C]0.999999781663882[/C][C]4.36672236179212e-07[/C][C]2.18336118089606e-07[/C][/ROW]
[ROW][C]42[/C][C]0.999999748860572[/C][C]5.0227885596095e-07[/C][C]2.51139427980475e-07[/C][/ROW]
[ROW][C]43[/C][C]0.999999779062887[/C][C]4.41874225261978e-07[/C][C]2.20937112630989e-07[/C][/ROW]
[ROW][C]44[/C][C]0.999999207789445[/C][C]1.58442111040375e-06[/C][C]7.92210555201875e-07[/C][/ROW]
[ROW][C]45[/C][C]0.999997839372923[/C][C]4.32125415419547e-06[/C][C]2.16062707709773e-06[/C][/ROW]
[ROW][C]46[/C][C]0.999991918793642[/C][C]1.61624127161642e-05[/C][C]8.08120635808211e-06[/C][/ROW]
[ROW][C]47[/C][C]0.99997275881434[/C][C]5.44823713198923e-05[/C][C]2.72411856599461e-05[/C][/ROW]
[ROW][C]48[/C][C]0.99990907325654[/C][C]0.000181853486918665[/C][C]9.09267434593327e-05[/C][/ROW]
[ROW][C]49[/C][C]0.999678958669376[/C][C]0.000642082661247853[/C][C]0.000321041330623927[/C][/ROW]
[ROW][C]50[/C][C]0.99892100139279[/C][C]0.00215799721442014[/C][C]0.00107899860721007[/C][/ROW]
[ROW][C]51[/C][C]0.9990535465235[/C][C]0.0018929069529983[/C][C]0.000946453476499152[/C][/ROW]
[ROW][C]52[/C][C]0.999936765678035[/C][C]0.000126468643929224[/C][C]6.3234321964612e-05[/C][/ROW]
[ROW][C]53[/C][C]0.999980527001919[/C][C]3.89459961628984e-05[/C][C]1.94729980814492e-05[/C][/ROW]
[ROW][C]54[/C][C]0.999781832533833[/C][C]0.000436334932334636[/C][C]0.000218167466167318[/C][/ROW]
[ROW][C]55[/C][C]0.99848497110345[/C][C]0.00303005779309898[/C][C]0.00151502889654949[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=102415&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=102415&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
50.002913940528744830.005827881057489670.997086059471255
60.01212551736746740.02425103473493480.987874482632533
70.006143323908198510.0122866478163970.993856676091802
80.002475267213945970.004950534427891950.997524732786054
90.001202507769958110.002405015539916220.998797492230042
100.0006143970823107710.001228794164621540.99938560291769
110.0001801641003683690.0003603282007367380.999819835899632
127.34488299279166e-050.0001468976598558330.999926551170072
133.50670782228352e-057.01341564456703e-050.999964932921777
141.76238949664762e-053.52477899329525e-050.999982376105033
153.51391228209298e-057.02782456418595e-050.99996486087718
162.58075748922336e-055.16151497844673e-050.999974192425108
171.10583733199668e-052.21167466399335e-050.99998894162668
183.46575979911539e-066.93151959823079e-060.9999965342402
191.03910903717346e-062.07821807434693e-060.999998960890963
203.29527734096344e-076.59055468192688e-070.999999670472266
211.25338542425687e-072.50677084851374e-070.999999874661458
223.88051874549753e-087.76103749099507e-080.999999961194812
232.07336826433361e-084.14673652866721e-080.999999979266317
242.08104302735882e-084.16208605471764e-080.99999997918957
257.06431014990399e-071.4128620299808e-060.999999293568985
262.44006662829363e-064.88013325658725e-060.999997559933372
273.12901114481542e-066.25802228963085e-060.999996870988855
285.43945157941561e-061.08789031588312e-050.99999456054842
294.86301616789662e-059.72603233579325e-050.99995136983832
300.0004280028922662060.0008560057845324110.999571997107734
310.01855458477689690.03710916955379380.981445415223103
320.2704190200911980.5408380401823970.729580979908802
330.8371980285330070.3256039429339860.162801971466993
340.9798863734777060.04022725304458770.0201136265222939
350.9995703667082920.0008592665834165650.000429633291708283
360.999998346906393.30618721767932e-061.65309360883966e-06
370.9999999069279451.86144109922944e-079.30720549614718e-08
380.99999977350674.52986597253314e-072.26493298626657e-07
390.9999996103700447.79259911292286e-073.89629955646143e-07
400.9999995293991869.41201628945368e-074.70600814472684e-07
410.9999997816638824.36672236179212e-072.18336118089606e-07
420.9999997488605725.0227885596095e-072.51139427980475e-07
430.9999997790628874.41874225261978e-072.20937112630989e-07
440.9999992077894451.58442111040375e-067.92210555201875e-07
450.9999978393729234.32125415419547e-062.16062707709773e-06
460.9999919187936421.61624127161642e-058.08120635808211e-06
470.999972758814345.44823713198923e-052.72411856599461e-05
480.999909073256540.0001818534869186659.09267434593327e-05
490.9996789586693760.0006420826612478530.000321041330623927
500.998921001392790.002157997214420140.00107899860721007
510.99905354652350.00189290695299830.000946453476499152
520.9999367656780350.0001264686439292246.3234321964612e-05
530.9999805270019193.89459961628984e-051.94729980814492e-05
540.9997818325338330.0004363349323346360.000218167466167318
550.998484971103450.003030057793098980.00151502889654949







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level450.88235294117647NOK
5% type I error level490.96078431372549NOK
10% type I error level490.96078431372549NOK

\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 & 45 & 0.88235294117647 & NOK \tabularnewline
5% type I error level & 49 & 0.96078431372549 & NOK \tabularnewline
10% type I error level & 49 & 0.96078431372549 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=102415&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]45[/C][C]0.88235294117647[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]49[/C][C]0.96078431372549[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]49[/C][C]0.96078431372549[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=102415&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=102415&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 level450.88235294117647NOK
5% type I error level490.96078431372549NOK
10% type I error level490.96078431372549NOK



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')
}