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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationTue, 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 Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 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 Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 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
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)5.816666666666641.0432635.57551e-060
X13.68958333333341.16640311.736600

\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
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)5.816666666666641.0432635.57551e-060
X13.68958333333341.16640311.736600







Multiple Linear Regression - Regression Statistics
Multiple R0.838867982627093
R-squared0.703699492276849
Adjusted R-squared0.698590862833346
F-TEST (value)137.747217734073
F-TEST (DF numerator)1
F-TEST (DF denominator)58
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.61396904664594
Sum Squared Residuals757.524791666667

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.838867982627093 \tabularnewline
R-squared & 0.703699492276849 \tabularnewline
Adjusted R-squared & 0.698590862833346 \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]Adjusted R-squared[/C][C]0.698590862833346[/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 R0.838867982627093
R-squared0.703699492276849
Adjusted R-squared0.698590862833346
F-TEST (value)137.747217734073
F-TEST (DF numerator)1
F-TEST (DF denominator)58
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.61396904664594
Sum Squared Residuals757.524791666667







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
13.25.81666666666671-2.61666666666671
21.95.8166666666667-3.9166666666667
305.81666666666666-5.81666666666666
40.65.81666666666666-5.21666666666666
50.25.81666666666666-5.61666666666666
60.95.81666666666666-4.91666666666666
72.45.81666666666666-3.41666666666666
84.75.81666666666666-1.11666666666666
99.45.816666666666663.58333333333334
1012.55.816666666666666.68333333333334
1115.85.816666666666669.98333333333334
1218.25.8166666666666612.3833333333333
1316.819.50625-2.70625
1417.319.50625-2.20625
1519.319.50625-0.206249999999999
1617.919.50625-1.60625
1720.219.506250.69375
1818.719.50625-0.80625
1920.119.506250.593750000000002
2018.219.50625-1.30625
2118.419.50625-1.10625000000000
2218.219.50625-1.30625
2318.919.50625-0.606250000000001
2419.919.506250.393749999999999
2521.319.506251.79375
262019.506250.49375
2719.519.50625-0.00624999999999976
2819.619.506250.0937500000000017
2920.919.506251.39375
302119.506251.49375
3119.919.506250.393749999999999
3219.619.506250.0937500000000017
3320.919.506251.39375
3421.719.506252.19375
3522.919.506253.39375
3621.519.506251.99375
3721.319.506251.79375
3823.519.506253.99375
3921.619.506252.09375
4024.519.506254.99375
4122.219.506252.69375
4223.519.506253.99375
4320.919.506251.39375
4420.719.506251.19375
4518.119.50625-1.40625000000000
4617.119.50625-2.40625
4714.819.50625-4.70625
4813.819.50625-5.70625
4915.219.50625-4.30625
501619.50625-3.50625
5117.619.50625-1.90625
521519.50625-4.50625
531519.50625-4.50625
5416.319.50625-3.20625
5519.419.50625-0.106250000000001
5621.319.506251.79375
5720.519.506250.99375
5821.119.506251.59375000000000
5921.619.506252.09375
6022.619.506253.09375

\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 IndexActualsInterpolationForecastResidualsPrediction Error
13.25.81666666666671-2.61666666666671
21.95.8166666666667-3.9166666666667
305.81666666666666-5.81666666666666
40.65.81666666666666-5.21666666666666
50.25.81666666666666-5.61666666666666
60.95.81666666666666-4.91666666666666
72.45.81666666666666-3.41666666666666
84.75.81666666666666-1.11666666666666
99.45.816666666666663.58333333333334
1012.55.816666666666666.68333333333334
1115.85.816666666666669.98333333333334
1218.25.8166666666666612.3833333333333
1316.819.50625-2.70625
1417.319.50625-2.20625
1519.319.50625-0.206249999999999
1617.919.50625-1.60625
1720.219.506250.69375
1818.719.50625-0.80625
1920.119.506250.593750000000002
2018.219.50625-1.30625
2118.419.50625-1.10625000000000
2218.219.50625-1.30625
2318.919.50625-0.606250000000001
2419.919.506250.393749999999999
2521.319.506251.79375
262019.506250.49375
2719.519.50625-0.00624999999999976
2819.619.506250.0937500000000017
2920.919.506251.39375
302119.506251.49375
3119.919.506250.393749999999999
3219.619.506250.0937500000000017
3320.919.506251.39375
3421.719.506252.19375
3522.919.506253.39375
3621.519.506251.99375
3721.319.506251.79375
3823.519.506253.99375
3921.619.506252.09375
4024.519.506254.99375
4122.219.506252.69375
4223.519.506253.99375
4320.919.506251.39375
4420.719.506251.19375
4518.119.50625-1.40625000000000
4617.119.50625-2.40625
4714.819.50625-4.70625
4813.819.50625-5.70625
4915.219.50625-4.30625
501619.50625-3.50625
5117.619.50625-1.90625
521519.50625-4.50625
531519.50625-4.50625
5416.319.50625-3.20625
5519.419.50625-0.106250000000001
5621.319.506251.79375
5720.519.506250.99375
5821.119.506251.59375000000000
5921.619.506252.09375
6022.619.506253.09375







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
50.1231505116097140.2463010232194280.876849488390286
60.06077206261919580.1215441252383920.939227937380804
70.04680953112788930.09361906225577860.95319046887211
80.165953326839690.331906653679380.83404667316031
90.8405656769795090.3188686460409830.159434323020491
100.9946589135756920.01068217284861680.00534108642430838
110.9999358702795950.0001282594408101056.41297204050523e-05
120.9999989574100452.0851799097921e-061.04258995489605e-06
130.9999978537390724.29252185671588e-062.14626092835794e-06
140.9999953802022739.23959545498073e-064.61979772749037e-06
150.9999891516272742.16967454527043e-051.08483727263521e-05
160.999976482427974.70351440596933e-052.35175720298467e-05
170.9999509395247229.81209505559624e-054.90604752779812e-05
180.9998938949886690.0002122100226620640.000106105011331032
190.9997851421357370.000429715728525140.00021485786426257
200.999588534624180.0008229307516405840.000411465375820292
210.99922488405890.001550231882199670.000775115941099837
220.9986199008900760.00276019821984770.00138009910992385
230.9974998670868820.005000265826237050.00250013291311852
240.9956363924673870.008727215065225920.00436360753261296
250.9935518950571710.01289620988565710.00644810494282857
260.9892968546090210.02140629078195800.0107031453909790
270.9825737050444730.03485258991105310.0174262949555265
280.9725371948437790.05492561031244210.0274628051562210
290.960576235127770.07884752974445970.0394237648722299
300.944998264727760.1100034705444800.0550017352722401
310.920346355150820.159307289698360.07965364484918
320.8876246025491180.2247507949017640.112375397450882
330.8525562124657460.2948875750685080.147443787534254
340.8218084163551140.3563831672897720.178191583644886
350.815570809684270.3688583806314610.184429190315731
360.7774070661530030.4451858676939930.222592933846996
370.731640999381930.5367180012361390.268359000618070
380.7527254675469860.4945490649060280.247274532453014
390.7150064017324490.5699871965351020.284993598267551
400.8002444569043260.3995110861913490.199755543095674
410.7952415262567260.4095169474865490.204758473743275
420.8515397505099420.2969204989801170.148460249490058
430.8281975321635480.3436049356729030.171802467836452
440.801514951848630.3969700963027390.198485048151370
450.7364371908548810.5271256182902380.263562809145119
460.6691609229567590.6616781540864820.330839077043241
470.6765180380131020.6469639239737970.323481961986898
480.7530277378049640.4939445243900720.246972262195036
490.7599132715476880.4801734569046240.240086728452312
500.7376441459154630.5247117081690730.262355854084537
510.653615238324420.6927695233511610.346384761675581
520.7282475267751110.5435049464497780.271752473224889
530.875325390994990.2493492180100210.124674609005010
540.9811583072577450.03768338548450930.0188416927422547
550.9817346285037340.03653074299253250.0182653714962663

\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-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
50.1231505116097140.2463010232194280.876849488390286
60.06077206261919580.1215441252383920.939227937380804
70.04680953112788930.09361906225577860.95319046887211
80.165953326839690.331906653679380.83404667316031
90.8405656769795090.3188686460409830.159434323020491
100.9946589135756920.01068217284861680.00534108642430838
110.9999358702795950.0001282594408101056.41297204050523e-05
120.9999989574100452.0851799097921e-061.04258995489605e-06
130.9999978537390724.29252185671588e-062.14626092835794e-06
140.9999953802022739.23959545498073e-064.61979772749037e-06
150.9999891516272742.16967454527043e-051.08483727263521e-05
160.999976482427974.70351440596933e-052.35175720298467e-05
170.9999509395247229.81209505559624e-054.90604752779812e-05
180.9998938949886690.0002122100226620640.000106105011331032
190.9997851421357370.000429715728525140.00021485786426257
200.999588534624180.0008229307516405840.000411465375820292
210.99922488405890.001550231882199670.000775115941099837
220.9986199008900760.00276019821984770.00138009910992385
230.9974998670868820.005000265826237050.00250013291311852
240.9956363924673870.008727215065225920.00436360753261296
250.9935518950571710.01289620988565710.00644810494282857
260.9892968546090210.02140629078195800.0107031453909790
270.9825737050444730.03485258991105310.0174262949555265
280.9725371948437790.05492561031244210.0274628051562210
290.960576235127770.07884752974445970.0394237648722299
300.944998264727760.1100034705444800.0550017352722401
310.920346355150820.159307289698360.07965364484918
320.8876246025491180.2247507949017640.112375397450882
330.8525562124657460.2948875750685080.147443787534254
340.8218084163551140.3563831672897720.178191583644886
350.815570809684270.3688583806314610.184429190315731
360.7774070661530030.4451858676939930.222592933846996
370.731640999381930.5367180012361390.268359000618070
380.7527254675469860.4945490649060280.247274532453014
390.7150064017324490.5699871965351020.284993598267551
400.8002444569043260.3995110861913490.199755543095674
410.7952415262567260.4095169474865490.204758473743275
420.8515397505099420.2969204989801170.148460249490058
430.8281975321635480.3436049356729030.171802467836452
440.801514951848630.3969700963027390.198485048151370
450.7364371908548810.5271256182902380.263562809145119
460.6691609229567590.6616781540864820.330839077043241
470.6765180380131020.6469639239737970.323481961986898
480.7530277378049640.4939445243900720.246972262195036
490.7599132715476880.4801734569046240.240086728452312
500.7376441459154630.5247117081690730.262355854084537
510.653615238324420.6927695233511610.346384761675581
520.7282475267751110.5435049464497780.271752473224889
530.875325390994990.2493492180100210.124674609005010
540.9811583072577450.03768338548450930.0188416927422547
550.9817346285037340.03653074299253250.0182653714962663







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level140.274509803921569NOK
5% type I error level200.392156862745098NOK
10% type I error level230.450980392156863NOK

\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 testsOK/NOK
1% type I error level140.274509803921569NOK
5% type I error level200.392156862745098NOK
10% type I error level230.450980392156863NOK



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')
}