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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationFri, 20 Nov 2009 10:32:49 -0700
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2009/Nov/20/t1258738486nljj2q63i1qsoce.htm/, Retrieved Thu, 28 Mar 2024 23:09:19 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=58356, Retrieved Thu, 28 Mar 2024 23:09:19 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordsETSHWP5
Estimated Impact93
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [Paper statistiek:...] [2009-11-20 17:32:49] [af31b947d6acaef3c71f428c4bb503e9] [Current]
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Dataseries X:
1,43	0
1,43	0
1,43	0
1,43	0
1,43	0
1,43	0
1,44	0
1,48	0
1,48	0
1,48	0
1,48	0
1,48	0
1,48	0
1,48	0
1,48	0
1,48	0
1,48	0
1,48	0
1,48	0
1,48	0
1,48	0
1,48	0
1,48	0
1,48	0
1,48	0
1,48	0
1,48	0
1,48	0
1,48	0
1,48	0
1,48	0
1,48	0
1,48	0
1,48	0
1,48	0
1,48	0
1,48	0
1,57	0
1,58	0
1,58	0
1,58	0
1,58	0
1,59	1
1,6	1
1,6	1
1,61	1
1,61	1
1,61	1
1,62	1
1,63	1
1,63	1
1,64	1
1,64	1
1,64	1
1,64	1
1,64	1
1,65	1
1,65	1
1,65	1
1,65	1




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 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 & 3 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58356&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]3 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=58356&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58356&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 time3 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







Multiple Linear Regression - Estimated Regression Equation
Broodprijzen[t] = + 1.4814 + 0.1465X[t] -0.0127000000000015M1[t] + 0.00730000000000005M2[t] + 0.00930000000000006M3[t] + 0.0113000000000000M4[t] + 0.0113000000000000M5[t] + 0.0113M6[t] -0.014M7[t] -0.00399999999999999M8[t] -0.00199999999999998M9[t] + 1.17154354015985e-17M10[t] + 8.22851594197665e-18M11[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Broodprijzen[t] =  +  1.4814 +  0.1465X[t] -0.0127000000000015M1[t] +  0.00730000000000005M2[t] +  0.00930000000000006M3[t] +  0.0113000000000000M4[t] +  0.0113000000000000M5[t] +  0.0113M6[t] -0.014M7[t] -0.00399999999999999M8[t] -0.00199999999999998M9[t] +  1.17154354015985e-17M10[t] +  8.22851594197665e-18M11[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58356&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Broodprijzen[t] =  +  1.4814 +  0.1465X[t] -0.0127000000000015M1[t] +  0.00730000000000005M2[t] +  0.00930000000000006M3[t] +  0.0113000000000000M4[t] +  0.0113000000000000M5[t] +  0.0113M6[t] -0.014M7[t] -0.00399999999999999M8[t] -0.00199999999999998M9[t] +  1.17154354015985e-17M10[t] +  8.22851594197665e-18M11[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58356&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58356&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
Broodprijzen[t] = + 1.4814 + 0.1465X[t] -0.0127000000000015M1[t] + 0.00730000000000005M2[t] + 0.00930000000000006M3[t] + 0.0113000000000000M4[t] + 0.0113000000000000M5[t] + 0.0113M6[t] -0.014M7[t] -0.00399999999999999M8[t] -0.00199999999999998M9[t] + 1.17154354015985e-17M10[t] + 8.22851594197665e-18M11[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)1.48140.01738385.220300
X0.14650.01086413.484300
M1-0.01270000000000150.023902-0.53130.5976860.298843
M20.007300000000000050.0239020.30540.7613990.380699
M30.009300000000000060.0239020.38910.6989660.349483
M40.01130000000000000.0239020.47280.6385680.319284
M50.01130000000000000.0239020.47280.6385680.319284
M60.01130.0239020.47280.6385680.319284
M7-0.0140.023803-0.58820.5592390.279619
M8-0.003999999999999990.023803-0.1680.8672680.433634
M9-0.001999999999999980.023803-0.0840.9333950.466697
M101.17154354015985e-170.023803010.5
M118.22851594197665e-180.023803010.5

\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) & 1.4814 & 0.017383 & 85.2203 & 0 & 0 \tabularnewline
X & 0.1465 & 0.010864 & 13.4843 & 0 & 0 \tabularnewline
M1 & -0.0127000000000015 & 0.023902 & -0.5313 & 0.597686 & 0.298843 \tabularnewline
M2 & 0.00730000000000005 & 0.023902 & 0.3054 & 0.761399 & 0.380699 \tabularnewline
M3 & 0.00930000000000006 & 0.023902 & 0.3891 & 0.698966 & 0.349483 \tabularnewline
M4 & 0.0113000000000000 & 0.023902 & 0.4728 & 0.638568 & 0.319284 \tabularnewline
M5 & 0.0113000000000000 & 0.023902 & 0.4728 & 0.638568 & 0.319284 \tabularnewline
M6 & 0.0113 & 0.023902 & 0.4728 & 0.638568 & 0.319284 \tabularnewline
M7 & -0.014 & 0.023803 & -0.5882 & 0.559239 & 0.279619 \tabularnewline
M8 & -0.00399999999999999 & 0.023803 & -0.168 & 0.867268 & 0.433634 \tabularnewline
M9 & -0.00199999999999998 & 0.023803 & -0.084 & 0.933395 & 0.466697 \tabularnewline
M10 & 1.17154354015985e-17 & 0.023803 & 0 & 1 & 0.5 \tabularnewline
M11 & 8.22851594197665e-18 & 0.023803 & 0 & 1 & 0.5 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58356&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]1.4814[/C][C]0.017383[/C][C]85.2203[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]X[/C][C]0.1465[/C][C]0.010864[/C][C]13.4843[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M1[/C][C]-0.0127000000000015[/C][C]0.023902[/C][C]-0.5313[/C][C]0.597686[/C][C]0.298843[/C][/ROW]
[ROW][C]M2[/C][C]0.00730000000000005[/C][C]0.023902[/C][C]0.3054[/C][C]0.761399[/C][C]0.380699[/C][/ROW]
[ROW][C]M3[/C][C]0.00930000000000006[/C][C]0.023902[/C][C]0.3891[/C][C]0.698966[/C][C]0.349483[/C][/ROW]
[ROW][C]M4[/C][C]0.0113000000000000[/C][C]0.023902[/C][C]0.4728[/C][C]0.638568[/C][C]0.319284[/C][/ROW]
[ROW][C]M5[/C][C]0.0113000000000000[/C][C]0.023902[/C][C]0.4728[/C][C]0.638568[/C][C]0.319284[/C][/ROW]
[ROW][C]M6[/C][C]0.0113[/C][C]0.023902[/C][C]0.4728[/C][C]0.638568[/C][C]0.319284[/C][/ROW]
[ROW][C]M7[/C][C]-0.014[/C][C]0.023803[/C][C]-0.5882[/C][C]0.559239[/C][C]0.279619[/C][/ROW]
[ROW][C]M8[/C][C]-0.00399999999999999[/C][C]0.023803[/C][C]-0.168[/C][C]0.867268[/C][C]0.433634[/C][/ROW]
[ROW][C]M9[/C][C]-0.00199999999999998[/C][C]0.023803[/C][C]-0.084[/C][C]0.933395[/C][C]0.466697[/C][/ROW]
[ROW][C]M10[/C][C]1.17154354015985e-17[/C][C]0.023803[/C][C]0[/C][C]1[/C][C]0.5[/C][/ROW]
[ROW][C]M11[/C][C]8.22851594197665e-18[/C][C]0.023803[/C][C]0[/C][C]1[/C][C]0.5[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58356&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58356&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)1.48140.01738385.220300
X0.14650.01086413.484300
M1-0.01270000000000150.023902-0.53130.5976860.298843
M20.007300000000000050.0239020.30540.7613990.380699
M30.009300000000000060.0239020.38910.6989660.349483
M40.01130000000000000.0239020.47280.6385680.319284
M50.01130000000000000.0239020.47280.6385680.319284
M60.01130.0239020.47280.6385680.319284
M7-0.0140.023803-0.58820.5592390.279619
M8-0.003999999999999990.023803-0.1680.8672680.433634
M9-0.001999999999999980.023803-0.0840.9333950.466697
M101.17154354015985e-170.023803010.5
M118.22851594197665e-180.023803010.5







Multiple Linear Regression - Regression Statistics
Multiple R0.894438385879152
R-squared0.800020026134103
Adjusted R-squared0.748961309402385
F-TEST (value)15.6686277553294
F-TEST (DF numerator)12
F-TEST (DF denominator)47
p-value1.39666056497845e-12
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.0376357118772935
Sum Squared Residuals0.0665730000000007

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.894438385879152 \tabularnewline
R-squared & 0.800020026134103 \tabularnewline
Adjusted R-squared & 0.748961309402385 \tabularnewline
F-TEST (value) & 15.6686277553294 \tabularnewline
F-TEST (DF numerator) & 12 \tabularnewline
F-TEST (DF denominator) & 47 \tabularnewline
p-value & 1.39666056497845e-12 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.0376357118772935 \tabularnewline
Sum Squared Residuals & 0.0665730000000007 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58356&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.894438385879152[/C][/ROW]
[ROW][C]R-squared[/C][C]0.800020026134103[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.748961309402385[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]15.6686277553294[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]12[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]47[/C][/ROW]
[ROW][C]p-value[/C][C]1.39666056497845e-12[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.0376357118772935[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]0.0665730000000007[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58356&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58356&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 R0.894438385879152
R-squared0.800020026134103
Adjusted R-squared0.748961309402385
F-TEST (value)15.6686277553294
F-TEST (DF numerator)12
F-TEST (DF denominator)47
p-value1.39666056497845e-12
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.0376357118772935
Sum Squared Residuals0.0665730000000007







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11.431.46870000000001-0.0387000000000059
21.431.4887-0.0587000000000001
31.431.4907-0.0607
41.431.4927-0.0627000000000001
51.431.4927-0.0627
61.431.4927-0.0627
71.441.4674-0.0274
81.481.47740.00260000000000006
91.481.47940.00060000000000003
101.481.4814-0.00139999999999997
111.481.4814-0.00139999999999997
121.481.4814-0.00139999999999995
131.481.46870.0113000000000015
141.481.4887-0.00869999999999997
151.481.4907-0.0107
161.481.4927-0.0127000000000000
171.481.4927-0.0127000000000000
181.481.4927-0.0127000000000000
191.481.46740.0126000000000000
201.481.47740.00260000000000005
211.481.47940.000600000000000037
221.481.4814-0.00139999999999996
231.481.4814-0.00139999999999995
241.481.4814-0.00139999999999994
251.481.46870.0113000000000015
261.481.4887-0.00869999999999997
271.481.4907-0.0107
281.481.4927-0.0127000000000000
291.481.4927-0.0127000000000000
301.481.4927-0.0127000000000000
311.481.46740.0126000000000000
321.481.47740.00260000000000005
331.481.47940.000600000000000037
341.481.4814-0.00139999999999996
351.481.4814-0.00139999999999995
361.481.4814-0.00139999999999994
371.481.46870.0113000000000015
381.571.48870.0813000000000001
391.581.49070.0893
401.581.49270.0873000000000002
411.581.49270.0873000000000001
421.581.49270.0873000000000002
431.591.6139-0.0239000000000000
441.61.6239-0.0239
451.61.6259-0.0259
461.611.6279-0.0179000000000000
471.611.6279-0.0179000000000000
481.611.6279-0.0179000000000000
491.621.61520.00480000000000148
501.631.6352-0.00520000000000016
511.631.6372-0.0072000000000002
521.641.63920.000799999999999853
531.641.63920.000799999999999835
541.641.63920.000799999999999856
551.641.61390.0260999999999999
561.641.62390.0160999999999999
571.651.62590.0240999999999999
581.651.62790.0220999999999998
591.651.62790.0220999999999999
601.651.62790.0220999999999999

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 1.43 & 1.46870000000001 & -0.0387000000000059 \tabularnewline
2 & 1.43 & 1.4887 & -0.0587000000000001 \tabularnewline
3 & 1.43 & 1.4907 & -0.0607 \tabularnewline
4 & 1.43 & 1.4927 & -0.0627000000000001 \tabularnewline
5 & 1.43 & 1.4927 & -0.0627 \tabularnewline
6 & 1.43 & 1.4927 & -0.0627 \tabularnewline
7 & 1.44 & 1.4674 & -0.0274 \tabularnewline
8 & 1.48 & 1.4774 & 0.00260000000000006 \tabularnewline
9 & 1.48 & 1.4794 & 0.00060000000000003 \tabularnewline
10 & 1.48 & 1.4814 & -0.00139999999999997 \tabularnewline
11 & 1.48 & 1.4814 & -0.00139999999999997 \tabularnewline
12 & 1.48 & 1.4814 & -0.00139999999999995 \tabularnewline
13 & 1.48 & 1.4687 & 0.0113000000000015 \tabularnewline
14 & 1.48 & 1.4887 & -0.00869999999999997 \tabularnewline
15 & 1.48 & 1.4907 & -0.0107 \tabularnewline
16 & 1.48 & 1.4927 & -0.0127000000000000 \tabularnewline
17 & 1.48 & 1.4927 & -0.0127000000000000 \tabularnewline
18 & 1.48 & 1.4927 & -0.0127000000000000 \tabularnewline
19 & 1.48 & 1.4674 & 0.0126000000000000 \tabularnewline
20 & 1.48 & 1.4774 & 0.00260000000000005 \tabularnewline
21 & 1.48 & 1.4794 & 0.000600000000000037 \tabularnewline
22 & 1.48 & 1.4814 & -0.00139999999999996 \tabularnewline
23 & 1.48 & 1.4814 & -0.00139999999999995 \tabularnewline
24 & 1.48 & 1.4814 & -0.00139999999999994 \tabularnewline
25 & 1.48 & 1.4687 & 0.0113000000000015 \tabularnewline
26 & 1.48 & 1.4887 & -0.00869999999999997 \tabularnewline
27 & 1.48 & 1.4907 & -0.0107 \tabularnewline
28 & 1.48 & 1.4927 & -0.0127000000000000 \tabularnewline
29 & 1.48 & 1.4927 & -0.0127000000000000 \tabularnewline
30 & 1.48 & 1.4927 & -0.0127000000000000 \tabularnewline
31 & 1.48 & 1.4674 & 0.0126000000000000 \tabularnewline
32 & 1.48 & 1.4774 & 0.00260000000000005 \tabularnewline
33 & 1.48 & 1.4794 & 0.000600000000000037 \tabularnewline
34 & 1.48 & 1.4814 & -0.00139999999999996 \tabularnewline
35 & 1.48 & 1.4814 & -0.00139999999999995 \tabularnewline
36 & 1.48 & 1.4814 & -0.00139999999999994 \tabularnewline
37 & 1.48 & 1.4687 & 0.0113000000000015 \tabularnewline
38 & 1.57 & 1.4887 & 0.0813000000000001 \tabularnewline
39 & 1.58 & 1.4907 & 0.0893 \tabularnewline
40 & 1.58 & 1.4927 & 0.0873000000000002 \tabularnewline
41 & 1.58 & 1.4927 & 0.0873000000000001 \tabularnewline
42 & 1.58 & 1.4927 & 0.0873000000000002 \tabularnewline
43 & 1.59 & 1.6139 & -0.0239000000000000 \tabularnewline
44 & 1.6 & 1.6239 & -0.0239 \tabularnewline
45 & 1.6 & 1.6259 & -0.0259 \tabularnewline
46 & 1.61 & 1.6279 & -0.0179000000000000 \tabularnewline
47 & 1.61 & 1.6279 & -0.0179000000000000 \tabularnewline
48 & 1.61 & 1.6279 & -0.0179000000000000 \tabularnewline
49 & 1.62 & 1.6152 & 0.00480000000000148 \tabularnewline
50 & 1.63 & 1.6352 & -0.00520000000000016 \tabularnewline
51 & 1.63 & 1.6372 & -0.0072000000000002 \tabularnewline
52 & 1.64 & 1.6392 & 0.000799999999999853 \tabularnewline
53 & 1.64 & 1.6392 & 0.000799999999999835 \tabularnewline
54 & 1.64 & 1.6392 & 0.000799999999999856 \tabularnewline
55 & 1.64 & 1.6139 & 0.0260999999999999 \tabularnewline
56 & 1.64 & 1.6239 & 0.0160999999999999 \tabularnewline
57 & 1.65 & 1.6259 & 0.0240999999999999 \tabularnewline
58 & 1.65 & 1.6279 & 0.0220999999999998 \tabularnewline
59 & 1.65 & 1.6279 & 0.0220999999999999 \tabularnewline
60 & 1.65 & 1.6279 & 0.0220999999999999 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58356&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]1.43[/C][C]1.46870000000001[/C][C]-0.0387000000000059[/C][/ROW]
[ROW][C]2[/C][C]1.43[/C][C]1.4887[/C][C]-0.0587000000000001[/C][/ROW]
[ROW][C]3[/C][C]1.43[/C][C]1.4907[/C][C]-0.0607[/C][/ROW]
[ROW][C]4[/C][C]1.43[/C][C]1.4927[/C][C]-0.0627000000000001[/C][/ROW]
[ROW][C]5[/C][C]1.43[/C][C]1.4927[/C][C]-0.0627[/C][/ROW]
[ROW][C]6[/C][C]1.43[/C][C]1.4927[/C][C]-0.0627[/C][/ROW]
[ROW][C]7[/C][C]1.44[/C][C]1.4674[/C][C]-0.0274[/C][/ROW]
[ROW][C]8[/C][C]1.48[/C][C]1.4774[/C][C]0.00260000000000006[/C][/ROW]
[ROW][C]9[/C][C]1.48[/C][C]1.4794[/C][C]0.00060000000000003[/C][/ROW]
[ROW][C]10[/C][C]1.48[/C][C]1.4814[/C][C]-0.00139999999999997[/C][/ROW]
[ROW][C]11[/C][C]1.48[/C][C]1.4814[/C][C]-0.00139999999999997[/C][/ROW]
[ROW][C]12[/C][C]1.48[/C][C]1.4814[/C][C]-0.00139999999999995[/C][/ROW]
[ROW][C]13[/C][C]1.48[/C][C]1.4687[/C][C]0.0113000000000015[/C][/ROW]
[ROW][C]14[/C][C]1.48[/C][C]1.4887[/C][C]-0.00869999999999997[/C][/ROW]
[ROW][C]15[/C][C]1.48[/C][C]1.4907[/C][C]-0.0107[/C][/ROW]
[ROW][C]16[/C][C]1.48[/C][C]1.4927[/C][C]-0.0127000000000000[/C][/ROW]
[ROW][C]17[/C][C]1.48[/C][C]1.4927[/C][C]-0.0127000000000000[/C][/ROW]
[ROW][C]18[/C][C]1.48[/C][C]1.4927[/C][C]-0.0127000000000000[/C][/ROW]
[ROW][C]19[/C][C]1.48[/C][C]1.4674[/C][C]0.0126000000000000[/C][/ROW]
[ROW][C]20[/C][C]1.48[/C][C]1.4774[/C][C]0.00260000000000005[/C][/ROW]
[ROW][C]21[/C][C]1.48[/C][C]1.4794[/C][C]0.000600000000000037[/C][/ROW]
[ROW][C]22[/C][C]1.48[/C][C]1.4814[/C][C]-0.00139999999999996[/C][/ROW]
[ROW][C]23[/C][C]1.48[/C][C]1.4814[/C][C]-0.00139999999999995[/C][/ROW]
[ROW][C]24[/C][C]1.48[/C][C]1.4814[/C][C]-0.00139999999999994[/C][/ROW]
[ROW][C]25[/C][C]1.48[/C][C]1.4687[/C][C]0.0113000000000015[/C][/ROW]
[ROW][C]26[/C][C]1.48[/C][C]1.4887[/C][C]-0.00869999999999997[/C][/ROW]
[ROW][C]27[/C][C]1.48[/C][C]1.4907[/C][C]-0.0107[/C][/ROW]
[ROW][C]28[/C][C]1.48[/C][C]1.4927[/C][C]-0.0127000000000000[/C][/ROW]
[ROW][C]29[/C][C]1.48[/C][C]1.4927[/C][C]-0.0127000000000000[/C][/ROW]
[ROW][C]30[/C][C]1.48[/C][C]1.4927[/C][C]-0.0127000000000000[/C][/ROW]
[ROW][C]31[/C][C]1.48[/C][C]1.4674[/C][C]0.0126000000000000[/C][/ROW]
[ROW][C]32[/C][C]1.48[/C][C]1.4774[/C][C]0.00260000000000005[/C][/ROW]
[ROW][C]33[/C][C]1.48[/C][C]1.4794[/C][C]0.000600000000000037[/C][/ROW]
[ROW][C]34[/C][C]1.48[/C][C]1.4814[/C][C]-0.00139999999999996[/C][/ROW]
[ROW][C]35[/C][C]1.48[/C][C]1.4814[/C][C]-0.00139999999999995[/C][/ROW]
[ROW][C]36[/C][C]1.48[/C][C]1.4814[/C][C]-0.00139999999999994[/C][/ROW]
[ROW][C]37[/C][C]1.48[/C][C]1.4687[/C][C]0.0113000000000015[/C][/ROW]
[ROW][C]38[/C][C]1.57[/C][C]1.4887[/C][C]0.0813000000000001[/C][/ROW]
[ROW][C]39[/C][C]1.58[/C][C]1.4907[/C][C]0.0893[/C][/ROW]
[ROW][C]40[/C][C]1.58[/C][C]1.4927[/C][C]0.0873000000000002[/C][/ROW]
[ROW][C]41[/C][C]1.58[/C][C]1.4927[/C][C]0.0873000000000001[/C][/ROW]
[ROW][C]42[/C][C]1.58[/C][C]1.4927[/C][C]0.0873000000000002[/C][/ROW]
[ROW][C]43[/C][C]1.59[/C][C]1.6139[/C][C]-0.0239000000000000[/C][/ROW]
[ROW][C]44[/C][C]1.6[/C][C]1.6239[/C][C]-0.0239[/C][/ROW]
[ROW][C]45[/C][C]1.6[/C][C]1.6259[/C][C]-0.0259[/C][/ROW]
[ROW][C]46[/C][C]1.61[/C][C]1.6279[/C][C]-0.0179000000000000[/C][/ROW]
[ROW][C]47[/C][C]1.61[/C][C]1.6279[/C][C]-0.0179000000000000[/C][/ROW]
[ROW][C]48[/C][C]1.61[/C][C]1.6279[/C][C]-0.0179000000000000[/C][/ROW]
[ROW][C]49[/C][C]1.62[/C][C]1.6152[/C][C]0.00480000000000148[/C][/ROW]
[ROW][C]50[/C][C]1.63[/C][C]1.6352[/C][C]-0.00520000000000016[/C][/ROW]
[ROW][C]51[/C][C]1.63[/C][C]1.6372[/C][C]-0.0072000000000002[/C][/ROW]
[ROW][C]52[/C][C]1.64[/C][C]1.6392[/C][C]0.000799999999999853[/C][/ROW]
[ROW][C]53[/C][C]1.64[/C][C]1.6392[/C][C]0.000799999999999835[/C][/ROW]
[ROW][C]54[/C][C]1.64[/C][C]1.6392[/C][C]0.000799999999999856[/C][/ROW]
[ROW][C]55[/C][C]1.64[/C][C]1.6139[/C][C]0.0260999999999999[/C][/ROW]
[ROW][C]56[/C][C]1.64[/C][C]1.6239[/C][C]0.0160999999999999[/C][/ROW]
[ROW][C]57[/C][C]1.65[/C][C]1.6259[/C][C]0.0240999999999999[/C][/ROW]
[ROW][C]58[/C][C]1.65[/C][C]1.6279[/C][C]0.0220999999999998[/C][/ROW]
[ROW][C]59[/C][C]1.65[/C][C]1.6279[/C][C]0.0220999999999999[/C][/ROW]
[ROW][C]60[/C][C]1.65[/C][C]1.6279[/C][C]0.0220999999999999[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58356&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58356&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
11.431.46870000000001-0.0387000000000059
21.431.4887-0.0587000000000001
31.431.4907-0.0607
41.431.4927-0.0627000000000001
51.431.4927-0.0627
61.431.4927-0.0627
71.441.4674-0.0274
81.481.47740.00260000000000006
91.481.47940.00060000000000003
101.481.4814-0.00139999999999997
111.481.4814-0.00139999999999997
121.481.4814-0.00139999999999995
131.481.46870.0113000000000015
141.481.4887-0.00869999999999997
151.481.4907-0.0107
161.481.4927-0.0127000000000000
171.481.4927-0.0127000000000000
181.481.4927-0.0127000000000000
191.481.46740.0126000000000000
201.481.47740.00260000000000005
211.481.47940.000600000000000037
221.481.4814-0.00139999999999996
231.481.4814-0.00139999999999995
241.481.4814-0.00139999999999994
251.481.46870.0113000000000015
261.481.4887-0.00869999999999997
271.481.4907-0.0107
281.481.4927-0.0127000000000000
291.481.4927-0.0127000000000000
301.481.4927-0.0127000000000000
311.481.46740.0126000000000000
321.481.47740.00260000000000005
331.481.47940.000600000000000037
341.481.4814-0.00139999999999996
351.481.4814-0.00139999999999995
361.481.4814-0.00139999999999994
371.481.46870.0113000000000015
381.571.48870.0813000000000001
391.581.49070.0893
401.581.49270.0873000000000002
411.581.49270.0873000000000001
421.581.49270.0873000000000002
431.591.6139-0.0239000000000000
441.61.6239-0.0239
451.61.6259-0.0259
461.611.6279-0.0179000000000000
471.611.6279-0.0179000000000000
481.611.6279-0.0179000000000000
491.621.61520.00480000000000148
501.631.6352-0.00520000000000016
511.631.6372-0.0072000000000002
521.641.63920.000799999999999853
531.641.63920.000799999999999835
541.641.63920.000799999999999856
551.641.61390.0260999999999999
561.641.62390.0160999999999999
571.651.62590.0240999999999999
581.651.62790.0220999999999998
591.651.62790.0220999999999999
601.651.62790.0220999999999999







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
160.7505237945988040.4989524108023910.249476205401196
170.7284120196626520.5431759606746950.271587980337348
180.7137502789468850.572499442106230.286249721053115
190.6492210083378550.7015579833242910.350778991662145
200.5253250569733460.9493498860533090.474674943026654
210.4046614022733390.8093228045466770.595338597726661
220.2971760862261240.5943521724522480.702823913773876
230.2076523702698010.4153047405396030.792347629730199
240.1381139985729000.2762279971458000.8618860014271
250.1007354453534830.2014708907069650.899264554646517
260.08970012750461230.1794002550092250.910299872495388
270.08712857717384370.1742571543476870.912871422826156
280.09562437359375270.1912487471875050.904375626406247
290.1149396641347990.2298793282695990.8850603358652
300.1537584724368690.3075169448737380.846241527563131
310.1184431970122180.2368863940244360.881556802987782
320.0868243889345210.1736487778690420.91317561106548
330.06907893446510160.1381578689302030.930921065534898
340.06617575064828030.1323515012965610.93382424935172
350.0810941671150970.1621883342301940.918905832884903
360.1604803762205010.3209607524410030.839519623779499
370.326718857840920.653437715681840.67328114215908
380.6161010055231760.7677979889536480.383898994476824
390.7554918279928850.4890163440142310.244508172007115
400.7915312574388140.4169374851223720.208468742561186
410.7826283947836730.4347432104326550.217371605216327
420.7405445717217490.5189108565565020.259455428278251
430.7001858815561370.5996282368877260.299814118443863
440.6061715091227210.7876569817545580.393828490877279

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
16 & 0.750523794598804 & 0.498952410802391 & 0.249476205401196 \tabularnewline
17 & 0.728412019662652 & 0.543175960674695 & 0.271587980337348 \tabularnewline
18 & 0.713750278946885 & 0.57249944210623 & 0.286249721053115 \tabularnewline
19 & 0.649221008337855 & 0.701557983324291 & 0.350778991662145 \tabularnewline
20 & 0.525325056973346 & 0.949349886053309 & 0.474674943026654 \tabularnewline
21 & 0.404661402273339 & 0.809322804546677 & 0.595338597726661 \tabularnewline
22 & 0.297176086226124 & 0.594352172452248 & 0.702823913773876 \tabularnewline
23 & 0.207652370269801 & 0.415304740539603 & 0.792347629730199 \tabularnewline
24 & 0.138113998572900 & 0.276227997145800 & 0.8618860014271 \tabularnewline
25 & 0.100735445353483 & 0.201470890706965 & 0.899264554646517 \tabularnewline
26 & 0.0897001275046123 & 0.179400255009225 & 0.910299872495388 \tabularnewline
27 & 0.0871285771738437 & 0.174257154347687 & 0.912871422826156 \tabularnewline
28 & 0.0956243735937527 & 0.191248747187505 & 0.904375626406247 \tabularnewline
29 & 0.114939664134799 & 0.229879328269599 & 0.8850603358652 \tabularnewline
30 & 0.153758472436869 & 0.307516944873738 & 0.846241527563131 \tabularnewline
31 & 0.118443197012218 & 0.236886394024436 & 0.881556802987782 \tabularnewline
32 & 0.086824388934521 & 0.173648777869042 & 0.91317561106548 \tabularnewline
33 & 0.0690789344651016 & 0.138157868930203 & 0.930921065534898 \tabularnewline
34 & 0.0661757506482803 & 0.132351501296561 & 0.93382424935172 \tabularnewline
35 & 0.081094167115097 & 0.162188334230194 & 0.918905832884903 \tabularnewline
36 & 0.160480376220501 & 0.320960752441003 & 0.839519623779499 \tabularnewline
37 & 0.32671885784092 & 0.65343771568184 & 0.67328114215908 \tabularnewline
38 & 0.616101005523176 & 0.767797988953648 & 0.383898994476824 \tabularnewline
39 & 0.755491827992885 & 0.489016344014231 & 0.244508172007115 \tabularnewline
40 & 0.791531257438814 & 0.416937485122372 & 0.208468742561186 \tabularnewline
41 & 0.782628394783673 & 0.434743210432655 & 0.217371605216327 \tabularnewline
42 & 0.740544571721749 & 0.518910856556502 & 0.259455428278251 \tabularnewline
43 & 0.700185881556137 & 0.599628236887726 & 0.299814118443863 \tabularnewline
44 & 0.606171509122721 & 0.787656981754558 & 0.393828490877279 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58356&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]16[/C][C]0.750523794598804[/C][C]0.498952410802391[/C][C]0.249476205401196[/C][/ROW]
[ROW][C]17[/C][C]0.728412019662652[/C][C]0.543175960674695[/C][C]0.271587980337348[/C][/ROW]
[ROW][C]18[/C][C]0.713750278946885[/C][C]0.57249944210623[/C][C]0.286249721053115[/C][/ROW]
[ROW][C]19[/C][C]0.649221008337855[/C][C]0.701557983324291[/C][C]0.350778991662145[/C][/ROW]
[ROW][C]20[/C][C]0.525325056973346[/C][C]0.949349886053309[/C][C]0.474674943026654[/C][/ROW]
[ROW][C]21[/C][C]0.404661402273339[/C][C]0.809322804546677[/C][C]0.595338597726661[/C][/ROW]
[ROW][C]22[/C][C]0.297176086226124[/C][C]0.594352172452248[/C][C]0.702823913773876[/C][/ROW]
[ROW][C]23[/C][C]0.207652370269801[/C][C]0.415304740539603[/C][C]0.792347629730199[/C][/ROW]
[ROW][C]24[/C][C]0.138113998572900[/C][C]0.276227997145800[/C][C]0.8618860014271[/C][/ROW]
[ROW][C]25[/C][C]0.100735445353483[/C][C]0.201470890706965[/C][C]0.899264554646517[/C][/ROW]
[ROW][C]26[/C][C]0.0897001275046123[/C][C]0.179400255009225[/C][C]0.910299872495388[/C][/ROW]
[ROW][C]27[/C][C]0.0871285771738437[/C][C]0.174257154347687[/C][C]0.912871422826156[/C][/ROW]
[ROW][C]28[/C][C]0.0956243735937527[/C][C]0.191248747187505[/C][C]0.904375626406247[/C][/ROW]
[ROW][C]29[/C][C]0.114939664134799[/C][C]0.229879328269599[/C][C]0.8850603358652[/C][/ROW]
[ROW][C]30[/C][C]0.153758472436869[/C][C]0.307516944873738[/C][C]0.846241527563131[/C][/ROW]
[ROW][C]31[/C][C]0.118443197012218[/C][C]0.236886394024436[/C][C]0.881556802987782[/C][/ROW]
[ROW][C]32[/C][C]0.086824388934521[/C][C]0.173648777869042[/C][C]0.91317561106548[/C][/ROW]
[ROW][C]33[/C][C]0.0690789344651016[/C][C]0.138157868930203[/C][C]0.930921065534898[/C][/ROW]
[ROW][C]34[/C][C]0.0661757506482803[/C][C]0.132351501296561[/C][C]0.93382424935172[/C][/ROW]
[ROW][C]35[/C][C]0.081094167115097[/C][C]0.162188334230194[/C][C]0.918905832884903[/C][/ROW]
[ROW][C]36[/C][C]0.160480376220501[/C][C]0.320960752441003[/C][C]0.839519623779499[/C][/ROW]
[ROW][C]37[/C][C]0.32671885784092[/C][C]0.65343771568184[/C][C]0.67328114215908[/C][/ROW]
[ROW][C]38[/C][C]0.616101005523176[/C][C]0.767797988953648[/C][C]0.383898994476824[/C][/ROW]
[ROW][C]39[/C][C]0.755491827992885[/C][C]0.489016344014231[/C][C]0.244508172007115[/C][/ROW]
[ROW][C]40[/C][C]0.791531257438814[/C][C]0.416937485122372[/C][C]0.208468742561186[/C][/ROW]
[ROW][C]41[/C][C]0.782628394783673[/C][C]0.434743210432655[/C][C]0.217371605216327[/C][/ROW]
[ROW][C]42[/C][C]0.740544571721749[/C][C]0.518910856556502[/C][C]0.259455428278251[/C][/ROW]
[ROW][C]43[/C][C]0.700185881556137[/C][C]0.599628236887726[/C][C]0.299814118443863[/C][/ROW]
[ROW][C]44[/C][C]0.606171509122721[/C][C]0.787656981754558[/C][C]0.393828490877279[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58356&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58356&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
160.7505237945988040.4989524108023910.249476205401196
170.7284120196626520.5431759606746950.271587980337348
180.7137502789468850.572499442106230.286249721053115
190.6492210083378550.7015579833242910.350778991662145
200.5253250569733460.9493498860533090.474674943026654
210.4046614022733390.8093228045466770.595338597726661
220.2971760862261240.5943521724522480.702823913773876
230.2076523702698010.4153047405396030.792347629730199
240.1381139985729000.2762279971458000.8618860014271
250.1007354453534830.2014708907069650.899264554646517
260.08970012750461230.1794002550092250.910299872495388
270.08712857717384370.1742571543476870.912871422826156
280.09562437359375270.1912487471875050.904375626406247
290.1149396641347990.2298793282695990.8850603358652
300.1537584724368690.3075169448737380.846241527563131
310.1184431970122180.2368863940244360.881556802987782
320.0868243889345210.1736487778690420.91317561106548
330.06907893446510160.1381578689302030.930921065534898
340.06617575064828030.1323515012965610.93382424935172
350.0810941671150970.1621883342301940.918905832884903
360.1604803762205010.3209607524410030.839519623779499
370.326718857840920.653437715681840.67328114215908
380.6161010055231760.7677979889536480.383898994476824
390.7554918279928850.4890163440142310.244508172007115
400.7915312574388140.4169374851223720.208468742561186
410.7826283947836730.4347432104326550.217371605216327
420.7405445717217490.5189108565565020.259455428278251
430.7001858815561370.5996282368877260.299814118443863
440.6061715091227210.7876569817545580.393828490877279







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

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 0 & 0 & OK \tabularnewline
10% type I error level & 0 & 0 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58356&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58356&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58356&T=6

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

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



Parameters (Session):
par1 = 1 ; par2 = Include Monthly Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 1 ; par2 = Include Monthly 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')
}