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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 08:32:51 -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/t12587312699hq338qgw9s453p.htm/, Retrieved Fri, 29 Mar 2024 11:26:09 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=58270, Retrieved Fri, 29 Mar 2024 11:26:09 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact94
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [WS7 Multiple Regr...] [2009-11-20 15:32:51] [2694a35f9be9144abd040893a0238ab5] [Current]
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Dataseries X:
101,6	79,8	103,9	110,3	114,1	96,8
94,6	71,9	101,6	103,9	110,3	114,1
95,9	82,9	94,6	101,6	103,9	110,3
104,7	90,1	95,9	94,6	101,6	103,9
102,8	100,7	104,7	95,9	94,6	101,6
98,1	90,7	102,8	104,7	95,9	94,6
113,9	108,8	98,1	102,8	104,7	95,9
80,9	44,1	113,9	98,1	102,8	104,7
95,7	93,6	80,9	113,9	98,1	102,8
113,2	107,4	95,7	80,9	113,9	98,1
105,9	96,5	113,2	95,7	80,9	113,9
108,8	93,6	105,9	113,2	95,7	80,9
102,3	76,5	108,8	105,9	113,2	95,7
99	76,7	102,3	108,8	105,9	113,2
100,7	84	99	102,3	108,8	105,9
115,5	103,3	100,7	99	102,3	108,8
100,7	88,5	115,5	100,7	99	102,3
109,9	99	100,7	115,5	100,7	99
114,6	105,9	109,9	100,7	115,5	100,7
85,4	44,7	114,6	109,9	100,7	115,5
100,5	94	85,4	114,6	109,9	100,7
114,8	107,1	100,5	85,4	114,6	109,9
116,5	104,8	114,8	100,5	85,4	114,6
112,9	102,5	116,5	114,8	100,5	85,4
102	77,7	112,9	116,5	114,8	100,5
106	85,2	102	112,9	116,5	114,8
105,3	91,3	106	102	112,9	116,5
118,8	106,5	105,3	106	102	112,9
106,1	92,4	118,8	105,3	106	102
109,3	97,5	106,1	118,8	105,3	106
117,2	107	109,3	106,1	118,8	105,3
92,5	51,1	117,2	109,3	106,1	118,8
104,2	98,6	92,5	117,2	109,3	106,1
112,5	102,2	104,2	92,5	117,2	109,3
122,4	114,3	112,5	104,2	92,5	117,2
113,3	99,4	122,4	112,5	104,2	92,5
100	72,5	113,3	122,4	112,5	104,2
110,7	92,3	100	113,3	122,4	112,5
112,8	99,4	110,7	100	113,3	122,4
109,8	85,9	112,8	110,7	100	113,3
117,3	109,4	109,8	112,8	110,7	100
109,1	97,6	117,3	109,8	112,8	110,7
115,9	104,7	109,1	117,3	109,8	112,8
96	56,9	115,9	109,1	117,3	109,8
99,8	86,7	96	115,9	109,1	117,3
116,8	108,5	99,8	96	115,9	109,1
115,7	103,4	116,8	99,8	96	115,9
99,4	86,2	115,7	116,8	99,8	96
94,3	71	99,4	115,7	116,8	99,8
91	75,9	94,3	99,4	115,7	116,8
93,2	87,1	91	94,3	99,4	115,7
103,1	102	93,2	91	94,3	99,4
94,1	88,5	103,1	93,2	91	94,3
91,8	87,8	94,1	103,1	93,2	91
102,7	100,8	91,8	94,1	103,1	93,2
82,6	50,6	102,7	91,8	94,1	103,1





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
R Framework error message
Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.

\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
R Framework error message & 
Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.
\tabularnewline \hline \end{tabular} %Source: https://freestatistics.org/blog/index.php?pk=58270&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]
[ROW][C]R Framework error message[/C][C]
Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.
[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=58270&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58270&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
R Framework error message
Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.







Multiple Linear Regression - Estimated Regression Equation
Totind[t] = -29.5139603340959 + 0.665616707652597Bouw[t] + 0.284368980405653`Yt-1`[t] + 0.289338994260551`Yt-2`[t] + 0.157257661996291`Yt-3`[t] -0.0641304861653082`Yt-4`[t] + 5.1106498875945M1[t] + 7.13200327844603M2[t] + 5.96646531169032M3[t] + 9.41343049457463M4[t] + 0.972856350450679M5[t] + 0.391300760931872M6[t] + 3.03306035447898M7[t] + 14.0194673929411M8[t] + 0.117239138123227M9[t] + 8.83732777976529M10[t] + 8.1309443614198M11[t] -0.0421476113852508t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Totind[t] =  -29.5139603340959 +  0.665616707652597Bouw[t] +  0.284368980405653`Yt-1`[t] +  0.289338994260551`Yt-2`[t] +  0.157257661996291`Yt-3`[t] -0.0641304861653082`Yt-4`[t] +  5.1106498875945M1[t] +  7.13200327844603M2[t] +  5.96646531169032M3[t] +  9.41343049457463M4[t] +  0.972856350450679M5[t] +  0.391300760931872M6[t] +  3.03306035447898M7[t] +  14.0194673929411M8[t] +  0.117239138123227M9[t] +  8.83732777976529M10[t] +  8.1309443614198M11[t] -0.0421476113852508t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58270&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Totind[t] =  -29.5139603340959 +  0.665616707652597Bouw[t] +  0.284368980405653`Yt-1`[t] +  0.289338994260551`Yt-2`[t] +  0.157257661996291`Yt-3`[t] -0.0641304861653082`Yt-4`[t] +  5.1106498875945M1[t] +  7.13200327844603M2[t] +  5.96646531169032M3[t] +  9.41343049457463M4[t] +  0.972856350450679M5[t] +  0.391300760931872M6[t] +  3.03306035447898M7[t] +  14.0194673929411M8[t] +  0.117239138123227M9[t] +  8.83732777976529M10[t] +  8.1309443614198M11[t] -0.0421476113852508t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58270&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58270&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
Totind[t] = -29.5139603340959 + 0.665616707652597Bouw[t] + 0.284368980405653`Yt-1`[t] + 0.289338994260551`Yt-2`[t] + 0.157257661996291`Yt-3`[t] -0.0641304861653082`Yt-4`[t] + 5.1106498875945M1[t] + 7.13200327844603M2[t] + 5.96646531169032M3[t] + 9.41343049457463M4[t] + 0.972856350450679M5[t] + 0.391300760931872M6[t] + 3.03306035447898M7[t] + 14.0194673929411M8[t] + 0.117239138123227M9[t] + 8.83732777976529M10[t] + 8.1309443614198M11[t] -0.0421476113852508t + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-29.51396033409596.195053-4.76412.8e-051.4e-05
Bouw0.6656167076525970.04900513.582500
`Yt-1`0.2843689804056530.0716423.96930.0003090.000155
`Yt-2`0.2893389942605510.0554345.21957e-063e-06
`Yt-3`0.1572576619962910.0677522.32110.0257460.012873
`Yt-4`-0.06413048616530820.07538-0.85080.4002330.200117
M15.11064988759452.5761041.98390.0545290.027264
M27.132003278446033.66091.94820.058810.029405
M35.966465311690323.4357641.73660.0905640.045282
M49.413430494574632.7092433.47460.0012950.000647
M50.9728563504506791.870420.52010.6059920.302996
M60.3913007609318721.9669850.19890.8433750.421687
M73.033060354478982.3970021.26540.2134480.106724
M814.01946739294113.31334.23130.0001417.1e-05
M90.1172391381232273.378040.03470.9724960.486248
M108.837327779765293.4965892.52740.0157710.007885
M118.13094436141982.8519222.8510.0070060.003503
t-0.04214761138525080.016227-2.59730.0132920.006646

\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) & -29.5139603340959 & 6.195053 & -4.7641 & 2.8e-05 & 1.4e-05 \tabularnewline
Bouw & 0.665616707652597 & 0.049005 & 13.5825 & 0 & 0 \tabularnewline
`Yt-1` & 0.284368980405653 & 0.071642 & 3.9693 & 0.000309 & 0.000155 \tabularnewline
`Yt-2` & 0.289338994260551 & 0.055434 & 5.2195 & 7e-06 & 3e-06 \tabularnewline
`Yt-3` & 0.157257661996291 & 0.067752 & 2.3211 & 0.025746 & 0.012873 \tabularnewline
`Yt-4` & -0.0641304861653082 & 0.07538 & -0.8508 & 0.400233 & 0.200117 \tabularnewline
M1 & 5.1106498875945 & 2.576104 & 1.9839 & 0.054529 & 0.027264 \tabularnewline
M2 & 7.13200327844603 & 3.6609 & 1.9482 & 0.05881 & 0.029405 \tabularnewline
M3 & 5.96646531169032 & 3.435764 & 1.7366 & 0.090564 & 0.045282 \tabularnewline
M4 & 9.41343049457463 & 2.709243 & 3.4746 & 0.001295 & 0.000647 \tabularnewline
M5 & 0.972856350450679 & 1.87042 & 0.5201 & 0.605992 & 0.302996 \tabularnewline
M6 & 0.391300760931872 & 1.966985 & 0.1989 & 0.843375 & 0.421687 \tabularnewline
M7 & 3.03306035447898 & 2.397002 & 1.2654 & 0.213448 & 0.106724 \tabularnewline
M8 & 14.0194673929411 & 3.3133 & 4.2313 & 0.000141 & 7.1e-05 \tabularnewline
M9 & 0.117239138123227 & 3.37804 & 0.0347 & 0.972496 & 0.486248 \tabularnewline
M10 & 8.83732777976529 & 3.496589 & 2.5274 & 0.015771 & 0.007885 \tabularnewline
M11 & 8.1309443614198 & 2.851922 & 2.851 & 0.007006 & 0.003503 \tabularnewline
t & -0.0421476113852508 & 0.016227 & -2.5973 & 0.013292 & 0.006646 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58270&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]-29.5139603340959[/C][C]6.195053[/C][C]-4.7641[/C][C]2.8e-05[/C][C]1.4e-05[/C][/ROW]
[ROW][C]Bouw[/C][C]0.665616707652597[/C][C]0.049005[/C][C]13.5825[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]`Yt-1`[/C][C]0.284368980405653[/C][C]0.071642[/C][C]3.9693[/C][C]0.000309[/C][C]0.000155[/C][/ROW]
[ROW][C]`Yt-2`[/C][C]0.289338994260551[/C][C]0.055434[/C][C]5.2195[/C][C]7e-06[/C][C]3e-06[/C][/ROW]
[ROW][C]`Yt-3`[/C][C]0.157257661996291[/C][C]0.067752[/C][C]2.3211[/C][C]0.025746[/C][C]0.012873[/C][/ROW]
[ROW][C]`Yt-4`[/C][C]-0.0641304861653082[/C][C]0.07538[/C][C]-0.8508[/C][C]0.400233[/C][C]0.200117[/C][/ROW]
[ROW][C]M1[/C][C]5.1106498875945[/C][C]2.576104[/C][C]1.9839[/C][C]0.054529[/C][C]0.027264[/C][/ROW]
[ROW][C]M2[/C][C]7.13200327844603[/C][C]3.6609[/C][C]1.9482[/C][C]0.05881[/C][C]0.029405[/C][/ROW]
[ROW][C]M3[/C][C]5.96646531169032[/C][C]3.435764[/C][C]1.7366[/C][C]0.090564[/C][C]0.045282[/C][/ROW]
[ROW][C]M4[/C][C]9.41343049457463[/C][C]2.709243[/C][C]3.4746[/C][C]0.001295[/C][C]0.000647[/C][/ROW]
[ROW][C]M5[/C][C]0.972856350450679[/C][C]1.87042[/C][C]0.5201[/C][C]0.605992[/C][C]0.302996[/C][/ROW]
[ROW][C]M6[/C][C]0.391300760931872[/C][C]1.966985[/C][C]0.1989[/C][C]0.843375[/C][C]0.421687[/C][/ROW]
[ROW][C]M7[/C][C]3.03306035447898[/C][C]2.397002[/C][C]1.2654[/C][C]0.213448[/C][C]0.106724[/C][/ROW]
[ROW][C]M8[/C][C]14.0194673929411[/C][C]3.3133[/C][C]4.2313[/C][C]0.000141[/C][C]7.1e-05[/C][/ROW]
[ROW][C]M9[/C][C]0.117239138123227[/C][C]3.37804[/C][C]0.0347[/C][C]0.972496[/C][C]0.486248[/C][/ROW]
[ROW][C]M10[/C][C]8.83732777976529[/C][C]3.496589[/C][C]2.5274[/C][C]0.015771[/C][C]0.007885[/C][/ROW]
[ROW][C]M11[/C][C]8.1309443614198[/C][C]2.851922[/C][C]2.851[/C][C]0.007006[/C][C]0.003503[/C][/ROW]
[ROW][C]t[/C][C]-0.0421476113852508[/C][C]0.016227[/C][C]-2.5973[/C][C]0.013292[/C][C]0.006646[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58270&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58270&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)-29.51396033409596.195053-4.76412.8e-051.4e-05
Bouw0.6656167076525970.04900513.582500
`Yt-1`0.2843689804056530.0716423.96930.0003090.000155
`Yt-2`0.2893389942605510.0554345.21957e-063e-06
`Yt-3`0.1572576619962910.0677522.32110.0257460.012873
`Yt-4`-0.06413048616530820.07538-0.85080.4002330.200117
M15.11064988759452.5761041.98390.0545290.027264
M27.132003278446033.66091.94820.058810.029405
M35.966465311690323.4357641.73660.0905640.045282
M49.413430494574632.7092433.47460.0012950.000647
M50.9728563504506791.870420.52010.6059920.302996
M60.3913007609318721.9669850.19890.8433750.421687
M73.033060354478982.3970021.26540.2134480.106724
M814.01946739294113.31334.23130.0001417.1e-05
M90.1172391381232273.378040.03470.9724960.486248
M108.837327779765293.4965892.52740.0157710.007885
M118.13094436141982.8519222.8510.0070060.003503
t-0.04214761138525080.016227-2.59730.0132920.006646







Multiple Linear Regression - Regression Statistics
Multiple R0.988348734440301
R-squared0.976833220869745
Adjusted R-squared0.966469135469368
F-TEST (value)94.2517533514537
F-TEST (DF numerator)17
F-TEST (DF denominator)38
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.76382842703576
Sum Squared Residuals118.221447360738

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.988348734440301 \tabularnewline
R-squared & 0.976833220869745 \tabularnewline
Adjusted R-squared & 0.966469135469368 \tabularnewline
F-TEST (value) & 94.2517533514537 \tabularnewline
F-TEST (DF numerator) & 17 \tabularnewline
F-TEST (DF denominator) & 38 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 1.76382842703576 \tabularnewline
Sum Squared Residuals & 118.221447360738 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58270&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.988348734440301[/C][/ROW]
[ROW][C]R-squared[/C][C]0.976833220869745[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.966469135469368[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]94.2517533514537[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]17[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]38[/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]1.76382842703576[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]118.221447360738[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58270&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58270&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 R0.988348734440301
R-squared0.976833220869745
Adjusted R-squared0.966469135469368
F-TEST (value)94.2517533514537
F-TEST (DF numerator)17
F-TEST (DF denominator)38
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.76382842703576
Sum Squared Residuals118.221447360738







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1101.6101.866051516852-0.266051516851691
294.694.37403056141620.225969438583834
395.997.0693130284668-1.16931302846682
4104.7103.6596200986351.04037990136542
5102.8104.157719648558-1.35771964855756
698.199.537079821602-1.43707982160198
7113.9113.5985737088270.301426291173327
880.983.7474309321176-2.84743093211761
995.797.3211987629797-1.62119876297968
10113.2112.6312088027660.568791197233832
11105.9107.683365404488-1.78336540448772
12108.8105.0112432630893.78875673691121
13102.399.21307311320153.08692688679845
149998.04582250445140.954177495548632
15100.799.80221756293810.89778243706188
16115.5114.3738929649071.12610703509319
17100.7100.6384790118730.0615209881274798
18109.9107.5562760761122.34372392388758
19114.6115.301012416817-0.701012416817162
2085.486.2314376978671-0.83143769786708
21100.5100.554186249729-0.0541862497289154
22114.8113.9460896606140.853910339386245
23116.5115.2087984211491.29120157885144
24112.9114.372963797529-1.47296379752900
25102103.682733910187-1.68273391018725
26106105.8630948045180.136905195481805
27105.3106.024202617573-0.724202617573007
28118.8119.021453070585-0.221453070585327
29106.1106.118032623855-0.0180326238554691
30109.3108.8169626952870.483037304713348
31117.2117.1431786876030.0568213123970843
3292.591.18893001315351.31106998684648
33104.2105.440893691775-1.24089369177498
34112.5113.732616756133-1.23261675613339
35122.4122.3926815672000.0073184327998856
36113.3112.9426048623900.357395137610363
37100100.937627930667-0.937627930667136
38110.7110.705520053080-0.00552005307985979
39112.8111.3523160287451.44768397125529
40109.8107.9564706639271.8435293360729
41117.3117.405838934343-0.105838934343046
42109.1109.836653841622-0.736653841622493
43115.9116.393914258810-0.49391425880954
449696.4546482973823-0.45464829738235
4599.896.88372129551642.91627870448357
46116.8116.990084780487-0.190084780486683
47115.7115.2151546071640.484845392836394
4899.4102.073188076993-2.67318807699256
4994.394.5005135290924-0.200513529092374
509192.3115320765344-1.31153207653441
5193.293.6519507622774-0.451950762277349
52103.1106.888563201946-3.78856320194618
5394.192.67992978137141.42007021862860
5491.892.4530275653764-0.653027565376453
55102.7101.8633209279440.836679072056288
5682.679.77755305947942.82244694052056

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 101.6 & 101.866051516852 & -0.266051516851691 \tabularnewline
2 & 94.6 & 94.3740305614162 & 0.225969438583834 \tabularnewline
3 & 95.9 & 97.0693130284668 & -1.16931302846682 \tabularnewline
4 & 104.7 & 103.659620098635 & 1.04037990136542 \tabularnewline
5 & 102.8 & 104.157719648558 & -1.35771964855756 \tabularnewline
6 & 98.1 & 99.537079821602 & -1.43707982160198 \tabularnewline
7 & 113.9 & 113.598573708827 & 0.301426291173327 \tabularnewline
8 & 80.9 & 83.7474309321176 & -2.84743093211761 \tabularnewline
9 & 95.7 & 97.3211987629797 & -1.62119876297968 \tabularnewline
10 & 113.2 & 112.631208802766 & 0.568791197233832 \tabularnewline
11 & 105.9 & 107.683365404488 & -1.78336540448772 \tabularnewline
12 & 108.8 & 105.011243263089 & 3.78875673691121 \tabularnewline
13 & 102.3 & 99.2130731132015 & 3.08692688679845 \tabularnewline
14 & 99 & 98.0458225044514 & 0.954177495548632 \tabularnewline
15 & 100.7 & 99.8022175629381 & 0.89778243706188 \tabularnewline
16 & 115.5 & 114.373892964907 & 1.12610703509319 \tabularnewline
17 & 100.7 & 100.638479011873 & 0.0615209881274798 \tabularnewline
18 & 109.9 & 107.556276076112 & 2.34372392388758 \tabularnewline
19 & 114.6 & 115.301012416817 & -0.701012416817162 \tabularnewline
20 & 85.4 & 86.2314376978671 & -0.83143769786708 \tabularnewline
21 & 100.5 & 100.554186249729 & -0.0541862497289154 \tabularnewline
22 & 114.8 & 113.946089660614 & 0.853910339386245 \tabularnewline
23 & 116.5 & 115.208798421149 & 1.29120157885144 \tabularnewline
24 & 112.9 & 114.372963797529 & -1.47296379752900 \tabularnewline
25 & 102 & 103.682733910187 & -1.68273391018725 \tabularnewline
26 & 106 & 105.863094804518 & 0.136905195481805 \tabularnewline
27 & 105.3 & 106.024202617573 & -0.724202617573007 \tabularnewline
28 & 118.8 & 119.021453070585 & -0.221453070585327 \tabularnewline
29 & 106.1 & 106.118032623855 & -0.0180326238554691 \tabularnewline
30 & 109.3 & 108.816962695287 & 0.483037304713348 \tabularnewline
31 & 117.2 & 117.143178687603 & 0.0568213123970843 \tabularnewline
32 & 92.5 & 91.1889300131535 & 1.31106998684648 \tabularnewline
33 & 104.2 & 105.440893691775 & -1.24089369177498 \tabularnewline
34 & 112.5 & 113.732616756133 & -1.23261675613339 \tabularnewline
35 & 122.4 & 122.392681567200 & 0.0073184327998856 \tabularnewline
36 & 113.3 & 112.942604862390 & 0.357395137610363 \tabularnewline
37 & 100 & 100.937627930667 & -0.937627930667136 \tabularnewline
38 & 110.7 & 110.705520053080 & -0.00552005307985979 \tabularnewline
39 & 112.8 & 111.352316028745 & 1.44768397125529 \tabularnewline
40 & 109.8 & 107.956470663927 & 1.8435293360729 \tabularnewline
41 & 117.3 & 117.405838934343 & -0.105838934343046 \tabularnewline
42 & 109.1 & 109.836653841622 & -0.736653841622493 \tabularnewline
43 & 115.9 & 116.393914258810 & -0.49391425880954 \tabularnewline
44 & 96 & 96.4546482973823 & -0.45464829738235 \tabularnewline
45 & 99.8 & 96.8837212955164 & 2.91627870448357 \tabularnewline
46 & 116.8 & 116.990084780487 & -0.190084780486683 \tabularnewline
47 & 115.7 & 115.215154607164 & 0.484845392836394 \tabularnewline
48 & 99.4 & 102.073188076993 & -2.67318807699256 \tabularnewline
49 & 94.3 & 94.5005135290924 & -0.200513529092374 \tabularnewline
50 & 91 & 92.3115320765344 & -1.31153207653441 \tabularnewline
51 & 93.2 & 93.6519507622774 & -0.451950762277349 \tabularnewline
52 & 103.1 & 106.888563201946 & -3.78856320194618 \tabularnewline
53 & 94.1 & 92.6799297813714 & 1.42007021862860 \tabularnewline
54 & 91.8 & 92.4530275653764 & -0.653027565376453 \tabularnewline
55 & 102.7 & 101.863320927944 & 0.836679072056288 \tabularnewline
56 & 82.6 & 79.7775530594794 & 2.82244694052056 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58270&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]101.6[/C][C]101.866051516852[/C][C]-0.266051516851691[/C][/ROW]
[ROW][C]2[/C][C]94.6[/C][C]94.3740305614162[/C][C]0.225969438583834[/C][/ROW]
[ROW][C]3[/C][C]95.9[/C][C]97.0693130284668[/C][C]-1.16931302846682[/C][/ROW]
[ROW][C]4[/C][C]104.7[/C][C]103.659620098635[/C][C]1.04037990136542[/C][/ROW]
[ROW][C]5[/C][C]102.8[/C][C]104.157719648558[/C][C]-1.35771964855756[/C][/ROW]
[ROW][C]6[/C][C]98.1[/C][C]99.537079821602[/C][C]-1.43707982160198[/C][/ROW]
[ROW][C]7[/C][C]113.9[/C][C]113.598573708827[/C][C]0.301426291173327[/C][/ROW]
[ROW][C]8[/C][C]80.9[/C][C]83.7474309321176[/C][C]-2.84743093211761[/C][/ROW]
[ROW][C]9[/C][C]95.7[/C][C]97.3211987629797[/C][C]-1.62119876297968[/C][/ROW]
[ROW][C]10[/C][C]113.2[/C][C]112.631208802766[/C][C]0.568791197233832[/C][/ROW]
[ROW][C]11[/C][C]105.9[/C][C]107.683365404488[/C][C]-1.78336540448772[/C][/ROW]
[ROW][C]12[/C][C]108.8[/C][C]105.011243263089[/C][C]3.78875673691121[/C][/ROW]
[ROW][C]13[/C][C]102.3[/C][C]99.2130731132015[/C][C]3.08692688679845[/C][/ROW]
[ROW][C]14[/C][C]99[/C][C]98.0458225044514[/C][C]0.954177495548632[/C][/ROW]
[ROW][C]15[/C][C]100.7[/C][C]99.8022175629381[/C][C]0.89778243706188[/C][/ROW]
[ROW][C]16[/C][C]115.5[/C][C]114.373892964907[/C][C]1.12610703509319[/C][/ROW]
[ROW][C]17[/C][C]100.7[/C][C]100.638479011873[/C][C]0.0615209881274798[/C][/ROW]
[ROW][C]18[/C][C]109.9[/C][C]107.556276076112[/C][C]2.34372392388758[/C][/ROW]
[ROW][C]19[/C][C]114.6[/C][C]115.301012416817[/C][C]-0.701012416817162[/C][/ROW]
[ROW][C]20[/C][C]85.4[/C][C]86.2314376978671[/C][C]-0.83143769786708[/C][/ROW]
[ROW][C]21[/C][C]100.5[/C][C]100.554186249729[/C][C]-0.0541862497289154[/C][/ROW]
[ROW][C]22[/C][C]114.8[/C][C]113.946089660614[/C][C]0.853910339386245[/C][/ROW]
[ROW][C]23[/C][C]116.5[/C][C]115.208798421149[/C][C]1.29120157885144[/C][/ROW]
[ROW][C]24[/C][C]112.9[/C][C]114.372963797529[/C][C]-1.47296379752900[/C][/ROW]
[ROW][C]25[/C][C]102[/C][C]103.682733910187[/C][C]-1.68273391018725[/C][/ROW]
[ROW][C]26[/C][C]106[/C][C]105.863094804518[/C][C]0.136905195481805[/C][/ROW]
[ROW][C]27[/C][C]105.3[/C][C]106.024202617573[/C][C]-0.724202617573007[/C][/ROW]
[ROW][C]28[/C][C]118.8[/C][C]119.021453070585[/C][C]-0.221453070585327[/C][/ROW]
[ROW][C]29[/C][C]106.1[/C][C]106.118032623855[/C][C]-0.0180326238554691[/C][/ROW]
[ROW][C]30[/C][C]109.3[/C][C]108.816962695287[/C][C]0.483037304713348[/C][/ROW]
[ROW][C]31[/C][C]117.2[/C][C]117.143178687603[/C][C]0.0568213123970843[/C][/ROW]
[ROW][C]32[/C][C]92.5[/C][C]91.1889300131535[/C][C]1.31106998684648[/C][/ROW]
[ROW][C]33[/C][C]104.2[/C][C]105.440893691775[/C][C]-1.24089369177498[/C][/ROW]
[ROW][C]34[/C][C]112.5[/C][C]113.732616756133[/C][C]-1.23261675613339[/C][/ROW]
[ROW][C]35[/C][C]122.4[/C][C]122.392681567200[/C][C]0.0073184327998856[/C][/ROW]
[ROW][C]36[/C][C]113.3[/C][C]112.942604862390[/C][C]0.357395137610363[/C][/ROW]
[ROW][C]37[/C][C]100[/C][C]100.937627930667[/C][C]-0.937627930667136[/C][/ROW]
[ROW][C]38[/C][C]110.7[/C][C]110.705520053080[/C][C]-0.00552005307985979[/C][/ROW]
[ROW][C]39[/C][C]112.8[/C][C]111.352316028745[/C][C]1.44768397125529[/C][/ROW]
[ROW][C]40[/C][C]109.8[/C][C]107.956470663927[/C][C]1.8435293360729[/C][/ROW]
[ROW][C]41[/C][C]117.3[/C][C]117.405838934343[/C][C]-0.105838934343046[/C][/ROW]
[ROW][C]42[/C][C]109.1[/C][C]109.836653841622[/C][C]-0.736653841622493[/C][/ROW]
[ROW][C]43[/C][C]115.9[/C][C]116.393914258810[/C][C]-0.49391425880954[/C][/ROW]
[ROW][C]44[/C][C]96[/C][C]96.4546482973823[/C][C]-0.45464829738235[/C][/ROW]
[ROW][C]45[/C][C]99.8[/C][C]96.8837212955164[/C][C]2.91627870448357[/C][/ROW]
[ROW][C]46[/C][C]116.8[/C][C]116.990084780487[/C][C]-0.190084780486683[/C][/ROW]
[ROW][C]47[/C][C]115.7[/C][C]115.215154607164[/C][C]0.484845392836394[/C][/ROW]
[ROW][C]48[/C][C]99.4[/C][C]102.073188076993[/C][C]-2.67318807699256[/C][/ROW]
[ROW][C]49[/C][C]94.3[/C][C]94.5005135290924[/C][C]-0.200513529092374[/C][/ROW]
[ROW][C]50[/C][C]91[/C][C]92.3115320765344[/C][C]-1.31153207653441[/C][/ROW]
[ROW][C]51[/C][C]93.2[/C][C]93.6519507622774[/C][C]-0.451950762277349[/C][/ROW]
[ROW][C]52[/C][C]103.1[/C][C]106.888563201946[/C][C]-3.78856320194618[/C][/ROW]
[ROW][C]53[/C][C]94.1[/C][C]92.6799297813714[/C][C]1.42007021862860[/C][/ROW]
[ROW][C]54[/C][C]91.8[/C][C]92.4530275653764[/C][C]-0.653027565376453[/C][/ROW]
[ROW][C]55[/C][C]102.7[/C][C]101.863320927944[/C][C]0.836679072056288[/C][/ROW]
[ROW][C]56[/C][C]82.6[/C][C]79.7775530594794[/C][C]2.82244694052056[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58270&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58270&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
1101.6101.866051516852-0.266051516851691
294.694.37403056141620.225969438583834
395.997.0693130284668-1.16931302846682
4104.7103.6596200986351.04037990136542
5102.8104.157719648558-1.35771964855756
698.199.537079821602-1.43707982160198
7113.9113.5985737088270.301426291173327
880.983.7474309321176-2.84743093211761
995.797.3211987629797-1.62119876297968
10113.2112.6312088027660.568791197233832
11105.9107.683365404488-1.78336540448772
12108.8105.0112432630893.78875673691121
13102.399.21307311320153.08692688679845
149998.04582250445140.954177495548632
15100.799.80221756293810.89778243706188
16115.5114.3738929649071.12610703509319
17100.7100.6384790118730.0615209881274798
18109.9107.5562760761122.34372392388758
19114.6115.301012416817-0.701012416817162
2085.486.2314376978671-0.83143769786708
21100.5100.554186249729-0.0541862497289154
22114.8113.9460896606140.853910339386245
23116.5115.2087984211491.29120157885144
24112.9114.372963797529-1.47296379752900
25102103.682733910187-1.68273391018725
26106105.8630948045180.136905195481805
27105.3106.024202617573-0.724202617573007
28118.8119.021453070585-0.221453070585327
29106.1106.118032623855-0.0180326238554691
30109.3108.8169626952870.483037304713348
31117.2117.1431786876030.0568213123970843
3292.591.18893001315351.31106998684648
33104.2105.440893691775-1.24089369177498
34112.5113.732616756133-1.23261675613339
35122.4122.3926815672000.0073184327998856
36113.3112.9426048623900.357395137610363
37100100.937627930667-0.937627930667136
38110.7110.705520053080-0.00552005307985979
39112.8111.3523160287451.44768397125529
40109.8107.9564706639271.8435293360729
41117.3117.405838934343-0.105838934343046
42109.1109.836653841622-0.736653841622493
43115.9116.393914258810-0.49391425880954
449696.4546482973823-0.45464829738235
4599.896.88372129551642.91627870448357
46116.8116.990084780487-0.190084780486683
47115.7115.2151546071640.484845392836394
4899.4102.073188076993-2.67318807699256
4994.394.5005135290924-0.200513529092374
509192.3115320765344-1.31153207653441
5193.293.6519507622774-0.451950762277349
52103.1106.888563201946-3.78856320194618
5394.192.67992978137141.42007021862860
5491.892.4530275653764-0.653027565376453
55102.7101.8633209279440.836679072056288
5682.679.77755305947942.82244694052056







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
210.2676883661896890.5353767323793780.732311633810311
220.1619965289748330.3239930579496660.838003471025167
230.1084263208262880.2168526416525750.891573679173713
240.1517943704467290.3035887408934590.84820562955327
250.5273558375998890.9452883248002230.472644162400111
260.6087190732760880.7825618534478240.391280926723912
270.4848865743274890.9697731486549790.515113425672511
280.3849730139475910.7699460278951820.615026986052409
290.2959096581985400.5918193163970790.70409034180146
300.2610850761052820.5221701522105630.738914923894718
310.1794409911954570.3588819823909140.820559008804543
320.1324212836945760.2648425673891520.867578716305424
330.1447300765251540.2894601530503090.855269923474846
340.1756548791062010.3513097582124020.824345120893799
350.1035930800158770.2071861600317530.896406919984123

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
21 & 0.267688366189689 & 0.535376732379378 & 0.732311633810311 \tabularnewline
22 & 0.161996528974833 & 0.323993057949666 & 0.838003471025167 \tabularnewline
23 & 0.108426320826288 & 0.216852641652575 & 0.891573679173713 \tabularnewline
24 & 0.151794370446729 & 0.303588740893459 & 0.84820562955327 \tabularnewline
25 & 0.527355837599889 & 0.945288324800223 & 0.472644162400111 \tabularnewline
26 & 0.608719073276088 & 0.782561853447824 & 0.391280926723912 \tabularnewline
27 & 0.484886574327489 & 0.969773148654979 & 0.515113425672511 \tabularnewline
28 & 0.384973013947591 & 0.769946027895182 & 0.615026986052409 \tabularnewline
29 & 0.295909658198540 & 0.591819316397079 & 0.70409034180146 \tabularnewline
30 & 0.261085076105282 & 0.522170152210563 & 0.738914923894718 \tabularnewline
31 & 0.179440991195457 & 0.358881982390914 & 0.820559008804543 \tabularnewline
32 & 0.132421283694576 & 0.264842567389152 & 0.867578716305424 \tabularnewline
33 & 0.144730076525154 & 0.289460153050309 & 0.855269923474846 \tabularnewline
34 & 0.175654879106201 & 0.351309758212402 & 0.824345120893799 \tabularnewline
35 & 0.103593080015877 & 0.207186160031753 & 0.896406919984123 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58270&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]21[/C][C]0.267688366189689[/C][C]0.535376732379378[/C][C]0.732311633810311[/C][/ROW]
[ROW][C]22[/C][C]0.161996528974833[/C][C]0.323993057949666[/C][C]0.838003471025167[/C][/ROW]
[ROW][C]23[/C][C]0.108426320826288[/C][C]0.216852641652575[/C][C]0.891573679173713[/C][/ROW]
[ROW][C]24[/C][C]0.151794370446729[/C][C]0.303588740893459[/C][C]0.84820562955327[/C][/ROW]
[ROW][C]25[/C][C]0.527355837599889[/C][C]0.945288324800223[/C][C]0.472644162400111[/C][/ROW]
[ROW][C]26[/C][C]0.608719073276088[/C][C]0.782561853447824[/C][C]0.391280926723912[/C][/ROW]
[ROW][C]27[/C][C]0.484886574327489[/C][C]0.969773148654979[/C][C]0.515113425672511[/C][/ROW]
[ROW][C]28[/C][C]0.384973013947591[/C][C]0.769946027895182[/C][C]0.615026986052409[/C][/ROW]
[ROW][C]29[/C][C]0.295909658198540[/C][C]0.591819316397079[/C][C]0.70409034180146[/C][/ROW]
[ROW][C]30[/C][C]0.261085076105282[/C][C]0.522170152210563[/C][C]0.738914923894718[/C][/ROW]
[ROW][C]31[/C][C]0.179440991195457[/C][C]0.358881982390914[/C][C]0.820559008804543[/C][/ROW]
[ROW][C]32[/C][C]0.132421283694576[/C][C]0.264842567389152[/C][C]0.867578716305424[/C][/ROW]
[ROW][C]33[/C][C]0.144730076525154[/C][C]0.289460153050309[/C][C]0.855269923474846[/C][/ROW]
[ROW][C]34[/C][C]0.175654879106201[/C][C]0.351309758212402[/C][C]0.824345120893799[/C][/ROW]
[ROW][C]35[/C][C]0.103593080015877[/C][C]0.207186160031753[/C][C]0.896406919984123[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58270&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58270&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
210.2676883661896890.5353767323793780.732311633810311
220.1619965289748330.3239930579496660.838003471025167
230.1084263208262880.2168526416525750.891573679173713
240.1517943704467290.3035887408934590.84820562955327
250.5273558375998890.9452883248002230.472644162400111
260.6087190732760880.7825618534478240.391280926723912
270.4848865743274890.9697731486549790.515113425672511
280.3849730139475910.7699460278951820.615026986052409
290.2959096581985400.5918193163970790.70409034180146
300.2610850761052820.5221701522105630.738914923894718
310.1794409911954570.3588819823909140.820559008804543
320.1324212836945760.2648425673891520.867578716305424
330.1447300765251540.2894601530503090.855269923474846
340.1756548791062010.3513097582124020.824345120893799
350.1035930800158770.2071861600317530.896406919984123







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=58270&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=58270&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58270&T=6

As an alternative you can also use a QR Code:  

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

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



Parameters (Session):
par1 = 1 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;
Parameters (R input):
par1 = 1 ; par2 = Include Monthly Dummies ; par3 = 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')
}