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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 11:05:17 -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/t12587403585h16o3zwkddpv2p.htm/, Retrieved Thu, 28 Mar 2024 08:02:38 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=58380, Retrieved Thu, 28 Mar 2024 08:02:38 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact119
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]
- RM D  [Multiple Regression] [Seatbelt] [2009-11-12 14:06:21] [b98453cac15ba1066b407e146608df68]
-    D    [Multiple Regression] [] [2009-11-19 22:46:07] [0e3da40906c04c6abfe5eb434331b3f1]
- R  D        [Multiple Regression] [] [2009-11-20 18:05:17] [85bc2b59254337d32abe63c415a20c60] [Current]
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Dataseries X:
6802.96	0
7132.68	0
7073.29	0
7264.5	0
7105.33	0
7218.71	0
7225.72	0
7354.25	0
7745.46	0
8070.26	0
8366.33	0
8667.51	0
8854.34	0
9218.1	0
9332.9	0
9358.31	0
9248.66	0
9401.2	0
9652.04	0
9957.38	0
10110.63	0
10169.26	0
10343.78	0
10750.21	0
11337.5	0
11786.96	0
12083.04	0
12007.74	0
11745.93	0
11051.51	0
11445.9	0
11924.88	0
12247.63	0
12690.91	0
12910.7	0
13202.12	0
13654.67	0
13862.82	0
13523.93	0
14211.17	0
14510.35	0
14289.23	0
14111.82	0
13086.59	0
13351.54	0
13747.69	0
12855.61	0
12926.93	0
12121.95	1
11731.65	1
11639.51	1
12163.78	1
12029.53	1
11234.18	1
9852.13	1
9709.04	1
9332.75	1
7108.6	1
6691.49	1
6143.05	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=58380&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=58380&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58380&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
Y[t] = + 5272.08916666666 -5964.88333333334X[t] + 2128.74684722222M1[t] + 2147.04786111111M2[t] + 1957.28287500000M3[t] + 2053.99188888889M4[t] + 1806.99490277778M5[t] + 1344.14391666667M6[t] + 988.842930555555M7[t] + 763.891944444445M8[t] + 741.208958333335M9[t] + 367.093972222224M10[t] + 69.474986111113M11[t] + 173.856986111111t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Y[t] =  +  5272.08916666666 -5964.88333333334X[t] +  2128.74684722222M1[t] +  2147.04786111111M2[t] +  1957.28287500000M3[t] +  2053.99188888889M4[t] +  1806.99490277778M5[t] +  1344.14391666667M6[t] +  988.842930555555M7[t] +  763.891944444445M8[t] +  741.208958333335M9[t] +  367.093972222224M10[t] +  69.474986111113M11[t] +  173.856986111111t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58380&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Y[t] =  +  5272.08916666666 -5964.88333333334X[t] +  2128.74684722222M1[t] +  2147.04786111111M2[t] +  1957.28287500000M3[t] +  2053.99188888889M4[t] +  1806.99490277778M5[t] +  1344.14391666667M6[t] +  988.842930555555M7[t] +  763.891944444445M8[t] +  741.208958333335M9[t] +  367.093972222224M10[t] +  69.474986111113M11[t] +  173.856986111111t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58380&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58380&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] = + 5272.08916666666 -5964.88333333334X[t] + 2128.74684722222M1[t] + 2147.04786111111M2[t] + 1957.28287500000M3[t] + 2053.99188888889M4[t] + 1806.99490277778M5[t] + 1344.14391666667M6[t] + 988.842930555555M7[t] + 763.891944444445M8[t] + 741.208958333335M9[t] + 367.093972222224M10[t] + 69.474986111113M11[t] + 173.856986111111t + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)5272.08916666666677.4761997.78200
X-5964.88333333334556.882005-10.711200
M12128.74684722222785.0302682.71170.0093820.004691
M22147.04786111111782.7224812.74310.0086470.004323
M31957.28287500000780.6286052.50730.0157590.00788
M42053.99188888889778.7503652.63750.0113540.005677
M51806.99490277778777.0893252.32530.0245190.01226
M61344.14391666667775.6468791.73290.0898080.044904
M7988.842930555555774.4242491.27690.2080550.104027
M8763.891944444445773.4224780.98770.3284780.164239
M9741.208958333335772.6424250.95930.3424140.171207
M10367.093972222224772.0847620.47550.6367110.318355
M1169.474986111113771.749970.090.928660.46433
t173.85698611111113.12583513.245400

\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) & 5272.08916666666 & 677.476199 & 7.782 & 0 & 0 \tabularnewline
X & -5964.88333333334 & 556.882005 & -10.7112 & 0 & 0 \tabularnewline
M1 & 2128.74684722222 & 785.030268 & 2.7117 & 0.009382 & 0.004691 \tabularnewline
M2 & 2147.04786111111 & 782.722481 & 2.7431 & 0.008647 & 0.004323 \tabularnewline
M3 & 1957.28287500000 & 780.628605 & 2.5073 & 0.015759 & 0.00788 \tabularnewline
M4 & 2053.99188888889 & 778.750365 & 2.6375 & 0.011354 & 0.005677 \tabularnewline
M5 & 1806.99490277778 & 777.089325 & 2.3253 & 0.024519 & 0.01226 \tabularnewline
M6 & 1344.14391666667 & 775.646879 & 1.7329 & 0.089808 & 0.044904 \tabularnewline
M7 & 988.842930555555 & 774.424249 & 1.2769 & 0.208055 & 0.104027 \tabularnewline
M8 & 763.891944444445 & 773.422478 & 0.9877 & 0.328478 & 0.164239 \tabularnewline
M9 & 741.208958333335 & 772.642425 & 0.9593 & 0.342414 & 0.171207 \tabularnewline
M10 & 367.093972222224 & 772.084762 & 0.4755 & 0.636711 & 0.318355 \tabularnewline
M11 & 69.474986111113 & 771.74997 & 0.09 & 0.92866 & 0.46433 \tabularnewline
t & 173.856986111111 & 13.125835 & 13.2454 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58380&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]5272.08916666666[/C][C]677.476199[/C][C]7.782[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]X[/C][C]-5964.88333333334[/C][C]556.882005[/C][C]-10.7112[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M1[/C][C]2128.74684722222[/C][C]785.030268[/C][C]2.7117[/C][C]0.009382[/C][C]0.004691[/C][/ROW]
[ROW][C]M2[/C][C]2147.04786111111[/C][C]782.722481[/C][C]2.7431[/C][C]0.008647[/C][C]0.004323[/C][/ROW]
[ROW][C]M3[/C][C]1957.28287500000[/C][C]780.628605[/C][C]2.5073[/C][C]0.015759[/C][C]0.00788[/C][/ROW]
[ROW][C]M4[/C][C]2053.99188888889[/C][C]778.750365[/C][C]2.6375[/C][C]0.011354[/C][C]0.005677[/C][/ROW]
[ROW][C]M5[/C][C]1806.99490277778[/C][C]777.089325[/C][C]2.3253[/C][C]0.024519[/C][C]0.01226[/C][/ROW]
[ROW][C]M6[/C][C]1344.14391666667[/C][C]775.646879[/C][C]1.7329[/C][C]0.089808[/C][C]0.044904[/C][/ROW]
[ROW][C]M7[/C][C]988.842930555555[/C][C]774.424249[/C][C]1.2769[/C][C]0.208055[/C][C]0.104027[/C][/ROW]
[ROW][C]M8[/C][C]763.891944444445[/C][C]773.422478[/C][C]0.9877[/C][C]0.328478[/C][C]0.164239[/C][/ROW]
[ROW][C]M9[/C][C]741.208958333335[/C][C]772.642425[/C][C]0.9593[/C][C]0.342414[/C][C]0.171207[/C][/ROW]
[ROW][C]M10[/C][C]367.093972222224[/C][C]772.084762[/C][C]0.4755[/C][C]0.636711[/C][C]0.318355[/C][/ROW]
[ROW][C]M11[/C][C]69.474986111113[/C][C]771.74997[/C][C]0.09[/C][C]0.92866[/C][C]0.46433[/C][/ROW]
[ROW][C]t[/C][C]173.856986111111[/C][C]13.125835[/C][C]13.2454[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58380&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58380&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)5272.08916666666677.4761997.78200
X-5964.88333333334556.882005-10.711200
M12128.74684722222785.0302682.71170.0093820.004691
M22147.04786111111782.7224812.74310.0086470.004323
M31957.28287500000780.6286052.50730.0157590.00788
M42053.99188888889778.7503652.63750.0113540.005677
M51806.99490277778777.0893252.32530.0245190.01226
M61344.14391666667775.6468791.73290.0898080.044904
M7988.842930555555774.4242491.27690.2080550.104027
M8763.891944444445773.4224780.98770.3284780.164239
M9741.208958333335772.6424250.95930.3424140.171207
M10367.093972222224772.0847620.47550.6367110.318355
M1169.474986111113771.749970.090.928660.46433
t173.85698611111113.12583513.245400







Multiple Linear Regression - Regression Statistics
Multiple R0.893031005209968
R-squared0.797504376266327
Adjusted R-squared0.74027735216768
F-TEST (value)13.9358002417110
F-TEST (DF numerator)13
F-TEST (DF denominator)46
p-value7.49089679175086e-12
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1220.06734362336
Sum Squared Residuals68473958.8569033

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.893031005209968 \tabularnewline
R-squared & 0.797504376266327 \tabularnewline
Adjusted R-squared & 0.74027735216768 \tabularnewline
F-TEST (value) & 13.9358002417110 \tabularnewline
F-TEST (DF numerator) & 13 \tabularnewline
F-TEST (DF denominator) & 46 \tabularnewline
p-value & 7.49089679175086e-12 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 1220.06734362336 \tabularnewline
Sum Squared Residuals & 68473958.8569033 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58380&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.893031005209968[/C][/ROW]
[ROW][C]R-squared[/C][C]0.797504376266327[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.74027735216768[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]13.9358002417110[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]13[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]46[/C][/ROW]
[ROW][C]p-value[/C][C]7.49089679175086e-12[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]1220.06734362336[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]68473958.8569033[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58380&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58380&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 R0.893031005209968
R-squared0.797504376266327
Adjusted R-squared0.74027735216768
F-TEST (value)13.9358002417110
F-TEST (DF numerator)13
F-TEST (DF denominator)46
p-value7.49089679175086e-12
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1220.06734362336
Sum Squared Residuals68473958.8569033







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
16802.967574.693-771.733000000007
27132.687766.85099999999-634.17099999999
37073.297750.943-677.653000000002
47264.58021.509-757.009000000002
57105.337948.369-843.038999999994
67218.717659.375-440.664999999997
77225.727477.931-252.211000000003
87354.257426.837-72.587000000002
97745.467578.011167.448999999999
108070.267377.753692.507
118366.337253.9911112.33900000000
128667.517358.3731309.137
138854.349660.97683333333-806.63683333333
149218.19853.13483333334-635.034833333337
159332.99837.22683333333-504.326833333334
169358.3110107.7928333333-749.482833333333
179248.6610034.6528333333-785.992833333336
189401.29745.65883333333-344.458833333333
199652.049564.2148333333387.8251666666692
209957.389513.12083333333444.259166666666
2110110.639664.29483333333446.335166666666
2210169.269464.03683333333705.223166666666
2310343.789340.274833333331003.50516666667
2410750.219444.656833333331305.55316666667
2511337.511747.2606666667-409.760666666666
2611786.9611939.4186666667-152.458666666669
2712083.0411923.5106666667159.529333333334
2812007.7412194.0766666667-186.336666666666
2911745.9312120.9366666667-375.006666666669
3011051.5111831.9426666667-780.432666666666
3111445.911650.4986666667-204.598666666665
3211924.8811599.4046666667325.475333333333
3312247.6311750.5786666667497.051333333333
3412690.9111550.32066666671140.58933333333
3512910.711426.55866666671484.14133333333
3613202.1211530.94066666671671.17933333334
3713654.6713833.5445-178.874499999998
3813862.8214025.7025-162.882500000002
3913523.9314009.7945-485.864499999999
4014211.1714280.3605-69.1904999999997
4114510.3514207.2205303.129499999998
4214289.2313918.2265371.003499999998
4314111.8213736.7825375.037499999999
4413086.5913685.6885-599.098499999999
4513351.5413836.8625-485.322499999999
4613747.6913636.6045111.085500000001
4712855.6113512.8425-657.2325
4812926.9313617.2245-690.294499999998
4912121.959954.9452167.005
5011731.6510147.1031584.54700000000
5111639.5110131.1951508.315
5212163.7810401.7611762.019
5312029.5310328.6211700.90900000000
5411234.1810039.6271194.553
559852.139858.183-6.05299999999912
569709.049807.089-98.0489999999993
579332.759958.263-625.513
587108.69758.005-2649.405
596691.499634.243-2942.753
606143.059738.625-3595.575

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 6802.96 & 7574.693 & -771.733000000007 \tabularnewline
2 & 7132.68 & 7766.85099999999 & -634.17099999999 \tabularnewline
3 & 7073.29 & 7750.943 & -677.653000000002 \tabularnewline
4 & 7264.5 & 8021.509 & -757.009000000002 \tabularnewline
5 & 7105.33 & 7948.369 & -843.038999999994 \tabularnewline
6 & 7218.71 & 7659.375 & -440.664999999997 \tabularnewline
7 & 7225.72 & 7477.931 & -252.211000000003 \tabularnewline
8 & 7354.25 & 7426.837 & -72.587000000002 \tabularnewline
9 & 7745.46 & 7578.011 & 167.448999999999 \tabularnewline
10 & 8070.26 & 7377.753 & 692.507 \tabularnewline
11 & 8366.33 & 7253.991 & 1112.33900000000 \tabularnewline
12 & 8667.51 & 7358.373 & 1309.137 \tabularnewline
13 & 8854.34 & 9660.97683333333 & -806.63683333333 \tabularnewline
14 & 9218.1 & 9853.13483333334 & -635.034833333337 \tabularnewline
15 & 9332.9 & 9837.22683333333 & -504.326833333334 \tabularnewline
16 & 9358.31 & 10107.7928333333 & -749.482833333333 \tabularnewline
17 & 9248.66 & 10034.6528333333 & -785.992833333336 \tabularnewline
18 & 9401.2 & 9745.65883333333 & -344.458833333333 \tabularnewline
19 & 9652.04 & 9564.21483333333 & 87.8251666666692 \tabularnewline
20 & 9957.38 & 9513.12083333333 & 444.259166666666 \tabularnewline
21 & 10110.63 & 9664.29483333333 & 446.335166666666 \tabularnewline
22 & 10169.26 & 9464.03683333333 & 705.223166666666 \tabularnewline
23 & 10343.78 & 9340.27483333333 & 1003.50516666667 \tabularnewline
24 & 10750.21 & 9444.65683333333 & 1305.55316666667 \tabularnewline
25 & 11337.5 & 11747.2606666667 & -409.760666666666 \tabularnewline
26 & 11786.96 & 11939.4186666667 & -152.458666666669 \tabularnewline
27 & 12083.04 & 11923.5106666667 & 159.529333333334 \tabularnewline
28 & 12007.74 & 12194.0766666667 & -186.336666666666 \tabularnewline
29 & 11745.93 & 12120.9366666667 & -375.006666666669 \tabularnewline
30 & 11051.51 & 11831.9426666667 & -780.432666666666 \tabularnewline
31 & 11445.9 & 11650.4986666667 & -204.598666666665 \tabularnewline
32 & 11924.88 & 11599.4046666667 & 325.475333333333 \tabularnewline
33 & 12247.63 & 11750.5786666667 & 497.051333333333 \tabularnewline
34 & 12690.91 & 11550.3206666667 & 1140.58933333333 \tabularnewline
35 & 12910.7 & 11426.5586666667 & 1484.14133333333 \tabularnewline
36 & 13202.12 & 11530.9406666667 & 1671.17933333334 \tabularnewline
37 & 13654.67 & 13833.5445 & -178.874499999998 \tabularnewline
38 & 13862.82 & 14025.7025 & -162.882500000002 \tabularnewline
39 & 13523.93 & 14009.7945 & -485.864499999999 \tabularnewline
40 & 14211.17 & 14280.3605 & -69.1904999999997 \tabularnewline
41 & 14510.35 & 14207.2205 & 303.129499999998 \tabularnewline
42 & 14289.23 & 13918.2265 & 371.003499999998 \tabularnewline
43 & 14111.82 & 13736.7825 & 375.037499999999 \tabularnewline
44 & 13086.59 & 13685.6885 & -599.098499999999 \tabularnewline
45 & 13351.54 & 13836.8625 & -485.322499999999 \tabularnewline
46 & 13747.69 & 13636.6045 & 111.085500000001 \tabularnewline
47 & 12855.61 & 13512.8425 & -657.2325 \tabularnewline
48 & 12926.93 & 13617.2245 & -690.294499999998 \tabularnewline
49 & 12121.95 & 9954.945 & 2167.005 \tabularnewline
50 & 11731.65 & 10147.103 & 1584.54700000000 \tabularnewline
51 & 11639.51 & 10131.195 & 1508.315 \tabularnewline
52 & 12163.78 & 10401.761 & 1762.019 \tabularnewline
53 & 12029.53 & 10328.621 & 1700.90900000000 \tabularnewline
54 & 11234.18 & 10039.627 & 1194.553 \tabularnewline
55 & 9852.13 & 9858.183 & -6.05299999999912 \tabularnewline
56 & 9709.04 & 9807.089 & -98.0489999999993 \tabularnewline
57 & 9332.75 & 9958.263 & -625.513 \tabularnewline
58 & 7108.6 & 9758.005 & -2649.405 \tabularnewline
59 & 6691.49 & 9634.243 & -2942.753 \tabularnewline
60 & 6143.05 & 9738.625 & -3595.575 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58380&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]6802.96[/C][C]7574.693[/C][C]-771.733000000007[/C][/ROW]
[ROW][C]2[/C][C]7132.68[/C][C]7766.85099999999[/C][C]-634.17099999999[/C][/ROW]
[ROW][C]3[/C][C]7073.29[/C][C]7750.943[/C][C]-677.653000000002[/C][/ROW]
[ROW][C]4[/C][C]7264.5[/C][C]8021.509[/C][C]-757.009000000002[/C][/ROW]
[ROW][C]5[/C][C]7105.33[/C][C]7948.369[/C][C]-843.038999999994[/C][/ROW]
[ROW][C]6[/C][C]7218.71[/C][C]7659.375[/C][C]-440.664999999997[/C][/ROW]
[ROW][C]7[/C][C]7225.72[/C][C]7477.931[/C][C]-252.211000000003[/C][/ROW]
[ROW][C]8[/C][C]7354.25[/C][C]7426.837[/C][C]-72.587000000002[/C][/ROW]
[ROW][C]9[/C][C]7745.46[/C][C]7578.011[/C][C]167.448999999999[/C][/ROW]
[ROW][C]10[/C][C]8070.26[/C][C]7377.753[/C][C]692.507[/C][/ROW]
[ROW][C]11[/C][C]8366.33[/C][C]7253.991[/C][C]1112.33900000000[/C][/ROW]
[ROW][C]12[/C][C]8667.51[/C][C]7358.373[/C][C]1309.137[/C][/ROW]
[ROW][C]13[/C][C]8854.34[/C][C]9660.97683333333[/C][C]-806.63683333333[/C][/ROW]
[ROW][C]14[/C][C]9218.1[/C][C]9853.13483333334[/C][C]-635.034833333337[/C][/ROW]
[ROW][C]15[/C][C]9332.9[/C][C]9837.22683333333[/C][C]-504.326833333334[/C][/ROW]
[ROW][C]16[/C][C]9358.31[/C][C]10107.7928333333[/C][C]-749.482833333333[/C][/ROW]
[ROW][C]17[/C][C]9248.66[/C][C]10034.6528333333[/C][C]-785.992833333336[/C][/ROW]
[ROW][C]18[/C][C]9401.2[/C][C]9745.65883333333[/C][C]-344.458833333333[/C][/ROW]
[ROW][C]19[/C][C]9652.04[/C][C]9564.21483333333[/C][C]87.8251666666692[/C][/ROW]
[ROW][C]20[/C][C]9957.38[/C][C]9513.12083333333[/C][C]444.259166666666[/C][/ROW]
[ROW][C]21[/C][C]10110.63[/C][C]9664.29483333333[/C][C]446.335166666666[/C][/ROW]
[ROW][C]22[/C][C]10169.26[/C][C]9464.03683333333[/C][C]705.223166666666[/C][/ROW]
[ROW][C]23[/C][C]10343.78[/C][C]9340.27483333333[/C][C]1003.50516666667[/C][/ROW]
[ROW][C]24[/C][C]10750.21[/C][C]9444.65683333333[/C][C]1305.55316666667[/C][/ROW]
[ROW][C]25[/C][C]11337.5[/C][C]11747.2606666667[/C][C]-409.760666666666[/C][/ROW]
[ROW][C]26[/C][C]11786.96[/C][C]11939.4186666667[/C][C]-152.458666666669[/C][/ROW]
[ROW][C]27[/C][C]12083.04[/C][C]11923.5106666667[/C][C]159.529333333334[/C][/ROW]
[ROW][C]28[/C][C]12007.74[/C][C]12194.0766666667[/C][C]-186.336666666666[/C][/ROW]
[ROW][C]29[/C][C]11745.93[/C][C]12120.9366666667[/C][C]-375.006666666669[/C][/ROW]
[ROW][C]30[/C][C]11051.51[/C][C]11831.9426666667[/C][C]-780.432666666666[/C][/ROW]
[ROW][C]31[/C][C]11445.9[/C][C]11650.4986666667[/C][C]-204.598666666665[/C][/ROW]
[ROW][C]32[/C][C]11924.88[/C][C]11599.4046666667[/C][C]325.475333333333[/C][/ROW]
[ROW][C]33[/C][C]12247.63[/C][C]11750.5786666667[/C][C]497.051333333333[/C][/ROW]
[ROW][C]34[/C][C]12690.91[/C][C]11550.3206666667[/C][C]1140.58933333333[/C][/ROW]
[ROW][C]35[/C][C]12910.7[/C][C]11426.5586666667[/C][C]1484.14133333333[/C][/ROW]
[ROW][C]36[/C][C]13202.12[/C][C]11530.9406666667[/C][C]1671.17933333334[/C][/ROW]
[ROW][C]37[/C][C]13654.67[/C][C]13833.5445[/C][C]-178.874499999998[/C][/ROW]
[ROW][C]38[/C][C]13862.82[/C][C]14025.7025[/C][C]-162.882500000002[/C][/ROW]
[ROW][C]39[/C][C]13523.93[/C][C]14009.7945[/C][C]-485.864499999999[/C][/ROW]
[ROW][C]40[/C][C]14211.17[/C][C]14280.3605[/C][C]-69.1904999999997[/C][/ROW]
[ROW][C]41[/C][C]14510.35[/C][C]14207.2205[/C][C]303.129499999998[/C][/ROW]
[ROW][C]42[/C][C]14289.23[/C][C]13918.2265[/C][C]371.003499999998[/C][/ROW]
[ROW][C]43[/C][C]14111.82[/C][C]13736.7825[/C][C]375.037499999999[/C][/ROW]
[ROW][C]44[/C][C]13086.59[/C][C]13685.6885[/C][C]-599.098499999999[/C][/ROW]
[ROW][C]45[/C][C]13351.54[/C][C]13836.8625[/C][C]-485.322499999999[/C][/ROW]
[ROW][C]46[/C][C]13747.69[/C][C]13636.6045[/C][C]111.085500000001[/C][/ROW]
[ROW][C]47[/C][C]12855.61[/C][C]13512.8425[/C][C]-657.2325[/C][/ROW]
[ROW][C]48[/C][C]12926.93[/C][C]13617.2245[/C][C]-690.294499999998[/C][/ROW]
[ROW][C]49[/C][C]12121.95[/C][C]9954.945[/C][C]2167.005[/C][/ROW]
[ROW][C]50[/C][C]11731.65[/C][C]10147.103[/C][C]1584.54700000000[/C][/ROW]
[ROW][C]51[/C][C]11639.51[/C][C]10131.195[/C][C]1508.315[/C][/ROW]
[ROW][C]52[/C][C]12163.78[/C][C]10401.761[/C][C]1762.019[/C][/ROW]
[ROW][C]53[/C][C]12029.53[/C][C]10328.621[/C][C]1700.90900000000[/C][/ROW]
[ROW][C]54[/C][C]11234.18[/C][C]10039.627[/C][C]1194.553[/C][/ROW]
[ROW][C]55[/C][C]9852.13[/C][C]9858.183[/C][C]-6.05299999999912[/C][/ROW]
[ROW][C]56[/C][C]9709.04[/C][C]9807.089[/C][C]-98.0489999999993[/C][/ROW]
[ROW][C]57[/C][C]9332.75[/C][C]9958.263[/C][C]-625.513[/C][/ROW]
[ROW][C]58[/C][C]7108.6[/C][C]9758.005[/C][C]-2649.405[/C][/ROW]
[ROW][C]59[/C][C]6691.49[/C][C]9634.243[/C][C]-2942.753[/C][/ROW]
[ROW][C]60[/C][C]6143.05[/C][C]9738.625[/C][C]-3595.575[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58380&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58380&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
16802.967574.693-771.733000000007
27132.687766.85099999999-634.17099999999
37073.297750.943-677.653000000002
47264.58021.509-757.009000000002
57105.337948.369-843.038999999994
67218.717659.375-440.664999999997
77225.727477.931-252.211000000003
87354.257426.837-72.587000000002
97745.467578.011167.448999999999
108070.267377.753692.507
118366.337253.9911112.33900000000
128667.517358.3731309.137
138854.349660.97683333333-806.63683333333
149218.19853.13483333334-635.034833333337
159332.99837.22683333333-504.326833333334
169358.3110107.7928333333-749.482833333333
179248.6610034.6528333333-785.992833333336
189401.29745.65883333333-344.458833333333
199652.049564.2148333333387.8251666666692
209957.389513.12083333333444.259166666666
2110110.639664.29483333333446.335166666666
2210169.269464.03683333333705.223166666666
2310343.789340.274833333331003.50516666667
2410750.219444.656833333331305.55316666667
2511337.511747.2606666667-409.760666666666
2611786.9611939.4186666667-152.458666666669
2712083.0411923.5106666667159.529333333334
2812007.7412194.0766666667-186.336666666666
2911745.9312120.9366666667-375.006666666669
3011051.5111831.9426666667-780.432666666666
3111445.911650.4986666667-204.598666666665
3211924.8811599.4046666667325.475333333333
3312247.6311750.5786666667497.051333333333
3412690.9111550.32066666671140.58933333333
3512910.711426.55866666671484.14133333333
3613202.1211530.94066666671671.17933333334
3713654.6713833.5445-178.874499999998
3813862.8214025.7025-162.882500000002
3913523.9314009.7945-485.864499999999
4014211.1714280.3605-69.1904999999997
4114510.3514207.2205303.129499999998
4214289.2313918.2265371.003499999998
4314111.8213736.7825375.037499999999
4413086.5913685.6885-599.098499999999
4513351.5413836.8625-485.322499999999
4613747.6913636.6045111.085500000001
4712855.6113512.8425-657.2325
4812926.9313617.2245-690.294499999998
4912121.959954.9452167.005
5011731.6510147.1031584.54700000000
5111639.5110131.1951508.315
5212163.7810401.7611762.019
5312029.5310328.6211700.90900000000
5411234.1810039.6271194.553
559852.139858.183-6.05299999999912
569709.049807.089-98.0489999999993
579332.759958.263-625.513
587108.69758.005-2649.405
596691.499634.243-2942.753
606143.059738.625-3595.575







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
170.0001797657725876340.0003595315451752670.999820234227412
189.20222302826022e-061.84044460565204e-050.999990797776972
198.10799139000903e-061.62159827800181e-050.99999189200861
208.69442663901354e-061.73888532780271e-050.99999130557336
211.01545632111591e-062.03091264223182e-060.999998984543679
221.18315219048744e-072.36630438097488e-070.99999988168478
232.31893599842789e-084.63787199685578e-080.99999997681064
242.82074966342616e-095.64149932685231e-090.99999999717925
256.07586489954362e-101.21517297990872e-090.999999999392414
261.84531909274842e-103.69063818549684e-100.999999999815468
273.21556725641831e-106.43113451283662e-100.999999999678443
289.07529960694118e-111.81505992138824e-100.999999999909247
292.40582355739146e-114.81164711478292e-110.999999999975942
301.15014333894896e-092.30028667789793e-090.999999998849857
311.51049312705473e-093.02098625410946e-090.999999998489507
325.40876349878247e-101.08175269975649e-090.999999999459124
332.17096364193548e-104.34192728387097e-100.999999999782904
345.72486400880437e-111.14497280176087e-100.999999999942751
351.08675399480315e-112.17350798960630e-110.999999999989132
361.62566723601535e-123.2513344720307e-120.999999999998374
371.04386890194342e-122.08773780388684e-120.999999999998956
384.43512258823131e-138.87024517646263e-130.999999999999556
393.46982349626026e-126.93964699252052e-120.99999999999653
401.07893202078003e-112.15786404156006e-110.99999999998921
415.43990873048885e-101.08798174609777e-090.99999999945601
429.44124944719725e-091.88824988943945e-080.99999999055875
434.66544290133591e-099.33088580267181e-090.999999995334557

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
17 & 0.000179765772587634 & 0.000359531545175267 & 0.999820234227412 \tabularnewline
18 & 9.20222302826022e-06 & 1.84044460565204e-05 & 0.999990797776972 \tabularnewline
19 & 8.10799139000903e-06 & 1.62159827800181e-05 & 0.99999189200861 \tabularnewline
20 & 8.69442663901354e-06 & 1.73888532780271e-05 & 0.99999130557336 \tabularnewline
21 & 1.01545632111591e-06 & 2.03091264223182e-06 & 0.999998984543679 \tabularnewline
22 & 1.18315219048744e-07 & 2.36630438097488e-07 & 0.99999988168478 \tabularnewline
23 & 2.31893599842789e-08 & 4.63787199685578e-08 & 0.99999997681064 \tabularnewline
24 & 2.82074966342616e-09 & 5.64149932685231e-09 & 0.99999999717925 \tabularnewline
25 & 6.07586489954362e-10 & 1.21517297990872e-09 & 0.999999999392414 \tabularnewline
26 & 1.84531909274842e-10 & 3.69063818549684e-10 & 0.999999999815468 \tabularnewline
27 & 3.21556725641831e-10 & 6.43113451283662e-10 & 0.999999999678443 \tabularnewline
28 & 9.07529960694118e-11 & 1.81505992138824e-10 & 0.999999999909247 \tabularnewline
29 & 2.40582355739146e-11 & 4.81164711478292e-11 & 0.999999999975942 \tabularnewline
30 & 1.15014333894896e-09 & 2.30028667789793e-09 & 0.999999998849857 \tabularnewline
31 & 1.51049312705473e-09 & 3.02098625410946e-09 & 0.999999998489507 \tabularnewline
32 & 5.40876349878247e-10 & 1.08175269975649e-09 & 0.999999999459124 \tabularnewline
33 & 2.17096364193548e-10 & 4.34192728387097e-10 & 0.999999999782904 \tabularnewline
34 & 5.72486400880437e-11 & 1.14497280176087e-10 & 0.999999999942751 \tabularnewline
35 & 1.08675399480315e-11 & 2.17350798960630e-11 & 0.999999999989132 \tabularnewline
36 & 1.62566723601535e-12 & 3.2513344720307e-12 & 0.999999999998374 \tabularnewline
37 & 1.04386890194342e-12 & 2.08773780388684e-12 & 0.999999999998956 \tabularnewline
38 & 4.43512258823131e-13 & 8.87024517646263e-13 & 0.999999999999556 \tabularnewline
39 & 3.46982349626026e-12 & 6.93964699252052e-12 & 0.99999999999653 \tabularnewline
40 & 1.07893202078003e-11 & 2.15786404156006e-11 & 0.99999999998921 \tabularnewline
41 & 5.43990873048885e-10 & 1.08798174609777e-09 & 0.99999999945601 \tabularnewline
42 & 9.44124944719725e-09 & 1.88824988943945e-08 & 0.99999999055875 \tabularnewline
43 & 4.66544290133591e-09 & 9.33088580267181e-09 & 0.999999995334557 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58380&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]17[/C][C]0.000179765772587634[/C][C]0.000359531545175267[/C][C]0.999820234227412[/C][/ROW]
[ROW][C]18[/C][C]9.20222302826022e-06[/C][C]1.84044460565204e-05[/C][C]0.999990797776972[/C][/ROW]
[ROW][C]19[/C][C]8.10799139000903e-06[/C][C]1.62159827800181e-05[/C][C]0.99999189200861[/C][/ROW]
[ROW][C]20[/C][C]8.69442663901354e-06[/C][C]1.73888532780271e-05[/C][C]0.99999130557336[/C][/ROW]
[ROW][C]21[/C][C]1.01545632111591e-06[/C][C]2.03091264223182e-06[/C][C]0.999998984543679[/C][/ROW]
[ROW][C]22[/C][C]1.18315219048744e-07[/C][C]2.36630438097488e-07[/C][C]0.99999988168478[/C][/ROW]
[ROW][C]23[/C][C]2.31893599842789e-08[/C][C]4.63787199685578e-08[/C][C]0.99999997681064[/C][/ROW]
[ROW][C]24[/C][C]2.82074966342616e-09[/C][C]5.64149932685231e-09[/C][C]0.99999999717925[/C][/ROW]
[ROW][C]25[/C][C]6.07586489954362e-10[/C][C]1.21517297990872e-09[/C][C]0.999999999392414[/C][/ROW]
[ROW][C]26[/C][C]1.84531909274842e-10[/C][C]3.69063818549684e-10[/C][C]0.999999999815468[/C][/ROW]
[ROW][C]27[/C][C]3.21556725641831e-10[/C][C]6.43113451283662e-10[/C][C]0.999999999678443[/C][/ROW]
[ROW][C]28[/C][C]9.07529960694118e-11[/C][C]1.81505992138824e-10[/C][C]0.999999999909247[/C][/ROW]
[ROW][C]29[/C][C]2.40582355739146e-11[/C][C]4.81164711478292e-11[/C][C]0.999999999975942[/C][/ROW]
[ROW][C]30[/C][C]1.15014333894896e-09[/C][C]2.30028667789793e-09[/C][C]0.999999998849857[/C][/ROW]
[ROW][C]31[/C][C]1.51049312705473e-09[/C][C]3.02098625410946e-09[/C][C]0.999999998489507[/C][/ROW]
[ROW][C]32[/C][C]5.40876349878247e-10[/C][C]1.08175269975649e-09[/C][C]0.999999999459124[/C][/ROW]
[ROW][C]33[/C][C]2.17096364193548e-10[/C][C]4.34192728387097e-10[/C][C]0.999999999782904[/C][/ROW]
[ROW][C]34[/C][C]5.72486400880437e-11[/C][C]1.14497280176087e-10[/C][C]0.999999999942751[/C][/ROW]
[ROW][C]35[/C][C]1.08675399480315e-11[/C][C]2.17350798960630e-11[/C][C]0.999999999989132[/C][/ROW]
[ROW][C]36[/C][C]1.62566723601535e-12[/C][C]3.2513344720307e-12[/C][C]0.999999999998374[/C][/ROW]
[ROW][C]37[/C][C]1.04386890194342e-12[/C][C]2.08773780388684e-12[/C][C]0.999999999998956[/C][/ROW]
[ROW][C]38[/C][C]4.43512258823131e-13[/C][C]8.87024517646263e-13[/C][C]0.999999999999556[/C][/ROW]
[ROW][C]39[/C][C]3.46982349626026e-12[/C][C]6.93964699252052e-12[/C][C]0.99999999999653[/C][/ROW]
[ROW][C]40[/C][C]1.07893202078003e-11[/C][C]2.15786404156006e-11[/C][C]0.99999999998921[/C][/ROW]
[ROW][C]41[/C][C]5.43990873048885e-10[/C][C]1.08798174609777e-09[/C][C]0.99999999945601[/C][/ROW]
[ROW][C]42[/C][C]9.44124944719725e-09[/C][C]1.88824988943945e-08[/C][C]0.99999999055875[/C][/ROW]
[ROW][C]43[/C][C]4.66544290133591e-09[/C][C]9.33088580267181e-09[/C][C]0.999999995334557[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58380&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58380&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
170.0001797657725876340.0003595315451752670.999820234227412
189.20222302826022e-061.84044460565204e-050.999990797776972
198.10799139000903e-061.62159827800181e-050.99999189200861
208.69442663901354e-061.73888532780271e-050.99999130557336
211.01545632111591e-062.03091264223182e-060.999998984543679
221.18315219048744e-072.36630438097488e-070.99999988168478
232.31893599842789e-084.63787199685578e-080.99999997681064
242.82074966342616e-095.64149932685231e-090.99999999717925
256.07586489954362e-101.21517297990872e-090.999999999392414
261.84531909274842e-103.69063818549684e-100.999999999815468
273.21556725641831e-106.43113451283662e-100.999999999678443
289.07529960694118e-111.81505992138824e-100.999999999909247
292.40582355739146e-114.81164711478292e-110.999999999975942
301.15014333894896e-092.30028667789793e-090.999999998849857
311.51049312705473e-093.02098625410946e-090.999999998489507
325.40876349878247e-101.08175269975649e-090.999999999459124
332.17096364193548e-104.34192728387097e-100.999999999782904
345.72486400880437e-111.14497280176087e-100.999999999942751
351.08675399480315e-112.17350798960630e-110.999999999989132
361.62566723601535e-123.2513344720307e-120.999999999998374
371.04386890194342e-122.08773780388684e-120.999999999998956
384.43512258823131e-138.87024517646263e-130.999999999999556
393.46982349626026e-126.93964699252052e-120.99999999999653
401.07893202078003e-112.15786404156006e-110.99999999998921
415.43990873048885e-101.08798174609777e-090.99999999945601
429.44124944719725e-091.88824988943945e-080.99999999055875
434.66544290133591e-099.33088580267181e-090.999999995334557







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level271NOK
5% type I error level271NOK
10% type I error level271NOK

\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 & 27 & 1 & NOK \tabularnewline
5% type I error level & 27 & 1 & NOK \tabularnewline
10% type I error level & 27 & 1 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58380&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]27[/C][C]1[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]27[/C][C]1[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]27[/C][C]1[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58380&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58380&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 level271NOK
5% type I error level271NOK
10% type I error level271NOK



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')
}