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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, 26 Nov 2010 10:30:30 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Nov/26/t1290767534twkxz33q4adt57a.htm/, Retrieved Thu, 28 Mar 2024 16:29:34 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=101741, Retrieved Thu, 28 Mar 2024 16:29:34 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact161
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Univariate Explorative Data Analysis] [time effect in su...] [2010-11-17 08:55:33] [b98453cac15ba1066b407e146608df68]
-    D  [Univariate Explorative Data Analysis] [Run sequence plot...] [2010-11-19 11:56:19] [2960375a246cc0628590c95c4038a43c]
- RMPD      [Multiple Regression] [Meervoudig regres...] [2010-11-26 10:30:30] [4b5105369ca2b03f8f7589f5d63124c0] [Current]
-    D        [Multiple Regression] [Regressiemodel 1] [2010-11-27 09:03:12] [62f7c80c4d96454bbd2b2b026ea9aad9]
-    D        [Multiple Regression] [Meervoudig regres...] [2010-11-30 12:23:01] [d7a673bc47e3999e70f4e1e2276e5189]
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Dataseries X:
16198,9	16896,2	0
16554,2	16698	0
19554,2	19691,6	0
15903,8	15930,7	0
18003,8	17444,6	0
18329,6	17699,4	0
16260,7	15189,8	0
14851,9	15672,7	0
18174,1	17180,8	0
18406,6	17664,9	0
18466,5	17862,9	0
16016,5	16162,3	0
17428,5	17463,6	0
17167,2	16772,1	0
19630	19106,9	0
17183,6	16721,3	0
18344,7	18161,3	0
19301,4	18509,9	0
18147,5	17802,7	0
16192,9	16409,9	0
18374,4	17967,7	0
20515,2	20286,6	0
18957,2	19537,3	0
16471,5	18021,9	0
18746,8	20194,3	0
19009,5	19049,6	0
19211,2	20244,7	0
20547,7	21473,3	0
19325,8	19673,6	0
20605,5	21053,2	0
20056,9	20159,5	0
16141,4	18203,6	0
20359,8	21289,5	0
19711,6	20432,3	1
15638,6	17180,4	1
14384,5	15816,8	1
13855,6	15071,8	1
14308,3	14521,1	1
15290,6	15668,8	1
14423,8	14346,9	1
13779,7	13881	1
15686,3	15465,9	1
14733,8	14238,2	1
12522,5	13557,7	1
16189,4	16127,6	1
16059,1	16793,9	1
16007,1	16014	1
15806,8	16867,9	1
15160	16014,6	0
15692,1	15878,6	0
18908,9	18664,9	0
16969,9	17962,5	0
16997,5	17332,7	0
19858,9	19542,1	0
17681,2	17203,6	0




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 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 & 6 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=101741&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]6 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=101741&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=101741&T=0

As an alternative you can also use a QR Code:  

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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







Multiple Linear Regression - Estimated Regression Equation
uitvoer[t] = + 3885.44637298981 + 0.734871978147499invoer[t] -1001.28315575325crisis[t] + 5.8095292526781M1[t] + 674.041557200105M2[t] + 1109.77681490461M3[t] + 616.882464243725M4[t] + 892.8244620994M5[t] + 1509.74928622909M6[t] + 1257.70762915807M7[t] -437.223855464266M8[t] + 1307.69291158348M9[t] + 1476.82392699202M10[t] + 913.046867753967M11[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
uitvoer[t] =  +  3885.44637298981 +  0.734871978147499invoer[t] -1001.28315575325crisis[t] +  5.8095292526781M1[t] +  674.041557200105M2[t] +  1109.77681490461M3[t] +  616.882464243725M4[t] +  892.8244620994M5[t] +  1509.74928622909M6[t] +  1257.70762915807M7[t] -437.223855464266M8[t] +  1307.69291158348M9[t] +  1476.82392699202M10[t] +  913.046867753967M11[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=101741&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]uitvoer[t] =  +  3885.44637298981 +  0.734871978147499invoer[t] -1001.28315575325crisis[t] +  5.8095292526781M1[t] +  674.041557200105M2[t] +  1109.77681490461M3[t] +  616.882464243725M4[t] +  892.8244620994M5[t] +  1509.74928622909M6[t] +  1257.70762915807M7[t] -437.223855464266M8[t] +  1307.69291158348M9[t] +  1476.82392699202M10[t] +  913.046867753967M11[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=101741&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=101741&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
uitvoer[t] = + 3885.44637298981 + 0.734871978147499invoer[t] -1001.28315575325crisis[t] + 5.8095292526781M1[t] + 674.041557200105M2[t] + 1109.77681490461M3[t] + 616.882464243725M4[t] + 892.8244620994M5[t] + 1509.74928622909M6[t] + 1257.70762915807M7[t] -437.223855464266M8[t] + 1307.69291158348M9[t] + 1476.82392699202M10[t] + 913.046867753967M11[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)3885.44637298981829.8350864.68223.1e-051.5e-05
invoer0.7348719781474990.04418316.632500
crisis-1001.28315575325181.284847-5.52332e-061e-06
M15.8095292526781298.2983040.01950.9845560.492278
M2674.041557200105300.6284282.24210.0304270.015213
M31109.77681490461302.2108933.67220.0006880.000344
M4616.882464243725297.9807992.07020.0447710.022385
M5892.8244620994297.9639352.99640.0046210.00231
M61509.74928622909300.7035495.02071e-055e-06
M71257.70762915807298.9679744.20680.0001376.9e-05
M8-437.223855464266319.001819-1.37060.1779570.088979
M91307.69291158348314.5810724.15690.000168e-05
M101476.82392699202324.1251624.55634.6e-052.3e-05
M11913.046867753967313.5725792.91180.005790.002895

\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) & 3885.44637298981 & 829.835086 & 4.6822 & 3.1e-05 & 1.5e-05 \tabularnewline
invoer & 0.734871978147499 & 0.044183 & 16.6325 & 0 & 0 \tabularnewline
crisis & -1001.28315575325 & 181.284847 & -5.5233 & 2e-06 & 1e-06 \tabularnewline
M1 & 5.8095292526781 & 298.298304 & 0.0195 & 0.984556 & 0.492278 \tabularnewline
M2 & 674.041557200105 & 300.628428 & 2.2421 & 0.030427 & 0.015213 \tabularnewline
M3 & 1109.77681490461 & 302.210893 & 3.6722 & 0.000688 & 0.000344 \tabularnewline
M4 & 616.882464243725 & 297.980799 & 2.0702 & 0.044771 & 0.022385 \tabularnewline
M5 & 892.8244620994 & 297.963935 & 2.9964 & 0.004621 & 0.00231 \tabularnewline
M6 & 1509.74928622909 & 300.703549 & 5.0207 & 1e-05 & 5e-06 \tabularnewline
M7 & 1257.70762915807 & 298.967974 & 4.2068 & 0.000137 & 6.9e-05 \tabularnewline
M8 & -437.223855464266 & 319.001819 & -1.3706 & 0.177957 & 0.088979 \tabularnewline
M9 & 1307.69291158348 & 314.581072 & 4.1569 & 0.00016 & 8e-05 \tabularnewline
M10 & 1476.82392699202 & 324.125162 & 4.5563 & 4.6e-05 & 2.3e-05 \tabularnewline
M11 & 913.046867753967 & 313.572579 & 2.9118 & 0.00579 & 0.002895 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=101741&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]3885.44637298981[/C][C]829.835086[/C][C]4.6822[/C][C]3.1e-05[/C][C]1.5e-05[/C][/ROW]
[ROW][C]invoer[/C][C]0.734871978147499[/C][C]0.044183[/C][C]16.6325[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]crisis[/C][C]-1001.28315575325[/C][C]181.284847[/C][C]-5.5233[/C][C]2e-06[/C][C]1e-06[/C][/ROW]
[ROW][C]M1[/C][C]5.8095292526781[/C][C]298.298304[/C][C]0.0195[/C][C]0.984556[/C][C]0.492278[/C][/ROW]
[ROW][C]M2[/C][C]674.041557200105[/C][C]300.628428[/C][C]2.2421[/C][C]0.030427[/C][C]0.015213[/C][/ROW]
[ROW][C]M3[/C][C]1109.77681490461[/C][C]302.210893[/C][C]3.6722[/C][C]0.000688[/C][C]0.000344[/C][/ROW]
[ROW][C]M4[/C][C]616.882464243725[/C][C]297.980799[/C][C]2.0702[/C][C]0.044771[/C][C]0.022385[/C][/ROW]
[ROW][C]M5[/C][C]892.8244620994[/C][C]297.963935[/C][C]2.9964[/C][C]0.004621[/C][C]0.00231[/C][/ROW]
[ROW][C]M6[/C][C]1509.74928622909[/C][C]300.703549[/C][C]5.0207[/C][C]1e-05[/C][C]5e-06[/C][/ROW]
[ROW][C]M7[/C][C]1257.70762915807[/C][C]298.967974[/C][C]4.2068[/C][C]0.000137[/C][C]6.9e-05[/C][/ROW]
[ROW][C]M8[/C][C]-437.223855464266[/C][C]319.001819[/C][C]-1.3706[/C][C]0.177957[/C][C]0.088979[/C][/ROW]
[ROW][C]M9[/C][C]1307.69291158348[/C][C]314.581072[/C][C]4.1569[/C][C]0.00016[/C][C]8e-05[/C][/ROW]
[ROW][C]M10[/C][C]1476.82392699202[/C][C]324.125162[/C][C]4.5563[/C][C]4.6e-05[/C][C]2.3e-05[/C][/ROW]
[ROW][C]M11[/C][C]913.046867753967[/C][C]313.572579[/C][C]2.9118[/C][C]0.00579[/C][C]0.002895[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=101741&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=101741&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)3885.44637298981829.8350864.68223.1e-051.5e-05
invoer0.7348719781474990.04418316.632500
crisis-1001.28315575325181.284847-5.52332e-061e-06
M15.8095292526781298.2983040.01950.9845560.492278
M2674.041557200105300.6284282.24210.0304270.015213
M31109.77681490461302.2108933.67220.0006880.000344
M4616.882464243725297.9807992.07020.0447710.022385
M5892.8244620994297.9639352.99640.0046210.00231
M61509.74928622909300.7035495.02071e-055e-06
M71257.70762915807298.9679744.20680.0001376.9e-05
M8-437.223855464266319.001819-1.37060.1779570.088979
M91307.69291158348314.5810724.15690.000168e-05
M101476.82392699202324.1251624.55634.6e-052.3e-05
M11913.046867753967313.5725792.91180.005790.002895







Multiple Linear Regression - Regression Statistics
Multiple R0.981837209466541
R-squared0.964004305893045
Adjusted R-squared0.952591037029864
F-TEST (value)84.4634711973627
F-TEST (DF numerator)13
F-TEST (DF denominator)41
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation439.623013186437
Sum Squared Residuals7924004.14264803

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.981837209466541 \tabularnewline
R-squared & 0.964004305893045 \tabularnewline
Adjusted R-squared & 0.952591037029864 \tabularnewline
F-TEST (value) & 84.4634711973627 \tabularnewline
F-TEST (DF numerator) & 13 \tabularnewline
F-TEST (DF denominator) & 41 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 439.623013186437 \tabularnewline
Sum Squared Residuals & 7924004.14264803 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=101741&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.981837209466541[/C][/ROW]
[ROW][C]R-squared[/C][C]0.964004305893045[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.952591037029864[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]84.4634711973627[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]13[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]41[/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]439.623013186437[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]7924004.14264803[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=101741&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=101741&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 R0.981837209466541
R-squared0.964004305893045
Adjusted R-squared0.952591037029864
F-TEST (value)84.4634711973627
F-TEST (DF numerator)13
F-TEST (DF denominator)41
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation439.623013186437
Sum Squared Residuals7924004.14264803







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
116198.916307.7998194182-108.899819418227
216554.216830.3802212968-276.180221296846
319554.219466.028232783788.1717672163006
415903.816209.3538595079-305.553859507887
518003.817597.8185450811405.981454918941
618329.618401.9887492427-72.3887492427344
716260.716305.7123758128-45.0123758127494
814851.914965.6505694378-113.750569437842
918174.117818.8277667298355.272233270175
1018406.618343.710306759662.8896932404267
1118466.517925.4378991947541.062100805275
1216016.515762.6677454031253.832254596878
1317428.516724.7661798191703.733820180863
1417167.216884.8342348776282.365765122431
151963019036.3485871609593.651412839141
1617183.616790.3436454313393.256354568701
1718344.718124.5012918194220.198708180631
1819301.418997.6024875313303.797512468721
1918147.518225.8593675143-78.3593675143503
2016192.915507.3981917282685.501808271822
2118374.418397.0985263341-22.6985263340898
2220515.220270.3241718689244.875828131136
2318957.219155.9075394049-198.707539404895
2416471.517129.2356759662-657.735675966212
2518746.818731.481090546515.3189094534857
2619009.518558.5051651085450.994834891502
2719211.219872.4859238971-661.285923897081
2820547.720282.4552855882265.244714411791
2919325.819235.848184371889.951815628168
3020605.520866.6023895538-261.102389553813
3120056.919957.805645612499.0943543876273
3216141.416825.5380589313-684.138058931344
3320359.820838.1962633445-478.396263344452
3419711.619376.1118633317335.48813666829
3515638.616422.6046183558-784.004618355811
3614384.514507.4863211999-122.986321199915
3713855.613965.8162267327-110.216226732706
3814308.314229.354256314378.9457436856934
3915290.615508.5020833387-217.902083338697
4014423.814044.1804647646379.619535235368
4113779.713977.7456080014-198.045608001386
4215686.315759.3690302970-73.0690302970484
4314733.814605.1250456543128.674954345654
4412522.512410.1131799026112.386820097364
4516189.416043.5774435916145.822556408367
4616059.116702.3536580399-643.253658039852
4716007.115565.4499430446441.650056955432
4815806.815279.9102574308526.889742569247
491516015659.9366834834-499.936683483416
5015692.116228.2261224028-536.126122402781
5118908.918711.5351728197197.364827180336
5216969.917702.4667447080-732.566744707972
5316997.517515.5863707264-518.086370726354
5419858.919756.1373433751102.762656624875
5517681.217785.5975654062-104.397565406182

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 16198.9 & 16307.7998194182 & -108.899819418227 \tabularnewline
2 & 16554.2 & 16830.3802212968 & -276.180221296846 \tabularnewline
3 & 19554.2 & 19466.0282327837 & 88.1717672163006 \tabularnewline
4 & 15903.8 & 16209.3538595079 & -305.553859507887 \tabularnewline
5 & 18003.8 & 17597.8185450811 & 405.981454918941 \tabularnewline
6 & 18329.6 & 18401.9887492427 & -72.3887492427344 \tabularnewline
7 & 16260.7 & 16305.7123758128 & -45.0123758127494 \tabularnewline
8 & 14851.9 & 14965.6505694378 & -113.750569437842 \tabularnewline
9 & 18174.1 & 17818.8277667298 & 355.272233270175 \tabularnewline
10 & 18406.6 & 18343.7103067596 & 62.8896932404267 \tabularnewline
11 & 18466.5 & 17925.4378991947 & 541.062100805275 \tabularnewline
12 & 16016.5 & 15762.6677454031 & 253.832254596878 \tabularnewline
13 & 17428.5 & 16724.7661798191 & 703.733820180863 \tabularnewline
14 & 17167.2 & 16884.8342348776 & 282.365765122431 \tabularnewline
15 & 19630 & 19036.3485871609 & 593.651412839141 \tabularnewline
16 & 17183.6 & 16790.3436454313 & 393.256354568701 \tabularnewline
17 & 18344.7 & 18124.5012918194 & 220.198708180631 \tabularnewline
18 & 19301.4 & 18997.6024875313 & 303.797512468721 \tabularnewline
19 & 18147.5 & 18225.8593675143 & -78.3593675143503 \tabularnewline
20 & 16192.9 & 15507.3981917282 & 685.501808271822 \tabularnewline
21 & 18374.4 & 18397.0985263341 & -22.6985263340898 \tabularnewline
22 & 20515.2 & 20270.3241718689 & 244.875828131136 \tabularnewline
23 & 18957.2 & 19155.9075394049 & -198.707539404895 \tabularnewline
24 & 16471.5 & 17129.2356759662 & -657.735675966212 \tabularnewline
25 & 18746.8 & 18731.4810905465 & 15.3189094534857 \tabularnewline
26 & 19009.5 & 18558.5051651085 & 450.994834891502 \tabularnewline
27 & 19211.2 & 19872.4859238971 & -661.285923897081 \tabularnewline
28 & 20547.7 & 20282.4552855882 & 265.244714411791 \tabularnewline
29 & 19325.8 & 19235.8481843718 & 89.951815628168 \tabularnewline
30 & 20605.5 & 20866.6023895538 & -261.102389553813 \tabularnewline
31 & 20056.9 & 19957.8056456124 & 99.0943543876273 \tabularnewline
32 & 16141.4 & 16825.5380589313 & -684.138058931344 \tabularnewline
33 & 20359.8 & 20838.1962633445 & -478.396263344452 \tabularnewline
34 & 19711.6 & 19376.1118633317 & 335.48813666829 \tabularnewline
35 & 15638.6 & 16422.6046183558 & -784.004618355811 \tabularnewline
36 & 14384.5 & 14507.4863211999 & -122.986321199915 \tabularnewline
37 & 13855.6 & 13965.8162267327 & -110.216226732706 \tabularnewline
38 & 14308.3 & 14229.3542563143 & 78.9457436856934 \tabularnewline
39 & 15290.6 & 15508.5020833387 & -217.902083338697 \tabularnewline
40 & 14423.8 & 14044.1804647646 & 379.619535235368 \tabularnewline
41 & 13779.7 & 13977.7456080014 & -198.045608001386 \tabularnewline
42 & 15686.3 & 15759.3690302970 & -73.0690302970484 \tabularnewline
43 & 14733.8 & 14605.1250456543 & 128.674954345654 \tabularnewline
44 & 12522.5 & 12410.1131799026 & 112.386820097364 \tabularnewline
45 & 16189.4 & 16043.5774435916 & 145.822556408367 \tabularnewline
46 & 16059.1 & 16702.3536580399 & -643.253658039852 \tabularnewline
47 & 16007.1 & 15565.4499430446 & 441.650056955432 \tabularnewline
48 & 15806.8 & 15279.9102574308 & 526.889742569247 \tabularnewline
49 & 15160 & 15659.9366834834 & -499.936683483416 \tabularnewline
50 & 15692.1 & 16228.2261224028 & -536.126122402781 \tabularnewline
51 & 18908.9 & 18711.5351728197 & 197.364827180336 \tabularnewline
52 & 16969.9 & 17702.4667447080 & -732.566744707972 \tabularnewline
53 & 16997.5 & 17515.5863707264 & -518.086370726354 \tabularnewline
54 & 19858.9 & 19756.1373433751 & 102.762656624875 \tabularnewline
55 & 17681.2 & 17785.5975654062 & -104.397565406182 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=101741&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]16198.9[/C][C]16307.7998194182[/C][C]-108.899819418227[/C][/ROW]
[ROW][C]2[/C][C]16554.2[/C][C]16830.3802212968[/C][C]-276.180221296846[/C][/ROW]
[ROW][C]3[/C][C]19554.2[/C][C]19466.0282327837[/C][C]88.1717672163006[/C][/ROW]
[ROW][C]4[/C][C]15903.8[/C][C]16209.3538595079[/C][C]-305.553859507887[/C][/ROW]
[ROW][C]5[/C][C]18003.8[/C][C]17597.8185450811[/C][C]405.981454918941[/C][/ROW]
[ROW][C]6[/C][C]18329.6[/C][C]18401.9887492427[/C][C]-72.3887492427344[/C][/ROW]
[ROW][C]7[/C][C]16260.7[/C][C]16305.7123758128[/C][C]-45.0123758127494[/C][/ROW]
[ROW][C]8[/C][C]14851.9[/C][C]14965.6505694378[/C][C]-113.750569437842[/C][/ROW]
[ROW][C]9[/C][C]18174.1[/C][C]17818.8277667298[/C][C]355.272233270175[/C][/ROW]
[ROW][C]10[/C][C]18406.6[/C][C]18343.7103067596[/C][C]62.8896932404267[/C][/ROW]
[ROW][C]11[/C][C]18466.5[/C][C]17925.4378991947[/C][C]541.062100805275[/C][/ROW]
[ROW][C]12[/C][C]16016.5[/C][C]15762.6677454031[/C][C]253.832254596878[/C][/ROW]
[ROW][C]13[/C][C]17428.5[/C][C]16724.7661798191[/C][C]703.733820180863[/C][/ROW]
[ROW][C]14[/C][C]17167.2[/C][C]16884.8342348776[/C][C]282.365765122431[/C][/ROW]
[ROW][C]15[/C][C]19630[/C][C]19036.3485871609[/C][C]593.651412839141[/C][/ROW]
[ROW][C]16[/C][C]17183.6[/C][C]16790.3436454313[/C][C]393.256354568701[/C][/ROW]
[ROW][C]17[/C][C]18344.7[/C][C]18124.5012918194[/C][C]220.198708180631[/C][/ROW]
[ROW][C]18[/C][C]19301.4[/C][C]18997.6024875313[/C][C]303.797512468721[/C][/ROW]
[ROW][C]19[/C][C]18147.5[/C][C]18225.8593675143[/C][C]-78.3593675143503[/C][/ROW]
[ROW][C]20[/C][C]16192.9[/C][C]15507.3981917282[/C][C]685.501808271822[/C][/ROW]
[ROW][C]21[/C][C]18374.4[/C][C]18397.0985263341[/C][C]-22.6985263340898[/C][/ROW]
[ROW][C]22[/C][C]20515.2[/C][C]20270.3241718689[/C][C]244.875828131136[/C][/ROW]
[ROW][C]23[/C][C]18957.2[/C][C]19155.9075394049[/C][C]-198.707539404895[/C][/ROW]
[ROW][C]24[/C][C]16471.5[/C][C]17129.2356759662[/C][C]-657.735675966212[/C][/ROW]
[ROW][C]25[/C][C]18746.8[/C][C]18731.4810905465[/C][C]15.3189094534857[/C][/ROW]
[ROW][C]26[/C][C]19009.5[/C][C]18558.5051651085[/C][C]450.994834891502[/C][/ROW]
[ROW][C]27[/C][C]19211.2[/C][C]19872.4859238971[/C][C]-661.285923897081[/C][/ROW]
[ROW][C]28[/C][C]20547.7[/C][C]20282.4552855882[/C][C]265.244714411791[/C][/ROW]
[ROW][C]29[/C][C]19325.8[/C][C]19235.8481843718[/C][C]89.951815628168[/C][/ROW]
[ROW][C]30[/C][C]20605.5[/C][C]20866.6023895538[/C][C]-261.102389553813[/C][/ROW]
[ROW][C]31[/C][C]20056.9[/C][C]19957.8056456124[/C][C]99.0943543876273[/C][/ROW]
[ROW][C]32[/C][C]16141.4[/C][C]16825.5380589313[/C][C]-684.138058931344[/C][/ROW]
[ROW][C]33[/C][C]20359.8[/C][C]20838.1962633445[/C][C]-478.396263344452[/C][/ROW]
[ROW][C]34[/C][C]19711.6[/C][C]19376.1118633317[/C][C]335.48813666829[/C][/ROW]
[ROW][C]35[/C][C]15638.6[/C][C]16422.6046183558[/C][C]-784.004618355811[/C][/ROW]
[ROW][C]36[/C][C]14384.5[/C][C]14507.4863211999[/C][C]-122.986321199915[/C][/ROW]
[ROW][C]37[/C][C]13855.6[/C][C]13965.8162267327[/C][C]-110.216226732706[/C][/ROW]
[ROW][C]38[/C][C]14308.3[/C][C]14229.3542563143[/C][C]78.9457436856934[/C][/ROW]
[ROW][C]39[/C][C]15290.6[/C][C]15508.5020833387[/C][C]-217.902083338697[/C][/ROW]
[ROW][C]40[/C][C]14423.8[/C][C]14044.1804647646[/C][C]379.619535235368[/C][/ROW]
[ROW][C]41[/C][C]13779.7[/C][C]13977.7456080014[/C][C]-198.045608001386[/C][/ROW]
[ROW][C]42[/C][C]15686.3[/C][C]15759.3690302970[/C][C]-73.0690302970484[/C][/ROW]
[ROW][C]43[/C][C]14733.8[/C][C]14605.1250456543[/C][C]128.674954345654[/C][/ROW]
[ROW][C]44[/C][C]12522.5[/C][C]12410.1131799026[/C][C]112.386820097364[/C][/ROW]
[ROW][C]45[/C][C]16189.4[/C][C]16043.5774435916[/C][C]145.822556408367[/C][/ROW]
[ROW][C]46[/C][C]16059.1[/C][C]16702.3536580399[/C][C]-643.253658039852[/C][/ROW]
[ROW][C]47[/C][C]16007.1[/C][C]15565.4499430446[/C][C]441.650056955432[/C][/ROW]
[ROW][C]48[/C][C]15806.8[/C][C]15279.9102574308[/C][C]526.889742569247[/C][/ROW]
[ROW][C]49[/C][C]15160[/C][C]15659.9366834834[/C][C]-499.936683483416[/C][/ROW]
[ROW][C]50[/C][C]15692.1[/C][C]16228.2261224028[/C][C]-536.126122402781[/C][/ROW]
[ROW][C]51[/C][C]18908.9[/C][C]18711.5351728197[/C][C]197.364827180336[/C][/ROW]
[ROW][C]52[/C][C]16969.9[/C][C]17702.4667447080[/C][C]-732.566744707972[/C][/ROW]
[ROW][C]53[/C][C]16997.5[/C][C]17515.5863707264[/C][C]-518.086370726354[/C][/ROW]
[ROW][C]54[/C][C]19858.9[/C][C]19756.1373433751[/C][C]102.762656624875[/C][/ROW]
[ROW][C]55[/C][C]17681.2[/C][C]17785.5975654062[/C][C]-104.397565406182[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=101741&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=101741&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
116198.916307.7998194182-108.899819418227
216554.216830.3802212968-276.180221296846
319554.219466.028232783788.1717672163006
415903.816209.3538595079-305.553859507887
518003.817597.8185450811405.981454918941
618329.618401.9887492427-72.3887492427344
716260.716305.7123758128-45.0123758127494
814851.914965.6505694378-113.750569437842
918174.117818.8277667298355.272233270175
1018406.618343.710306759662.8896932404267
1118466.517925.4378991947541.062100805275
1216016.515762.6677454031253.832254596878
1317428.516724.7661798191703.733820180863
1417167.216884.8342348776282.365765122431
151963019036.3485871609593.651412839141
1617183.616790.3436454313393.256354568701
1718344.718124.5012918194220.198708180631
1819301.418997.6024875313303.797512468721
1918147.518225.8593675143-78.3593675143503
2016192.915507.3981917282685.501808271822
2118374.418397.0985263341-22.6985263340898
2220515.220270.3241718689244.875828131136
2318957.219155.9075394049-198.707539404895
2416471.517129.2356759662-657.735675966212
2518746.818731.481090546515.3189094534857
2619009.518558.5051651085450.994834891502
2719211.219872.4859238971-661.285923897081
2820547.720282.4552855882265.244714411791
2919325.819235.848184371889.951815628168
3020605.520866.6023895538-261.102389553813
3120056.919957.805645612499.0943543876273
3216141.416825.5380589313-684.138058931344
3320359.820838.1962633445-478.396263344452
3419711.619376.1118633317335.48813666829
3515638.616422.6046183558-784.004618355811
3614384.514507.4863211999-122.986321199915
3713855.613965.8162267327-110.216226732706
3814308.314229.354256314378.9457436856934
3915290.615508.5020833387-217.902083338697
4014423.814044.1804647646379.619535235368
4113779.713977.7456080014-198.045608001386
4215686.315759.3690302970-73.0690302970484
4314733.814605.1250456543128.674954345654
4412522.512410.1131799026112.386820097364
4516189.416043.5774435916145.822556408367
4616059.116702.3536580399-643.253658039852
4716007.115565.4499430446441.650056955432
4815806.815279.9102574308526.889742569247
491516015659.9366834834-499.936683483416
5015692.116228.2261224028-536.126122402781
5118908.918711.5351728197197.364827180336
5216969.917702.4667447080-732.566744707972
5316997.517515.5863707264-518.086370726354
5419858.919756.1373433751102.762656624875
5517681.217785.5975654062-104.397565406182







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
170.681846654831890.636306690336220.31815334516811
180.5367507374192290.9264985251615420.463249262580771
190.4644917964066960.9289835928133920.535508203593304
200.580707349210770.838585301578460.41929265078923
210.5327706932696250.934458613460750.467229306730375
220.5093269711409610.9813460577180770.490673028859039
230.570925119517280.858149760965440.42907488048272
240.6090664678223930.7818670643552140.390933532177607
250.5004615797014870.9990768405970260.499538420298513
260.5221427803002990.9557144393994030.477857219699701
270.6538968734395990.6922062531208020.346103126560401
280.5785680879175660.8428638241648680.421431912082434
290.5134401241158420.9731197517683170.486559875884158
300.4259780914053040.8519561828106080.574021908594696
310.3270827936831280.6541655873662570.672917206316872
320.3717443396044670.7434886792089340.628255660395533
330.3366309611508330.6732619223016670.663369038849167
340.2975134133786310.5950268267572620.702486586621369
350.650531179251790.698937641496420.34946882074821
360.6093462585325530.7813074829348930.390653741467447
370.4572341201765430.9144682403530870.542765879823457
380.3288391584030510.6576783168061020.671160841596949

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
17 & 0.68184665483189 & 0.63630669033622 & 0.31815334516811 \tabularnewline
18 & 0.536750737419229 & 0.926498525161542 & 0.463249262580771 \tabularnewline
19 & 0.464491796406696 & 0.928983592813392 & 0.535508203593304 \tabularnewline
20 & 0.58070734921077 & 0.83858530157846 & 0.41929265078923 \tabularnewline
21 & 0.532770693269625 & 0.93445861346075 & 0.467229306730375 \tabularnewline
22 & 0.509326971140961 & 0.981346057718077 & 0.490673028859039 \tabularnewline
23 & 0.57092511951728 & 0.85814976096544 & 0.42907488048272 \tabularnewline
24 & 0.609066467822393 & 0.781867064355214 & 0.390933532177607 \tabularnewline
25 & 0.500461579701487 & 0.999076840597026 & 0.499538420298513 \tabularnewline
26 & 0.522142780300299 & 0.955714439399403 & 0.477857219699701 \tabularnewline
27 & 0.653896873439599 & 0.692206253120802 & 0.346103126560401 \tabularnewline
28 & 0.578568087917566 & 0.842863824164868 & 0.421431912082434 \tabularnewline
29 & 0.513440124115842 & 0.973119751768317 & 0.486559875884158 \tabularnewline
30 & 0.425978091405304 & 0.851956182810608 & 0.574021908594696 \tabularnewline
31 & 0.327082793683128 & 0.654165587366257 & 0.672917206316872 \tabularnewline
32 & 0.371744339604467 & 0.743488679208934 & 0.628255660395533 \tabularnewline
33 & 0.336630961150833 & 0.673261922301667 & 0.663369038849167 \tabularnewline
34 & 0.297513413378631 & 0.595026826757262 & 0.702486586621369 \tabularnewline
35 & 0.65053117925179 & 0.69893764149642 & 0.34946882074821 \tabularnewline
36 & 0.609346258532553 & 0.781307482934893 & 0.390653741467447 \tabularnewline
37 & 0.457234120176543 & 0.914468240353087 & 0.542765879823457 \tabularnewline
38 & 0.328839158403051 & 0.657678316806102 & 0.671160841596949 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=101741&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]0.68184665483189[/C][C]0.63630669033622[/C][C]0.31815334516811[/C][/ROW]
[ROW][C]18[/C][C]0.536750737419229[/C][C]0.926498525161542[/C][C]0.463249262580771[/C][/ROW]
[ROW][C]19[/C][C]0.464491796406696[/C][C]0.928983592813392[/C][C]0.535508203593304[/C][/ROW]
[ROW][C]20[/C][C]0.58070734921077[/C][C]0.83858530157846[/C][C]0.41929265078923[/C][/ROW]
[ROW][C]21[/C][C]0.532770693269625[/C][C]0.93445861346075[/C][C]0.467229306730375[/C][/ROW]
[ROW][C]22[/C][C]0.509326971140961[/C][C]0.981346057718077[/C][C]0.490673028859039[/C][/ROW]
[ROW][C]23[/C][C]0.57092511951728[/C][C]0.85814976096544[/C][C]0.42907488048272[/C][/ROW]
[ROW][C]24[/C][C]0.609066467822393[/C][C]0.781867064355214[/C][C]0.390933532177607[/C][/ROW]
[ROW][C]25[/C][C]0.500461579701487[/C][C]0.999076840597026[/C][C]0.499538420298513[/C][/ROW]
[ROW][C]26[/C][C]0.522142780300299[/C][C]0.955714439399403[/C][C]0.477857219699701[/C][/ROW]
[ROW][C]27[/C][C]0.653896873439599[/C][C]0.692206253120802[/C][C]0.346103126560401[/C][/ROW]
[ROW][C]28[/C][C]0.578568087917566[/C][C]0.842863824164868[/C][C]0.421431912082434[/C][/ROW]
[ROW][C]29[/C][C]0.513440124115842[/C][C]0.973119751768317[/C][C]0.486559875884158[/C][/ROW]
[ROW][C]30[/C][C]0.425978091405304[/C][C]0.851956182810608[/C][C]0.574021908594696[/C][/ROW]
[ROW][C]31[/C][C]0.327082793683128[/C][C]0.654165587366257[/C][C]0.672917206316872[/C][/ROW]
[ROW][C]32[/C][C]0.371744339604467[/C][C]0.743488679208934[/C][C]0.628255660395533[/C][/ROW]
[ROW][C]33[/C][C]0.336630961150833[/C][C]0.673261922301667[/C][C]0.663369038849167[/C][/ROW]
[ROW][C]34[/C][C]0.297513413378631[/C][C]0.595026826757262[/C][C]0.702486586621369[/C][/ROW]
[ROW][C]35[/C][C]0.65053117925179[/C][C]0.69893764149642[/C][C]0.34946882074821[/C][/ROW]
[ROW][C]36[/C][C]0.609346258532553[/C][C]0.781307482934893[/C][C]0.390653741467447[/C][/ROW]
[ROW][C]37[/C][C]0.457234120176543[/C][C]0.914468240353087[/C][C]0.542765879823457[/C][/ROW]
[ROW][C]38[/C][C]0.328839158403051[/C][C]0.657678316806102[/C][C]0.671160841596949[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=101741&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=101741&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
170.681846654831890.636306690336220.31815334516811
180.5367507374192290.9264985251615420.463249262580771
190.4644917964066960.9289835928133920.535508203593304
200.580707349210770.838585301578460.41929265078923
210.5327706932696250.934458613460750.467229306730375
220.5093269711409610.9813460577180770.490673028859039
230.570925119517280.858149760965440.42907488048272
240.6090664678223930.7818670643552140.390933532177607
250.5004615797014870.9990768405970260.499538420298513
260.5221427803002990.9557144393994030.477857219699701
270.6538968734395990.6922062531208020.346103126560401
280.5785680879175660.8428638241648680.421431912082434
290.5134401241158420.9731197517683170.486559875884158
300.4259780914053040.8519561828106080.574021908594696
310.3270827936831280.6541655873662570.672917206316872
320.3717443396044670.7434886792089340.628255660395533
330.3366309611508330.6732619223016670.663369038849167
340.2975134133786310.5950268267572620.702486586621369
350.650531179251790.698937641496420.34946882074821
360.6093462585325530.7813074829348930.390653741467447
370.4572341201765430.9144682403530870.542765879823457
380.3288391584030510.6576783168061020.671160841596949







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=101741&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=101741&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=101741&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 = No Linear Trend ;
Parameters (R input):
par1 = 1 ; par2 = Include Monthly Dummies ; par3 = No Linear Trend ;
R code (references can be found in the software module):
library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,mysum$coefficients[i,1])
a<-table.element(a, round(mysum$coefficients[i,2],6))
a<-table.element(a, round(mysum$coefficients[i,3],4))
a<-table.element(a, round(mysum$coefficients[i,4],6))
a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, sqrt(mysum$r.squared))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, mysum$r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, mysum$adj.r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, mysum$fstatistic[1])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[3])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a, mysum$sigma)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, sum(myerror*myerror))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,x[i])
a<-table.element(a,x[i]-mysum$resid[i])
a<-table.element(a,mysum$resid[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
if (n > n25) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-values',header=TRUE)
a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'breakpoint index',header=TRUE)
a<-table.element(a,'greater',header=TRUE)
a<-table.element(a,'2-sided',header=TRUE)
a<-table.element(a,'less',header=TRUE)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
a<-table.element(a,mypoint,header=TRUE)
a<-table.element(a,gqarr[mypoint-kp3+1,1])
a<-table.element(a,gqarr[mypoint-kp3+1,2])
a<-table.element(a,gqarr[mypoint-kp3+1,3])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,numsignificant1)
a<-table.element(a,numsignificant1/numgqtests)
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,numsignificant5)
a<-table.element(a,numsignificant5/numgqtests)
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,numsignificant10)
a<-table.element(a,numsignificant10/numgqtests)
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}