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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 03:53:05 -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/t1258714672b1nv4zrvjt7lhkn.htm/, Retrieved Fri, 29 Mar 2024 10:42:51 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=58025, Retrieved Fri, 29 Mar 2024 10:42:51 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact153
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Q1 The Seatbeltlaw] [2007-11-14 19:27:43] [8cd6641b921d30ebe00b648d1481bba0]
- RMPD  [Multiple Regression] [Seatbelt] [2009-11-12 13:54:52] [b98453cac15ba1066b407e146608df68]
-   PD    [Multiple Regression] [W7] [2009-11-18 21:32:41] [315ba876df544ad397193b5931d5f354]
-    D        [Multiple Regression] [WS 7: Multiple Re...] [2009-11-20 10:53:05] [ac86848d66148c9c4c9404e0c9a511eb] [Current]
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Dataseries X:
104	120.28	112.9	113.6	83.4	79.8
109.9	115.33	104	112.9	113.6	83.4
99	110.4	109.9	104	112.9	113.6
106.3	114.49	99	109.9	104	112.9
128.9	132.03	106.3	99	109.9	104
111.1	123.16	128.9	106.3	99	109.9
102.9	118.82	111.1	128.9	106.3	99
130	128.32	102.9	111.1	128.9	106.3
87	112.24	130	102.9	111.1	128.9
87.5	104.53	87	130	102.9	111.1
117.6	132.57	87.5	87	130	102.9
103.4	122.52	117.6	87.5	87	130
110.8	131.8	103.4	117.6	87.5	87
112.6	124.55	110.8	103.4	117.6	87.5
102.5	120.96	112.6	110.8	103.4	117.6
112.4	122.6	102.5	112.6	110.8	103.4
135.6	145.52	112.4	102.5	112.6	110.8
105.1	118.57	135.6	112.4	102.5	112.6
127.7	134.25	105.1	135.6	112.4	102.5
137	136.7	127.7	105.1	135.6	112.4
91	121.37	137	127.7	105.1	135.6
90.5	111.63	91	137	127.7	105.1
122.4	134.42	90.5	91	137	127.7
123.3	137.65	122.4	90.5	91	137
124.3	137.86	123.3	122.4	90.5	91
120	119.77	124.3	123.3	122.4	90.5
118.1	130.69	120	124.3	123.3	122.4
119	128.28	118.1	120	124.3	123.3
142.7	147.45	119	118.1	120	124.3
123.6	128.42	142.7	119	118.1	120
129.6	136.9	123.6	142.7	119	118.1
151.6	143.95	129.6	123.6	142.7	119
110.4	135.64	151.6	129.6	123.6	142.7
99.2	122.48	110.4	151.6	129.6	123.6
130.5	136.83	99.2	110.4	151.6	129.6
136.2	153.04	130.5	99.2	110.4	151.6
129.7	142.71	136.2	130.5	99.2	110.4
128	123.46	129.7	136.2	130.5	99.2
121.6	144.37	128	129.7	136.2	130.5
135.8	146.15	121.6	128	129.7	136.2
143.8	147.61	135.8	121.6	128	129.7
147.5	158.51	143.8	135.8	121.6	128
136.2	147.4	147.5	143.8	135.8	121.6
156.6	165.05	136.2	147.5	143.8	135.8
123.3	154.64	156.6	136.2	147.5	143.8
104.5	126.2	123.3	156.6	136.2	147.5
139.8	157.36	104.5	123.3	156.6	136.2
136.5	154.15	139.8	104.5	123.3	156.6
112.1	123.21	136.5	139.8	104.5	123.3
118.5	113.07	112.1	136.5	139.8	104.5
94.4	110.45	118.5	112.1	136.5	139.8
102.3	113.57	94.4	118.5	112.1	136.5
111.4	122.44	102.3	94.4	118.5	112.1
99.2	114.93	111.4	102.3	94.4	118.5
87.8	111.85	99.2	111.4	102.3	94.4
115.8	126.04	87.8	99.2	111.4	102.3




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=58025&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=58025&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58025&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
I[t] = -25.1447875446987 + 0.730220332179792U[t] + 0.0643404999581819m1[t] + 0.194820257073925m2[t] + 0.170133757972612m3[t] + 0.046402945892129m4[t] -3.23374546523884M1[t] + 2.95184473503329M2[t] -10.5575350599474M3[t] -2.0182951502947M4[t] + 6.81571807814321M5[t] -1.63565124828532M6[t] -6.03568977932192M7[t] + 7.71868122050343M8[t] -24.0107549608620M9[t] -21.4349262435542M10[t] -1.73157334236155M11[t] -0.152079578124883t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
I[t] =  -25.1447875446987 +  0.730220332179792U[t] +  0.0643404999581819m1[t] +  0.194820257073925m2[t] +  0.170133757972612m3[t] +  0.046402945892129m4[t] -3.23374546523884M1[t] +  2.95184473503329M2[t] -10.5575350599474M3[t] -2.0182951502947M4[t] +  6.81571807814321M5[t] -1.63565124828532M6[t] -6.03568977932192M7[t] +  7.71868122050343M8[t] -24.0107549608620M9[t] -21.4349262435542M10[t] -1.73157334236155M11[t] -0.152079578124883t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58025&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]I[t] =  -25.1447875446987 +  0.730220332179792U[t] +  0.0643404999581819m1[t] +  0.194820257073925m2[t] +  0.170133757972612m3[t] +  0.046402945892129m4[t] -3.23374546523884M1[t] +  2.95184473503329M2[t] -10.5575350599474M3[t] -2.0182951502947M4[t] +  6.81571807814321M5[t] -1.63565124828532M6[t] -6.03568977932192M7[t] +  7.71868122050343M8[t] -24.0107549608620M9[t] -21.4349262435542M10[t] -1.73157334236155M11[t] -0.152079578124883t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58025&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58025&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
I[t] = -25.1447875446987 + 0.730220332179792U[t] + 0.0643404999581819m1[t] + 0.194820257073925m2[t] + 0.170133757972612m3[t] + 0.046402945892129m4[t] -3.23374546523884M1[t] + 2.95184473503329M2[t] -10.5575350599474M3[t] -2.0182951502947M4[t] + 6.81571807814321M5[t] -1.63565124828532M6[t] -6.03568977932192M7[t] + 7.71868122050343M8[t] -24.0107549608620M9[t] -21.4349262435542M10[t] -1.73157334236155M11[t] -0.152079578124883t + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-25.144787544698710.733265-2.34270.0244830.012241
U0.7302203321797920.0789839.245300
m10.06434049995818190.0943110.68220.499240.24962
m20.1948202570739250.0995561.95690.0577360.028868
m30.1701337579726120.0988321.72140.0933030.046652
m40.0464029458921290.1080660.42940.6700620.335031
M1-3.233745465238847.362942-0.43920.663010.331505
M22.951844735033298.4972450.34740.7302160.365108
M3-10.55753505994745.519921-1.91260.0633550.031678
M4-2.01829515029475.641557-0.35780.7225060.361253
M56.815718078143214.8673961.40030.1695420.084771
M6-1.635651248285325.41332-0.30220.7641830.382092
M7-6.035689779321927.446678-0.81050.4226880.211344
M87.718681220503436.1327891.25860.2158550.107928
M9-24.01075496086204.966142-4.83492.2e-051.1e-05
M10-21.43492624355428.439853-2.53970.0153060.007653
M11-1.731573342361556.847911-0.25290.8017380.400869
t-0.1520795781248830.053864-2.82340.0075240.003762

\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) & -25.1447875446987 & 10.733265 & -2.3427 & 0.024483 & 0.012241 \tabularnewline
U & 0.730220332179792 & 0.078983 & 9.2453 & 0 & 0 \tabularnewline
m1 & 0.0643404999581819 & 0.094311 & 0.6822 & 0.49924 & 0.24962 \tabularnewline
m2 & 0.194820257073925 & 0.099556 & 1.9569 & 0.057736 & 0.028868 \tabularnewline
m3 & 0.170133757972612 & 0.098832 & 1.7214 & 0.093303 & 0.046652 \tabularnewline
m4 & 0.046402945892129 & 0.108066 & 0.4294 & 0.670062 & 0.335031 \tabularnewline
M1 & -3.23374546523884 & 7.362942 & -0.4392 & 0.66301 & 0.331505 \tabularnewline
M2 & 2.95184473503329 & 8.497245 & 0.3474 & 0.730216 & 0.365108 \tabularnewline
M3 & -10.5575350599474 & 5.519921 & -1.9126 & 0.063355 & 0.031678 \tabularnewline
M4 & -2.0182951502947 & 5.641557 & -0.3578 & 0.722506 & 0.361253 \tabularnewline
M5 & 6.81571807814321 & 4.867396 & 1.4003 & 0.169542 & 0.084771 \tabularnewline
M6 & -1.63565124828532 & 5.41332 & -0.3022 & 0.764183 & 0.382092 \tabularnewline
M7 & -6.03568977932192 & 7.446678 & -0.8105 & 0.422688 & 0.211344 \tabularnewline
M8 & 7.71868122050343 & 6.132789 & 1.2586 & 0.215855 & 0.107928 \tabularnewline
M9 & -24.0107549608620 & 4.966142 & -4.8349 & 2.2e-05 & 1.1e-05 \tabularnewline
M10 & -21.4349262435542 & 8.439853 & -2.5397 & 0.015306 & 0.007653 \tabularnewline
M11 & -1.73157334236155 & 6.847911 & -0.2529 & 0.801738 & 0.400869 \tabularnewline
t & -0.152079578124883 & 0.053864 & -2.8234 & 0.007524 & 0.003762 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58025&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]-25.1447875446987[/C][C]10.733265[/C][C]-2.3427[/C][C]0.024483[/C][C]0.012241[/C][/ROW]
[ROW][C]U[/C][C]0.730220332179792[/C][C]0.078983[/C][C]9.2453[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]m1[/C][C]0.0643404999581819[/C][C]0.094311[/C][C]0.6822[/C][C]0.49924[/C][C]0.24962[/C][/ROW]
[ROW][C]m2[/C][C]0.194820257073925[/C][C]0.099556[/C][C]1.9569[/C][C]0.057736[/C][C]0.028868[/C][/ROW]
[ROW][C]m3[/C][C]0.170133757972612[/C][C]0.098832[/C][C]1.7214[/C][C]0.093303[/C][C]0.046652[/C][/ROW]
[ROW][C]m4[/C][C]0.046402945892129[/C][C]0.108066[/C][C]0.4294[/C][C]0.670062[/C][C]0.335031[/C][/ROW]
[ROW][C]M1[/C][C]-3.23374546523884[/C][C]7.362942[/C][C]-0.4392[/C][C]0.66301[/C][C]0.331505[/C][/ROW]
[ROW][C]M2[/C][C]2.95184473503329[/C][C]8.497245[/C][C]0.3474[/C][C]0.730216[/C][C]0.365108[/C][/ROW]
[ROW][C]M3[/C][C]-10.5575350599474[/C][C]5.519921[/C][C]-1.9126[/C][C]0.063355[/C][C]0.031678[/C][/ROW]
[ROW][C]M4[/C][C]-2.0182951502947[/C][C]5.641557[/C][C]-0.3578[/C][C]0.722506[/C][C]0.361253[/C][/ROW]
[ROW][C]M5[/C][C]6.81571807814321[/C][C]4.867396[/C][C]1.4003[/C][C]0.169542[/C][C]0.084771[/C][/ROW]
[ROW][C]M6[/C][C]-1.63565124828532[/C][C]5.41332[/C][C]-0.3022[/C][C]0.764183[/C][C]0.382092[/C][/ROW]
[ROW][C]M7[/C][C]-6.03568977932192[/C][C]7.446678[/C][C]-0.8105[/C][C]0.422688[/C][C]0.211344[/C][/ROW]
[ROW][C]M8[/C][C]7.71868122050343[/C][C]6.132789[/C][C]1.2586[/C][C]0.215855[/C][C]0.107928[/C][/ROW]
[ROW][C]M9[/C][C]-24.0107549608620[/C][C]4.966142[/C][C]-4.8349[/C][C]2.2e-05[/C][C]1.1e-05[/C][/ROW]
[ROW][C]M10[/C][C]-21.4349262435542[/C][C]8.439853[/C][C]-2.5397[/C][C]0.015306[/C][C]0.007653[/C][/ROW]
[ROW][C]M11[/C][C]-1.73157334236155[/C][C]6.847911[/C][C]-0.2529[/C][C]0.801738[/C][C]0.400869[/C][/ROW]
[ROW][C]t[/C][C]-0.152079578124883[/C][C]0.053864[/C][C]-2.8234[/C][C]0.007524[/C][C]0.003762[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58025&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58025&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)-25.144787544698710.733265-2.34270.0244830.012241
U0.7302203321797920.0789839.245300
m10.06434049995818190.0943110.68220.499240.24962
m20.1948202570739250.0995561.95690.0577360.028868
m30.1701337579726120.0988321.72140.0933030.046652
m40.0464029458921290.1080660.42940.6700620.335031
M1-3.233745465238847.362942-0.43920.663010.331505
M22.951844735033298.4972450.34740.7302160.365108
M3-10.55753505994745.519921-1.91260.0633550.031678
M4-2.01829515029475.641557-0.35780.7225060.361253
M56.815718078143214.8673961.40030.1695420.084771
M6-1.635651248285325.41332-0.30220.7641830.382092
M7-6.035689779321927.446678-0.81050.4226880.211344
M87.718681220503436.1327891.25860.2158550.107928
M9-24.01075496086204.966142-4.83492.2e-051.1e-05
M10-21.43492624355428.439853-2.53970.0153060.007653
M11-1.731573342361556.847911-0.25290.8017380.400869
t-0.1520795781248830.053864-2.82340.0075240.003762







Multiple Linear Regression - Regression Statistics
Multiple R0.980509202308032
R-squared0.961398295810733
Adjusted R-squared0.94412911235764
F-TEST (value)55.6713233385993
F-TEST (DF numerator)17
F-TEST (DF denominator)38
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation4.05308389685655
Sum Squared Residuals624.244584848399

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.980509202308032 \tabularnewline
R-squared & 0.961398295810733 \tabularnewline
Adjusted R-squared & 0.94412911235764 \tabularnewline
F-TEST (value) & 55.6713233385993 \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 & 4.05308389685655 \tabularnewline
Sum Squared Residuals & 624.244584848399 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58025&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.980509202308032[/C][/ROW]
[ROW][C]R-squared[/C][C]0.961398295810733[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.94412911235764[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]55.6713233385993[/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]4.05308389685655[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]624.244584848399[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58025&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58025&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 R0.980509202308032
R-squared0.961398295810733
Adjusted R-squared0.94412911235764
F-TEST (value)55.6713233385993
F-TEST (DF numerator)17
F-TEST (DF denominator)38
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation4.05308389685655
Sum Squared Residuals624.244584848399







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1104106.588023112507-2.5880231125075
2109.9113.603028556769-3.70302855676944
39996.26956694317442.73043305682563
4106.3106.544783992429-0.244783992428747
5128.9126.9717300703631.92826992963728
6111.1113.186829413931-2.08682941393122
7102.9109.459416296700-6.55941629670036
8130130.187172633729-0.187172633729466
98784.73014105889822.26985894110176
1087.581.81580965322135.68419034677873
11117.6117.727480971153-0.127480971152990
12103.4107.944087815115-4.54408781511537
13110.8114.374902298524-3.57490229852384
14112.6117.96821514953-5.3682151495301
15102.5102.2235768943130.27642310568715
16112.4112.10920396110.290796038899986
17135.6136.846696542066-1.24669654206571
18105.1110.350404176411-5.25040417641103
19127.7121.0212400416386.67875995836246
20137136.3311410847750.668858915224647
219194.1441208189833-3.14412081898335
2290.590.7374223959174-0.237422395917378
23122.4120.5674655402901.83253445970969
24123.3119.0660173276534.23398267234656
25124.3119.8866088146444.41339118535619
26120118.3541777653641.64582223463643
27118.1114.2182548830493.88174511695064
28119120.097506567961-1.09750656796095
29142.7141.7803397342920.919660265707606
30123.6120.4582191812493.14178081875114
31129.6125.5516608174034.04833918259692
32151.6145.0409143858626.55908561413800
33110.4107.5258752478482.8741247521523
3499.2102.10964815419-2.90964815418991
35130.5127.4137354038113.08626459618946
36136.2134.67332550331.5266744967
37129.7126.3916398646463.30836013535443
38128123.8661449384764.13385506152412
39121.6126.520056817208-4.92005681720789
40135.8134.6226630680211.17733693197917
41143.8143.4466573205970.35334267940286
42147.5144.9160406278772.58395937212268
43136.2136.1667170441360.033282955863508
44156.6164.671176525905-8.0711765259055
45123.3125.299862874271-1.99986287427072
46104.5107.037119796671-2.53711979667143
47139.8144.591318084746-4.79131808474616
48136.5137.716569353931-1.2165693539312
49112.1113.658825909679-1.55882590967928
50118.5115.2084335898613.291566410139
5194.496.3685444622555-1.96854446225553
52102.3102.425842410489-0.125842410489466
53111.4113.354576332682-1.95457633268204
5499.297.58850660053161.61149339946844
5587.892.0009658001225-4.20096580012253
56115.8114.7695953697281.03040463027231

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 104 & 106.588023112507 & -2.5880231125075 \tabularnewline
2 & 109.9 & 113.603028556769 & -3.70302855676944 \tabularnewline
3 & 99 & 96.2695669431744 & 2.73043305682563 \tabularnewline
4 & 106.3 & 106.544783992429 & -0.244783992428747 \tabularnewline
5 & 128.9 & 126.971730070363 & 1.92826992963728 \tabularnewline
6 & 111.1 & 113.186829413931 & -2.08682941393122 \tabularnewline
7 & 102.9 & 109.459416296700 & -6.55941629670036 \tabularnewline
8 & 130 & 130.187172633729 & -0.187172633729466 \tabularnewline
9 & 87 & 84.7301410588982 & 2.26985894110176 \tabularnewline
10 & 87.5 & 81.8158096532213 & 5.68419034677873 \tabularnewline
11 & 117.6 & 117.727480971153 & -0.127480971152990 \tabularnewline
12 & 103.4 & 107.944087815115 & -4.54408781511537 \tabularnewline
13 & 110.8 & 114.374902298524 & -3.57490229852384 \tabularnewline
14 & 112.6 & 117.96821514953 & -5.3682151495301 \tabularnewline
15 & 102.5 & 102.223576894313 & 0.27642310568715 \tabularnewline
16 & 112.4 & 112.1092039611 & 0.290796038899986 \tabularnewline
17 & 135.6 & 136.846696542066 & -1.24669654206571 \tabularnewline
18 & 105.1 & 110.350404176411 & -5.25040417641103 \tabularnewline
19 & 127.7 & 121.021240041638 & 6.67875995836246 \tabularnewline
20 & 137 & 136.331141084775 & 0.668858915224647 \tabularnewline
21 & 91 & 94.1441208189833 & -3.14412081898335 \tabularnewline
22 & 90.5 & 90.7374223959174 & -0.237422395917378 \tabularnewline
23 & 122.4 & 120.567465540290 & 1.83253445970969 \tabularnewline
24 & 123.3 & 119.066017327653 & 4.23398267234656 \tabularnewline
25 & 124.3 & 119.886608814644 & 4.41339118535619 \tabularnewline
26 & 120 & 118.354177765364 & 1.64582223463643 \tabularnewline
27 & 118.1 & 114.218254883049 & 3.88174511695064 \tabularnewline
28 & 119 & 120.097506567961 & -1.09750656796095 \tabularnewline
29 & 142.7 & 141.780339734292 & 0.919660265707606 \tabularnewline
30 & 123.6 & 120.458219181249 & 3.14178081875114 \tabularnewline
31 & 129.6 & 125.551660817403 & 4.04833918259692 \tabularnewline
32 & 151.6 & 145.040914385862 & 6.55908561413800 \tabularnewline
33 & 110.4 & 107.525875247848 & 2.8741247521523 \tabularnewline
34 & 99.2 & 102.10964815419 & -2.90964815418991 \tabularnewline
35 & 130.5 & 127.413735403811 & 3.08626459618946 \tabularnewline
36 & 136.2 & 134.6733255033 & 1.5266744967 \tabularnewline
37 & 129.7 & 126.391639864646 & 3.30836013535443 \tabularnewline
38 & 128 & 123.866144938476 & 4.13385506152412 \tabularnewline
39 & 121.6 & 126.520056817208 & -4.92005681720789 \tabularnewline
40 & 135.8 & 134.622663068021 & 1.17733693197917 \tabularnewline
41 & 143.8 & 143.446657320597 & 0.35334267940286 \tabularnewline
42 & 147.5 & 144.916040627877 & 2.58395937212268 \tabularnewline
43 & 136.2 & 136.166717044136 & 0.033282955863508 \tabularnewline
44 & 156.6 & 164.671176525905 & -8.0711765259055 \tabularnewline
45 & 123.3 & 125.299862874271 & -1.99986287427072 \tabularnewline
46 & 104.5 & 107.037119796671 & -2.53711979667143 \tabularnewline
47 & 139.8 & 144.591318084746 & -4.79131808474616 \tabularnewline
48 & 136.5 & 137.716569353931 & -1.2165693539312 \tabularnewline
49 & 112.1 & 113.658825909679 & -1.55882590967928 \tabularnewline
50 & 118.5 & 115.208433589861 & 3.291566410139 \tabularnewline
51 & 94.4 & 96.3685444622555 & -1.96854446225553 \tabularnewline
52 & 102.3 & 102.425842410489 & -0.125842410489466 \tabularnewline
53 & 111.4 & 113.354576332682 & -1.95457633268204 \tabularnewline
54 & 99.2 & 97.5885066005316 & 1.61149339946844 \tabularnewline
55 & 87.8 & 92.0009658001225 & -4.20096580012253 \tabularnewline
56 & 115.8 & 114.769595369728 & 1.03040463027231 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58025&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]104[/C][C]106.588023112507[/C][C]-2.5880231125075[/C][/ROW]
[ROW][C]2[/C][C]109.9[/C][C]113.603028556769[/C][C]-3.70302855676944[/C][/ROW]
[ROW][C]3[/C][C]99[/C][C]96.2695669431744[/C][C]2.73043305682563[/C][/ROW]
[ROW][C]4[/C][C]106.3[/C][C]106.544783992429[/C][C]-0.244783992428747[/C][/ROW]
[ROW][C]5[/C][C]128.9[/C][C]126.971730070363[/C][C]1.92826992963728[/C][/ROW]
[ROW][C]6[/C][C]111.1[/C][C]113.186829413931[/C][C]-2.08682941393122[/C][/ROW]
[ROW][C]7[/C][C]102.9[/C][C]109.459416296700[/C][C]-6.55941629670036[/C][/ROW]
[ROW][C]8[/C][C]130[/C][C]130.187172633729[/C][C]-0.187172633729466[/C][/ROW]
[ROW][C]9[/C][C]87[/C][C]84.7301410588982[/C][C]2.26985894110176[/C][/ROW]
[ROW][C]10[/C][C]87.5[/C][C]81.8158096532213[/C][C]5.68419034677873[/C][/ROW]
[ROW][C]11[/C][C]117.6[/C][C]117.727480971153[/C][C]-0.127480971152990[/C][/ROW]
[ROW][C]12[/C][C]103.4[/C][C]107.944087815115[/C][C]-4.54408781511537[/C][/ROW]
[ROW][C]13[/C][C]110.8[/C][C]114.374902298524[/C][C]-3.57490229852384[/C][/ROW]
[ROW][C]14[/C][C]112.6[/C][C]117.96821514953[/C][C]-5.3682151495301[/C][/ROW]
[ROW][C]15[/C][C]102.5[/C][C]102.223576894313[/C][C]0.27642310568715[/C][/ROW]
[ROW][C]16[/C][C]112.4[/C][C]112.1092039611[/C][C]0.290796038899986[/C][/ROW]
[ROW][C]17[/C][C]135.6[/C][C]136.846696542066[/C][C]-1.24669654206571[/C][/ROW]
[ROW][C]18[/C][C]105.1[/C][C]110.350404176411[/C][C]-5.25040417641103[/C][/ROW]
[ROW][C]19[/C][C]127.7[/C][C]121.021240041638[/C][C]6.67875995836246[/C][/ROW]
[ROW][C]20[/C][C]137[/C][C]136.331141084775[/C][C]0.668858915224647[/C][/ROW]
[ROW][C]21[/C][C]91[/C][C]94.1441208189833[/C][C]-3.14412081898335[/C][/ROW]
[ROW][C]22[/C][C]90.5[/C][C]90.7374223959174[/C][C]-0.237422395917378[/C][/ROW]
[ROW][C]23[/C][C]122.4[/C][C]120.567465540290[/C][C]1.83253445970969[/C][/ROW]
[ROW][C]24[/C][C]123.3[/C][C]119.066017327653[/C][C]4.23398267234656[/C][/ROW]
[ROW][C]25[/C][C]124.3[/C][C]119.886608814644[/C][C]4.41339118535619[/C][/ROW]
[ROW][C]26[/C][C]120[/C][C]118.354177765364[/C][C]1.64582223463643[/C][/ROW]
[ROW][C]27[/C][C]118.1[/C][C]114.218254883049[/C][C]3.88174511695064[/C][/ROW]
[ROW][C]28[/C][C]119[/C][C]120.097506567961[/C][C]-1.09750656796095[/C][/ROW]
[ROW][C]29[/C][C]142.7[/C][C]141.780339734292[/C][C]0.919660265707606[/C][/ROW]
[ROW][C]30[/C][C]123.6[/C][C]120.458219181249[/C][C]3.14178081875114[/C][/ROW]
[ROW][C]31[/C][C]129.6[/C][C]125.551660817403[/C][C]4.04833918259692[/C][/ROW]
[ROW][C]32[/C][C]151.6[/C][C]145.040914385862[/C][C]6.55908561413800[/C][/ROW]
[ROW][C]33[/C][C]110.4[/C][C]107.525875247848[/C][C]2.8741247521523[/C][/ROW]
[ROW][C]34[/C][C]99.2[/C][C]102.10964815419[/C][C]-2.90964815418991[/C][/ROW]
[ROW][C]35[/C][C]130.5[/C][C]127.413735403811[/C][C]3.08626459618946[/C][/ROW]
[ROW][C]36[/C][C]136.2[/C][C]134.6733255033[/C][C]1.5266744967[/C][/ROW]
[ROW][C]37[/C][C]129.7[/C][C]126.391639864646[/C][C]3.30836013535443[/C][/ROW]
[ROW][C]38[/C][C]128[/C][C]123.866144938476[/C][C]4.13385506152412[/C][/ROW]
[ROW][C]39[/C][C]121.6[/C][C]126.520056817208[/C][C]-4.92005681720789[/C][/ROW]
[ROW][C]40[/C][C]135.8[/C][C]134.622663068021[/C][C]1.17733693197917[/C][/ROW]
[ROW][C]41[/C][C]143.8[/C][C]143.446657320597[/C][C]0.35334267940286[/C][/ROW]
[ROW][C]42[/C][C]147.5[/C][C]144.916040627877[/C][C]2.58395937212268[/C][/ROW]
[ROW][C]43[/C][C]136.2[/C][C]136.166717044136[/C][C]0.033282955863508[/C][/ROW]
[ROW][C]44[/C][C]156.6[/C][C]164.671176525905[/C][C]-8.0711765259055[/C][/ROW]
[ROW][C]45[/C][C]123.3[/C][C]125.299862874271[/C][C]-1.99986287427072[/C][/ROW]
[ROW][C]46[/C][C]104.5[/C][C]107.037119796671[/C][C]-2.53711979667143[/C][/ROW]
[ROW][C]47[/C][C]139.8[/C][C]144.591318084746[/C][C]-4.79131808474616[/C][/ROW]
[ROW][C]48[/C][C]136.5[/C][C]137.716569353931[/C][C]-1.2165693539312[/C][/ROW]
[ROW][C]49[/C][C]112.1[/C][C]113.658825909679[/C][C]-1.55882590967928[/C][/ROW]
[ROW][C]50[/C][C]118.5[/C][C]115.208433589861[/C][C]3.291566410139[/C][/ROW]
[ROW][C]51[/C][C]94.4[/C][C]96.3685444622555[/C][C]-1.96854446225553[/C][/ROW]
[ROW][C]52[/C][C]102.3[/C][C]102.425842410489[/C][C]-0.125842410489466[/C][/ROW]
[ROW][C]53[/C][C]111.4[/C][C]113.354576332682[/C][C]-1.95457633268204[/C][/ROW]
[ROW][C]54[/C][C]99.2[/C][C]97.5885066005316[/C][C]1.61149339946844[/C][/ROW]
[ROW][C]55[/C][C]87.8[/C][C]92.0009658001225[/C][C]-4.20096580012253[/C][/ROW]
[ROW][C]56[/C][C]115.8[/C][C]114.769595369728[/C][C]1.03040463027231[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58025&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58025&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
1104106.588023112507-2.5880231125075
2109.9113.603028556769-3.70302855676944
39996.26956694317442.73043305682563
4106.3106.544783992429-0.244783992428747
5128.9126.9717300703631.92826992963728
6111.1113.186829413931-2.08682941393122
7102.9109.459416296700-6.55941629670036
8130130.187172633729-0.187172633729466
98784.73014105889822.26985894110176
1087.581.81580965322135.68419034677873
11117.6117.727480971153-0.127480971152990
12103.4107.944087815115-4.54408781511537
13110.8114.374902298524-3.57490229852384
14112.6117.96821514953-5.3682151495301
15102.5102.2235768943130.27642310568715
16112.4112.10920396110.290796038899986
17135.6136.846696542066-1.24669654206571
18105.1110.350404176411-5.25040417641103
19127.7121.0212400416386.67875995836246
20137136.3311410847750.668858915224647
219194.1441208189833-3.14412081898335
2290.590.7374223959174-0.237422395917378
23122.4120.5674655402901.83253445970969
24123.3119.0660173276534.23398267234656
25124.3119.8866088146444.41339118535619
26120118.3541777653641.64582223463643
27118.1114.2182548830493.88174511695064
28119120.097506567961-1.09750656796095
29142.7141.7803397342920.919660265707606
30123.6120.4582191812493.14178081875114
31129.6125.5516608174034.04833918259692
32151.6145.0409143858626.55908561413800
33110.4107.5258752478482.8741247521523
3499.2102.10964815419-2.90964815418991
35130.5127.4137354038113.08626459618946
36136.2134.67332550331.5266744967
37129.7126.3916398646463.30836013535443
38128123.8661449384764.13385506152412
39121.6126.520056817208-4.92005681720789
40135.8134.6226630680211.17733693197917
41143.8143.4466573205970.35334267940286
42147.5144.9160406278772.58395937212268
43136.2136.1667170441360.033282955863508
44156.6164.671176525905-8.0711765259055
45123.3125.299862874271-1.99986287427072
46104.5107.037119796671-2.53711979667143
47139.8144.591318084746-4.79131808474616
48136.5137.716569353931-1.2165693539312
49112.1113.658825909679-1.55882590967928
50118.5115.2084335898613.291566410139
5194.496.3685444622555-1.96854446225553
52102.3102.425842410489-0.125842410489466
53111.4113.354576332682-1.95457633268204
5499.297.58850660053161.61149339946844
5587.892.0009658001225-4.20096580012253
56115.8114.7695953697281.03040463027231







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
210.8564147275216850.2871705449566290.143585272478315
220.9007050382094170.1985899235811660.0992949617905828
230.8600960393981460.2798079212037080.139903960601854
240.8351438346362660.3297123307274670.164856165363734
250.8587446233671080.2825107532657840.141255376632892
260.946782259937330.1064354801253390.0532177400626696
270.9566597763930930.08668044721381350.0433402236069068
280.957461431680050.0850771366398990.0425385683199495
290.9360890620996250.1278218758007510.0639109379003754
300.959182636609740.0816347267805180.040817363390259
310.928219799314570.1435604013708590.0717802006854293
320.8785055473381210.2429889053237580.121494452661879
330.7917740606629840.4164518786740310.208225939337016
340.7254681470522830.5490637058954330.274531852947717
350.5544957723082690.8910084553834630.445504227691731

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
21 & 0.856414727521685 & 0.287170544956629 & 0.143585272478315 \tabularnewline
22 & 0.900705038209417 & 0.198589923581166 & 0.0992949617905828 \tabularnewline
23 & 0.860096039398146 & 0.279807921203708 & 0.139903960601854 \tabularnewline
24 & 0.835143834636266 & 0.329712330727467 & 0.164856165363734 \tabularnewline
25 & 0.858744623367108 & 0.282510753265784 & 0.141255376632892 \tabularnewline
26 & 0.94678225993733 & 0.106435480125339 & 0.0532177400626696 \tabularnewline
27 & 0.956659776393093 & 0.0866804472138135 & 0.0433402236069068 \tabularnewline
28 & 0.95746143168005 & 0.085077136639899 & 0.0425385683199495 \tabularnewline
29 & 0.936089062099625 & 0.127821875800751 & 0.0639109379003754 \tabularnewline
30 & 0.95918263660974 & 0.081634726780518 & 0.040817363390259 \tabularnewline
31 & 0.92821979931457 & 0.143560401370859 & 0.0717802006854293 \tabularnewline
32 & 0.878505547338121 & 0.242988905323758 & 0.121494452661879 \tabularnewline
33 & 0.791774060662984 & 0.416451878674031 & 0.208225939337016 \tabularnewline
34 & 0.725468147052283 & 0.549063705895433 & 0.274531852947717 \tabularnewline
35 & 0.554495772308269 & 0.891008455383463 & 0.445504227691731 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58025&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]0.856414727521685[/C][C]0.287170544956629[/C][C]0.143585272478315[/C][/ROW]
[ROW][C]22[/C][C]0.900705038209417[/C][C]0.198589923581166[/C][C]0.0992949617905828[/C][/ROW]
[ROW][C]23[/C][C]0.860096039398146[/C][C]0.279807921203708[/C][C]0.139903960601854[/C][/ROW]
[ROW][C]24[/C][C]0.835143834636266[/C][C]0.329712330727467[/C][C]0.164856165363734[/C][/ROW]
[ROW][C]25[/C][C]0.858744623367108[/C][C]0.282510753265784[/C][C]0.141255376632892[/C][/ROW]
[ROW][C]26[/C][C]0.94678225993733[/C][C]0.106435480125339[/C][C]0.0532177400626696[/C][/ROW]
[ROW][C]27[/C][C]0.956659776393093[/C][C]0.0866804472138135[/C][C]0.0433402236069068[/C][/ROW]
[ROW][C]28[/C][C]0.95746143168005[/C][C]0.085077136639899[/C][C]0.0425385683199495[/C][/ROW]
[ROW][C]29[/C][C]0.936089062099625[/C][C]0.127821875800751[/C][C]0.0639109379003754[/C][/ROW]
[ROW][C]30[/C][C]0.95918263660974[/C][C]0.081634726780518[/C][C]0.040817363390259[/C][/ROW]
[ROW][C]31[/C][C]0.92821979931457[/C][C]0.143560401370859[/C][C]0.0717802006854293[/C][/ROW]
[ROW][C]32[/C][C]0.878505547338121[/C][C]0.242988905323758[/C][C]0.121494452661879[/C][/ROW]
[ROW][C]33[/C][C]0.791774060662984[/C][C]0.416451878674031[/C][C]0.208225939337016[/C][/ROW]
[ROW][C]34[/C][C]0.725468147052283[/C][C]0.549063705895433[/C][C]0.274531852947717[/C][/ROW]
[ROW][C]35[/C][C]0.554495772308269[/C][C]0.891008455383463[/C][C]0.445504227691731[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58025&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58025&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
210.8564147275216850.2871705449566290.143585272478315
220.9007050382094170.1985899235811660.0992949617905828
230.8600960393981460.2798079212037080.139903960601854
240.8351438346362660.3297123307274670.164856165363734
250.8587446233671080.2825107532657840.141255376632892
260.946782259937330.1064354801253390.0532177400626696
270.9566597763930930.08668044721381350.0433402236069068
280.957461431680050.0850771366398990.0425385683199495
290.9360890620996250.1278218758007510.0639109379003754
300.959182636609740.0816347267805180.040817363390259
310.928219799314570.1435604013708590.0717802006854293
320.8785055473381210.2429889053237580.121494452661879
330.7917740606629840.4164518786740310.208225939337016
340.7254681470522830.5490637058954330.274531852947717
350.5544957723082690.8910084553834630.445504227691731







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 level30.2NOK

\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 & 3 & 0.2 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58025&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]3[/C][C]0.2[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58025&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58025&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 level30.2NOK



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')
}