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Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationFri, 20 Nov 2009 08:15:58 -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/t125873028019ebivgbpl0ygeb.htm/, Retrieved Thu, 28 Mar 2024 14:42:56 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=58260, Retrieved Thu, 28 Mar 2024 14:42:56 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact171
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [WS7 Multiple Regr...] [2009-11-20 15:15:58] [2694a35f9be9144abd040893a0238ab5] [Current]
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Dataseries X:
96.8	92.9
114.1	107.7
110.3	103.5
103.9	91.1
101.6	79.8
94.6	71.9
95.9	82.9
104.7	90.1
102.8	100.7
98.1	90.7
113.9	108.8
80.9	44.1
95.7	93.6
113.2	107.4
105.9	96.5
108.8	93.6
102.3	76.5
99	76.7
100.7	84
115.5	103.3
100.7	88.5
109.9	99
114.6	105.9
85.4	44.7
100.5	94
114.8	107.1
116.5	104.8
112.9	102.5
102	77.7
106	85.2
105.3	91.3
118.8	106.5
106.1	92.4
109.3	97.5
117.2	107
92.5	51.1
104.2	98.6
112.5	102.2
122.4	114.3
113.3	99.4
100	72.5
110.7	92.3
112.8	99.4
109.8	85.9
117.3	109.4
109.1	97.6
115.9	104.7
96	56.9
99.8	86.7
116.8	108.5
115.7	103.4
99.4	86.2
94.3	71
91	75.9
93.2	87.1
103.1	102
94.1	88.5
91.8	87.8
102.7	100.8
82.6	50.6




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=58260&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=58260&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58260&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
Totind[t] = + 46.8345190965529 + 0.831862377966146Bouw[t] -24.5731310346029M1[t] -20.8424166591775M2[t] -19.2178354252768M3[t] -17.4348159005623M4[t] -9.18521148879648M5[t] -13.0270296530995M6[t] -18.7968268731994M7[t] -17.1531730835365M8[t] -21.9379740483817M9[t] -21.3356964790573M10[t] -21.1853261587166M11[t] -0.0143074877310507t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Totind[t] =  +  46.8345190965529 +  0.831862377966146Bouw[t] -24.5731310346029M1[t] -20.8424166591775M2[t] -19.2178354252768M3[t] -17.4348159005623M4[t] -9.18521148879648M5[t] -13.0270296530995M6[t] -18.7968268731994M7[t] -17.1531730835365M8[t] -21.9379740483817M9[t] -21.3356964790573M10[t] -21.1853261587166M11[t] -0.0143074877310507t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58260&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Totind[t] =  +  46.8345190965529 +  0.831862377966146Bouw[t] -24.5731310346029M1[t] -20.8424166591775M2[t] -19.2178354252768M3[t] -17.4348159005623M4[t] -9.18521148879648M5[t] -13.0270296530995M6[t] -18.7968268731994M7[t] -17.1531730835365M8[t] -21.9379740483817M9[t] -21.3356964790573M10[t] -21.1853261587166M11[t] -0.0143074877310507t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58260&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58260&T=1

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Estimated Regression Equation
Totind[t] = + 46.8345190965529 + 0.831862377966146Bouw[t] -24.5731310346029M1[t] -20.8424166591775M2[t] -19.2178354252768M3[t] -17.4348159005623M4[t] -9.18521148879648M5[t] -13.0270296530995M6[t] -18.7968268731994M7[t] -17.1531730835365M8[t] -21.9379740483817M9[t] -21.3356964790573M10[t] -21.1853261587166M11[t] -0.0143074877310507t + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)46.83451909655294.8882139.581100
Bouw0.8318623779661460.0912449.116900
M1-24.57313103460294.714995-5.21174e-062e-06
M2-20.84241665917755.788456-3.60070.0007750.000387
M3-19.21783542527685.614245-3.4230.0013110.000655
M4-17.43481590056234.813629-3.6220.0007270.000363
M5-9.185211488796483.436444-2.67290.0103710.005185
M6-13.02702965309953.757899-3.46660.0011540.000577
M7-18.79682687319944.372697-4.29878.8e-054.4e-05
M8-17.15317308353655.039153-3.4040.0013860.000693
M9-21.93797404838174.90522-4.47245e-052.5e-05
M10-21.33569647905734.794551-4.455.4e-052.7e-05
M11-21.18532615871665.670517-3.7360.0005150.000257
t-0.01430748773105070.029684-0.4820.6320920.316046

\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) & 46.8345190965529 & 4.888213 & 9.5811 & 0 & 0 \tabularnewline
Bouw & 0.831862377966146 & 0.091244 & 9.1169 & 0 & 0 \tabularnewline
M1 & -24.5731310346029 & 4.714995 & -5.2117 & 4e-06 & 2e-06 \tabularnewline
M2 & -20.8424166591775 & 5.788456 & -3.6007 & 0.000775 & 0.000387 \tabularnewline
M3 & -19.2178354252768 & 5.614245 & -3.423 & 0.001311 & 0.000655 \tabularnewline
M4 & -17.4348159005623 & 4.813629 & -3.622 & 0.000727 & 0.000363 \tabularnewline
M5 & -9.18521148879648 & 3.436444 & -2.6729 & 0.010371 & 0.005185 \tabularnewline
M6 & -13.0270296530995 & 3.757899 & -3.4666 & 0.001154 & 0.000577 \tabularnewline
M7 & -18.7968268731994 & 4.372697 & -4.2987 & 8.8e-05 & 4.4e-05 \tabularnewline
M8 & -17.1531730835365 & 5.039153 & -3.404 & 0.001386 & 0.000693 \tabularnewline
M9 & -21.9379740483817 & 4.90522 & -4.4724 & 5e-05 & 2.5e-05 \tabularnewline
M10 & -21.3356964790573 & 4.794551 & -4.45 & 5.4e-05 & 2.7e-05 \tabularnewline
M11 & -21.1853261587166 & 5.670517 & -3.736 & 0.000515 & 0.000257 \tabularnewline
t & -0.0143074877310507 & 0.029684 & -0.482 & 0.632092 & 0.316046 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58260&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]46.8345190965529[/C][C]4.888213[/C][C]9.5811[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Bouw[/C][C]0.831862377966146[/C][C]0.091244[/C][C]9.1169[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M1[/C][C]-24.5731310346029[/C][C]4.714995[/C][C]-5.2117[/C][C]4e-06[/C][C]2e-06[/C][/ROW]
[ROW][C]M2[/C][C]-20.8424166591775[/C][C]5.788456[/C][C]-3.6007[/C][C]0.000775[/C][C]0.000387[/C][/ROW]
[ROW][C]M3[/C][C]-19.2178354252768[/C][C]5.614245[/C][C]-3.423[/C][C]0.001311[/C][C]0.000655[/C][/ROW]
[ROW][C]M4[/C][C]-17.4348159005623[/C][C]4.813629[/C][C]-3.622[/C][C]0.000727[/C][C]0.000363[/C][/ROW]
[ROW][C]M5[/C][C]-9.18521148879648[/C][C]3.436444[/C][C]-2.6729[/C][C]0.010371[/C][C]0.005185[/C][/ROW]
[ROW][C]M6[/C][C]-13.0270296530995[/C][C]3.757899[/C][C]-3.4666[/C][C]0.001154[/C][C]0.000577[/C][/ROW]
[ROW][C]M7[/C][C]-18.7968268731994[/C][C]4.372697[/C][C]-4.2987[/C][C]8.8e-05[/C][C]4.4e-05[/C][/ROW]
[ROW][C]M8[/C][C]-17.1531730835365[/C][C]5.039153[/C][C]-3.404[/C][C]0.001386[/C][C]0.000693[/C][/ROW]
[ROW][C]M9[/C][C]-21.9379740483817[/C][C]4.90522[/C][C]-4.4724[/C][C]5e-05[/C][C]2.5e-05[/C][/ROW]
[ROW][C]M10[/C][C]-21.3356964790573[/C][C]4.794551[/C][C]-4.45[/C][C]5.4e-05[/C][C]2.7e-05[/C][/ROW]
[ROW][C]M11[/C][C]-21.1853261587166[/C][C]5.670517[/C][C]-3.736[/C][C]0.000515[/C][C]0.000257[/C][/ROW]
[ROW][C]t[/C][C]-0.0143074877310507[/C][C]0.029684[/C][C]-0.482[/C][C]0.632092[/C][C]0.316046[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58260&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58260&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)46.83451909655294.8882139.581100
Bouw0.8318623779661460.0912449.116900
M1-24.57313103460294.714995-5.21174e-062e-06
M2-20.84241665917755.788456-3.60070.0007750.000387
M3-19.21783542527685.614245-3.4230.0013110.000655
M4-17.43481590056234.813629-3.6220.0007270.000363
M5-9.185211488796483.436444-2.67290.0103710.005185
M6-13.02702965309953.757899-3.46660.0011540.000577
M7-18.79682687319944.372697-4.29878.8e-054.4e-05
M8-17.15317308353655.039153-3.4040.0013860.000693
M9-21.93797404838174.90522-4.47245e-052.5e-05
M10-21.33569647905734.794551-4.455.4e-052.7e-05
M11-21.18532615871665.670517-3.7360.0005150.000257
t-0.01430748773105070.029684-0.4820.6320920.316046







Multiple Linear Regression - Regression Statistics
Multiple R0.931746328205055
R-squared0.868151220123602
Adjusted R-squared0.830889608419402
F-TEST (value)23.2988102343882
F-TEST (DF numerator)13
F-TEST (DF denominator)46
p-value6.66133814775094e-16
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.89283805003852
Sum Squared Residuals697.092651856073

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.931746328205055 \tabularnewline
R-squared & 0.868151220123602 \tabularnewline
Adjusted R-squared & 0.830889608419402 \tabularnewline
F-TEST (value) & 23.2988102343882 \tabularnewline
F-TEST (DF numerator) & 13 \tabularnewline
F-TEST (DF denominator) & 46 \tabularnewline
p-value & 6.66133814775094e-16 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 3.89283805003852 \tabularnewline
Sum Squared Residuals & 697.092651856073 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58260&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.931746328205055[/C][/ROW]
[ROW][C]R-squared[/C][C]0.868151220123602[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.830889608419402[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]23.2988102343882[/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]6.66133814775094e-16[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]3.89283805003852[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]697.092651856073[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58260&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58260&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 R0.931746328205055
R-squared0.868151220123602
Adjusted R-squared0.830889608419402
F-TEST (value)23.2988102343882
F-TEST (DF numerator)13
F-TEST (DF denominator)46
p-value6.66133814775094e-16
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.89283805003852
Sum Squared Residuals697.092651856073







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
196.899.5270954872742-2.72709548727416
2114.1115.555065568867-1.45506556886731
3110.3113.671517327579-3.37151732757907
4103.9105.125135877782-1.22513587778233
5101.6103.960387930800-2.36038793079965
694.693.5325494928331.06745050716704
795.996.8989309426297-0.998930942629699
8104.7104.5176863659180.182313634082246
9102.8108.536319119783-5.73631911978272
1098.1100.805665421715-2.70566542171457
11113.9115.998437295511-2.09843729551147
1280.983.3479601120873-2.44796011208735
1395.799.9377092990777-4.23770929907767
14113.2115.133817002705-1.93381700270485
15105.9107.676790829043-1.77679082904343
16108.8107.0331019699251.76689803007489
17102.3101.0435522307391.25644776926124
189997.35379905429791.64620094570213
19100.797.64228970561983.05771029438016
20115.5115.3265799022980.173420097701714
21100.798.21590825582312.48409174417688
22109.9107.5384333060612.36156669393907
23114.6113.4143465466371.18565345336296
2485.483.67538768609441.72461231390557
25100.5100.0987643974920.401235602508471
26114.8114.7125684365420.0874315634576082
27116.5114.4095587133902.09044128661015
28112.9114.264987281051-1.36498728105120
29102101.8700972315260.129902768474479
30106104.2529394142381.74706058576250
31105.3103.5431952120001.75680478799990
32118.8117.8168496590170.983150340982653
33106.1101.2884816771184.8115183228815
34109.3106.1189498863393.18105011366088
35117.2114.1577053096273.04229469037281
3692.588.82761705230523.67238294769485
37104.2103.7536414833630.446358516636813
38112.5110.4647529317362.03524706826433
39122.4122.1405614512960.259438548704380
40113.3111.5145240565841.78547594341645
4110097.3727230133292.62727698667105
42110.7109.9874724450250.71252755497547
43112.8110.1095906207532.69040937924671
44109.8100.5087948201429.29120517985787
45117.3115.2584522497702.04154775022963
46109.1106.0304462713633.06955372863688
47115.9112.0727319875323.82726801246756
489693.48072899173622.51927100826381
4999.893.68278933279346.11721066720655
50116.8115.5337960601501.26620393985022
51115.7112.9015716786922.79842832130797
5299.4100.362250814658-0.962250814657801
5394.395.9532395936071-1.65323959360713
549196.1732395936071-5.17323959360713
5593.299.705993518997-6.50599351899707
56103.1113.730089252624-10.6300892526245
5794.197.7008386975053-3.6008386975053
5891.897.7065051145223-5.90650511452227
59102.7108.656778860692-5.95677886069186
6082.688.0683061577769-5.46830615777687

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 96.8 & 99.5270954872742 & -2.72709548727416 \tabularnewline
2 & 114.1 & 115.555065568867 & -1.45506556886731 \tabularnewline
3 & 110.3 & 113.671517327579 & -3.37151732757907 \tabularnewline
4 & 103.9 & 105.125135877782 & -1.22513587778233 \tabularnewline
5 & 101.6 & 103.960387930800 & -2.36038793079965 \tabularnewline
6 & 94.6 & 93.532549492833 & 1.06745050716704 \tabularnewline
7 & 95.9 & 96.8989309426297 & -0.998930942629699 \tabularnewline
8 & 104.7 & 104.517686365918 & 0.182313634082246 \tabularnewline
9 & 102.8 & 108.536319119783 & -5.73631911978272 \tabularnewline
10 & 98.1 & 100.805665421715 & -2.70566542171457 \tabularnewline
11 & 113.9 & 115.998437295511 & -2.09843729551147 \tabularnewline
12 & 80.9 & 83.3479601120873 & -2.44796011208735 \tabularnewline
13 & 95.7 & 99.9377092990777 & -4.23770929907767 \tabularnewline
14 & 113.2 & 115.133817002705 & -1.93381700270485 \tabularnewline
15 & 105.9 & 107.676790829043 & -1.77679082904343 \tabularnewline
16 & 108.8 & 107.033101969925 & 1.76689803007489 \tabularnewline
17 & 102.3 & 101.043552230739 & 1.25644776926124 \tabularnewline
18 & 99 & 97.3537990542979 & 1.64620094570213 \tabularnewline
19 & 100.7 & 97.6422897056198 & 3.05771029438016 \tabularnewline
20 & 115.5 & 115.326579902298 & 0.173420097701714 \tabularnewline
21 & 100.7 & 98.2159082558231 & 2.48409174417688 \tabularnewline
22 & 109.9 & 107.538433306061 & 2.36156669393907 \tabularnewline
23 & 114.6 & 113.414346546637 & 1.18565345336296 \tabularnewline
24 & 85.4 & 83.6753876860944 & 1.72461231390557 \tabularnewline
25 & 100.5 & 100.098764397492 & 0.401235602508471 \tabularnewline
26 & 114.8 & 114.712568436542 & 0.0874315634576082 \tabularnewline
27 & 116.5 & 114.409558713390 & 2.09044128661015 \tabularnewline
28 & 112.9 & 114.264987281051 & -1.36498728105120 \tabularnewline
29 & 102 & 101.870097231526 & 0.129902768474479 \tabularnewline
30 & 106 & 104.252939414238 & 1.74706058576250 \tabularnewline
31 & 105.3 & 103.543195212000 & 1.75680478799990 \tabularnewline
32 & 118.8 & 117.816849659017 & 0.983150340982653 \tabularnewline
33 & 106.1 & 101.288481677118 & 4.8115183228815 \tabularnewline
34 & 109.3 & 106.118949886339 & 3.18105011366088 \tabularnewline
35 & 117.2 & 114.157705309627 & 3.04229469037281 \tabularnewline
36 & 92.5 & 88.8276170523052 & 3.67238294769485 \tabularnewline
37 & 104.2 & 103.753641483363 & 0.446358516636813 \tabularnewline
38 & 112.5 & 110.464752931736 & 2.03524706826433 \tabularnewline
39 & 122.4 & 122.140561451296 & 0.259438548704380 \tabularnewline
40 & 113.3 & 111.514524056584 & 1.78547594341645 \tabularnewline
41 & 100 & 97.372723013329 & 2.62727698667105 \tabularnewline
42 & 110.7 & 109.987472445025 & 0.71252755497547 \tabularnewline
43 & 112.8 & 110.109590620753 & 2.69040937924671 \tabularnewline
44 & 109.8 & 100.508794820142 & 9.29120517985787 \tabularnewline
45 & 117.3 & 115.258452249770 & 2.04154775022963 \tabularnewline
46 & 109.1 & 106.030446271363 & 3.06955372863688 \tabularnewline
47 & 115.9 & 112.072731987532 & 3.82726801246756 \tabularnewline
48 & 96 & 93.4807289917362 & 2.51927100826381 \tabularnewline
49 & 99.8 & 93.6827893327934 & 6.11721066720655 \tabularnewline
50 & 116.8 & 115.533796060150 & 1.26620393985022 \tabularnewline
51 & 115.7 & 112.901571678692 & 2.79842832130797 \tabularnewline
52 & 99.4 & 100.362250814658 & -0.962250814657801 \tabularnewline
53 & 94.3 & 95.9532395936071 & -1.65323959360713 \tabularnewline
54 & 91 & 96.1732395936071 & -5.17323959360713 \tabularnewline
55 & 93.2 & 99.705993518997 & -6.50599351899707 \tabularnewline
56 & 103.1 & 113.730089252624 & -10.6300892526245 \tabularnewline
57 & 94.1 & 97.7008386975053 & -3.6008386975053 \tabularnewline
58 & 91.8 & 97.7065051145223 & -5.90650511452227 \tabularnewline
59 & 102.7 & 108.656778860692 & -5.95677886069186 \tabularnewline
60 & 82.6 & 88.0683061577769 & -5.46830615777687 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58260&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]96.8[/C][C]99.5270954872742[/C][C]-2.72709548727416[/C][/ROW]
[ROW][C]2[/C][C]114.1[/C][C]115.555065568867[/C][C]-1.45506556886731[/C][/ROW]
[ROW][C]3[/C][C]110.3[/C][C]113.671517327579[/C][C]-3.37151732757907[/C][/ROW]
[ROW][C]4[/C][C]103.9[/C][C]105.125135877782[/C][C]-1.22513587778233[/C][/ROW]
[ROW][C]5[/C][C]101.6[/C][C]103.960387930800[/C][C]-2.36038793079965[/C][/ROW]
[ROW][C]6[/C][C]94.6[/C][C]93.532549492833[/C][C]1.06745050716704[/C][/ROW]
[ROW][C]7[/C][C]95.9[/C][C]96.8989309426297[/C][C]-0.998930942629699[/C][/ROW]
[ROW][C]8[/C][C]104.7[/C][C]104.517686365918[/C][C]0.182313634082246[/C][/ROW]
[ROW][C]9[/C][C]102.8[/C][C]108.536319119783[/C][C]-5.73631911978272[/C][/ROW]
[ROW][C]10[/C][C]98.1[/C][C]100.805665421715[/C][C]-2.70566542171457[/C][/ROW]
[ROW][C]11[/C][C]113.9[/C][C]115.998437295511[/C][C]-2.09843729551147[/C][/ROW]
[ROW][C]12[/C][C]80.9[/C][C]83.3479601120873[/C][C]-2.44796011208735[/C][/ROW]
[ROW][C]13[/C][C]95.7[/C][C]99.9377092990777[/C][C]-4.23770929907767[/C][/ROW]
[ROW][C]14[/C][C]113.2[/C][C]115.133817002705[/C][C]-1.93381700270485[/C][/ROW]
[ROW][C]15[/C][C]105.9[/C][C]107.676790829043[/C][C]-1.77679082904343[/C][/ROW]
[ROW][C]16[/C][C]108.8[/C][C]107.033101969925[/C][C]1.76689803007489[/C][/ROW]
[ROW][C]17[/C][C]102.3[/C][C]101.043552230739[/C][C]1.25644776926124[/C][/ROW]
[ROW][C]18[/C][C]99[/C][C]97.3537990542979[/C][C]1.64620094570213[/C][/ROW]
[ROW][C]19[/C][C]100.7[/C][C]97.6422897056198[/C][C]3.05771029438016[/C][/ROW]
[ROW][C]20[/C][C]115.5[/C][C]115.326579902298[/C][C]0.173420097701714[/C][/ROW]
[ROW][C]21[/C][C]100.7[/C][C]98.2159082558231[/C][C]2.48409174417688[/C][/ROW]
[ROW][C]22[/C][C]109.9[/C][C]107.538433306061[/C][C]2.36156669393907[/C][/ROW]
[ROW][C]23[/C][C]114.6[/C][C]113.414346546637[/C][C]1.18565345336296[/C][/ROW]
[ROW][C]24[/C][C]85.4[/C][C]83.6753876860944[/C][C]1.72461231390557[/C][/ROW]
[ROW][C]25[/C][C]100.5[/C][C]100.098764397492[/C][C]0.401235602508471[/C][/ROW]
[ROW][C]26[/C][C]114.8[/C][C]114.712568436542[/C][C]0.0874315634576082[/C][/ROW]
[ROW][C]27[/C][C]116.5[/C][C]114.409558713390[/C][C]2.09044128661015[/C][/ROW]
[ROW][C]28[/C][C]112.9[/C][C]114.264987281051[/C][C]-1.36498728105120[/C][/ROW]
[ROW][C]29[/C][C]102[/C][C]101.870097231526[/C][C]0.129902768474479[/C][/ROW]
[ROW][C]30[/C][C]106[/C][C]104.252939414238[/C][C]1.74706058576250[/C][/ROW]
[ROW][C]31[/C][C]105.3[/C][C]103.543195212000[/C][C]1.75680478799990[/C][/ROW]
[ROW][C]32[/C][C]118.8[/C][C]117.816849659017[/C][C]0.983150340982653[/C][/ROW]
[ROW][C]33[/C][C]106.1[/C][C]101.288481677118[/C][C]4.8115183228815[/C][/ROW]
[ROW][C]34[/C][C]109.3[/C][C]106.118949886339[/C][C]3.18105011366088[/C][/ROW]
[ROW][C]35[/C][C]117.2[/C][C]114.157705309627[/C][C]3.04229469037281[/C][/ROW]
[ROW][C]36[/C][C]92.5[/C][C]88.8276170523052[/C][C]3.67238294769485[/C][/ROW]
[ROW][C]37[/C][C]104.2[/C][C]103.753641483363[/C][C]0.446358516636813[/C][/ROW]
[ROW][C]38[/C][C]112.5[/C][C]110.464752931736[/C][C]2.03524706826433[/C][/ROW]
[ROW][C]39[/C][C]122.4[/C][C]122.140561451296[/C][C]0.259438548704380[/C][/ROW]
[ROW][C]40[/C][C]113.3[/C][C]111.514524056584[/C][C]1.78547594341645[/C][/ROW]
[ROW][C]41[/C][C]100[/C][C]97.372723013329[/C][C]2.62727698667105[/C][/ROW]
[ROW][C]42[/C][C]110.7[/C][C]109.987472445025[/C][C]0.71252755497547[/C][/ROW]
[ROW][C]43[/C][C]112.8[/C][C]110.109590620753[/C][C]2.69040937924671[/C][/ROW]
[ROW][C]44[/C][C]109.8[/C][C]100.508794820142[/C][C]9.29120517985787[/C][/ROW]
[ROW][C]45[/C][C]117.3[/C][C]115.258452249770[/C][C]2.04154775022963[/C][/ROW]
[ROW][C]46[/C][C]109.1[/C][C]106.030446271363[/C][C]3.06955372863688[/C][/ROW]
[ROW][C]47[/C][C]115.9[/C][C]112.072731987532[/C][C]3.82726801246756[/C][/ROW]
[ROW][C]48[/C][C]96[/C][C]93.4807289917362[/C][C]2.51927100826381[/C][/ROW]
[ROW][C]49[/C][C]99.8[/C][C]93.6827893327934[/C][C]6.11721066720655[/C][/ROW]
[ROW][C]50[/C][C]116.8[/C][C]115.533796060150[/C][C]1.26620393985022[/C][/ROW]
[ROW][C]51[/C][C]115.7[/C][C]112.901571678692[/C][C]2.79842832130797[/C][/ROW]
[ROW][C]52[/C][C]99.4[/C][C]100.362250814658[/C][C]-0.962250814657801[/C][/ROW]
[ROW][C]53[/C][C]94.3[/C][C]95.9532395936071[/C][C]-1.65323959360713[/C][/ROW]
[ROW][C]54[/C][C]91[/C][C]96.1732395936071[/C][C]-5.17323959360713[/C][/ROW]
[ROW][C]55[/C][C]93.2[/C][C]99.705993518997[/C][C]-6.50599351899707[/C][/ROW]
[ROW][C]56[/C][C]103.1[/C][C]113.730089252624[/C][C]-10.6300892526245[/C][/ROW]
[ROW][C]57[/C][C]94.1[/C][C]97.7008386975053[/C][C]-3.6008386975053[/C][/ROW]
[ROW][C]58[/C][C]91.8[/C][C]97.7065051145223[/C][C]-5.90650511452227[/C][/ROW]
[ROW][C]59[/C][C]102.7[/C][C]108.656778860692[/C][C]-5.95677886069186[/C][/ROW]
[ROW][C]60[/C][C]82.6[/C][C]88.0683061577769[/C][C]-5.46830615777687[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58260&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58260&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
196.899.5270954872742-2.72709548727416
2114.1115.555065568867-1.45506556886731
3110.3113.671517327579-3.37151732757907
4103.9105.125135877782-1.22513587778233
5101.6103.960387930800-2.36038793079965
694.693.5325494928331.06745050716704
795.996.8989309426297-0.998930942629699
8104.7104.5176863659180.182313634082246
9102.8108.536319119783-5.73631911978272
1098.1100.805665421715-2.70566542171457
11113.9115.998437295511-2.09843729551147
1280.983.3479601120873-2.44796011208735
1395.799.9377092990777-4.23770929907767
14113.2115.133817002705-1.93381700270485
15105.9107.676790829043-1.77679082904343
16108.8107.0331019699251.76689803007489
17102.3101.0435522307391.25644776926124
189997.35379905429791.64620094570213
19100.797.64228970561983.05771029438016
20115.5115.3265799022980.173420097701714
21100.798.21590825582312.48409174417688
22109.9107.5384333060612.36156669393907
23114.6113.4143465466371.18565345336296
2485.483.67538768609441.72461231390557
25100.5100.0987643974920.401235602508471
26114.8114.7125684365420.0874315634576082
27116.5114.4095587133902.09044128661015
28112.9114.264987281051-1.36498728105120
29102101.8700972315260.129902768474479
30106104.2529394142381.74706058576250
31105.3103.5431952120001.75680478799990
32118.8117.8168496590170.983150340982653
33106.1101.2884816771184.8115183228815
34109.3106.1189498863393.18105011366088
35117.2114.1577053096273.04229469037281
3692.588.82761705230523.67238294769485
37104.2103.7536414833630.446358516636813
38112.5110.4647529317362.03524706826433
39122.4122.1405614512960.259438548704380
40113.3111.5145240565841.78547594341645
4110097.3727230133292.62727698667105
42110.7109.9874724450250.71252755497547
43112.8110.1095906207532.69040937924671
44109.8100.5087948201429.29120517985787
45117.3115.2584522497702.04154775022963
46109.1106.0304462713633.06955372863688
47115.9112.0727319875323.82726801246756
489693.48072899173622.51927100826381
4999.893.68278933279346.11721066720655
50116.8115.5337960601501.26620393985022
51115.7112.9015716786922.79842832130797
5299.4100.362250814658-0.962250814657801
5394.395.9532395936071-1.65323959360713
549196.1732395936071-5.17323959360713
5593.299.705993518997-6.50599351899707
56103.1113.730089252624-10.6300892526245
5794.197.7008386975053-3.6008386975053
5891.897.7065051145223-5.90650511452227
59102.7108.656778860692-5.95677886069186
6082.688.0683061577769-5.46830615777687







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
170.1138742132782350.2277484265564710.886125786721764
180.04100113116486180.08200226232972370.958998868835138
190.02945426099005380.05890852198010760.970545739009946
200.0107958715893670.0215917431787340.989204128410633
210.01500315660336950.03000631320673890.98499684339663
220.02056919314707650.04113838629415310.979430806852923
230.009429959739319240.01885991947863850.99057004026068
240.005117957681369930.01023591536273990.99488204231863
250.002948148285623650.00589629657124730.997051851714376
260.002434396678696100.004868793357392210.997565603321304
270.001662364176734000.003324728353468010.998337635823266
280.002549717573293340.005099435146586680.997450282426707
290.002858025631221810.005716051262443610.997141974368778
300.001334981765271750.002669963530543510.998665018234728
310.000687830402221060.001375660804442120.999312169597779
320.000329401764575450.00065880352915090.999670598235425
330.000274297117185060.000548594234370120.999725702882815
340.0001217505945054410.0002435011890108820.999878249405495
356.14326897591207e-050.0001228653795182410.99993856731024
367.80544792508097e-050.0001561089585016190.99992194552075
370.0003467020087520650.0006934040175041310.999653297991248
380.00629848356770870.01259696713541740.993701516432291
390.08298075414673070.1659615082934610.917019245853269
400.09981260476260750.1996252095252150.900187395237393
410.8261016484256650.3477967031486690.173898351574335
420.7177429029804330.5645141940391340.282257097019567
430.6303185126456920.7393629747086150.369681487354307

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
17 & 0.113874213278235 & 0.227748426556471 & 0.886125786721764 \tabularnewline
18 & 0.0410011311648618 & 0.0820022623297237 & 0.958998868835138 \tabularnewline
19 & 0.0294542609900538 & 0.0589085219801076 & 0.970545739009946 \tabularnewline
20 & 0.010795871589367 & 0.021591743178734 & 0.989204128410633 \tabularnewline
21 & 0.0150031566033695 & 0.0300063132067389 & 0.98499684339663 \tabularnewline
22 & 0.0205691931470765 & 0.0411383862941531 & 0.979430806852923 \tabularnewline
23 & 0.00942995973931924 & 0.0188599194786385 & 0.99057004026068 \tabularnewline
24 & 0.00511795768136993 & 0.0102359153627399 & 0.99488204231863 \tabularnewline
25 & 0.00294814828562365 & 0.0058962965712473 & 0.997051851714376 \tabularnewline
26 & 0.00243439667869610 & 0.00486879335739221 & 0.997565603321304 \tabularnewline
27 & 0.00166236417673400 & 0.00332472835346801 & 0.998337635823266 \tabularnewline
28 & 0.00254971757329334 & 0.00509943514658668 & 0.997450282426707 \tabularnewline
29 & 0.00285802563122181 & 0.00571605126244361 & 0.997141974368778 \tabularnewline
30 & 0.00133498176527175 & 0.00266996353054351 & 0.998665018234728 \tabularnewline
31 & 0.00068783040222106 & 0.00137566080444212 & 0.999312169597779 \tabularnewline
32 & 0.00032940176457545 & 0.0006588035291509 & 0.999670598235425 \tabularnewline
33 & 0.00027429711718506 & 0.00054859423437012 & 0.999725702882815 \tabularnewline
34 & 0.000121750594505441 & 0.000243501189010882 & 0.999878249405495 \tabularnewline
35 & 6.14326897591207e-05 & 0.000122865379518241 & 0.99993856731024 \tabularnewline
36 & 7.80544792508097e-05 & 0.000156108958501619 & 0.99992194552075 \tabularnewline
37 & 0.000346702008752065 & 0.000693404017504131 & 0.999653297991248 \tabularnewline
38 & 0.0062984835677087 & 0.0125969671354174 & 0.993701516432291 \tabularnewline
39 & 0.0829807541467307 & 0.165961508293461 & 0.917019245853269 \tabularnewline
40 & 0.0998126047626075 & 0.199625209525215 & 0.900187395237393 \tabularnewline
41 & 0.826101648425665 & 0.347796703148669 & 0.173898351574335 \tabularnewline
42 & 0.717742902980433 & 0.564514194039134 & 0.282257097019567 \tabularnewline
43 & 0.630318512645692 & 0.739362974708615 & 0.369681487354307 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58260&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]0.113874213278235[/C][C]0.227748426556471[/C][C]0.886125786721764[/C][/ROW]
[ROW][C]18[/C][C]0.0410011311648618[/C][C]0.0820022623297237[/C][C]0.958998868835138[/C][/ROW]
[ROW][C]19[/C][C]0.0294542609900538[/C][C]0.0589085219801076[/C][C]0.970545739009946[/C][/ROW]
[ROW][C]20[/C][C]0.010795871589367[/C][C]0.021591743178734[/C][C]0.989204128410633[/C][/ROW]
[ROW][C]21[/C][C]0.0150031566033695[/C][C]0.0300063132067389[/C][C]0.98499684339663[/C][/ROW]
[ROW][C]22[/C][C]0.0205691931470765[/C][C]0.0411383862941531[/C][C]0.979430806852923[/C][/ROW]
[ROW][C]23[/C][C]0.00942995973931924[/C][C]0.0188599194786385[/C][C]0.99057004026068[/C][/ROW]
[ROW][C]24[/C][C]0.00511795768136993[/C][C]0.0102359153627399[/C][C]0.99488204231863[/C][/ROW]
[ROW][C]25[/C][C]0.00294814828562365[/C][C]0.0058962965712473[/C][C]0.997051851714376[/C][/ROW]
[ROW][C]26[/C][C]0.00243439667869610[/C][C]0.00486879335739221[/C][C]0.997565603321304[/C][/ROW]
[ROW][C]27[/C][C]0.00166236417673400[/C][C]0.00332472835346801[/C][C]0.998337635823266[/C][/ROW]
[ROW][C]28[/C][C]0.00254971757329334[/C][C]0.00509943514658668[/C][C]0.997450282426707[/C][/ROW]
[ROW][C]29[/C][C]0.00285802563122181[/C][C]0.00571605126244361[/C][C]0.997141974368778[/C][/ROW]
[ROW][C]30[/C][C]0.00133498176527175[/C][C]0.00266996353054351[/C][C]0.998665018234728[/C][/ROW]
[ROW][C]31[/C][C]0.00068783040222106[/C][C]0.00137566080444212[/C][C]0.999312169597779[/C][/ROW]
[ROW][C]32[/C][C]0.00032940176457545[/C][C]0.0006588035291509[/C][C]0.999670598235425[/C][/ROW]
[ROW][C]33[/C][C]0.00027429711718506[/C][C]0.00054859423437012[/C][C]0.999725702882815[/C][/ROW]
[ROW][C]34[/C][C]0.000121750594505441[/C][C]0.000243501189010882[/C][C]0.999878249405495[/C][/ROW]
[ROW][C]35[/C][C]6.14326897591207e-05[/C][C]0.000122865379518241[/C][C]0.99993856731024[/C][/ROW]
[ROW][C]36[/C][C]7.80544792508097e-05[/C][C]0.000156108958501619[/C][C]0.99992194552075[/C][/ROW]
[ROW][C]37[/C][C]0.000346702008752065[/C][C]0.000693404017504131[/C][C]0.999653297991248[/C][/ROW]
[ROW][C]38[/C][C]0.0062984835677087[/C][C]0.0125969671354174[/C][C]0.993701516432291[/C][/ROW]
[ROW][C]39[/C][C]0.0829807541467307[/C][C]0.165961508293461[/C][C]0.917019245853269[/C][/ROW]
[ROW][C]40[/C][C]0.0998126047626075[/C][C]0.199625209525215[/C][C]0.900187395237393[/C][/ROW]
[ROW][C]41[/C][C]0.826101648425665[/C][C]0.347796703148669[/C][C]0.173898351574335[/C][/ROW]
[ROW][C]42[/C][C]0.717742902980433[/C][C]0.564514194039134[/C][C]0.282257097019567[/C][/ROW]
[ROW][C]43[/C][C]0.630318512645692[/C][C]0.739362974708615[/C][C]0.369681487354307[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58260&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58260&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
170.1138742132782350.2277484265564710.886125786721764
180.04100113116486180.08200226232972370.958998868835138
190.02945426099005380.05890852198010760.970545739009946
200.0107958715893670.0215917431787340.989204128410633
210.01500315660336950.03000631320673890.98499684339663
220.02056919314707650.04113838629415310.979430806852923
230.009429959739319240.01885991947863850.99057004026068
240.005117957681369930.01023591536273990.99488204231863
250.002948148285623650.00589629657124730.997051851714376
260.002434396678696100.004868793357392210.997565603321304
270.001662364176734000.003324728353468010.998337635823266
280.002549717573293340.005099435146586680.997450282426707
290.002858025631221810.005716051262443610.997141974368778
300.001334981765271750.002669963530543510.998665018234728
310.000687830402221060.001375660804442120.999312169597779
320.000329401764575450.00065880352915090.999670598235425
330.000274297117185060.000548594234370120.999725702882815
340.0001217505945054410.0002435011890108820.999878249405495
356.14326897591207e-050.0001228653795182410.99993856731024
367.80544792508097e-050.0001561089585016190.99992194552075
370.0003467020087520650.0006934040175041310.999653297991248
380.00629848356770870.01259696713541740.993701516432291
390.08298075414673070.1659615082934610.917019245853269
400.09981260476260750.1996252095252150.900187395237393
410.8261016484256650.3477967031486690.173898351574335
420.7177429029804330.5645141940391340.282257097019567
430.6303185126456920.7393629747086150.369681487354307







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level130.481481481481481NOK
5% type I error level190.703703703703704NOK
10% type I error level210.777777777777778NOK

\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 & 13 & 0.481481481481481 & NOK \tabularnewline
5% type I error level & 19 & 0.703703703703704 & NOK \tabularnewline
10% type I error level & 21 & 0.777777777777778 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58260&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]13[/C][C]0.481481481481481[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]19[/C][C]0.703703703703704[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]21[/C][C]0.777777777777778[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58260&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58260&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 level130.481481481481481NOK
5% type I error level190.703703703703704NOK
10% type I error level210.777777777777778NOK



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')
}