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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 computationThu, 19 Nov 2009 13:41:50 -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/19/t1258663387dqm0ly9zx17p7lm.htm/, Retrieved Thu, 28 Mar 2024 17:25:23 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=57945, Retrieved Thu, 28 Mar 2024 17:25:23 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact154
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Q1 The Seatbeltlaw] [2007-11-14 19:27:43] [8cd6641b921d30ebe00b648d1481bba0]
- RM D  [Multiple Regression] [Seatbelt] [2009-11-12 14:06:21] [b98453cac15ba1066b407e146608df68]
-   PD      [Multiple Regression] [multiple -lineair...] [2009-11-19 20:41:50] [244731fa3e7e6c85774b8c0902c58f85] [Current]
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Dataseries X:
0	6,3
0	6,2
0	6,1
0	6,3
0	6,5
0	6,6
0	6,5
0	6,2
0	6,2
0	5,9
0	6,1
0	6,1
0	6,1
0	6,1
0	6,1
0	6,4
0	6,7
0	6,9
0	7
0	7
0	6,8
0	6,4
0	5,9
0	5,5
0	5,5
0	5,6
0	5,8
0	5,9
0	6,1
0	6,1
0	6
0	6
0	5,9
0	5,5
0	5,6
0	5,4
0	5,2
0	5,2
0	5,2
0	5,5
1	5,8
1	5,8
1	5,5
1	5,3
1	5,1
1	5,2
1	5,8
1	5,8
1	5,5
1	5
1	4,9
1	5,3
1	6,1
1	6,5
1	6,8
1	6,6
1	6,4
1	6,4




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

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 6 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57945&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]6 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57945&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57945&T=0

As an alternative you can also use a QR Code:  

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

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







Multiple Linear Regression - Estimated Regression Equation
Y[t] = + 6.16057692307692 + 0.0480769230769208X[t] -0.0563621794871855M1[t] -0.140608974358974M2[t] -0.124855769230769M3[t] + 0.150897435897435M4[t] + 0.517035256410256M5[t] + 0.672788461538462M6[t] + 0.668541666666667M7[t] + 0.544294871794872M8[t] + 0.420048076923077M9[t] + 0.235801282051282M10[t] + 0.134246794871795M11[t] -0.0157532051282050t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Y[t] =  +  6.16057692307692 +  0.0480769230769208X[t] -0.0563621794871855M1[t] -0.140608974358974M2[t] -0.124855769230769M3[t] +  0.150897435897435M4[t] +  0.517035256410256M5[t] +  0.672788461538462M6[t] +  0.668541666666667M7[t] +  0.544294871794872M8[t] +  0.420048076923077M9[t] +  0.235801282051282M10[t] +  0.134246794871795M11[t] -0.0157532051282050t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57945&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Y[t] =  +  6.16057692307692 +  0.0480769230769208X[t] -0.0563621794871855M1[t] -0.140608974358974M2[t] -0.124855769230769M3[t] +  0.150897435897435M4[t] +  0.517035256410256M5[t] +  0.672788461538462M6[t] +  0.668541666666667M7[t] +  0.544294871794872M8[t] +  0.420048076923077M9[t] +  0.235801282051282M10[t] +  0.134246794871795M11[t] -0.0157532051282050t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57945&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57945&T=1

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Estimated Regression Equation
Y[t] = + 6.16057692307692 + 0.0480769230769208X[t] -0.0563621794871855M1[t] -0.140608974358974M2[t] -0.124855769230769M3[t] + 0.150897435897435M4[t] + 0.517035256410256M5[t] + 0.672788461538462M6[t] + 0.668541666666667M7[t] + 0.544294871794872M8[t] + 0.420048076923077M9[t] + 0.235801282051282M10[t] + 0.134246794871795M11[t] -0.0157532051282050t + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)6.160576923076920.2550224.157200
X0.04807692307692080.2101420.22880.8200980.410049
M1-0.05636217948718550.291549-0.19330.8475980.423799
M2-0.1406089743589740.291203-0.48290.6315920.315796
M3-0.1248557692307690.290971-0.42910.6699430.334972
M40.1508974358974350.2908520.51880.6064920.303246
M50.5170352564102560.2930251.76450.0845930.042297
M60.6727884615384620.2924712.30040.0262280.013114
M70.6685416666666670.292032.28930.0269180.013459
M80.5442948717948720.2917021.86590.0687260.034363
M90.4200480769230770.2914871.44110.156650.078325
M100.2358012820512820.2913870.80920.4227320.211366
M110.1342467948717950.306550.43790.6635810.331791
t-0.01575320512820500.00576-2.7350.0089570.004478

\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) & 6.16057692307692 & 0.25502 & 24.1572 & 0 & 0 \tabularnewline
X & 0.0480769230769208 & 0.210142 & 0.2288 & 0.820098 & 0.410049 \tabularnewline
M1 & -0.0563621794871855 & 0.291549 & -0.1933 & 0.847598 & 0.423799 \tabularnewline
M2 & -0.140608974358974 & 0.291203 & -0.4829 & 0.631592 & 0.315796 \tabularnewline
M3 & -0.124855769230769 & 0.290971 & -0.4291 & 0.669943 & 0.334972 \tabularnewline
M4 & 0.150897435897435 & 0.290852 & 0.5188 & 0.606492 & 0.303246 \tabularnewline
M5 & 0.517035256410256 & 0.293025 & 1.7645 & 0.084593 & 0.042297 \tabularnewline
M6 & 0.672788461538462 & 0.292471 & 2.3004 & 0.026228 & 0.013114 \tabularnewline
M7 & 0.668541666666667 & 0.29203 & 2.2893 & 0.026918 & 0.013459 \tabularnewline
M8 & 0.544294871794872 & 0.291702 & 1.8659 & 0.068726 & 0.034363 \tabularnewline
M9 & 0.420048076923077 & 0.291487 & 1.4411 & 0.15665 & 0.078325 \tabularnewline
M10 & 0.235801282051282 & 0.291387 & 0.8092 & 0.422732 & 0.211366 \tabularnewline
M11 & 0.134246794871795 & 0.30655 & 0.4379 & 0.663581 & 0.331791 \tabularnewline
t & -0.0157532051282050 & 0.00576 & -2.735 & 0.008957 & 0.004478 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57945&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]6.16057692307692[/C][C]0.25502[/C][C]24.1572[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]X[/C][C]0.0480769230769208[/C][C]0.210142[/C][C]0.2288[/C][C]0.820098[/C][C]0.410049[/C][/ROW]
[ROW][C]M1[/C][C]-0.0563621794871855[/C][C]0.291549[/C][C]-0.1933[/C][C]0.847598[/C][C]0.423799[/C][/ROW]
[ROW][C]M2[/C][C]-0.140608974358974[/C][C]0.291203[/C][C]-0.4829[/C][C]0.631592[/C][C]0.315796[/C][/ROW]
[ROW][C]M3[/C][C]-0.124855769230769[/C][C]0.290971[/C][C]-0.4291[/C][C]0.669943[/C][C]0.334972[/C][/ROW]
[ROW][C]M4[/C][C]0.150897435897435[/C][C]0.290852[/C][C]0.5188[/C][C]0.606492[/C][C]0.303246[/C][/ROW]
[ROW][C]M5[/C][C]0.517035256410256[/C][C]0.293025[/C][C]1.7645[/C][C]0.084593[/C][C]0.042297[/C][/ROW]
[ROW][C]M6[/C][C]0.672788461538462[/C][C]0.292471[/C][C]2.3004[/C][C]0.026228[/C][C]0.013114[/C][/ROW]
[ROW][C]M7[/C][C]0.668541666666667[/C][C]0.29203[/C][C]2.2893[/C][C]0.026918[/C][C]0.013459[/C][/ROW]
[ROW][C]M8[/C][C]0.544294871794872[/C][C]0.291702[/C][C]1.8659[/C][C]0.068726[/C][C]0.034363[/C][/ROW]
[ROW][C]M9[/C][C]0.420048076923077[/C][C]0.291487[/C][C]1.4411[/C][C]0.15665[/C][C]0.078325[/C][/ROW]
[ROW][C]M10[/C][C]0.235801282051282[/C][C]0.291387[/C][C]0.8092[/C][C]0.422732[/C][C]0.211366[/C][/ROW]
[ROW][C]M11[/C][C]0.134246794871795[/C][C]0.30655[/C][C]0.4379[/C][C]0.663581[/C][C]0.331791[/C][/ROW]
[ROW][C]t[/C][C]-0.0157532051282050[/C][C]0.00576[/C][C]-2.735[/C][C]0.008957[/C][C]0.004478[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57945&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57945&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)6.160576923076920.2550224.157200
X0.04807692307692080.2101420.22880.8200980.410049
M1-0.05636217948718550.291549-0.19330.8475980.423799
M2-0.1406089743589740.291203-0.48290.6315920.315796
M3-0.1248557692307690.290971-0.42910.6699430.334972
M40.1508974358974350.2908520.51880.6064920.303246
M50.5170352564102560.2930251.76450.0845930.042297
M60.6727884615384620.2924712.30040.0262280.013114
M70.6685416666666670.292032.28930.0269180.013459
M80.5442948717948720.2917021.86590.0687260.034363
M90.4200480769230770.2914871.44110.156650.078325
M100.2358012820512820.2913870.80920.4227320.211366
M110.1342467948717950.306550.43790.6635810.331791
t-0.01575320512820500.00576-2.7350.0089570.004478







Multiple Linear Regression - Regression Statistics
Multiple R0.695315838819151
R-squared0.48346411571278
Adjusted R-squared0.330851240809738
F-TEST (value)3.16791172448546
F-TEST (DF numerator)13
F-TEST (DF denominator)44
p-value0.0020884763094301
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.433451044170282
Sum Squared Residuals8.26671153846153

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.695315838819151 \tabularnewline
R-squared & 0.48346411571278 \tabularnewline
Adjusted R-squared & 0.330851240809738 \tabularnewline
F-TEST (value) & 3.16791172448546 \tabularnewline
F-TEST (DF numerator) & 13 \tabularnewline
F-TEST (DF denominator) & 44 \tabularnewline
p-value & 0.0020884763094301 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.433451044170282 \tabularnewline
Sum Squared Residuals & 8.26671153846153 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57945&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.695315838819151[/C][/ROW]
[ROW][C]R-squared[/C][C]0.48346411571278[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.330851240809738[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]3.16791172448546[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]13[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]44[/C][/ROW]
[ROW][C]p-value[/C][C]0.0020884763094301[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.433451044170282[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]8.26671153846153[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57945&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57945&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 R0.695315838819151
R-squared0.48346411571278
Adjusted R-squared0.330851240809738
F-TEST (value)3.16791172448546
F-TEST (DF numerator)13
F-TEST (DF denominator)44
p-value0.0020884763094301
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.433451044170282
Sum Squared Residuals8.26671153846153







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
16.36.088461538461560.211538461538438
26.25.988461538461540.211538461538464
36.15.988461538461540.111538461538463
46.36.248461538461540.0515384615384633
56.56.59884615384615-0.0988461538461528
66.66.73884615384615-0.138846153846152
76.56.71884615384615-0.218846153846152
86.26.57884615384615-0.378846153846153
96.26.43884615384615-0.238846153846152
105.96.23884615384615-0.338846153846153
116.16.12153846153846-0.0215384615384604
126.15.971538461538460.128461538461539
136.15.899423076923070.20057692307693
146.15.799423076923080.300576923076924
156.15.799423076923080.300576923076924
166.46.059423076923080.340576923076924
176.76.409807692307690.290192307692308
186.96.54980769230770.350192307692308
1976.529807692307690.470192307692308
2076.389807692307690.610192307692308
216.86.249807692307690.550192307692308
226.46.049807692307690.350192307692308
235.95.9325-0.0324999999999996
245.55.7825-0.2825
255.55.71038461538461-0.210384615384610
265.65.61038461538462-0.0103846153846166
275.85.610384615384620.189615384615384
285.95.870384615384620.0296153846153845
296.16.22076923076923-0.120769230769232
306.16.36076923076923-0.260769230769232
3166.34076923076923-0.340769230769232
3266.20076923076923-0.200769230769232
335.96.06076923076923-0.160769230769232
345.55.86076923076923-0.360769230769232
355.65.74346153846154-0.143461538461540
365.45.59346153846154-0.193461538461539
375.25.52134615384615-0.321346153846149
385.25.42134615384616-0.221346153846155
395.25.42134615384616-0.221346153846155
405.55.68134615384616-0.181346153846156
415.86.07980769230769-0.279807692307692
425.86.21980769230769-0.419807692307692
435.56.19980769230769-0.699807692307692
445.36.05980769230769-0.759807692307692
455.15.91980769230769-0.819807692307692
465.25.71980769230769-0.519807692307692
475.85.60250.1975
485.85.45250.3475
495.55.380384615384610.119615384615391
5055.28038461538462-0.280384615384616
514.95.28038461538462-0.380384615384615
525.35.54038461538462-0.240384615384616
536.15.890769230769230.209230769230768
546.56.030769230769230.469230769230768
556.86.010769230769230.789230769230768
566.65.870769230769230.729230769230768
576.45.730769230769230.669230769230769
586.45.530769230769230.869230769230769

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 6.3 & 6.08846153846156 & 0.211538461538438 \tabularnewline
2 & 6.2 & 5.98846153846154 & 0.211538461538464 \tabularnewline
3 & 6.1 & 5.98846153846154 & 0.111538461538463 \tabularnewline
4 & 6.3 & 6.24846153846154 & 0.0515384615384633 \tabularnewline
5 & 6.5 & 6.59884615384615 & -0.0988461538461528 \tabularnewline
6 & 6.6 & 6.73884615384615 & -0.138846153846152 \tabularnewline
7 & 6.5 & 6.71884615384615 & -0.218846153846152 \tabularnewline
8 & 6.2 & 6.57884615384615 & -0.378846153846153 \tabularnewline
9 & 6.2 & 6.43884615384615 & -0.238846153846152 \tabularnewline
10 & 5.9 & 6.23884615384615 & -0.338846153846153 \tabularnewline
11 & 6.1 & 6.12153846153846 & -0.0215384615384604 \tabularnewline
12 & 6.1 & 5.97153846153846 & 0.128461538461539 \tabularnewline
13 & 6.1 & 5.89942307692307 & 0.20057692307693 \tabularnewline
14 & 6.1 & 5.79942307692308 & 0.300576923076924 \tabularnewline
15 & 6.1 & 5.79942307692308 & 0.300576923076924 \tabularnewline
16 & 6.4 & 6.05942307692308 & 0.340576923076924 \tabularnewline
17 & 6.7 & 6.40980769230769 & 0.290192307692308 \tabularnewline
18 & 6.9 & 6.5498076923077 & 0.350192307692308 \tabularnewline
19 & 7 & 6.52980769230769 & 0.470192307692308 \tabularnewline
20 & 7 & 6.38980769230769 & 0.610192307692308 \tabularnewline
21 & 6.8 & 6.24980769230769 & 0.550192307692308 \tabularnewline
22 & 6.4 & 6.04980769230769 & 0.350192307692308 \tabularnewline
23 & 5.9 & 5.9325 & -0.0324999999999996 \tabularnewline
24 & 5.5 & 5.7825 & -0.2825 \tabularnewline
25 & 5.5 & 5.71038461538461 & -0.210384615384610 \tabularnewline
26 & 5.6 & 5.61038461538462 & -0.0103846153846166 \tabularnewline
27 & 5.8 & 5.61038461538462 & 0.189615384615384 \tabularnewline
28 & 5.9 & 5.87038461538462 & 0.0296153846153845 \tabularnewline
29 & 6.1 & 6.22076923076923 & -0.120769230769232 \tabularnewline
30 & 6.1 & 6.36076923076923 & -0.260769230769232 \tabularnewline
31 & 6 & 6.34076923076923 & -0.340769230769232 \tabularnewline
32 & 6 & 6.20076923076923 & -0.200769230769232 \tabularnewline
33 & 5.9 & 6.06076923076923 & -0.160769230769232 \tabularnewline
34 & 5.5 & 5.86076923076923 & -0.360769230769232 \tabularnewline
35 & 5.6 & 5.74346153846154 & -0.143461538461540 \tabularnewline
36 & 5.4 & 5.59346153846154 & -0.193461538461539 \tabularnewline
37 & 5.2 & 5.52134615384615 & -0.321346153846149 \tabularnewline
38 & 5.2 & 5.42134615384616 & -0.221346153846155 \tabularnewline
39 & 5.2 & 5.42134615384616 & -0.221346153846155 \tabularnewline
40 & 5.5 & 5.68134615384616 & -0.181346153846156 \tabularnewline
41 & 5.8 & 6.07980769230769 & -0.279807692307692 \tabularnewline
42 & 5.8 & 6.21980769230769 & -0.419807692307692 \tabularnewline
43 & 5.5 & 6.19980769230769 & -0.699807692307692 \tabularnewline
44 & 5.3 & 6.05980769230769 & -0.759807692307692 \tabularnewline
45 & 5.1 & 5.91980769230769 & -0.819807692307692 \tabularnewline
46 & 5.2 & 5.71980769230769 & -0.519807692307692 \tabularnewline
47 & 5.8 & 5.6025 & 0.1975 \tabularnewline
48 & 5.8 & 5.4525 & 0.3475 \tabularnewline
49 & 5.5 & 5.38038461538461 & 0.119615384615391 \tabularnewline
50 & 5 & 5.28038461538462 & -0.280384615384616 \tabularnewline
51 & 4.9 & 5.28038461538462 & -0.380384615384615 \tabularnewline
52 & 5.3 & 5.54038461538462 & -0.240384615384616 \tabularnewline
53 & 6.1 & 5.89076923076923 & 0.209230769230768 \tabularnewline
54 & 6.5 & 6.03076923076923 & 0.469230769230768 \tabularnewline
55 & 6.8 & 6.01076923076923 & 0.789230769230768 \tabularnewline
56 & 6.6 & 5.87076923076923 & 0.729230769230768 \tabularnewline
57 & 6.4 & 5.73076923076923 & 0.669230769230769 \tabularnewline
58 & 6.4 & 5.53076923076923 & 0.869230769230769 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57945&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]6.3[/C][C]6.08846153846156[/C][C]0.211538461538438[/C][/ROW]
[ROW][C]2[/C][C]6.2[/C][C]5.98846153846154[/C][C]0.211538461538464[/C][/ROW]
[ROW][C]3[/C][C]6.1[/C][C]5.98846153846154[/C][C]0.111538461538463[/C][/ROW]
[ROW][C]4[/C][C]6.3[/C][C]6.24846153846154[/C][C]0.0515384615384633[/C][/ROW]
[ROW][C]5[/C][C]6.5[/C][C]6.59884615384615[/C][C]-0.0988461538461528[/C][/ROW]
[ROW][C]6[/C][C]6.6[/C][C]6.73884615384615[/C][C]-0.138846153846152[/C][/ROW]
[ROW][C]7[/C][C]6.5[/C][C]6.71884615384615[/C][C]-0.218846153846152[/C][/ROW]
[ROW][C]8[/C][C]6.2[/C][C]6.57884615384615[/C][C]-0.378846153846153[/C][/ROW]
[ROW][C]9[/C][C]6.2[/C][C]6.43884615384615[/C][C]-0.238846153846152[/C][/ROW]
[ROW][C]10[/C][C]5.9[/C][C]6.23884615384615[/C][C]-0.338846153846153[/C][/ROW]
[ROW][C]11[/C][C]6.1[/C][C]6.12153846153846[/C][C]-0.0215384615384604[/C][/ROW]
[ROW][C]12[/C][C]6.1[/C][C]5.97153846153846[/C][C]0.128461538461539[/C][/ROW]
[ROW][C]13[/C][C]6.1[/C][C]5.89942307692307[/C][C]0.20057692307693[/C][/ROW]
[ROW][C]14[/C][C]6.1[/C][C]5.79942307692308[/C][C]0.300576923076924[/C][/ROW]
[ROW][C]15[/C][C]6.1[/C][C]5.79942307692308[/C][C]0.300576923076924[/C][/ROW]
[ROW][C]16[/C][C]6.4[/C][C]6.05942307692308[/C][C]0.340576923076924[/C][/ROW]
[ROW][C]17[/C][C]6.7[/C][C]6.40980769230769[/C][C]0.290192307692308[/C][/ROW]
[ROW][C]18[/C][C]6.9[/C][C]6.5498076923077[/C][C]0.350192307692308[/C][/ROW]
[ROW][C]19[/C][C]7[/C][C]6.52980769230769[/C][C]0.470192307692308[/C][/ROW]
[ROW][C]20[/C][C]7[/C][C]6.38980769230769[/C][C]0.610192307692308[/C][/ROW]
[ROW][C]21[/C][C]6.8[/C][C]6.24980769230769[/C][C]0.550192307692308[/C][/ROW]
[ROW][C]22[/C][C]6.4[/C][C]6.04980769230769[/C][C]0.350192307692308[/C][/ROW]
[ROW][C]23[/C][C]5.9[/C][C]5.9325[/C][C]-0.0324999999999996[/C][/ROW]
[ROW][C]24[/C][C]5.5[/C][C]5.7825[/C][C]-0.2825[/C][/ROW]
[ROW][C]25[/C][C]5.5[/C][C]5.71038461538461[/C][C]-0.210384615384610[/C][/ROW]
[ROW][C]26[/C][C]5.6[/C][C]5.61038461538462[/C][C]-0.0103846153846166[/C][/ROW]
[ROW][C]27[/C][C]5.8[/C][C]5.61038461538462[/C][C]0.189615384615384[/C][/ROW]
[ROW][C]28[/C][C]5.9[/C][C]5.87038461538462[/C][C]0.0296153846153845[/C][/ROW]
[ROW][C]29[/C][C]6.1[/C][C]6.22076923076923[/C][C]-0.120769230769232[/C][/ROW]
[ROW][C]30[/C][C]6.1[/C][C]6.36076923076923[/C][C]-0.260769230769232[/C][/ROW]
[ROW][C]31[/C][C]6[/C][C]6.34076923076923[/C][C]-0.340769230769232[/C][/ROW]
[ROW][C]32[/C][C]6[/C][C]6.20076923076923[/C][C]-0.200769230769232[/C][/ROW]
[ROW][C]33[/C][C]5.9[/C][C]6.06076923076923[/C][C]-0.160769230769232[/C][/ROW]
[ROW][C]34[/C][C]5.5[/C][C]5.86076923076923[/C][C]-0.360769230769232[/C][/ROW]
[ROW][C]35[/C][C]5.6[/C][C]5.74346153846154[/C][C]-0.143461538461540[/C][/ROW]
[ROW][C]36[/C][C]5.4[/C][C]5.59346153846154[/C][C]-0.193461538461539[/C][/ROW]
[ROW][C]37[/C][C]5.2[/C][C]5.52134615384615[/C][C]-0.321346153846149[/C][/ROW]
[ROW][C]38[/C][C]5.2[/C][C]5.42134615384616[/C][C]-0.221346153846155[/C][/ROW]
[ROW][C]39[/C][C]5.2[/C][C]5.42134615384616[/C][C]-0.221346153846155[/C][/ROW]
[ROW][C]40[/C][C]5.5[/C][C]5.68134615384616[/C][C]-0.181346153846156[/C][/ROW]
[ROW][C]41[/C][C]5.8[/C][C]6.07980769230769[/C][C]-0.279807692307692[/C][/ROW]
[ROW][C]42[/C][C]5.8[/C][C]6.21980769230769[/C][C]-0.419807692307692[/C][/ROW]
[ROW][C]43[/C][C]5.5[/C][C]6.19980769230769[/C][C]-0.699807692307692[/C][/ROW]
[ROW][C]44[/C][C]5.3[/C][C]6.05980769230769[/C][C]-0.759807692307692[/C][/ROW]
[ROW][C]45[/C][C]5.1[/C][C]5.91980769230769[/C][C]-0.819807692307692[/C][/ROW]
[ROW][C]46[/C][C]5.2[/C][C]5.71980769230769[/C][C]-0.519807692307692[/C][/ROW]
[ROW][C]47[/C][C]5.8[/C][C]5.6025[/C][C]0.1975[/C][/ROW]
[ROW][C]48[/C][C]5.8[/C][C]5.4525[/C][C]0.3475[/C][/ROW]
[ROW][C]49[/C][C]5.5[/C][C]5.38038461538461[/C][C]0.119615384615391[/C][/ROW]
[ROW][C]50[/C][C]5[/C][C]5.28038461538462[/C][C]-0.280384615384616[/C][/ROW]
[ROW][C]51[/C][C]4.9[/C][C]5.28038461538462[/C][C]-0.380384615384615[/C][/ROW]
[ROW][C]52[/C][C]5.3[/C][C]5.54038461538462[/C][C]-0.240384615384616[/C][/ROW]
[ROW][C]53[/C][C]6.1[/C][C]5.89076923076923[/C][C]0.209230769230768[/C][/ROW]
[ROW][C]54[/C][C]6.5[/C][C]6.03076923076923[/C][C]0.469230769230768[/C][/ROW]
[ROW][C]55[/C][C]6.8[/C][C]6.01076923076923[/C][C]0.789230769230768[/C][/ROW]
[ROW][C]56[/C][C]6.6[/C][C]5.87076923076923[/C][C]0.729230769230768[/C][/ROW]
[ROW][C]57[/C][C]6.4[/C][C]5.73076923076923[/C][C]0.669230769230769[/C][/ROW]
[ROW][C]58[/C][C]6.4[/C][C]5.53076923076923[/C][C]0.869230769230769[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57945&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57945&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
16.36.088461538461560.211538461538438
26.25.988461538461540.211538461538464
36.15.988461538461540.111538461538463
46.36.248461538461540.0515384615384633
56.56.59884615384615-0.0988461538461528
66.66.73884615384615-0.138846153846152
76.56.71884615384615-0.218846153846152
86.26.57884615384615-0.378846153846153
96.26.43884615384615-0.238846153846152
105.96.23884615384615-0.338846153846153
116.16.12153846153846-0.0215384615384604
126.15.971538461538460.128461538461539
136.15.899423076923070.20057692307693
146.15.799423076923080.300576923076924
156.15.799423076923080.300576923076924
166.46.059423076923080.340576923076924
176.76.409807692307690.290192307692308
186.96.54980769230770.350192307692308
1976.529807692307690.470192307692308
2076.389807692307690.610192307692308
216.86.249807692307690.550192307692308
226.46.049807692307690.350192307692308
235.95.9325-0.0324999999999996
245.55.7825-0.2825
255.55.71038461538461-0.210384615384610
265.65.61038461538462-0.0103846153846166
275.85.610384615384620.189615384615384
285.95.870384615384620.0296153846153845
296.16.22076923076923-0.120769230769232
306.16.36076923076923-0.260769230769232
3166.34076923076923-0.340769230769232
3266.20076923076923-0.200769230769232
335.96.06076923076923-0.160769230769232
345.55.86076923076923-0.360769230769232
355.65.74346153846154-0.143461538461540
365.45.59346153846154-0.193461538461539
375.25.52134615384615-0.321346153846149
385.25.42134615384616-0.221346153846155
395.25.42134615384616-0.221346153846155
405.55.68134615384616-0.181346153846156
415.86.07980769230769-0.279807692307692
425.86.21980769230769-0.419807692307692
435.56.19980769230769-0.699807692307692
445.36.05980769230769-0.759807692307692
455.15.91980769230769-0.819807692307692
465.25.71980769230769-0.519807692307692
475.85.60250.1975
485.85.45250.3475
495.55.380384615384610.119615384615391
5055.28038461538462-0.280384615384616
514.95.28038461538462-0.380384615384615
525.35.54038461538462-0.240384615384616
536.15.890769230769230.209230769230768
546.56.030769230769230.469230769230768
556.86.010769230769230.789230769230768
566.65.870769230769230.729230769230768
576.45.730769230769230.669230769230769
586.45.530769230769230.869230769230769







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
170.02249527619509500.04499055239019000.977504723804905
180.01403002073354370.02806004146708740.985969979266456
190.01908673950067510.03817347900135010.980913260499325
200.05857915494820750.1171583098964150.941420845051792
210.06455414939777760.1291082987955550.935445850602222
220.05703530245458920.1140706049091780.94296469754541
230.05603417583278370.1120683516655670.943965824167216
240.1104885506884670.2209771013769350.889511449311533
250.2280994369604630.4561988739209270.771900563039537
260.303830628577620.607661257155240.69616937142238
270.5039610855051420.9920778289897160.496038914494858
280.825834362062650.34833127587470.17416563793735
290.8237060920718480.3525878158563030.176293907928152
300.8055733710077040.3888532579845920.194426628992296
310.7814442603524660.4371114792950680.218555739647534
320.7738297038055910.4523405923888170.226170296194409
330.8420720703314080.3158558593371840.157927929668592
340.7998274320291690.4003451359416630.200172567970831
350.7217450127907950.556509974418410.278254987209205
360.7087398958124330.5825202083751340.291260104187567
370.723348837663180.5533023246736420.276651162336821
380.6121555125862260.7756889748275480.387844487413774
390.4854964325675380.9709928651350760.514503567432462
400.3401751351332430.6803502702664860.659824864866757
410.6835219769690710.6329560460618580.316478023030929

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
17 & 0.0224952761950950 & 0.0449905523901900 & 0.977504723804905 \tabularnewline
18 & 0.0140300207335437 & 0.0280600414670874 & 0.985969979266456 \tabularnewline
19 & 0.0190867395006751 & 0.0381734790013501 & 0.980913260499325 \tabularnewline
20 & 0.0585791549482075 & 0.117158309896415 & 0.941420845051792 \tabularnewline
21 & 0.0645541493977776 & 0.129108298795555 & 0.935445850602222 \tabularnewline
22 & 0.0570353024545892 & 0.114070604909178 & 0.94296469754541 \tabularnewline
23 & 0.0560341758327837 & 0.112068351665567 & 0.943965824167216 \tabularnewline
24 & 0.110488550688467 & 0.220977101376935 & 0.889511449311533 \tabularnewline
25 & 0.228099436960463 & 0.456198873920927 & 0.771900563039537 \tabularnewline
26 & 0.30383062857762 & 0.60766125715524 & 0.69616937142238 \tabularnewline
27 & 0.503961085505142 & 0.992077828989716 & 0.496038914494858 \tabularnewline
28 & 0.82583436206265 & 0.3483312758747 & 0.17416563793735 \tabularnewline
29 & 0.823706092071848 & 0.352587815856303 & 0.176293907928152 \tabularnewline
30 & 0.805573371007704 & 0.388853257984592 & 0.194426628992296 \tabularnewline
31 & 0.781444260352466 & 0.437111479295068 & 0.218555739647534 \tabularnewline
32 & 0.773829703805591 & 0.452340592388817 & 0.226170296194409 \tabularnewline
33 & 0.842072070331408 & 0.315855859337184 & 0.157927929668592 \tabularnewline
34 & 0.799827432029169 & 0.400345135941663 & 0.200172567970831 \tabularnewline
35 & 0.721745012790795 & 0.55650997441841 & 0.278254987209205 \tabularnewline
36 & 0.708739895812433 & 0.582520208375134 & 0.291260104187567 \tabularnewline
37 & 0.72334883766318 & 0.553302324673642 & 0.276651162336821 \tabularnewline
38 & 0.612155512586226 & 0.775688974827548 & 0.387844487413774 \tabularnewline
39 & 0.485496432567538 & 0.970992865135076 & 0.514503567432462 \tabularnewline
40 & 0.340175135133243 & 0.680350270266486 & 0.659824864866757 \tabularnewline
41 & 0.683521976969071 & 0.632956046061858 & 0.316478023030929 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57945&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]0.0224952761950950[/C][C]0.0449905523901900[/C][C]0.977504723804905[/C][/ROW]
[ROW][C]18[/C][C]0.0140300207335437[/C][C]0.0280600414670874[/C][C]0.985969979266456[/C][/ROW]
[ROW][C]19[/C][C]0.0190867395006751[/C][C]0.0381734790013501[/C][C]0.980913260499325[/C][/ROW]
[ROW][C]20[/C][C]0.0585791549482075[/C][C]0.117158309896415[/C][C]0.941420845051792[/C][/ROW]
[ROW][C]21[/C][C]0.0645541493977776[/C][C]0.129108298795555[/C][C]0.935445850602222[/C][/ROW]
[ROW][C]22[/C][C]0.0570353024545892[/C][C]0.114070604909178[/C][C]0.94296469754541[/C][/ROW]
[ROW][C]23[/C][C]0.0560341758327837[/C][C]0.112068351665567[/C][C]0.943965824167216[/C][/ROW]
[ROW][C]24[/C][C]0.110488550688467[/C][C]0.220977101376935[/C][C]0.889511449311533[/C][/ROW]
[ROW][C]25[/C][C]0.228099436960463[/C][C]0.456198873920927[/C][C]0.771900563039537[/C][/ROW]
[ROW][C]26[/C][C]0.30383062857762[/C][C]0.60766125715524[/C][C]0.69616937142238[/C][/ROW]
[ROW][C]27[/C][C]0.503961085505142[/C][C]0.992077828989716[/C][C]0.496038914494858[/C][/ROW]
[ROW][C]28[/C][C]0.82583436206265[/C][C]0.3483312758747[/C][C]0.17416563793735[/C][/ROW]
[ROW][C]29[/C][C]0.823706092071848[/C][C]0.352587815856303[/C][C]0.176293907928152[/C][/ROW]
[ROW][C]30[/C][C]0.805573371007704[/C][C]0.388853257984592[/C][C]0.194426628992296[/C][/ROW]
[ROW][C]31[/C][C]0.781444260352466[/C][C]0.437111479295068[/C][C]0.218555739647534[/C][/ROW]
[ROW][C]32[/C][C]0.773829703805591[/C][C]0.452340592388817[/C][C]0.226170296194409[/C][/ROW]
[ROW][C]33[/C][C]0.842072070331408[/C][C]0.315855859337184[/C][C]0.157927929668592[/C][/ROW]
[ROW][C]34[/C][C]0.799827432029169[/C][C]0.400345135941663[/C][C]0.200172567970831[/C][/ROW]
[ROW][C]35[/C][C]0.721745012790795[/C][C]0.55650997441841[/C][C]0.278254987209205[/C][/ROW]
[ROW][C]36[/C][C]0.708739895812433[/C][C]0.582520208375134[/C][C]0.291260104187567[/C][/ROW]
[ROW][C]37[/C][C]0.72334883766318[/C][C]0.553302324673642[/C][C]0.276651162336821[/C][/ROW]
[ROW][C]38[/C][C]0.612155512586226[/C][C]0.775688974827548[/C][C]0.387844487413774[/C][/ROW]
[ROW][C]39[/C][C]0.485496432567538[/C][C]0.970992865135076[/C][C]0.514503567432462[/C][/ROW]
[ROW][C]40[/C][C]0.340175135133243[/C][C]0.680350270266486[/C][C]0.659824864866757[/C][/ROW]
[ROW][C]41[/C][C]0.683521976969071[/C][C]0.632956046061858[/C][C]0.316478023030929[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57945&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57945&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
170.02249527619509500.04499055239019000.977504723804905
180.01403002073354370.02806004146708740.985969979266456
190.01908673950067510.03817347900135010.980913260499325
200.05857915494820750.1171583098964150.941420845051792
210.06455414939777760.1291082987955550.935445850602222
220.05703530245458920.1140706049091780.94296469754541
230.05603417583278370.1120683516655670.943965824167216
240.1104885506884670.2209771013769350.889511449311533
250.2280994369604630.4561988739209270.771900563039537
260.303830628577620.607661257155240.69616937142238
270.5039610855051420.9920778289897160.496038914494858
280.825834362062650.34833127587470.17416563793735
290.8237060920718480.3525878158563030.176293907928152
300.8055733710077040.3888532579845920.194426628992296
310.7814442603524660.4371114792950680.218555739647534
320.7738297038055910.4523405923888170.226170296194409
330.8420720703314080.3158558593371840.157927929668592
340.7998274320291690.4003451359416630.200172567970831
350.7217450127907950.556509974418410.278254987209205
360.7087398958124330.5825202083751340.291260104187567
370.723348837663180.5533023246736420.276651162336821
380.6121555125862260.7756889748275480.387844487413774
390.4854964325675380.9709928651350760.514503567432462
400.3401751351332430.6803502702664860.659824864866757
410.6835219769690710.6329560460618580.316478023030929







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level30.12NOK
10% type I error level30.12NOK

\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 & 3 & 0.12 & NOK \tabularnewline
10% type I error level & 3 & 0.12 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57945&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]3[/C][C]0.12[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]3[/C][C]0.12[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57945&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57945&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 level30.12NOK
10% type I error level30.12NOK



Parameters (Session):
par1 = 2 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;
Parameters (R input):
par1 = 2 ; 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')
}