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Description of Statistical Computation

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, 12 Dec 2014 12:30:20 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2014/Dec/12/t1418387449f9asdy00hhwvk0q.htm/, Retrieved Sun, 19 May 2024 16:28:11 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=266602, Retrieved Sun, 19 May 2024 16:28:11 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact75

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [multicollinialiteit] [2014-12-12 12:30:20] [ec1b40d1a9751af99658fe8fca4f9eca] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
0 1.7 1 1.6 4.3
0 1.7 1.3 1.8 4.9
0 1.8 2 1.8 5.6
0 2 2 1.8 5.7
0 2 1.8 2.1 5.9
2.5 1.9 1.8 0.1 6.3
0.5 2.2 2.1 1.6 6.4
0.5 2.1 1.8 1.9 6.4
1 1.7 1.8 1.9 6.4
1 2.1 2 1.6 6.7
1 1.8 1.7 2.2 6.7
2.5 1.9 1.1 1.8 7.3
1 2.3 2.1 1.9 7.4
1.5 1.8 1.9 2.4 7.6
1.5 2.1 2 2.1 7.7
1.5 1.9 1.8 2.4 7.7
1 2.6 2 2.2 7.9
1 2.4 2 2.4 7.9
3.5 1.7 1.6 1.2 8
2 2.2 2.1 1.9 8.2
2.5 1.9 1.9 1.9 8.3
3 1.8 1.6 1.8 8.3
2.5 2.2 2 1.8 8.5
2.5 2.1 1.9 2.1 8.6
3.5 1.8 1.8 1.6 8.8
3 1.9 2 1.9 8.8
3.5 2 2 1.5 9
3.5 1.7 2 1.8 9
3.5 2 1.8 1.8 9.1
3.5 2.1 2.1 1.5 9.2
3.5 2.2 2 1.6 9.3
2.5 2.8 2.2 1.8 9.3
3.5 2.1 2.1 1.5 9.3
3 2.3 2.1 2.2 9.6
3 2.1 2.2 2.2 9.6
3.5 1.8 1.9 2.4 9.6
2.5 3.2 2.1 1.9 9.7
4 1.9 2 1.9 9.9
3.5 2.2 1.8 2.4 9.9
4.5 1.5 2 1.9 9.9
4 1.9 2 2.1 10
4.5 1.9 1.9 1.8 10.1
4.5 2.5 1.8 1.5 10.3
3.5 2.2 2.2 2.4 10.3
4.5 1.9 2.1 1.8 10.3
4.5 1.8 2 2.1 10.4
4 2.1 2.1 2.2 10.5
5 2.2 2 1.5 10.6
5 1.8 1.9 1.9 10.7
4.5 2.1 2.2 1.9 10.8
4.5 2.3 2 2.1 10.8
5 1.8 2 1.9 10.8
4 2.2 2.3 2.4 10.9
5 1.8 2 2.1 10.9
4.5 2 2.1 2.2 10.9
4.5 2.9 2.2 1.5 11.1
4 2.8 2.3 2.1 11.1
5.5 1.8 1.8 1.9 11.1
5 2.3 1.9 1.9 11.2
5 2 2 2.2 11.3
4.5 2.6 2.2 2.1 11.3
5 2.1 2.1 2.2 11.4
5 2.5 1.9 1.9 11.4
5.5 2 1.9 1.9 11.4
4.5 2.4 2 2.4 11.4
5.5 1.9 1.8 2.1 11.4
5.5 1.9 2 2.1 11.5
5 2.1 2.2 2.4 11.6
5.5 1.9 2.1 2.1 11.6
5.5 2.3 2.1 1.8 11.7
5.5 2.1 2 2.1 11.7
5 2.3 2.1 2.4 11.8
5.5 2 2 2.2 11.8
5.5 2.2 2.1 2.1 11.8
5.5 2.5 2.2 1.8 11.9
5 2.7 2.3 1.9 12
6.5 1.9 2.2 1.5 12.1
6 2.1 2 2.1 12.2
6 2.1 2.1 1.9 12.2
6 1.9 1.9 2.4 12.3
6 2.2 2 2.1 12.3
5 2.9 2.2 2.1 12.3
6 2.4 2 2.1 12.5
5.5 2.7 2.3 2.1 12.6
6.5 2.1 2 1.9 12.6
6.5 2.2 2.1 1.8 12.6
7 1.7 1.8 2.1 12.6
6 2.4 2.2 2.1 12.7
7.5 1.6 1.7 1.8 12.7
6.5 2.2 2 2.1 12.8
7.5 1.8 2.1 1.5 12.9
6.5 2.3 2.1 2.1 13
6.5 2 2.3 2.2 13
6.5 2.3 2.1 2.1 13
7 2 2.1 2.1 13.2
7.5 1.9 2 1.8 13.2
6.5 2.4 2.1 2.2 13.3
7 2.4 2 1.9 13.3
7.5 1.9 2.1 1.8 13.3
7 1.9 1.9 2.5 13.4
7.5 1.9 1.9 2.1 13.4
7 2 2.1 2.4 13.5
8 1.8 1.8 2.1 13.6
6.5 3.5 2.2 1.6 13.8
7.5 2.3 1.7 2.2 13.8
8 1.9 1.9 2.4 14.2
8.5 2.1 2 1.6 14.3
7.5 2.7 2.2 2.1 14.5
7.5 2.6 2.1 2.4 14.6
8.5 2.1 2.1 2.1 14.8
9 2.3 2.2 2.4 15.9
7.5 3.5 2.7 2.4 16.1

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time11 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net

\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 & 11 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=266602&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]11 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=266602&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=266602&T=0

As an alternative you can also use a QR Code:  
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 time11 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net






Multiple Linear Regression - Estimated Regression Equation
TOT[t] = + 0.0971908 + 1.00017Ex[t] + 0.996133PR[t] + 0.948559PE[t] + 1.01828PA[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
TOT[t] =  +  0.0971908 +  1.00017Ex[t] +  0.996133PR[t] +  0.948559PE[t] +  1.01828PA[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=266602&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]TOT[t] =  +  0.0971908 +  1.00017Ex[t] +  0.996133PR[t] +  0.948559PE[t] +  1.01828PA[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=266602&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=266602&T=1

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Estimated Regression Equation
TOT[t] = + 0.0971908 + 1.00017Ex[t] + 0.996133PR[t] + 0.948559PE[t] + 1.01828PA[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)0.09719080.0568131.7110.09003210.045016
Ex1.000170.00269088371.72.69931e-1681.34965e-168
PR0.9961330.018594253.574.15992e-792.07996e-79
PE0.9485590.03244129.247.66885e-533.83443e-53
PA1.018280.017574157.941.24582e-826.2291e-83

\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) & 0.0971908 & 0.056813 & 1.711 & 0.0900321 & 0.045016 \tabularnewline
Ex & 1.00017 & 0.00269088 & 371.7 & 2.69931e-168 & 1.34965e-168 \tabularnewline
PR & 0.996133 & 0.0185942 & 53.57 & 4.15992e-79 & 2.07996e-79 \tabularnewline
PE & 0.948559 & 0.032441 & 29.24 & 7.66885e-53 & 3.83443e-53 \tabularnewline
PA & 1.01828 & 0.0175741 & 57.94 & 1.24582e-82 & 6.2291e-83 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=266602&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]0.0971908[/C][C]0.056813[/C][C]1.711[/C][C]0.0900321[/C][C]0.045016[/C][/ROW]
[ROW][C]Ex[/C][C]1.00017[/C][C]0.00269088[/C][C]371.7[/C][C]2.69931e-168[/C][C]1.34965e-168[/C][/ROW]
[ROW][C]PR[/C][C]0.996133[/C][C]0.0185942[/C][C]53.57[/C][C]4.15992e-79[/C][C]2.07996e-79[/C][/ROW]
[ROW][C]PE[/C][C]0.948559[/C][C]0.032441[/C][C]29.24[/C][C]7.66885e-53[/C][C]3.83443e-53[/C][/ROW]
[ROW][C]PA[/C][C]1.01828[/C][C]0.0175741[/C][C]57.94[/C][C]1.24582e-82[/C][C]6.2291e-83[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=266602&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=266602&T=2

As an alternative you can also use a QR Code:  
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)0.09719080.0568131.7110.09003210.045016
Ex1.000170.00269088371.72.69931e-1681.34965e-168
PR0.9961330.018594253.574.15992e-792.07996e-79
PE0.9485590.03244129.247.66885e-533.83443e-53
PA1.018280.017574157.941.24582e-826.2291e-83






Multiple Linear Regression - Regression Statistics
Multiple R0.999732
R-squared0.999464
Adjusted R-squared0.999444
F-TEST (value)49894.9
F-TEST (DF numerator)4
F-TEST (DF denominator)107
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.0582666
Sum Squared Residuals0.363265

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.999732 \tabularnewline
R-squared & 0.999464 \tabularnewline
Adjusted R-squared & 0.999444 \tabularnewline
F-TEST (value) & 49894.9 \tabularnewline
F-TEST (DF numerator) & 4 \tabularnewline
F-TEST (DF denominator) & 107 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.0582666 \tabularnewline
Sum Squared Residuals & 0.363265 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=266602&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.999732[/C][/ROW]
[ROW][C]R-squared[/C][C]0.999464[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.999444[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]49894.9[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]4[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]107[/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]0.0582666[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]0.363265[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=266602&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=266602&T=3

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Regression Statistics
Multiple R0.999732
R-squared0.999464
Adjusted R-squared0.999444
F-TEST (value)49894.9
F-TEST (DF numerator)4
F-TEST (DF denominator)107
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.0582666
Sum Squared Residuals0.363265






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
14.34.36843-0.0684276
24.94.856650.0433482
35.65.62026-0.0202566
45.75.81948-0.119483
55.95.93526-0.035256
66.36.29950.000500134
76.46.40999-0.00999364
86.46.33130.0687028
96.46.43293-0.0329281
106.76.71561-0.0156086
116.76.74317-0.0431701
127.37.36659-0.0665879
137.47.315180.0848242
147.67.63662-0.0366227
157.77.72483-0.0248339
167.77.641380.05862
177.97.824640.0753556
187.97.829070.0709258
1988.03084-0.0308401
208.28.21573-0.015731
218.38.227260.0727365
228.38.241340.0586616
238.58.51913-0.0191311
248.68.63015-0.0301465
258.88.727480.0725219
268.88.8222-0.0222036
2799.01459-0.0145884
2899.02123-0.021233
299.19.13036-0.0303611
309.29.20906-0.00905762
319.39.31564-0.0156432
329.39.30652-0.00652286
339.39.209060.0909424
349.69.621-0.0209974
359.69.516630.0833733
369.69.63696-0.0369597
379.79.71195-0.0119484
389.99.822370.0776279
399.99.94056-0.040557
409.99.924-0.0240031
411010.026-0.0260285
4210.110.1258-0.0257723
4310.310.3231-0.0231116
4410.310.32-0.0199807
4510.310.3155-0.0154841
4610.410.4265-0.0264995
4710.510.42190.0780607
4810.610.7141-0.114068
4910.710.62810.0719286
5010.810.71140.0886051
5110.810.9246-0.124566
5210.810.72290.0770727
5310.910.9149-0.0149209
5410.910.9266-0.0265837
5510.910.82240.0775898
5611.111.101-0.00098857
5711.111.2071-0.107116
5811.111.03330.0667003
5911.211.12610.073862
6011.311.22760.0723614
6111.311.4131-0.113118
6211.411.4221-0.0221078
6311.411.32540.0746354
6411.411.32740.0726177
6511.411.32970.070336
6611.411.33660.0634306
6711.511.5263-0.0262813
6811.611.7206-0.12062
6911.611.6211-0.0211372
7011.711.7141-0.0141059
7111.711.7255-0.0255079
7211.811.825-0.0249908
7311.811.72770.0722772
7411.811.92-0.119977
7511.912.0082-0.108188
761211.9040.0959851
7712.112.1052-0.00519241
7812.212.2256-0.0255922
7912.212.11680.0832083
8012.312.2370.0630058
8112.312.3252-0.0252055
8212.312.2120.087958
8312.512.5244-0.0244321
8412.612.6078-0.00775558
8512.612.5220.07798
8612.612.6147-0.014661
8712.612.6376-0.0375956
8812.712.7141-0.014144
8912.712.63770.062274
9012.812.8253-0.0252897
9112.912.9109-0.0108917
921313.0198-0.019759
931313.0125-0.0124591
941313.0198-0.019759
9513.213.221-0.0210033
9613.213.2211-0.0211337
9713.313.22120.0787995
9813.313.3209-0.0209442
9913.313.316-0.0159896
10013.413.3390.0610091
10113.413.4318-0.0317624
10213.513.5265-0.0264879
10313.613.7374-0.137377
10413.813.8008-0.00083367
10513.813.74230.057668
10614.214.2373-0.0373312
10714.314.21690.0831276
10814.514.5132-0.0132367
10914.614.6243-0.024252
11014.814.8209-0.0208693
11115.915.9205-0.0205208
11216.116.08990.0100926

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 4.3 & 4.36843 & -0.0684276 \tabularnewline
2 & 4.9 & 4.85665 & 0.0433482 \tabularnewline
3 & 5.6 & 5.62026 & -0.0202566 \tabularnewline
4 & 5.7 & 5.81948 & -0.119483 \tabularnewline
5 & 5.9 & 5.93526 & -0.035256 \tabularnewline
6 & 6.3 & 6.2995 & 0.000500134 \tabularnewline
7 & 6.4 & 6.40999 & -0.00999364 \tabularnewline
8 & 6.4 & 6.3313 & 0.0687028 \tabularnewline
9 & 6.4 & 6.43293 & -0.0329281 \tabularnewline
10 & 6.7 & 6.71561 & -0.0156086 \tabularnewline
11 & 6.7 & 6.74317 & -0.0431701 \tabularnewline
12 & 7.3 & 7.36659 & -0.0665879 \tabularnewline
13 & 7.4 & 7.31518 & 0.0848242 \tabularnewline
14 & 7.6 & 7.63662 & -0.0366227 \tabularnewline
15 & 7.7 & 7.72483 & -0.0248339 \tabularnewline
16 & 7.7 & 7.64138 & 0.05862 \tabularnewline
17 & 7.9 & 7.82464 & 0.0753556 \tabularnewline
18 & 7.9 & 7.82907 & 0.0709258 \tabularnewline
19 & 8 & 8.03084 & -0.0308401 \tabularnewline
20 & 8.2 & 8.21573 & -0.015731 \tabularnewline
21 & 8.3 & 8.22726 & 0.0727365 \tabularnewline
22 & 8.3 & 8.24134 & 0.0586616 \tabularnewline
23 & 8.5 & 8.51913 & -0.0191311 \tabularnewline
24 & 8.6 & 8.63015 & -0.0301465 \tabularnewline
25 & 8.8 & 8.72748 & 0.0725219 \tabularnewline
26 & 8.8 & 8.8222 & -0.0222036 \tabularnewline
27 & 9 & 9.01459 & -0.0145884 \tabularnewline
28 & 9 & 9.02123 & -0.021233 \tabularnewline
29 & 9.1 & 9.13036 & -0.0303611 \tabularnewline
30 & 9.2 & 9.20906 & -0.00905762 \tabularnewline
31 & 9.3 & 9.31564 & -0.0156432 \tabularnewline
32 & 9.3 & 9.30652 & -0.00652286 \tabularnewline
33 & 9.3 & 9.20906 & 0.0909424 \tabularnewline
34 & 9.6 & 9.621 & -0.0209974 \tabularnewline
35 & 9.6 & 9.51663 & 0.0833733 \tabularnewline
36 & 9.6 & 9.63696 & -0.0369597 \tabularnewline
37 & 9.7 & 9.71195 & -0.0119484 \tabularnewline
38 & 9.9 & 9.82237 & 0.0776279 \tabularnewline
39 & 9.9 & 9.94056 & -0.040557 \tabularnewline
40 & 9.9 & 9.924 & -0.0240031 \tabularnewline
41 & 10 & 10.026 & -0.0260285 \tabularnewline
42 & 10.1 & 10.1258 & -0.0257723 \tabularnewline
43 & 10.3 & 10.3231 & -0.0231116 \tabularnewline
44 & 10.3 & 10.32 & -0.0199807 \tabularnewline
45 & 10.3 & 10.3155 & -0.0154841 \tabularnewline
46 & 10.4 & 10.4265 & -0.0264995 \tabularnewline
47 & 10.5 & 10.4219 & 0.0780607 \tabularnewline
48 & 10.6 & 10.7141 & -0.114068 \tabularnewline
49 & 10.7 & 10.6281 & 0.0719286 \tabularnewline
50 & 10.8 & 10.7114 & 0.0886051 \tabularnewline
51 & 10.8 & 10.9246 & -0.124566 \tabularnewline
52 & 10.8 & 10.7229 & 0.0770727 \tabularnewline
53 & 10.9 & 10.9149 & -0.0149209 \tabularnewline
54 & 10.9 & 10.9266 & -0.0265837 \tabularnewline
55 & 10.9 & 10.8224 & 0.0775898 \tabularnewline
56 & 11.1 & 11.101 & -0.00098857 \tabularnewline
57 & 11.1 & 11.2071 & -0.107116 \tabularnewline
58 & 11.1 & 11.0333 & 0.0667003 \tabularnewline
59 & 11.2 & 11.1261 & 0.073862 \tabularnewline
60 & 11.3 & 11.2276 & 0.0723614 \tabularnewline
61 & 11.3 & 11.4131 & -0.113118 \tabularnewline
62 & 11.4 & 11.4221 & -0.0221078 \tabularnewline
63 & 11.4 & 11.3254 & 0.0746354 \tabularnewline
64 & 11.4 & 11.3274 & 0.0726177 \tabularnewline
65 & 11.4 & 11.3297 & 0.070336 \tabularnewline
66 & 11.4 & 11.3366 & 0.0634306 \tabularnewline
67 & 11.5 & 11.5263 & -0.0262813 \tabularnewline
68 & 11.6 & 11.7206 & -0.12062 \tabularnewline
69 & 11.6 & 11.6211 & -0.0211372 \tabularnewline
70 & 11.7 & 11.7141 & -0.0141059 \tabularnewline
71 & 11.7 & 11.7255 & -0.0255079 \tabularnewline
72 & 11.8 & 11.825 & -0.0249908 \tabularnewline
73 & 11.8 & 11.7277 & 0.0722772 \tabularnewline
74 & 11.8 & 11.92 & -0.119977 \tabularnewline
75 & 11.9 & 12.0082 & -0.108188 \tabularnewline
76 & 12 & 11.904 & 0.0959851 \tabularnewline
77 & 12.1 & 12.1052 & -0.00519241 \tabularnewline
78 & 12.2 & 12.2256 & -0.0255922 \tabularnewline
79 & 12.2 & 12.1168 & 0.0832083 \tabularnewline
80 & 12.3 & 12.237 & 0.0630058 \tabularnewline
81 & 12.3 & 12.3252 & -0.0252055 \tabularnewline
82 & 12.3 & 12.212 & 0.087958 \tabularnewline
83 & 12.5 & 12.5244 & -0.0244321 \tabularnewline
84 & 12.6 & 12.6078 & -0.00775558 \tabularnewline
85 & 12.6 & 12.522 & 0.07798 \tabularnewline
86 & 12.6 & 12.6147 & -0.014661 \tabularnewline
87 & 12.6 & 12.6376 & -0.0375956 \tabularnewline
88 & 12.7 & 12.7141 & -0.014144 \tabularnewline
89 & 12.7 & 12.6377 & 0.062274 \tabularnewline
90 & 12.8 & 12.8253 & -0.0252897 \tabularnewline
91 & 12.9 & 12.9109 & -0.0108917 \tabularnewline
92 & 13 & 13.0198 & -0.019759 \tabularnewline
93 & 13 & 13.0125 & -0.0124591 \tabularnewline
94 & 13 & 13.0198 & -0.019759 \tabularnewline
95 & 13.2 & 13.221 & -0.0210033 \tabularnewline
96 & 13.2 & 13.2211 & -0.0211337 \tabularnewline
97 & 13.3 & 13.2212 & 0.0787995 \tabularnewline
98 & 13.3 & 13.3209 & -0.0209442 \tabularnewline
99 & 13.3 & 13.316 & -0.0159896 \tabularnewline
100 & 13.4 & 13.339 & 0.0610091 \tabularnewline
101 & 13.4 & 13.4318 & -0.0317624 \tabularnewline
102 & 13.5 & 13.5265 & -0.0264879 \tabularnewline
103 & 13.6 & 13.7374 & -0.137377 \tabularnewline
104 & 13.8 & 13.8008 & -0.00083367 \tabularnewline
105 & 13.8 & 13.7423 & 0.057668 \tabularnewline
106 & 14.2 & 14.2373 & -0.0373312 \tabularnewline
107 & 14.3 & 14.2169 & 0.0831276 \tabularnewline
108 & 14.5 & 14.5132 & -0.0132367 \tabularnewline
109 & 14.6 & 14.6243 & -0.024252 \tabularnewline
110 & 14.8 & 14.8209 & -0.0208693 \tabularnewline
111 & 15.9 & 15.9205 & -0.0205208 \tabularnewline
112 & 16.1 & 16.0899 & 0.0100926 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=266602&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]4.3[/C][C]4.36843[/C][C]-0.0684276[/C][/ROW]
[ROW][C]2[/C][C]4.9[/C][C]4.85665[/C][C]0.0433482[/C][/ROW]
[ROW][C]3[/C][C]5.6[/C][C]5.62026[/C][C]-0.0202566[/C][/ROW]
[ROW][C]4[/C][C]5.7[/C][C]5.81948[/C][C]-0.119483[/C][/ROW]
[ROW][C]5[/C][C]5.9[/C][C]5.93526[/C][C]-0.035256[/C][/ROW]
[ROW][C]6[/C][C]6.3[/C][C]6.2995[/C][C]0.000500134[/C][/ROW]
[ROW][C]7[/C][C]6.4[/C][C]6.40999[/C][C]-0.00999364[/C][/ROW]
[ROW][C]8[/C][C]6.4[/C][C]6.3313[/C][C]0.0687028[/C][/ROW]
[ROW][C]9[/C][C]6.4[/C][C]6.43293[/C][C]-0.0329281[/C][/ROW]
[ROW][C]10[/C][C]6.7[/C][C]6.71561[/C][C]-0.0156086[/C][/ROW]
[ROW][C]11[/C][C]6.7[/C][C]6.74317[/C][C]-0.0431701[/C][/ROW]
[ROW][C]12[/C][C]7.3[/C][C]7.36659[/C][C]-0.0665879[/C][/ROW]
[ROW][C]13[/C][C]7.4[/C][C]7.31518[/C][C]0.0848242[/C][/ROW]
[ROW][C]14[/C][C]7.6[/C][C]7.63662[/C][C]-0.0366227[/C][/ROW]
[ROW][C]15[/C][C]7.7[/C][C]7.72483[/C][C]-0.0248339[/C][/ROW]
[ROW][C]16[/C][C]7.7[/C][C]7.64138[/C][C]0.05862[/C][/ROW]
[ROW][C]17[/C][C]7.9[/C][C]7.82464[/C][C]0.0753556[/C][/ROW]
[ROW][C]18[/C][C]7.9[/C][C]7.82907[/C][C]0.0709258[/C][/ROW]
[ROW][C]19[/C][C]8[/C][C]8.03084[/C][C]-0.0308401[/C][/ROW]
[ROW][C]20[/C][C]8.2[/C][C]8.21573[/C][C]-0.015731[/C][/ROW]
[ROW][C]21[/C][C]8.3[/C][C]8.22726[/C][C]0.0727365[/C][/ROW]
[ROW][C]22[/C][C]8.3[/C][C]8.24134[/C][C]0.0586616[/C][/ROW]
[ROW][C]23[/C][C]8.5[/C][C]8.51913[/C][C]-0.0191311[/C][/ROW]
[ROW][C]24[/C][C]8.6[/C][C]8.63015[/C][C]-0.0301465[/C][/ROW]
[ROW][C]25[/C][C]8.8[/C][C]8.72748[/C][C]0.0725219[/C][/ROW]
[ROW][C]26[/C][C]8.8[/C][C]8.8222[/C][C]-0.0222036[/C][/ROW]
[ROW][C]27[/C][C]9[/C][C]9.01459[/C][C]-0.0145884[/C][/ROW]
[ROW][C]28[/C][C]9[/C][C]9.02123[/C][C]-0.021233[/C][/ROW]
[ROW][C]29[/C][C]9.1[/C][C]9.13036[/C][C]-0.0303611[/C][/ROW]
[ROW][C]30[/C][C]9.2[/C][C]9.20906[/C][C]-0.00905762[/C][/ROW]
[ROW][C]31[/C][C]9.3[/C][C]9.31564[/C][C]-0.0156432[/C][/ROW]
[ROW][C]32[/C][C]9.3[/C][C]9.30652[/C][C]-0.00652286[/C][/ROW]
[ROW][C]33[/C][C]9.3[/C][C]9.20906[/C][C]0.0909424[/C][/ROW]
[ROW][C]34[/C][C]9.6[/C][C]9.621[/C][C]-0.0209974[/C][/ROW]
[ROW][C]35[/C][C]9.6[/C][C]9.51663[/C][C]0.0833733[/C][/ROW]
[ROW][C]36[/C][C]9.6[/C][C]9.63696[/C][C]-0.0369597[/C][/ROW]
[ROW][C]37[/C][C]9.7[/C][C]9.71195[/C][C]-0.0119484[/C][/ROW]
[ROW][C]38[/C][C]9.9[/C][C]9.82237[/C][C]0.0776279[/C][/ROW]
[ROW][C]39[/C][C]9.9[/C][C]9.94056[/C][C]-0.040557[/C][/ROW]
[ROW][C]40[/C][C]9.9[/C][C]9.924[/C][C]-0.0240031[/C][/ROW]
[ROW][C]41[/C][C]10[/C][C]10.026[/C][C]-0.0260285[/C][/ROW]
[ROW][C]42[/C][C]10.1[/C][C]10.1258[/C][C]-0.0257723[/C][/ROW]
[ROW][C]43[/C][C]10.3[/C][C]10.3231[/C][C]-0.0231116[/C][/ROW]
[ROW][C]44[/C][C]10.3[/C][C]10.32[/C][C]-0.0199807[/C][/ROW]
[ROW][C]45[/C][C]10.3[/C][C]10.3155[/C][C]-0.0154841[/C][/ROW]
[ROW][C]46[/C][C]10.4[/C][C]10.4265[/C][C]-0.0264995[/C][/ROW]
[ROW][C]47[/C][C]10.5[/C][C]10.4219[/C][C]0.0780607[/C][/ROW]
[ROW][C]48[/C][C]10.6[/C][C]10.7141[/C][C]-0.114068[/C][/ROW]
[ROW][C]49[/C][C]10.7[/C][C]10.6281[/C][C]0.0719286[/C][/ROW]
[ROW][C]50[/C][C]10.8[/C][C]10.7114[/C][C]0.0886051[/C][/ROW]
[ROW][C]51[/C][C]10.8[/C][C]10.9246[/C][C]-0.124566[/C][/ROW]
[ROW][C]52[/C][C]10.8[/C][C]10.7229[/C][C]0.0770727[/C][/ROW]
[ROW][C]53[/C][C]10.9[/C][C]10.9149[/C][C]-0.0149209[/C][/ROW]
[ROW][C]54[/C][C]10.9[/C][C]10.9266[/C][C]-0.0265837[/C][/ROW]
[ROW][C]55[/C][C]10.9[/C][C]10.8224[/C][C]0.0775898[/C][/ROW]
[ROW][C]56[/C][C]11.1[/C][C]11.101[/C][C]-0.00098857[/C][/ROW]
[ROW][C]57[/C][C]11.1[/C][C]11.2071[/C][C]-0.107116[/C][/ROW]
[ROW][C]58[/C][C]11.1[/C][C]11.0333[/C][C]0.0667003[/C][/ROW]
[ROW][C]59[/C][C]11.2[/C][C]11.1261[/C][C]0.073862[/C][/ROW]
[ROW][C]60[/C][C]11.3[/C][C]11.2276[/C][C]0.0723614[/C][/ROW]
[ROW][C]61[/C][C]11.3[/C][C]11.4131[/C][C]-0.113118[/C][/ROW]
[ROW][C]62[/C][C]11.4[/C][C]11.4221[/C][C]-0.0221078[/C][/ROW]
[ROW][C]63[/C][C]11.4[/C][C]11.3254[/C][C]0.0746354[/C][/ROW]
[ROW][C]64[/C][C]11.4[/C][C]11.3274[/C][C]0.0726177[/C][/ROW]
[ROW][C]65[/C][C]11.4[/C][C]11.3297[/C][C]0.070336[/C][/ROW]
[ROW][C]66[/C][C]11.4[/C][C]11.3366[/C][C]0.0634306[/C][/ROW]
[ROW][C]67[/C][C]11.5[/C][C]11.5263[/C][C]-0.0262813[/C][/ROW]
[ROW][C]68[/C][C]11.6[/C][C]11.7206[/C][C]-0.12062[/C][/ROW]
[ROW][C]69[/C][C]11.6[/C][C]11.6211[/C][C]-0.0211372[/C][/ROW]
[ROW][C]70[/C][C]11.7[/C][C]11.7141[/C][C]-0.0141059[/C][/ROW]
[ROW][C]71[/C][C]11.7[/C][C]11.7255[/C][C]-0.0255079[/C][/ROW]
[ROW][C]72[/C][C]11.8[/C][C]11.825[/C][C]-0.0249908[/C][/ROW]
[ROW][C]73[/C][C]11.8[/C][C]11.7277[/C][C]0.0722772[/C][/ROW]
[ROW][C]74[/C][C]11.8[/C][C]11.92[/C][C]-0.119977[/C][/ROW]
[ROW][C]75[/C][C]11.9[/C][C]12.0082[/C][C]-0.108188[/C][/ROW]
[ROW][C]76[/C][C]12[/C][C]11.904[/C][C]0.0959851[/C][/ROW]
[ROW][C]77[/C][C]12.1[/C][C]12.1052[/C][C]-0.00519241[/C][/ROW]
[ROW][C]78[/C][C]12.2[/C][C]12.2256[/C][C]-0.0255922[/C][/ROW]
[ROW][C]79[/C][C]12.2[/C][C]12.1168[/C][C]0.0832083[/C][/ROW]
[ROW][C]80[/C][C]12.3[/C][C]12.237[/C][C]0.0630058[/C][/ROW]
[ROW][C]81[/C][C]12.3[/C][C]12.3252[/C][C]-0.0252055[/C][/ROW]
[ROW][C]82[/C][C]12.3[/C][C]12.212[/C][C]0.087958[/C][/ROW]
[ROW][C]83[/C][C]12.5[/C][C]12.5244[/C][C]-0.0244321[/C][/ROW]
[ROW][C]84[/C][C]12.6[/C][C]12.6078[/C][C]-0.00775558[/C][/ROW]
[ROW][C]85[/C][C]12.6[/C][C]12.522[/C][C]0.07798[/C][/ROW]
[ROW][C]86[/C][C]12.6[/C][C]12.6147[/C][C]-0.014661[/C][/ROW]
[ROW][C]87[/C][C]12.6[/C][C]12.6376[/C][C]-0.0375956[/C][/ROW]
[ROW][C]88[/C][C]12.7[/C][C]12.7141[/C][C]-0.014144[/C][/ROW]
[ROW][C]89[/C][C]12.7[/C][C]12.6377[/C][C]0.062274[/C][/ROW]
[ROW][C]90[/C][C]12.8[/C][C]12.8253[/C][C]-0.0252897[/C][/ROW]
[ROW][C]91[/C][C]12.9[/C][C]12.9109[/C][C]-0.0108917[/C][/ROW]
[ROW][C]92[/C][C]13[/C][C]13.0198[/C][C]-0.019759[/C][/ROW]
[ROW][C]93[/C][C]13[/C][C]13.0125[/C][C]-0.0124591[/C][/ROW]
[ROW][C]94[/C][C]13[/C][C]13.0198[/C][C]-0.019759[/C][/ROW]
[ROW][C]95[/C][C]13.2[/C][C]13.221[/C][C]-0.0210033[/C][/ROW]
[ROW][C]96[/C][C]13.2[/C][C]13.2211[/C][C]-0.0211337[/C][/ROW]
[ROW][C]97[/C][C]13.3[/C][C]13.2212[/C][C]0.0787995[/C][/ROW]
[ROW][C]98[/C][C]13.3[/C][C]13.3209[/C][C]-0.0209442[/C][/ROW]
[ROW][C]99[/C][C]13.3[/C][C]13.316[/C][C]-0.0159896[/C][/ROW]
[ROW][C]100[/C][C]13.4[/C][C]13.339[/C][C]0.0610091[/C][/ROW]
[ROW][C]101[/C][C]13.4[/C][C]13.4318[/C][C]-0.0317624[/C][/ROW]
[ROW][C]102[/C][C]13.5[/C][C]13.5265[/C][C]-0.0264879[/C][/ROW]
[ROW][C]103[/C][C]13.6[/C][C]13.7374[/C][C]-0.137377[/C][/ROW]
[ROW][C]104[/C][C]13.8[/C][C]13.8008[/C][C]-0.00083367[/C][/ROW]
[ROW][C]105[/C][C]13.8[/C][C]13.7423[/C][C]0.057668[/C][/ROW]
[ROW][C]106[/C][C]14.2[/C][C]14.2373[/C][C]-0.0373312[/C][/ROW]
[ROW][C]107[/C][C]14.3[/C][C]14.2169[/C][C]0.0831276[/C][/ROW]
[ROW][C]108[/C][C]14.5[/C][C]14.5132[/C][C]-0.0132367[/C][/ROW]
[ROW][C]109[/C][C]14.6[/C][C]14.6243[/C][C]-0.024252[/C][/ROW]
[ROW][C]110[/C][C]14.8[/C][C]14.8209[/C][C]-0.0208693[/C][/ROW]
[ROW][C]111[/C][C]15.9[/C][C]15.9205[/C][C]-0.0205208[/C][/ROW]
[ROW][C]112[/C][C]16.1[/C][C]16.0899[/C][C]0.0100926[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=266602&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=266602&T=4

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
14.34.36843-0.0684276
24.94.856650.0433482
35.65.62026-0.0202566
45.75.81948-0.119483
55.95.93526-0.035256
66.36.29950.000500134
76.46.40999-0.00999364
86.46.33130.0687028
96.46.43293-0.0329281
106.76.71561-0.0156086
116.76.74317-0.0431701
127.37.36659-0.0665879
137.47.315180.0848242
147.67.63662-0.0366227
157.77.72483-0.0248339
167.77.641380.05862
177.97.824640.0753556
187.97.829070.0709258
1988.03084-0.0308401
208.28.21573-0.015731
218.38.227260.0727365
228.38.241340.0586616
238.58.51913-0.0191311
248.68.63015-0.0301465
258.88.727480.0725219
268.88.8222-0.0222036
2799.01459-0.0145884
2899.02123-0.021233
299.19.13036-0.0303611
309.29.20906-0.00905762
319.39.31564-0.0156432
329.39.30652-0.00652286
339.39.209060.0909424
349.69.621-0.0209974
359.69.516630.0833733
369.69.63696-0.0369597
379.79.71195-0.0119484
389.99.822370.0776279
399.99.94056-0.040557
409.99.924-0.0240031
411010.026-0.0260285
4210.110.1258-0.0257723
4310.310.3231-0.0231116
4410.310.32-0.0199807
4510.310.3155-0.0154841
4610.410.4265-0.0264995
4710.510.42190.0780607
4810.610.7141-0.114068
4910.710.62810.0719286
5010.810.71140.0886051
5110.810.9246-0.124566
5210.810.72290.0770727
5310.910.9149-0.0149209
5410.910.9266-0.0265837
5510.910.82240.0775898
5611.111.101-0.00098857
5711.111.2071-0.107116
5811.111.03330.0667003
5911.211.12610.073862
6011.311.22760.0723614
6111.311.4131-0.113118
6211.411.4221-0.0221078
6311.411.32540.0746354
6411.411.32740.0726177
6511.411.32970.070336
6611.411.33660.0634306
6711.511.5263-0.0262813
6811.611.7206-0.12062
6911.611.6211-0.0211372
7011.711.7141-0.0141059
7111.711.7255-0.0255079
7211.811.825-0.0249908
7311.811.72770.0722772
7411.811.92-0.119977
7511.912.0082-0.108188
761211.9040.0959851
7712.112.1052-0.00519241
7812.212.2256-0.0255922
7912.212.11680.0832083
8012.312.2370.0630058
8112.312.3252-0.0252055
8212.312.2120.087958
8312.512.5244-0.0244321
8412.612.6078-0.00775558
8512.612.5220.07798
8612.612.6147-0.014661
8712.612.6376-0.0375956
8812.712.7141-0.014144
8912.712.63770.062274
9012.812.8253-0.0252897
9112.912.9109-0.0108917
921313.0198-0.019759
931313.0125-0.0124591
941313.0198-0.019759
9513.213.221-0.0210033
9613.213.2211-0.0211337
9713.313.22120.0787995
9813.313.3209-0.0209442
9913.313.316-0.0159896
10013.413.3390.0610091
10113.413.4318-0.0317624
10213.513.5265-0.0264879
10313.613.7374-0.137377
10413.813.8008-0.00083367
10513.813.74230.057668
10614.214.2373-0.0373312
10714.314.21690.0831276
10814.514.5132-0.0132367
10914.614.6243-0.024252
11014.814.8209-0.0208693
11115.915.9205-0.0205208
11216.116.08990.0100926






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
80.6138550.7722910.386145
90.7788820.4422360.221118
100.6792240.6415510.320776
110.6329840.7340320.367016
120.6421290.7157410.357871
130.7068920.5862170.293108
140.620630.7587410.37937
150.5375520.9248950.462448
160.5979010.8041990.402099
170.5247740.9504520.475226
180.4626590.9253180.537341
190.3904710.7809410.609529
200.3439290.6878570.656071
210.4071910.8143820.592809
220.4008420.8016830.599158
230.385210.770420.61479
240.3717850.743570.628215
250.3949120.7898240.605088
260.3516510.7033010.648349
270.3058640.6117290.694136
280.2528870.5057750.747113
290.2347140.4694280.765286
300.1930360.3860710.806964
310.1656350.3312690.834365
320.1527210.3054420.847279
330.2010370.4020740.798963
340.1793310.3586620.820669
350.2057830.4115660.794217
360.1878680.3757370.812132
370.1721520.3443030.827848
380.1883510.3767020.811649
390.1839250.3678490.816075
400.1560190.3120380.843981
410.1331970.2663940.866803
420.1120710.2241420.887929
430.09682230.1936450.903178
440.07922080.1584420.920779
450.06145270.1229050.938547
460.04900690.09801380.950993
470.05998570.1199710.940014
480.1483550.2967090.851645
490.1666130.3332250.833387
500.211820.423640.78818
510.4250460.8500920.574954
520.4596620.9193230.540338
530.4148520.8297040.585148
540.3732610.7465220.626739
550.4199990.8399990.580001
560.3701230.7402450.629877
570.5088080.9823840.491192
580.506520.9869610.49348
590.5142170.9715650.485783
600.5356090.9287820.464391
610.7012910.5974190.298709
620.6590840.6818320.340916
630.6602740.6794510.339726
640.6639080.6721850.336092
650.6702180.6595640.329782
660.6629810.6740390.337019
670.6236860.7526290.376314
680.7698760.4602480.230124
690.7300740.5398520.269926
700.6857520.6284970.314248
710.6473680.7052630.352632
720.6078060.7843890.392194
730.6252250.7495510.374775
740.8108910.3782180.189109
750.9324950.1350110.0675054
760.9475150.1049710.0524854
770.92860.14280.0713998
780.915570.168860.0844301
790.9336720.1326560.0663282
800.9380070.1239860.0619932
810.9232050.1535910.0767954
820.9408270.1183450.0591726
830.9249510.1500980.0750489
840.8980920.2038150.101908
850.9255390.1489210.0744607
860.8982610.2034780.101739
870.8763470.2473060.123653
880.8358390.3283210.164161
890.8561440.2877120.143856
900.8167670.3664660.183233
910.7602170.4795660.239783
920.7012070.5975860.298793
930.6258880.7482250.374112
940.5559330.8881330.444067
950.4763660.9527330.523634
960.3940760.7881530.605924
970.4495540.8991070.550446
980.3646330.7292650.635367
990.2747180.5494360.725282
1000.4135920.8271840.586408
1010.3071780.6143560.692822
1020.3518840.7037680.648116
1030.713350.5733010.28665
1040.9129120.1741770.0870883

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
8 & 0.613855 & 0.772291 & 0.386145 \tabularnewline
9 & 0.778882 & 0.442236 & 0.221118 \tabularnewline
10 & 0.679224 & 0.641551 & 0.320776 \tabularnewline
11 & 0.632984 & 0.734032 & 0.367016 \tabularnewline
12 & 0.642129 & 0.715741 & 0.357871 \tabularnewline
13 & 0.706892 & 0.586217 & 0.293108 \tabularnewline
14 & 0.62063 & 0.758741 & 0.37937 \tabularnewline
15 & 0.537552 & 0.924895 & 0.462448 \tabularnewline
16 & 0.597901 & 0.804199 & 0.402099 \tabularnewline
17 & 0.524774 & 0.950452 & 0.475226 \tabularnewline
18 & 0.462659 & 0.925318 & 0.537341 \tabularnewline
19 & 0.390471 & 0.780941 & 0.609529 \tabularnewline
20 & 0.343929 & 0.687857 & 0.656071 \tabularnewline
21 & 0.407191 & 0.814382 & 0.592809 \tabularnewline
22 & 0.400842 & 0.801683 & 0.599158 \tabularnewline
23 & 0.38521 & 0.77042 & 0.61479 \tabularnewline
24 & 0.371785 & 0.74357 & 0.628215 \tabularnewline
25 & 0.394912 & 0.789824 & 0.605088 \tabularnewline
26 & 0.351651 & 0.703301 & 0.648349 \tabularnewline
27 & 0.305864 & 0.611729 & 0.694136 \tabularnewline
28 & 0.252887 & 0.505775 & 0.747113 \tabularnewline
29 & 0.234714 & 0.469428 & 0.765286 \tabularnewline
30 & 0.193036 & 0.386071 & 0.806964 \tabularnewline
31 & 0.165635 & 0.331269 & 0.834365 \tabularnewline
32 & 0.152721 & 0.305442 & 0.847279 \tabularnewline
33 & 0.201037 & 0.402074 & 0.798963 \tabularnewline
34 & 0.179331 & 0.358662 & 0.820669 \tabularnewline
35 & 0.205783 & 0.411566 & 0.794217 \tabularnewline
36 & 0.187868 & 0.375737 & 0.812132 \tabularnewline
37 & 0.172152 & 0.344303 & 0.827848 \tabularnewline
38 & 0.188351 & 0.376702 & 0.811649 \tabularnewline
39 & 0.183925 & 0.367849 & 0.816075 \tabularnewline
40 & 0.156019 & 0.312038 & 0.843981 \tabularnewline
41 & 0.133197 & 0.266394 & 0.866803 \tabularnewline
42 & 0.112071 & 0.224142 & 0.887929 \tabularnewline
43 & 0.0968223 & 0.193645 & 0.903178 \tabularnewline
44 & 0.0792208 & 0.158442 & 0.920779 \tabularnewline
45 & 0.0614527 & 0.122905 & 0.938547 \tabularnewline
46 & 0.0490069 & 0.0980138 & 0.950993 \tabularnewline
47 & 0.0599857 & 0.119971 & 0.940014 \tabularnewline
48 & 0.148355 & 0.296709 & 0.851645 \tabularnewline
49 & 0.166613 & 0.333225 & 0.833387 \tabularnewline
50 & 0.21182 & 0.42364 & 0.78818 \tabularnewline
51 & 0.425046 & 0.850092 & 0.574954 \tabularnewline
52 & 0.459662 & 0.919323 & 0.540338 \tabularnewline
53 & 0.414852 & 0.829704 & 0.585148 \tabularnewline
54 & 0.373261 & 0.746522 & 0.626739 \tabularnewline
55 & 0.419999 & 0.839999 & 0.580001 \tabularnewline
56 & 0.370123 & 0.740245 & 0.629877 \tabularnewline
57 & 0.508808 & 0.982384 & 0.491192 \tabularnewline
58 & 0.50652 & 0.986961 & 0.49348 \tabularnewline
59 & 0.514217 & 0.971565 & 0.485783 \tabularnewline
60 & 0.535609 & 0.928782 & 0.464391 \tabularnewline
61 & 0.701291 & 0.597419 & 0.298709 \tabularnewline
62 & 0.659084 & 0.681832 & 0.340916 \tabularnewline
63 & 0.660274 & 0.679451 & 0.339726 \tabularnewline
64 & 0.663908 & 0.672185 & 0.336092 \tabularnewline
65 & 0.670218 & 0.659564 & 0.329782 \tabularnewline
66 & 0.662981 & 0.674039 & 0.337019 \tabularnewline
67 & 0.623686 & 0.752629 & 0.376314 \tabularnewline
68 & 0.769876 & 0.460248 & 0.230124 \tabularnewline
69 & 0.730074 & 0.539852 & 0.269926 \tabularnewline
70 & 0.685752 & 0.628497 & 0.314248 \tabularnewline
71 & 0.647368 & 0.705263 & 0.352632 \tabularnewline
72 & 0.607806 & 0.784389 & 0.392194 \tabularnewline
73 & 0.625225 & 0.749551 & 0.374775 \tabularnewline
74 & 0.810891 & 0.378218 & 0.189109 \tabularnewline
75 & 0.932495 & 0.135011 & 0.0675054 \tabularnewline
76 & 0.947515 & 0.104971 & 0.0524854 \tabularnewline
77 & 0.9286 & 0.1428 & 0.0713998 \tabularnewline
78 & 0.91557 & 0.16886 & 0.0844301 \tabularnewline
79 & 0.933672 & 0.132656 & 0.0663282 \tabularnewline
80 & 0.938007 & 0.123986 & 0.0619932 \tabularnewline
81 & 0.923205 & 0.153591 & 0.0767954 \tabularnewline
82 & 0.940827 & 0.118345 & 0.0591726 \tabularnewline
83 & 0.924951 & 0.150098 & 0.0750489 \tabularnewline
84 & 0.898092 & 0.203815 & 0.101908 \tabularnewline
85 & 0.925539 & 0.148921 & 0.0744607 \tabularnewline
86 & 0.898261 & 0.203478 & 0.101739 \tabularnewline
87 & 0.876347 & 0.247306 & 0.123653 \tabularnewline
88 & 0.835839 & 0.328321 & 0.164161 \tabularnewline
89 & 0.856144 & 0.287712 & 0.143856 \tabularnewline
90 & 0.816767 & 0.366466 & 0.183233 \tabularnewline
91 & 0.760217 & 0.479566 & 0.239783 \tabularnewline
92 & 0.701207 & 0.597586 & 0.298793 \tabularnewline
93 & 0.625888 & 0.748225 & 0.374112 \tabularnewline
94 & 0.555933 & 0.888133 & 0.444067 \tabularnewline
95 & 0.476366 & 0.952733 & 0.523634 \tabularnewline
96 & 0.394076 & 0.788153 & 0.605924 \tabularnewline
97 & 0.449554 & 0.899107 & 0.550446 \tabularnewline
98 & 0.364633 & 0.729265 & 0.635367 \tabularnewline
99 & 0.274718 & 0.549436 & 0.725282 \tabularnewline
100 & 0.413592 & 0.827184 & 0.586408 \tabularnewline
101 & 0.307178 & 0.614356 & 0.692822 \tabularnewline
102 & 0.351884 & 0.703768 & 0.648116 \tabularnewline
103 & 0.71335 & 0.573301 & 0.28665 \tabularnewline
104 & 0.912912 & 0.174177 & 0.0870883 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=266602&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]8[/C][C]0.613855[/C][C]0.772291[/C][C]0.386145[/C][/ROW]
[ROW][C]9[/C][C]0.778882[/C][C]0.442236[/C][C]0.221118[/C][/ROW]
[ROW][C]10[/C][C]0.679224[/C][C]0.641551[/C][C]0.320776[/C][/ROW]
[ROW][C]11[/C][C]0.632984[/C][C]0.734032[/C][C]0.367016[/C][/ROW]
[ROW][C]12[/C][C]0.642129[/C][C]0.715741[/C][C]0.357871[/C][/ROW]
[ROW][C]13[/C][C]0.706892[/C][C]0.586217[/C][C]0.293108[/C][/ROW]
[ROW][C]14[/C][C]0.62063[/C][C]0.758741[/C][C]0.37937[/C][/ROW]
[ROW][C]15[/C][C]0.537552[/C][C]0.924895[/C][C]0.462448[/C][/ROW]
[ROW][C]16[/C][C]0.597901[/C][C]0.804199[/C][C]0.402099[/C][/ROW]
[ROW][C]17[/C][C]0.524774[/C][C]0.950452[/C][C]0.475226[/C][/ROW]
[ROW][C]18[/C][C]0.462659[/C][C]0.925318[/C][C]0.537341[/C][/ROW]
[ROW][C]19[/C][C]0.390471[/C][C]0.780941[/C][C]0.609529[/C][/ROW]
[ROW][C]20[/C][C]0.343929[/C][C]0.687857[/C][C]0.656071[/C][/ROW]
[ROW][C]21[/C][C]0.407191[/C][C]0.814382[/C][C]0.592809[/C][/ROW]
[ROW][C]22[/C][C]0.400842[/C][C]0.801683[/C][C]0.599158[/C][/ROW]
[ROW][C]23[/C][C]0.38521[/C][C]0.77042[/C][C]0.61479[/C][/ROW]
[ROW][C]24[/C][C]0.371785[/C][C]0.74357[/C][C]0.628215[/C][/ROW]
[ROW][C]25[/C][C]0.394912[/C][C]0.789824[/C][C]0.605088[/C][/ROW]
[ROW][C]26[/C][C]0.351651[/C][C]0.703301[/C][C]0.648349[/C][/ROW]
[ROW][C]27[/C][C]0.305864[/C][C]0.611729[/C][C]0.694136[/C][/ROW]
[ROW][C]28[/C][C]0.252887[/C][C]0.505775[/C][C]0.747113[/C][/ROW]
[ROW][C]29[/C][C]0.234714[/C][C]0.469428[/C][C]0.765286[/C][/ROW]
[ROW][C]30[/C][C]0.193036[/C][C]0.386071[/C][C]0.806964[/C][/ROW]
[ROW][C]31[/C][C]0.165635[/C][C]0.331269[/C][C]0.834365[/C][/ROW]
[ROW][C]32[/C][C]0.152721[/C][C]0.305442[/C][C]0.847279[/C][/ROW]
[ROW][C]33[/C][C]0.201037[/C][C]0.402074[/C][C]0.798963[/C][/ROW]
[ROW][C]34[/C][C]0.179331[/C][C]0.358662[/C][C]0.820669[/C][/ROW]
[ROW][C]35[/C][C]0.205783[/C][C]0.411566[/C][C]0.794217[/C][/ROW]
[ROW][C]36[/C][C]0.187868[/C][C]0.375737[/C][C]0.812132[/C][/ROW]
[ROW][C]37[/C][C]0.172152[/C][C]0.344303[/C][C]0.827848[/C][/ROW]
[ROW][C]38[/C][C]0.188351[/C][C]0.376702[/C][C]0.811649[/C][/ROW]
[ROW][C]39[/C][C]0.183925[/C][C]0.367849[/C][C]0.816075[/C][/ROW]
[ROW][C]40[/C][C]0.156019[/C][C]0.312038[/C][C]0.843981[/C][/ROW]
[ROW][C]41[/C][C]0.133197[/C][C]0.266394[/C][C]0.866803[/C][/ROW]
[ROW][C]42[/C][C]0.112071[/C][C]0.224142[/C][C]0.887929[/C][/ROW]
[ROW][C]43[/C][C]0.0968223[/C][C]0.193645[/C][C]0.903178[/C][/ROW]
[ROW][C]44[/C][C]0.0792208[/C][C]0.158442[/C][C]0.920779[/C][/ROW]
[ROW][C]45[/C][C]0.0614527[/C][C]0.122905[/C][C]0.938547[/C][/ROW]
[ROW][C]46[/C][C]0.0490069[/C][C]0.0980138[/C][C]0.950993[/C][/ROW]
[ROW][C]47[/C][C]0.0599857[/C][C]0.119971[/C][C]0.940014[/C][/ROW]
[ROW][C]48[/C][C]0.148355[/C][C]0.296709[/C][C]0.851645[/C][/ROW]
[ROW][C]49[/C][C]0.166613[/C][C]0.333225[/C][C]0.833387[/C][/ROW]
[ROW][C]50[/C][C]0.21182[/C][C]0.42364[/C][C]0.78818[/C][/ROW]
[ROW][C]51[/C][C]0.425046[/C][C]0.850092[/C][C]0.574954[/C][/ROW]
[ROW][C]52[/C][C]0.459662[/C][C]0.919323[/C][C]0.540338[/C][/ROW]
[ROW][C]53[/C][C]0.414852[/C][C]0.829704[/C][C]0.585148[/C][/ROW]
[ROW][C]54[/C][C]0.373261[/C][C]0.746522[/C][C]0.626739[/C][/ROW]
[ROW][C]55[/C][C]0.419999[/C][C]0.839999[/C][C]0.580001[/C][/ROW]
[ROW][C]56[/C][C]0.370123[/C][C]0.740245[/C][C]0.629877[/C][/ROW]
[ROW][C]57[/C][C]0.508808[/C][C]0.982384[/C][C]0.491192[/C][/ROW]
[ROW][C]58[/C][C]0.50652[/C][C]0.986961[/C][C]0.49348[/C][/ROW]
[ROW][C]59[/C][C]0.514217[/C][C]0.971565[/C][C]0.485783[/C][/ROW]
[ROW][C]60[/C][C]0.535609[/C][C]0.928782[/C][C]0.464391[/C][/ROW]
[ROW][C]61[/C][C]0.701291[/C][C]0.597419[/C][C]0.298709[/C][/ROW]
[ROW][C]62[/C][C]0.659084[/C][C]0.681832[/C][C]0.340916[/C][/ROW]
[ROW][C]63[/C][C]0.660274[/C][C]0.679451[/C][C]0.339726[/C][/ROW]
[ROW][C]64[/C][C]0.663908[/C][C]0.672185[/C][C]0.336092[/C][/ROW]
[ROW][C]65[/C][C]0.670218[/C][C]0.659564[/C][C]0.329782[/C][/ROW]
[ROW][C]66[/C][C]0.662981[/C][C]0.674039[/C][C]0.337019[/C][/ROW]
[ROW][C]67[/C][C]0.623686[/C][C]0.752629[/C][C]0.376314[/C][/ROW]
[ROW][C]68[/C][C]0.769876[/C][C]0.460248[/C][C]0.230124[/C][/ROW]
[ROW][C]69[/C][C]0.730074[/C][C]0.539852[/C][C]0.269926[/C][/ROW]
[ROW][C]70[/C][C]0.685752[/C][C]0.628497[/C][C]0.314248[/C][/ROW]
[ROW][C]71[/C][C]0.647368[/C][C]0.705263[/C][C]0.352632[/C][/ROW]
[ROW][C]72[/C][C]0.607806[/C][C]0.784389[/C][C]0.392194[/C][/ROW]
[ROW][C]73[/C][C]0.625225[/C][C]0.749551[/C][C]0.374775[/C][/ROW]
[ROW][C]74[/C][C]0.810891[/C][C]0.378218[/C][C]0.189109[/C][/ROW]
[ROW][C]75[/C][C]0.932495[/C][C]0.135011[/C][C]0.0675054[/C][/ROW]
[ROW][C]76[/C][C]0.947515[/C][C]0.104971[/C][C]0.0524854[/C][/ROW]
[ROW][C]77[/C][C]0.9286[/C][C]0.1428[/C][C]0.0713998[/C][/ROW]
[ROW][C]78[/C][C]0.91557[/C][C]0.16886[/C][C]0.0844301[/C][/ROW]
[ROW][C]79[/C][C]0.933672[/C][C]0.132656[/C][C]0.0663282[/C][/ROW]
[ROW][C]80[/C][C]0.938007[/C][C]0.123986[/C][C]0.0619932[/C][/ROW]
[ROW][C]81[/C][C]0.923205[/C][C]0.153591[/C][C]0.0767954[/C][/ROW]
[ROW][C]82[/C][C]0.940827[/C][C]0.118345[/C][C]0.0591726[/C][/ROW]
[ROW][C]83[/C][C]0.924951[/C][C]0.150098[/C][C]0.0750489[/C][/ROW]
[ROW][C]84[/C][C]0.898092[/C][C]0.203815[/C][C]0.101908[/C][/ROW]
[ROW][C]85[/C][C]0.925539[/C][C]0.148921[/C][C]0.0744607[/C][/ROW]
[ROW][C]86[/C][C]0.898261[/C][C]0.203478[/C][C]0.101739[/C][/ROW]
[ROW][C]87[/C][C]0.876347[/C][C]0.247306[/C][C]0.123653[/C][/ROW]
[ROW][C]88[/C][C]0.835839[/C][C]0.328321[/C][C]0.164161[/C][/ROW]
[ROW][C]89[/C][C]0.856144[/C][C]0.287712[/C][C]0.143856[/C][/ROW]
[ROW][C]90[/C][C]0.816767[/C][C]0.366466[/C][C]0.183233[/C][/ROW]
[ROW][C]91[/C][C]0.760217[/C][C]0.479566[/C][C]0.239783[/C][/ROW]
[ROW][C]92[/C][C]0.701207[/C][C]0.597586[/C][C]0.298793[/C][/ROW]
[ROW][C]93[/C][C]0.625888[/C][C]0.748225[/C][C]0.374112[/C][/ROW]
[ROW][C]94[/C][C]0.555933[/C][C]0.888133[/C][C]0.444067[/C][/ROW]
[ROW][C]95[/C][C]0.476366[/C][C]0.952733[/C][C]0.523634[/C][/ROW]
[ROW][C]96[/C][C]0.394076[/C][C]0.788153[/C][C]0.605924[/C][/ROW]
[ROW][C]97[/C][C]0.449554[/C][C]0.899107[/C][C]0.550446[/C][/ROW]
[ROW][C]98[/C][C]0.364633[/C][C]0.729265[/C][C]0.635367[/C][/ROW]
[ROW][C]99[/C][C]0.274718[/C][C]0.549436[/C][C]0.725282[/C][/ROW]
[ROW][C]100[/C][C]0.413592[/C][C]0.827184[/C][C]0.586408[/C][/ROW]
[ROW][C]101[/C][C]0.307178[/C][C]0.614356[/C][C]0.692822[/C][/ROW]
[ROW][C]102[/C][C]0.351884[/C][C]0.703768[/C][C]0.648116[/C][/ROW]
[ROW][C]103[/C][C]0.71335[/C][C]0.573301[/C][C]0.28665[/C][/ROW]
[ROW][C]104[/C][C]0.912912[/C][C]0.174177[/C][C]0.0870883[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=266602&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=266602&T=5

As an alternative you can also use a QR Code:  
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The GUIDs for individual cells are displayed in the table below:

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
80.6138550.7722910.386145
90.7788820.4422360.221118
100.6792240.6415510.320776
110.6329840.7340320.367016
120.6421290.7157410.357871
130.7068920.5862170.293108
140.620630.7587410.37937
150.5375520.9248950.462448
160.5979010.8041990.402099
170.5247740.9504520.475226
180.4626590.9253180.537341
190.3904710.7809410.609529
200.3439290.6878570.656071
210.4071910.8143820.592809
220.4008420.8016830.599158
230.385210.770420.61479
240.3717850.743570.628215
250.3949120.7898240.605088
260.3516510.7033010.648349
270.3058640.6117290.694136
280.2528870.5057750.747113
290.2347140.4694280.765286
300.1930360.3860710.806964
310.1656350.3312690.834365
320.1527210.3054420.847279
330.2010370.4020740.798963
340.1793310.3586620.820669
350.2057830.4115660.794217
360.1878680.3757370.812132
370.1721520.3443030.827848
380.1883510.3767020.811649
390.1839250.3678490.816075
400.1560190.3120380.843981
410.1331970.2663940.866803
420.1120710.2241420.887929
430.09682230.1936450.903178
440.07922080.1584420.920779
450.06145270.1229050.938547
460.04900690.09801380.950993
470.05998570.1199710.940014
480.1483550.2967090.851645
490.1666130.3332250.833387
500.211820.423640.78818
510.4250460.8500920.574954
520.4596620.9193230.540338
530.4148520.8297040.585148
540.3732610.7465220.626739
550.4199990.8399990.580001
560.3701230.7402450.629877
570.5088080.9823840.491192
580.506520.9869610.49348
590.5142170.9715650.485783
600.5356090.9287820.464391
610.7012910.5974190.298709
620.6590840.6818320.340916
630.6602740.6794510.339726
640.6639080.6721850.336092
650.6702180.6595640.329782
660.6629810.6740390.337019
670.6236860.7526290.376314
680.7698760.4602480.230124
690.7300740.5398520.269926
700.6857520.6284970.314248
710.6473680.7052630.352632
720.6078060.7843890.392194
730.6252250.7495510.374775
740.8108910.3782180.189109
750.9324950.1350110.0675054
760.9475150.1049710.0524854
770.92860.14280.0713998
780.915570.168860.0844301
790.9336720.1326560.0663282
800.9380070.1239860.0619932
810.9232050.1535910.0767954
820.9408270.1183450.0591726
830.9249510.1500980.0750489
840.8980920.2038150.101908
850.9255390.1489210.0744607
860.8982610.2034780.101739
870.8763470.2473060.123653
880.8358390.3283210.164161
890.8561440.2877120.143856
900.8167670.3664660.183233
910.7602170.4795660.239783
920.7012070.5975860.298793
930.6258880.7482250.374112
940.5559330.8881330.444067
950.4763660.9527330.523634
960.3940760.7881530.605924
970.4495540.8991070.550446
980.3646330.7292650.635367
990.2747180.5494360.725282
1000.4135920.8271840.586408
1010.3071780.6143560.692822
1020.3518840.7037680.648116
1030.713350.5733010.28665
1040.9129120.1741770.0870883






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 level10.0103093OK

\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 & 1 & 0.0103093 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=266602&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]1[/C][C]0.0103093[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=266602&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=266602&T=6

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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 level10.0103093OK


Figures (Output of Computation)

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Input Parameters & R Code

Parameters (Session):
par1 = 5 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 5 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
R code (references can be found in the software module):
library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, signif(mysum$coefficients[i,1],6), 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,signif(mysum$coefficients[i,1],6))
a<-table.element(a, signif(mysum$coefficients[i,2],6))
a<-table.element(a, signif(mysum$coefficients[i,3],4))
a<-table.element(a, signif(mysum$coefficients[i,4],6))
a<-table.element(a, signif(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, signif(sqrt(mysum$r.squared),6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, signif(mysum$r.squared,6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, signif(mysum$adj.r.squared,6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[1],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[2],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[3],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, signif(1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]),6))
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, signif(mysum$sigma,6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, signif(sum(myerror*myerror),6))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,signif(x[i],6))
a<-table.element(a,signif(x[i]-mysum$resid[i],6))
a<-table.element(a,signif(mysum$resid[i],6))
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,signif(gqarr[mypoint-kp3+1,1],6))
a<-table.element(a,signif(gqarr[mypoint-kp3+1,2],6))
a<-table.element(a,signif(gqarr[mypoint-kp3+1,3],6))
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,signif(numsignificant1,6))
a<-table.element(a,signif(numsignificant1/numgqtests,6))
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,signif(numsignificant5,6))
a<-table.element(a,signif(numsignificant5/numgqtests,6))
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,signif(numsignificant10,6))
a<-table.element(a,signif(numsignificant10/numgqtests,6))
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')
}