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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 12:41:26 +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/t14183881522wamqnfn1dizlwx.htm/, Retrieved Thu, 16 May 2024 09:47:35 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=266618, Retrieved Thu, 16 May 2024 09:47:35 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact89

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [] [2013-11-04 07:18:26] [0307e7a6407eb638caabc417e3a6b260]
- RM D    [Multiple Regression] [] [2014-12-12 12:41:26] [2b74e5be20a95dee0bfccc444f4c1798] [Current]
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Dataset

Dataseries X:
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Histogram
Boxplots 
7,5	1,8	2,1	1,5	12,9
6	2,1	2	2,1	12,2
6,5	2,2	2	2,1	12,8
1	2,3	2,1	1,9	7,4
1	2,1	2	1,6	6,7
5,5	2,7	2,3	2,1	12,6
8,5	2,1	2,1	2,1	14,8
6,5	2,4	2,1	2,2	13,3
4,5	2,9	2,2	1,5	11,1
2	2,2	2,1	1,9	8,2
5	2,1	2,1	2,2	11,4
0,5	2,2	2,1	1,6	6,4
5	2,2	2	1,5	10,6
5	2,7	2,3	1,9	12
2,5	1,9	1,8	0,1	6,3
5	2	2	2,2	11,3
5,5	2,5	2,2	1,8	11,9
3,5	2,2	2	1,6	9,3
3	2,3	2,1	2,2	9,6
4	1,9	2	2,1	10
0,5	2,1	1,8	1,9	6,4
6,5	3,5	2,2	1,6	13,8
4,5	2,1	2,2	1,9	10,8
7,5	2,3	1,7	2,2	13,8
5,5	2,3	2,1	1,8	11,7
4	2,2	2,3	2,4	10,9
7,5	3,5	2,7	2,4	16,1
7	1,9	1,9	2,5	13,4
4	1,9	2	1,9	9,9
5,5	1,9	2	2,1	11,5
2,5	1,9	1,9	1,9	8,3
5,5	2,1	2	2,1	11,7
3,5	2	2	1,5	9
2,5	3,2	2,1	1,9	9,7
4,5	2,3	2	2,1	10,8
4,5	2,5	1,8	1,5	10,3
4,5	1,8	2	2,1	10,4
6	2,4	2,2	2,1	12,7
2,5	2,8	2,2	1,8	9,3
5	2,3	2,1	2,4	11,8
0	2	1,8	2,1	5,9
5	2,5	1,9	1,9	11,4
6,5	2,3	2,1	2,1	13
5	1,8	2	1,9	10,8
6	1,9	1,9	2,4	12,3
4,5	2,6	2,2	2,1	11,3
5,5	2	2	2,2	11,8
1	2,6	2	2,2	7,9
7,5	1,6	1,7	1,8	12,7
6	2,2	2	2,1	12,3
5	2,1	2,2	2,4	11,6
1	1,8	1,7	2,2	6,7
5	1,8	2	2,1	10,9
6,5	1,9	2,2	1,5	12,1
7	2,4	2	1,9	13,3
4,5	1,9	1,9	1,8	10,1
0	2	2	1,8	5,7
8,5	2,1	2	1,6	14,3
3,5	1,7	1,6	1,2	8
7,5	1,9	2,1	1,8	13,3
3,5	2,1	2,1	1,5	9,3
6	2,4	2	2,1	12,5
1,5	1,8	1,9	2,4	7,6
9	2,3	2,2	2,4	15,9
3,5	2,1	2,1	1,5	9,2
3,5	2	1,8	1,8	9,1
4	2,8	2,3	2,1	11,1
6,5	2	2,3	2,2	13
7,5	2,7	2,2	2,1	14,5
6	2,1	2,1	1,9	12,2
5	2,9	2,2	2,1	12,3
5,5	2	1,9	1,9	11,4
3,5	1,8	1,8	1,6	8,8
7,5	2,6	2,1	2,4	14,6
6,5	2,1	2	1,9	12,6
NA	2,3	1,7	1,9	NA
6,5	2,3	2,1	2,1	13
6,5	2,2	2,1	1,8	12,6
7	2	2,1	2,1	13,2
3,5	2,2	1,8	2,4	9,9
1,5	2,1	2	2,1	7,7
4	2,1	2,1	2,2	10,5
7,5	1,9	1,9	2,1	13,4
4,5	2	2,1	2,2	10,9
0	1,7	1	1,6	4,3
3,5	2,2	2,2	2,4	10,3
5,5	2,2	2,1	2,1	11,8
5	2,3	1,9	1,9	11,2
4,5	2,4	2	2,4	11,4
2,5	2,1	1,9	2,1	8,6
7,5	1,9	2	1,8	13,2
7	1,7	1,8	2,1	12,6
0	1,8	2	1,8	5,6
4,5	1,5	2	1,9	9,9
3	1,9	2	1,9	8,8
1,5	1,9	1,8	2,4	7,7
3,5	1,7	2	1,8	9
2,5	1,9	1,1	1,8	7,3
5,5	1,9	1,8	2,1	11,4
8	1,8	1,8	2,1	13,6
1	2,4	2	2,4	7,9
5	1,8	1,9	1,9	10,7
4,5	1,9	2,1	1,8	10,3
3	1,8	1,6	1,8	8,3
3	2,1	2,2	2,2	9,6
8	1,9	1,9	2,4	14,2
2,5	2,2	2	1,8	8,5
7	2	2,1	2,4	13,5
0	1,7	1,3	1,8	4,9
1	1,7	1,8	1,9	6,4
3,5	1,8	1,9	2,4	9,6
5,5	1,9	2,1	2,1	11,6
5,5	1,8	1,8	1,9	11,1

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 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 & 6 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=266618&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' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=266618&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=266618&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 time6 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=266618&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=266618&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=266618&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=266618&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=266618&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=266618&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=266618&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=266618&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=266618&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
112.912.9109-0.0108917
212.212.2256-0.0255922
312.812.8253-0.0252897
47.47.315180.0848242
56.76.71561-0.0156086
612.612.6078-0.00775558
714.814.8209-0.0208693
813.313.22120.0787995
911.111.101-0.00098857
108.28.21573-0.015731
1111.411.4221-0.0221078
126.46.40999-0.00999364
1310.610.7141-0.114068
141211.9040.0959851
156.36.29950.000500134
1611.311.22760.0723614
1711.912.0082-0.108188
189.39.31564-0.0156432
199.69.621-0.0209974
201010.026-0.0260285
216.46.33130.0687028
2213.813.8008-0.00083367
2310.810.71140.0886051
2413.813.74230.057668
2511.711.7141-0.0141059
2610.910.9149-0.0149209
2716.116.08990.0100926
2813.413.3390.0610091
299.99.822370.0776279
3011.511.5263-0.0262813
318.38.227260.0727365
3211.711.7255-0.0255079
3399.01459-0.0145884
349.79.71195-0.0119484
3510.810.9246-0.124566
3610.310.3231-0.0231116
3710.410.4265-0.0264995
3812.712.7141-0.014144
399.39.30652-0.00652286
4011.811.825-0.0249908
415.95.93526-0.035256
4211.411.32540.0746354
431313.0198-0.019759
4410.810.72290.0770727
4512.312.2370.0630058
4611.311.4131-0.113118
4711.811.72770.0722772
487.97.824640.0753556
4912.712.63770.062274
5012.312.3252-0.0252055
5111.611.7206-0.12062
526.76.74317-0.0431701
5310.910.9266-0.0265837
5412.112.1052-0.00519241
5513.313.3209-0.0209442
5610.110.1258-0.0257723
575.75.81948-0.119483
5814.314.21690.0831276
5988.03084-0.0308401
6013.313.316-0.0159896
619.39.209060.0909424
6212.512.5244-0.0244321
637.67.63662-0.0366227
6415.915.9205-0.0205208
659.29.20906-0.00905762
669.19.13036-0.0303611
6711.111.2071-0.107116
681313.0125-0.0124591
6914.514.5132-0.0132367
7012.212.11680.0832083
7112.312.2120.087958
7211.411.32740.0726177
738.88.727480.0725219
7414.614.6243-0.024252
7512.612.5220.07798
76NANA-0.019759
771313.0147-0.014661
7812.612.621-0.0210033
7913.213.2406-0.040557
809.99.92483-0.0248339
817.77.621940.0780607
8210.510.5318-0.0317624
8313.413.32240.0775898
8410.910.9684-0.0684276
854.34.31998-0.0199807
8610.310.42-0.119977
8711.811.72610.073862
8811.211.12970.070336
8911.411.4301-0.0301465
908.68.62113-0.0211337
9113.213.2376-0.0375956
9212.612.6203-0.0202566
935.65.624-0.0240031
949.99.9222-0.0222036
958.88.741380.05862
967.77.72123-0.021233
9799.06659-0.0665879
987.37.236570.0634306
9911.411.5374-0.137377
10013.613.52910.0709258
1017.97.828070.0719286
10210.710.7155-0.0154841
10310.310.24130.0586616
1048.38.216630.0833733
1059.69.63733-0.0373312
10614.214.2191-0.0191311
1078.58.52649-0.0264879
10813.513.45670.0433482
1094.94.93293-0.0329281
1106.46.43696-0.0369597
1119.69.62114-0.0211372
11211.611.53330.0667003
11311.1NANA

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 12.9 & 12.9109 & -0.0108917 \tabularnewline
2 & 12.2 & 12.2256 & -0.0255922 \tabularnewline
3 & 12.8 & 12.8253 & -0.0252897 \tabularnewline
4 & 7.4 & 7.31518 & 0.0848242 \tabularnewline
5 & 6.7 & 6.71561 & -0.0156086 \tabularnewline
6 & 12.6 & 12.6078 & -0.00775558 \tabularnewline
7 & 14.8 & 14.8209 & -0.0208693 \tabularnewline
8 & 13.3 & 13.2212 & 0.0787995 \tabularnewline
9 & 11.1 & 11.101 & -0.00098857 \tabularnewline
10 & 8.2 & 8.21573 & -0.015731 \tabularnewline
11 & 11.4 & 11.4221 & -0.0221078 \tabularnewline
12 & 6.4 & 6.40999 & -0.00999364 \tabularnewline
13 & 10.6 & 10.7141 & -0.114068 \tabularnewline
14 & 12 & 11.904 & 0.0959851 \tabularnewline
15 & 6.3 & 6.2995 & 0.000500134 \tabularnewline
16 & 11.3 & 11.2276 & 0.0723614 \tabularnewline
17 & 11.9 & 12.0082 & -0.108188 \tabularnewline
18 & 9.3 & 9.31564 & -0.0156432 \tabularnewline
19 & 9.6 & 9.621 & -0.0209974 \tabularnewline
20 & 10 & 10.026 & -0.0260285 \tabularnewline
21 & 6.4 & 6.3313 & 0.0687028 \tabularnewline
22 & 13.8 & 13.8008 & -0.00083367 \tabularnewline
23 & 10.8 & 10.7114 & 0.0886051 \tabularnewline
24 & 13.8 & 13.7423 & 0.057668 \tabularnewline
25 & 11.7 & 11.7141 & -0.0141059 \tabularnewline
26 & 10.9 & 10.9149 & -0.0149209 \tabularnewline
27 & 16.1 & 16.0899 & 0.0100926 \tabularnewline
28 & 13.4 & 13.339 & 0.0610091 \tabularnewline
29 & 9.9 & 9.82237 & 0.0776279 \tabularnewline
30 & 11.5 & 11.5263 & -0.0262813 \tabularnewline
31 & 8.3 & 8.22726 & 0.0727365 \tabularnewline
32 & 11.7 & 11.7255 & -0.0255079 \tabularnewline
33 & 9 & 9.01459 & -0.0145884 \tabularnewline
34 & 9.7 & 9.71195 & -0.0119484 \tabularnewline
35 & 10.8 & 10.9246 & -0.124566 \tabularnewline
36 & 10.3 & 10.3231 & -0.0231116 \tabularnewline
37 & 10.4 & 10.4265 & -0.0264995 \tabularnewline
38 & 12.7 & 12.7141 & -0.014144 \tabularnewline
39 & 9.3 & 9.30652 & -0.00652286 \tabularnewline
40 & 11.8 & 11.825 & -0.0249908 \tabularnewline
41 & 5.9 & 5.93526 & -0.035256 \tabularnewline
42 & 11.4 & 11.3254 & 0.0746354 \tabularnewline
43 & 13 & 13.0198 & -0.019759 \tabularnewline
44 & 10.8 & 10.7229 & 0.0770727 \tabularnewline
45 & 12.3 & 12.237 & 0.0630058 \tabularnewline
46 & 11.3 & 11.4131 & -0.113118 \tabularnewline
47 & 11.8 & 11.7277 & 0.0722772 \tabularnewline
48 & 7.9 & 7.82464 & 0.0753556 \tabularnewline
49 & 12.7 & 12.6377 & 0.062274 \tabularnewline
50 & 12.3 & 12.3252 & -0.0252055 \tabularnewline
51 & 11.6 & 11.7206 & -0.12062 \tabularnewline
52 & 6.7 & 6.74317 & -0.0431701 \tabularnewline
53 & 10.9 & 10.9266 & -0.0265837 \tabularnewline
54 & 12.1 & 12.1052 & -0.00519241 \tabularnewline
55 & 13.3 & 13.3209 & -0.0209442 \tabularnewline
56 & 10.1 & 10.1258 & -0.0257723 \tabularnewline
57 & 5.7 & 5.81948 & -0.119483 \tabularnewline
58 & 14.3 & 14.2169 & 0.0831276 \tabularnewline
59 & 8 & 8.03084 & -0.0308401 \tabularnewline
60 & 13.3 & 13.316 & -0.0159896 \tabularnewline
61 & 9.3 & 9.20906 & 0.0909424 \tabularnewline
62 & 12.5 & 12.5244 & -0.0244321 \tabularnewline
63 & 7.6 & 7.63662 & -0.0366227 \tabularnewline
64 & 15.9 & 15.9205 & -0.0205208 \tabularnewline
65 & 9.2 & 9.20906 & -0.00905762 \tabularnewline
66 & 9.1 & 9.13036 & -0.0303611 \tabularnewline
67 & 11.1 & 11.2071 & -0.107116 \tabularnewline
68 & 13 & 13.0125 & -0.0124591 \tabularnewline
69 & 14.5 & 14.5132 & -0.0132367 \tabularnewline
70 & 12.2 & 12.1168 & 0.0832083 \tabularnewline
71 & 12.3 & 12.212 & 0.087958 \tabularnewline
72 & 11.4 & 11.3274 & 0.0726177 \tabularnewline
73 & 8.8 & 8.72748 & 0.0725219 \tabularnewline
74 & 14.6 & 14.6243 & -0.024252 \tabularnewline
75 & 12.6 & 12.522 & 0.07798 \tabularnewline
76 & NA & NA & -0.019759 \tabularnewline
77 & 13 & 13.0147 & -0.014661 \tabularnewline
78 & 12.6 & 12.621 & -0.0210033 \tabularnewline
79 & 13.2 & 13.2406 & -0.040557 \tabularnewline
80 & 9.9 & 9.92483 & -0.0248339 \tabularnewline
81 & 7.7 & 7.62194 & 0.0780607 \tabularnewline
82 & 10.5 & 10.5318 & -0.0317624 \tabularnewline
83 & 13.4 & 13.3224 & 0.0775898 \tabularnewline
84 & 10.9 & 10.9684 & -0.0684276 \tabularnewline
85 & 4.3 & 4.31998 & -0.0199807 \tabularnewline
86 & 10.3 & 10.42 & -0.119977 \tabularnewline
87 & 11.8 & 11.7261 & 0.073862 \tabularnewline
88 & 11.2 & 11.1297 & 0.070336 \tabularnewline
89 & 11.4 & 11.4301 & -0.0301465 \tabularnewline
90 & 8.6 & 8.62113 & -0.0211337 \tabularnewline
91 & 13.2 & 13.2376 & -0.0375956 \tabularnewline
92 & 12.6 & 12.6203 & -0.0202566 \tabularnewline
93 & 5.6 & 5.624 & -0.0240031 \tabularnewline
94 & 9.9 & 9.9222 & -0.0222036 \tabularnewline
95 & 8.8 & 8.74138 & 0.05862 \tabularnewline
96 & 7.7 & 7.72123 & -0.021233 \tabularnewline
97 & 9 & 9.06659 & -0.0665879 \tabularnewline
98 & 7.3 & 7.23657 & 0.0634306 \tabularnewline
99 & 11.4 & 11.5374 & -0.137377 \tabularnewline
100 & 13.6 & 13.5291 & 0.0709258 \tabularnewline
101 & 7.9 & 7.82807 & 0.0719286 \tabularnewline
102 & 10.7 & 10.7155 & -0.0154841 \tabularnewline
103 & 10.3 & 10.2413 & 0.0586616 \tabularnewline
104 & 8.3 & 8.21663 & 0.0833733 \tabularnewline
105 & 9.6 & 9.63733 & -0.0373312 \tabularnewline
106 & 14.2 & 14.2191 & -0.0191311 \tabularnewline
107 & 8.5 & 8.52649 & -0.0264879 \tabularnewline
108 & 13.5 & 13.4567 & 0.0433482 \tabularnewline
109 & 4.9 & 4.93293 & -0.0329281 \tabularnewline
110 & 6.4 & 6.43696 & -0.0369597 \tabularnewline
111 & 9.6 & 9.62114 & -0.0211372 \tabularnewline
112 & 11.6 & 11.5333 & 0.0667003 \tabularnewline
113 & 11.1 & NA & NA \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=266618&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]12.9[/C][C]12.9109[/C][C]-0.0108917[/C][/ROW]
[ROW][C]2[/C][C]12.2[/C][C]12.2256[/C][C]-0.0255922[/C][/ROW]
[ROW][C]3[/C][C]12.8[/C][C]12.8253[/C][C]-0.0252897[/C][/ROW]
[ROW][C]4[/C][C]7.4[/C][C]7.31518[/C][C]0.0848242[/C][/ROW]
[ROW][C]5[/C][C]6.7[/C][C]6.71561[/C][C]-0.0156086[/C][/ROW]
[ROW][C]6[/C][C]12.6[/C][C]12.6078[/C][C]-0.00775558[/C][/ROW]
[ROW][C]7[/C][C]14.8[/C][C]14.8209[/C][C]-0.0208693[/C][/ROW]
[ROW][C]8[/C][C]13.3[/C][C]13.2212[/C][C]0.0787995[/C][/ROW]
[ROW][C]9[/C][C]11.1[/C][C]11.101[/C][C]-0.00098857[/C][/ROW]
[ROW][C]10[/C][C]8.2[/C][C]8.21573[/C][C]-0.015731[/C][/ROW]
[ROW][C]11[/C][C]11.4[/C][C]11.4221[/C][C]-0.0221078[/C][/ROW]
[ROW][C]12[/C][C]6.4[/C][C]6.40999[/C][C]-0.00999364[/C][/ROW]
[ROW][C]13[/C][C]10.6[/C][C]10.7141[/C][C]-0.114068[/C][/ROW]
[ROW][C]14[/C][C]12[/C][C]11.904[/C][C]0.0959851[/C][/ROW]
[ROW][C]15[/C][C]6.3[/C][C]6.2995[/C][C]0.000500134[/C][/ROW]
[ROW][C]16[/C][C]11.3[/C][C]11.2276[/C][C]0.0723614[/C][/ROW]
[ROW][C]17[/C][C]11.9[/C][C]12.0082[/C][C]-0.108188[/C][/ROW]
[ROW][C]18[/C][C]9.3[/C][C]9.31564[/C][C]-0.0156432[/C][/ROW]
[ROW][C]19[/C][C]9.6[/C][C]9.621[/C][C]-0.0209974[/C][/ROW]
[ROW][C]20[/C][C]10[/C][C]10.026[/C][C]-0.0260285[/C][/ROW]
[ROW][C]21[/C][C]6.4[/C][C]6.3313[/C][C]0.0687028[/C][/ROW]
[ROW][C]22[/C][C]13.8[/C][C]13.8008[/C][C]-0.00083367[/C][/ROW]
[ROW][C]23[/C][C]10.8[/C][C]10.7114[/C][C]0.0886051[/C][/ROW]
[ROW][C]24[/C][C]13.8[/C][C]13.7423[/C][C]0.057668[/C][/ROW]
[ROW][C]25[/C][C]11.7[/C][C]11.7141[/C][C]-0.0141059[/C][/ROW]
[ROW][C]26[/C][C]10.9[/C][C]10.9149[/C][C]-0.0149209[/C][/ROW]
[ROW][C]27[/C][C]16.1[/C][C]16.0899[/C][C]0.0100926[/C][/ROW]
[ROW][C]28[/C][C]13.4[/C][C]13.339[/C][C]0.0610091[/C][/ROW]
[ROW][C]29[/C][C]9.9[/C][C]9.82237[/C][C]0.0776279[/C][/ROW]
[ROW][C]30[/C][C]11.5[/C][C]11.5263[/C][C]-0.0262813[/C][/ROW]
[ROW][C]31[/C][C]8.3[/C][C]8.22726[/C][C]0.0727365[/C][/ROW]
[ROW][C]32[/C][C]11.7[/C][C]11.7255[/C][C]-0.0255079[/C][/ROW]
[ROW][C]33[/C][C]9[/C][C]9.01459[/C][C]-0.0145884[/C][/ROW]
[ROW][C]34[/C][C]9.7[/C][C]9.71195[/C][C]-0.0119484[/C][/ROW]
[ROW][C]35[/C][C]10.8[/C][C]10.9246[/C][C]-0.124566[/C][/ROW]
[ROW][C]36[/C][C]10.3[/C][C]10.3231[/C][C]-0.0231116[/C][/ROW]
[ROW][C]37[/C][C]10.4[/C][C]10.4265[/C][C]-0.0264995[/C][/ROW]
[ROW][C]38[/C][C]12.7[/C][C]12.7141[/C][C]-0.014144[/C][/ROW]
[ROW][C]39[/C][C]9.3[/C][C]9.30652[/C][C]-0.00652286[/C][/ROW]
[ROW][C]40[/C][C]11.8[/C][C]11.825[/C][C]-0.0249908[/C][/ROW]
[ROW][C]41[/C][C]5.9[/C][C]5.93526[/C][C]-0.035256[/C][/ROW]
[ROW][C]42[/C][C]11.4[/C][C]11.3254[/C][C]0.0746354[/C][/ROW]
[ROW][C]43[/C][C]13[/C][C]13.0198[/C][C]-0.019759[/C][/ROW]
[ROW][C]44[/C][C]10.8[/C][C]10.7229[/C][C]0.0770727[/C][/ROW]
[ROW][C]45[/C][C]12.3[/C][C]12.237[/C][C]0.0630058[/C][/ROW]
[ROW][C]46[/C][C]11.3[/C][C]11.4131[/C][C]-0.113118[/C][/ROW]
[ROW][C]47[/C][C]11.8[/C][C]11.7277[/C][C]0.0722772[/C][/ROW]
[ROW][C]48[/C][C]7.9[/C][C]7.82464[/C][C]0.0753556[/C][/ROW]
[ROW][C]49[/C][C]12.7[/C][C]12.6377[/C][C]0.062274[/C][/ROW]
[ROW][C]50[/C][C]12.3[/C][C]12.3252[/C][C]-0.0252055[/C][/ROW]
[ROW][C]51[/C][C]11.6[/C][C]11.7206[/C][C]-0.12062[/C][/ROW]
[ROW][C]52[/C][C]6.7[/C][C]6.74317[/C][C]-0.0431701[/C][/ROW]
[ROW][C]53[/C][C]10.9[/C][C]10.9266[/C][C]-0.0265837[/C][/ROW]
[ROW][C]54[/C][C]12.1[/C][C]12.1052[/C][C]-0.00519241[/C][/ROW]
[ROW][C]55[/C][C]13.3[/C][C]13.3209[/C][C]-0.0209442[/C][/ROW]
[ROW][C]56[/C][C]10.1[/C][C]10.1258[/C][C]-0.0257723[/C][/ROW]
[ROW][C]57[/C][C]5.7[/C][C]5.81948[/C][C]-0.119483[/C][/ROW]
[ROW][C]58[/C][C]14.3[/C][C]14.2169[/C][C]0.0831276[/C][/ROW]
[ROW][C]59[/C][C]8[/C][C]8.03084[/C][C]-0.0308401[/C][/ROW]
[ROW][C]60[/C][C]13.3[/C][C]13.316[/C][C]-0.0159896[/C][/ROW]
[ROW][C]61[/C][C]9.3[/C][C]9.20906[/C][C]0.0909424[/C][/ROW]
[ROW][C]62[/C][C]12.5[/C][C]12.5244[/C][C]-0.0244321[/C][/ROW]
[ROW][C]63[/C][C]7.6[/C][C]7.63662[/C][C]-0.0366227[/C][/ROW]
[ROW][C]64[/C][C]15.9[/C][C]15.9205[/C][C]-0.0205208[/C][/ROW]
[ROW][C]65[/C][C]9.2[/C][C]9.20906[/C][C]-0.00905762[/C][/ROW]
[ROW][C]66[/C][C]9.1[/C][C]9.13036[/C][C]-0.0303611[/C][/ROW]
[ROW][C]67[/C][C]11.1[/C][C]11.2071[/C][C]-0.107116[/C][/ROW]
[ROW][C]68[/C][C]13[/C][C]13.0125[/C][C]-0.0124591[/C][/ROW]
[ROW][C]69[/C][C]14.5[/C][C]14.5132[/C][C]-0.0132367[/C][/ROW]
[ROW][C]70[/C][C]12.2[/C][C]12.1168[/C][C]0.0832083[/C][/ROW]
[ROW][C]71[/C][C]12.3[/C][C]12.212[/C][C]0.087958[/C][/ROW]
[ROW][C]72[/C][C]11.4[/C][C]11.3274[/C][C]0.0726177[/C][/ROW]
[ROW][C]73[/C][C]8.8[/C][C]8.72748[/C][C]0.0725219[/C][/ROW]
[ROW][C]74[/C][C]14.6[/C][C]14.6243[/C][C]-0.024252[/C][/ROW]
[ROW][C]75[/C][C]12.6[/C][C]12.522[/C][C]0.07798[/C][/ROW]
[ROW][C]76[/C][C]NA[/C][C]NA[/C][C]-0.019759[/C][/ROW]
[ROW][C]77[/C][C]13[/C][C]13.0147[/C][C]-0.014661[/C][/ROW]
[ROW][C]78[/C][C]12.6[/C][C]12.621[/C][C]-0.0210033[/C][/ROW]
[ROW][C]79[/C][C]13.2[/C][C]13.2406[/C][C]-0.040557[/C][/ROW]
[ROW][C]80[/C][C]9.9[/C][C]9.92483[/C][C]-0.0248339[/C][/ROW]
[ROW][C]81[/C][C]7.7[/C][C]7.62194[/C][C]0.0780607[/C][/ROW]
[ROW][C]82[/C][C]10.5[/C][C]10.5318[/C][C]-0.0317624[/C][/ROW]
[ROW][C]83[/C][C]13.4[/C][C]13.3224[/C][C]0.0775898[/C][/ROW]
[ROW][C]84[/C][C]10.9[/C][C]10.9684[/C][C]-0.0684276[/C][/ROW]
[ROW][C]85[/C][C]4.3[/C][C]4.31998[/C][C]-0.0199807[/C][/ROW]
[ROW][C]86[/C][C]10.3[/C][C]10.42[/C][C]-0.119977[/C][/ROW]
[ROW][C]87[/C][C]11.8[/C][C]11.7261[/C][C]0.073862[/C][/ROW]
[ROW][C]88[/C][C]11.2[/C][C]11.1297[/C][C]0.070336[/C][/ROW]
[ROW][C]89[/C][C]11.4[/C][C]11.4301[/C][C]-0.0301465[/C][/ROW]
[ROW][C]90[/C][C]8.6[/C][C]8.62113[/C][C]-0.0211337[/C][/ROW]
[ROW][C]91[/C][C]13.2[/C][C]13.2376[/C][C]-0.0375956[/C][/ROW]
[ROW][C]92[/C][C]12.6[/C][C]12.6203[/C][C]-0.0202566[/C][/ROW]
[ROW][C]93[/C][C]5.6[/C][C]5.624[/C][C]-0.0240031[/C][/ROW]
[ROW][C]94[/C][C]9.9[/C][C]9.9222[/C][C]-0.0222036[/C][/ROW]
[ROW][C]95[/C][C]8.8[/C][C]8.74138[/C][C]0.05862[/C][/ROW]
[ROW][C]96[/C][C]7.7[/C][C]7.72123[/C][C]-0.021233[/C][/ROW]
[ROW][C]97[/C][C]9[/C][C]9.06659[/C][C]-0.0665879[/C][/ROW]
[ROW][C]98[/C][C]7.3[/C][C]7.23657[/C][C]0.0634306[/C][/ROW]
[ROW][C]99[/C][C]11.4[/C][C]11.5374[/C][C]-0.137377[/C][/ROW]
[ROW][C]100[/C][C]13.6[/C][C]13.5291[/C][C]0.0709258[/C][/ROW]
[ROW][C]101[/C][C]7.9[/C][C]7.82807[/C][C]0.0719286[/C][/ROW]
[ROW][C]102[/C][C]10.7[/C][C]10.7155[/C][C]-0.0154841[/C][/ROW]
[ROW][C]103[/C][C]10.3[/C][C]10.2413[/C][C]0.0586616[/C][/ROW]
[ROW][C]104[/C][C]8.3[/C][C]8.21663[/C][C]0.0833733[/C][/ROW]
[ROW][C]105[/C][C]9.6[/C][C]9.63733[/C][C]-0.0373312[/C][/ROW]
[ROW][C]106[/C][C]14.2[/C][C]14.2191[/C][C]-0.0191311[/C][/ROW]
[ROW][C]107[/C][C]8.5[/C][C]8.52649[/C][C]-0.0264879[/C][/ROW]
[ROW][C]108[/C][C]13.5[/C][C]13.4567[/C][C]0.0433482[/C][/ROW]
[ROW][C]109[/C][C]4.9[/C][C]4.93293[/C][C]-0.0329281[/C][/ROW]
[ROW][C]110[/C][C]6.4[/C][C]6.43696[/C][C]-0.0369597[/C][/ROW]
[ROW][C]111[/C][C]9.6[/C][C]9.62114[/C][C]-0.0211372[/C][/ROW]
[ROW][C]112[/C][C]11.6[/C][C]11.5333[/C][C]0.0667003[/C][/ROW]
[ROW][C]113[/C][C]11.1[/C][C]NA[/C][C]NA[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=266618&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=266618&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
112.912.9109-0.0108917
212.212.2256-0.0255922
312.812.8253-0.0252897
47.47.315180.0848242
56.76.71561-0.0156086
612.612.6078-0.00775558
714.814.8209-0.0208693
813.313.22120.0787995
911.111.101-0.00098857
108.28.21573-0.015731
1111.411.4221-0.0221078
126.46.40999-0.00999364
1310.610.7141-0.114068
141211.9040.0959851
156.36.29950.000500134
1611.311.22760.0723614
1711.912.0082-0.108188
189.39.31564-0.0156432
199.69.621-0.0209974
201010.026-0.0260285
216.46.33130.0687028
2213.813.8008-0.00083367
2310.810.71140.0886051
2413.813.74230.057668
2511.711.7141-0.0141059
2610.910.9149-0.0149209
2716.116.08990.0100926
2813.413.3390.0610091
299.99.822370.0776279
3011.511.5263-0.0262813
318.38.227260.0727365
3211.711.7255-0.0255079
3399.01459-0.0145884
349.79.71195-0.0119484
3510.810.9246-0.124566
3610.310.3231-0.0231116
3710.410.4265-0.0264995
3812.712.7141-0.014144
399.39.30652-0.00652286
4011.811.825-0.0249908
415.95.93526-0.035256
4211.411.32540.0746354
431313.0198-0.019759
4410.810.72290.0770727
4512.312.2370.0630058
4611.311.4131-0.113118
4711.811.72770.0722772
487.97.824640.0753556
4912.712.63770.062274
5012.312.3252-0.0252055
5111.611.7206-0.12062
526.76.74317-0.0431701
5310.910.9266-0.0265837
5412.112.1052-0.00519241
5513.313.3209-0.0209442
5610.110.1258-0.0257723
575.75.81948-0.119483
5814.314.21690.0831276
5988.03084-0.0308401
6013.313.316-0.0159896
619.39.209060.0909424
6212.512.5244-0.0244321
637.67.63662-0.0366227
6415.915.9205-0.0205208
659.29.20906-0.00905762
669.19.13036-0.0303611
6711.111.2071-0.107116
681313.0125-0.0124591
6914.514.5132-0.0132367
7012.212.11680.0832083
7112.312.2120.087958
7211.411.32740.0726177
738.88.727480.0725219
7414.614.6243-0.024252
7512.612.5220.07798
76NANA-0.019759
771313.0147-0.014661
7812.612.621-0.0210033
7913.213.2406-0.040557
809.99.92483-0.0248339
817.77.621940.0780607
8210.510.5318-0.0317624
8313.413.32240.0775898
8410.910.9684-0.0684276
854.34.31998-0.0199807
8610.310.42-0.119977
8711.811.72610.073862
8811.211.12970.070336
8911.411.4301-0.0301465
908.68.62113-0.0211337
9113.213.2376-0.0375956
9212.612.6203-0.0202566
935.65.624-0.0240031
949.99.9222-0.0222036
958.88.741380.05862
967.77.72123-0.021233
9799.06659-0.0665879
987.37.236570.0634306
9911.411.5374-0.137377
10013.613.52910.0709258
1017.97.828070.0719286
10210.710.7155-0.0154841
10310.310.24130.0586616
1048.38.216630.0833733
1059.69.63733-0.0373312
10614.214.2191-0.0191311
1078.58.52649-0.0264879
10813.513.45670.0433482
1094.94.93293-0.0329281
1106.46.43696-0.0369597
1119.69.62114-0.0211372
11211.611.53330.0667003
11311.1NANA






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
80.5864490.8271020.413551
90.4496280.8992560.550372
100.3847240.7694480.615276
110.2996640.5993280.700336
120.2082710.4165410.791729
130.3137220.6274430.686278
140.3284710.6569430.671529
150.4464570.8929130.553543
160.5353760.9292490.464624
170.7323560.5352880.267644
180.6613490.6773010.338651
190.6067490.7865020.393251
200.5388770.9222470.461123
210.5190290.9619430.480971
220.4439750.887950.556025
230.5435670.9128660.456433
240.5292110.9415780.470789
250.4599850.9199690.540015
260.3986480.7972950.601352
270.3359890.6719770.664011
280.3177310.6354620.682269
290.3507980.7015960.649202
300.3109270.6218540.689073
310.3074640.6149280.692536
320.2735620.5471250.726438
330.2255370.4510740.774463
340.1978020.3956040.802198
350.4393760.8787520.560624
360.3913890.7827790.608611
370.3505460.7010920.649454
380.2983470.5966940.701653
390.2501390.5002780.749861
400.2178020.4356040.782198
410.2069550.4139110.793045
420.2278330.4556660.772167
430.1900470.3800950.809953
440.2203240.4406470.779676
450.2196630.4393250.780337
460.3622930.7245860.637707
470.383760.767520.61624
480.395140.7902810.60486
490.3913130.7826260.608687
500.3524610.7049220.647539
510.5220840.9558320.477916
520.519360.961280.48064
530.4735070.9470140.526493
540.4210030.8420050.578997
550.3776240.7552480.622376
560.3378660.6757320.662134
570.5282470.9435060.471753
580.5729540.8540920.427046
590.5447260.9105480.455274
600.4925280.9850560.507472
610.55290.89420.4471
620.5088830.9822340.491117
630.4743950.9487910.525605
640.4235560.8471120.576444
650.3756580.7513170.624342
660.3413820.6827650.658618
670.5580.8840.442
680.5036310.9927370.496369
690.4705760.9411510.529424
700.5089760.9820480.491024
710.5190330.9619330.480967
720.5481130.9037740.451887
730.577430.845140.42257
740.5382950.9234090.461705
750.5965850.8068290.403415
760.5449540.9100910.455046
770.4869630.9739250.513037
780.4299750.8599510.570025
790.4196590.8393180.580341
800.3977740.7955470.602226
810.4187910.8375820.581209
820.3670810.7341610.632919
830.4034240.8068480.596576
840.4289810.8579630.571019
850.3883280.7766560.611672
860.6647270.6705450.335273
870.6591050.681790.340895
880.6374350.725130.362565
890.619020.761960.38098
900.5497360.9005290.450264
910.4800710.9601430.519929
920.4603680.9207360.539632
930.3845740.7691480.615426
940.3403210.6806430.659679
950.2926070.5852140.707393
960.2433130.4866270.756687
970.2755850.5511690.724415
980.2943570.5887130.705643
990.5867980.8264050.413202
1000.539740.9205210.46026
1010.5494310.9011380.450569
1020.4418630.8837260.558137
1030.3455290.6910580.654471
1040.8504530.2990940.149547
1050.9392690.1214620.060731

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
8 & 0.586449 & 0.827102 & 0.413551 \tabularnewline
9 & 0.449628 & 0.899256 & 0.550372 \tabularnewline
10 & 0.384724 & 0.769448 & 0.615276 \tabularnewline
11 & 0.299664 & 0.599328 & 0.700336 \tabularnewline
12 & 0.208271 & 0.416541 & 0.791729 \tabularnewline
13 & 0.313722 & 0.627443 & 0.686278 \tabularnewline
14 & 0.328471 & 0.656943 & 0.671529 \tabularnewline
15 & 0.446457 & 0.892913 & 0.553543 \tabularnewline
16 & 0.535376 & 0.929249 & 0.464624 \tabularnewline
17 & 0.732356 & 0.535288 & 0.267644 \tabularnewline
18 & 0.661349 & 0.677301 & 0.338651 \tabularnewline
19 & 0.606749 & 0.786502 & 0.393251 \tabularnewline
20 & 0.538877 & 0.922247 & 0.461123 \tabularnewline
21 & 0.519029 & 0.961943 & 0.480971 \tabularnewline
22 & 0.443975 & 0.88795 & 0.556025 \tabularnewline
23 & 0.543567 & 0.912866 & 0.456433 \tabularnewline
24 & 0.529211 & 0.941578 & 0.470789 \tabularnewline
25 & 0.459985 & 0.919969 & 0.540015 \tabularnewline
26 & 0.398648 & 0.797295 & 0.601352 \tabularnewline
27 & 0.335989 & 0.671977 & 0.664011 \tabularnewline
28 & 0.317731 & 0.635462 & 0.682269 \tabularnewline
29 & 0.350798 & 0.701596 & 0.649202 \tabularnewline
30 & 0.310927 & 0.621854 & 0.689073 \tabularnewline
31 & 0.307464 & 0.614928 & 0.692536 \tabularnewline
32 & 0.273562 & 0.547125 & 0.726438 \tabularnewline
33 & 0.225537 & 0.451074 & 0.774463 \tabularnewline
34 & 0.197802 & 0.395604 & 0.802198 \tabularnewline
35 & 0.439376 & 0.878752 & 0.560624 \tabularnewline
36 & 0.391389 & 0.782779 & 0.608611 \tabularnewline
37 & 0.350546 & 0.701092 & 0.649454 \tabularnewline
38 & 0.298347 & 0.596694 & 0.701653 \tabularnewline
39 & 0.250139 & 0.500278 & 0.749861 \tabularnewline
40 & 0.217802 & 0.435604 & 0.782198 \tabularnewline
41 & 0.206955 & 0.413911 & 0.793045 \tabularnewline
42 & 0.227833 & 0.455666 & 0.772167 \tabularnewline
43 & 0.190047 & 0.380095 & 0.809953 \tabularnewline
44 & 0.220324 & 0.440647 & 0.779676 \tabularnewline
45 & 0.219663 & 0.439325 & 0.780337 \tabularnewline
46 & 0.362293 & 0.724586 & 0.637707 \tabularnewline
47 & 0.38376 & 0.76752 & 0.61624 \tabularnewline
48 & 0.39514 & 0.790281 & 0.60486 \tabularnewline
49 & 0.391313 & 0.782626 & 0.608687 \tabularnewline
50 & 0.352461 & 0.704922 & 0.647539 \tabularnewline
51 & 0.522084 & 0.955832 & 0.477916 \tabularnewline
52 & 0.51936 & 0.96128 & 0.48064 \tabularnewline
53 & 0.473507 & 0.947014 & 0.526493 \tabularnewline
54 & 0.421003 & 0.842005 & 0.578997 \tabularnewline
55 & 0.377624 & 0.755248 & 0.622376 \tabularnewline
56 & 0.337866 & 0.675732 & 0.662134 \tabularnewline
57 & 0.528247 & 0.943506 & 0.471753 \tabularnewline
58 & 0.572954 & 0.854092 & 0.427046 \tabularnewline
59 & 0.544726 & 0.910548 & 0.455274 \tabularnewline
60 & 0.492528 & 0.985056 & 0.507472 \tabularnewline
61 & 0.5529 & 0.8942 & 0.4471 \tabularnewline
62 & 0.508883 & 0.982234 & 0.491117 \tabularnewline
63 & 0.474395 & 0.948791 & 0.525605 \tabularnewline
64 & 0.423556 & 0.847112 & 0.576444 \tabularnewline
65 & 0.375658 & 0.751317 & 0.624342 \tabularnewline
66 & 0.341382 & 0.682765 & 0.658618 \tabularnewline
67 & 0.558 & 0.884 & 0.442 \tabularnewline
68 & 0.503631 & 0.992737 & 0.496369 \tabularnewline
69 & 0.470576 & 0.941151 & 0.529424 \tabularnewline
70 & 0.508976 & 0.982048 & 0.491024 \tabularnewline
71 & 0.519033 & 0.961933 & 0.480967 \tabularnewline
72 & 0.548113 & 0.903774 & 0.451887 \tabularnewline
73 & 0.57743 & 0.84514 & 0.42257 \tabularnewline
74 & 0.538295 & 0.923409 & 0.461705 \tabularnewline
75 & 0.596585 & 0.806829 & 0.403415 \tabularnewline
76 & 0.544954 & 0.910091 & 0.455046 \tabularnewline
77 & 0.486963 & 0.973925 & 0.513037 \tabularnewline
78 & 0.429975 & 0.859951 & 0.570025 \tabularnewline
79 & 0.419659 & 0.839318 & 0.580341 \tabularnewline
80 & 0.397774 & 0.795547 & 0.602226 \tabularnewline
81 & 0.418791 & 0.837582 & 0.581209 \tabularnewline
82 & 0.367081 & 0.734161 & 0.632919 \tabularnewline
83 & 0.403424 & 0.806848 & 0.596576 \tabularnewline
84 & 0.428981 & 0.857963 & 0.571019 \tabularnewline
85 & 0.388328 & 0.776656 & 0.611672 \tabularnewline
86 & 0.664727 & 0.670545 & 0.335273 \tabularnewline
87 & 0.659105 & 0.68179 & 0.340895 \tabularnewline
88 & 0.637435 & 0.72513 & 0.362565 \tabularnewline
89 & 0.61902 & 0.76196 & 0.38098 \tabularnewline
90 & 0.549736 & 0.900529 & 0.450264 \tabularnewline
91 & 0.480071 & 0.960143 & 0.519929 \tabularnewline
92 & 0.460368 & 0.920736 & 0.539632 \tabularnewline
93 & 0.384574 & 0.769148 & 0.615426 \tabularnewline
94 & 0.340321 & 0.680643 & 0.659679 \tabularnewline
95 & 0.292607 & 0.585214 & 0.707393 \tabularnewline
96 & 0.243313 & 0.486627 & 0.756687 \tabularnewline
97 & 0.275585 & 0.551169 & 0.724415 \tabularnewline
98 & 0.294357 & 0.588713 & 0.705643 \tabularnewline
99 & 0.586798 & 0.826405 & 0.413202 \tabularnewline
100 & 0.53974 & 0.920521 & 0.46026 \tabularnewline
101 & 0.549431 & 0.901138 & 0.450569 \tabularnewline
102 & 0.441863 & 0.883726 & 0.558137 \tabularnewline
103 & 0.345529 & 0.691058 & 0.654471 \tabularnewline
104 & 0.850453 & 0.299094 & 0.149547 \tabularnewline
105 & 0.939269 & 0.121462 & 0.060731 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=266618&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]0.586449[/C][C]0.827102[/C][C]0.413551[/C][/ROW]
[ROW][C]9[/C][C]0.449628[/C][C]0.899256[/C][C]0.550372[/C][/ROW]
[ROW][C]10[/C][C]0.384724[/C][C]0.769448[/C][C]0.615276[/C][/ROW]
[ROW][C]11[/C][C]0.299664[/C][C]0.599328[/C][C]0.700336[/C][/ROW]
[ROW][C]12[/C][C]0.208271[/C][C]0.416541[/C][C]0.791729[/C][/ROW]
[ROW][C]13[/C][C]0.313722[/C][C]0.627443[/C][C]0.686278[/C][/ROW]
[ROW][C]14[/C][C]0.328471[/C][C]0.656943[/C][C]0.671529[/C][/ROW]
[ROW][C]15[/C][C]0.446457[/C][C]0.892913[/C][C]0.553543[/C][/ROW]
[ROW][C]16[/C][C]0.535376[/C][C]0.929249[/C][C]0.464624[/C][/ROW]
[ROW][C]17[/C][C]0.732356[/C][C]0.535288[/C][C]0.267644[/C][/ROW]
[ROW][C]18[/C][C]0.661349[/C][C]0.677301[/C][C]0.338651[/C][/ROW]
[ROW][C]19[/C][C]0.606749[/C][C]0.786502[/C][C]0.393251[/C][/ROW]
[ROW][C]20[/C][C]0.538877[/C][C]0.922247[/C][C]0.461123[/C][/ROW]
[ROW][C]21[/C][C]0.519029[/C][C]0.961943[/C][C]0.480971[/C][/ROW]
[ROW][C]22[/C][C]0.443975[/C][C]0.88795[/C][C]0.556025[/C][/ROW]
[ROW][C]23[/C][C]0.543567[/C][C]0.912866[/C][C]0.456433[/C][/ROW]
[ROW][C]24[/C][C]0.529211[/C][C]0.941578[/C][C]0.470789[/C][/ROW]
[ROW][C]25[/C][C]0.459985[/C][C]0.919969[/C][C]0.540015[/C][/ROW]
[ROW][C]26[/C][C]0.398648[/C][C]0.797295[/C][C]0.601352[/C][/ROW]
[ROW][C]27[/C][C]0.335989[/C][C]0.671977[/C][C]0.664011[/C][/ROW]
[ROW][C]28[/C][C]0.317731[/C][C]0.635462[/C][C]0.682269[/C][/ROW]
[ROW][C]29[/C][C]0.350798[/C][C]0.701596[/C][C]0.649202[/C][/ROW]
[ROW][C]30[/C][C]0.310927[/C][C]0.621854[/C][C]0.689073[/C][/ROW]
[ROW][C]31[/C][C]0.307464[/C][C]0.614928[/C][C]0.692536[/C][/ROW]
[ROW][C]32[/C][C]0.273562[/C][C]0.547125[/C][C]0.726438[/C][/ROW]
[ROW][C]33[/C][C]0.225537[/C][C]0.451074[/C][C]0.774463[/C][/ROW]
[ROW][C]34[/C][C]0.197802[/C][C]0.395604[/C][C]0.802198[/C][/ROW]
[ROW][C]35[/C][C]0.439376[/C][C]0.878752[/C][C]0.560624[/C][/ROW]
[ROW][C]36[/C][C]0.391389[/C][C]0.782779[/C][C]0.608611[/C][/ROW]
[ROW][C]37[/C][C]0.350546[/C][C]0.701092[/C][C]0.649454[/C][/ROW]
[ROW][C]38[/C][C]0.298347[/C][C]0.596694[/C][C]0.701653[/C][/ROW]
[ROW][C]39[/C][C]0.250139[/C][C]0.500278[/C][C]0.749861[/C][/ROW]
[ROW][C]40[/C][C]0.217802[/C][C]0.435604[/C][C]0.782198[/C][/ROW]
[ROW][C]41[/C][C]0.206955[/C][C]0.413911[/C][C]0.793045[/C][/ROW]
[ROW][C]42[/C][C]0.227833[/C][C]0.455666[/C][C]0.772167[/C][/ROW]
[ROW][C]43[/C][C]0.190047[/C][C]0.380095[/C][C]0.809953[/C][/ROW]
[ROW][C]44[/C][C]0.220324[/C][C]0.440647[/C][C]0.779676[/C][/ROW]
[ROW][C]45[/C][C]0.219663[/C][C]0.439325[/C][C]0.780337[/C][/ROW]
[ROW][C]46[/C][C]0.362293[/C][C]0.724586[/C][C]0.637707[/C][/ROW]
[ROW][C]47[/C][C]0.38376[/C][C]0.76752[/C][C]0.61624[/C][/ROW]
[ROW][C]48[/C][C]0.39514[/C][C]0.790281[/C][C]0.60486[/C][/ROW]
[ROW][C]49[/C][C]0.391313[/C][C]0.782626[/C][C]0.608687[/C][/ROW]
[ROW][C]50[/C][C]0.352461[/C][C]0.704922[/C][C]0.647539[/C][/ROW]
[ROW][C]51[/C][C]0.522084[/C][C]0.955832[/C][C]0.477916[/C][/ROW]
[ROW][C]52[/C][C]0.51936[/C][C]0.96128[/C][C]0.48064[/C][/ROW]
[ROW][C]53[/C][C]0.473507[/C][C]0.947014[/C][C]0.526493[/C][/ROW]
[ROW][C]54[/C][C]0.421003[/C][C]0.842005[/C][C]0.578997[/C][/ROW]
[ROW][C]55[/C][C]0.377624[/C][C]0.755248[/C][C]0.622376[/C][/ROW]
[ROW][C]56[/C][C]0.337866[/C][C]0.675732[/C][C]0.662134[/C][/ROW]
[ROW][C]57[/C][C]0.528247[/C][C]0.943506[/C][C]0.471753[/C][/ROW]
[ROW][C]58[/C][C]0.572954[/C][C]0.854092[/C][C]0.427046[/C][/ROW]
[ROW][C]59[/C][C]0.544726[/C][C]0.910548[/C][C]0.455274[/C][/ROW]
[ROW][C]60[/C][C]0.492528[/C][C]0.985056[/C][C]0.507472[/C][/ROW]
[ROW][C]61[/C][C]0.5529[/C][C]0.8942[/C][C]0.4471[/C][/ROW]
[ROW][C]62[/C][C]0.508883[/C][C]0.982234[/C][C]0.491117[/C][/ROW]
[ROW][C]63[/C][C]0.474395[/C][C]0.948791[/C][C]0.525605[/C][/ROW]
[ROW][C]64[/C][C]0.423556[/C][C]0.847112[/C][C]0.576444[/C][/ROW]
[ROW][C]65[/C][C]0.375658[/C][C]0.751317[/C][C]0.624342[/C][/ROW]
[ROW][C]66[/C][C]0.341382[/C][C]0.682765[/C][C]0.658618[/C][/ROW]
[ROW][C]67[/C][C]0.558[/C][C]0.884[/C][C]0.442[/C][/ROW]
[ROW][C]68[/C][C]0.503631[/C][C]0.992737[/C][C]0.496369[/C][/ROW]
[ROW][C]69[/C][C]0.470576[/C][C]0.941151[/C][C]0.529424[/C][/ROW]
[ROW][C]70[/C][C]0.508976[/C][C]0.982048[/C][C]0.491024[/C][/ROW]
[ROW][C]71[/C][C]0.519033[/C][C]0.961933[/C][C]0.480967[/C][/ROW]
[ROW][C]72[/C][C]0.548113[/C][C]0.903774[/C][C]0.451887[/C][/ROW]
[ROW][C]73[/C][C]0.57743[/C][C]0.84514[/C][C]0.42257[/C][/ROW]
[ROW][C]74[/C][C]0.538295[/C][C]0.923409[/C][C]0.461705[/C][/ROW]
[ROW][C]75[/C][C]0.596585[/C][C]0.806829[/C][C]0.403415[/C][/ROW]
[ROW][C]76[/C][C]0.544954[/C][C]0.910091[/C][C]0.455046[/C][/ROW]
[ROW][C]77[/C][C]0.486963[/C][C]0.973925[/C][C]0.513037[/C][/ROW]
[ROW][C]78[/C][C]0.429975[/C][C]0.859951[/C][C]0.570025[/C][/ROW]
[ROW][C]79[/C][C]0.419659[/C][C]0.839318[/C][C]0.580341[/C][/ROW]
[ROW][C]80[/C][C]0.397774[/C][C]0.795547[/C][C]0.602226[/C][/ROW]
[ROW][C]81[/C][C]0.418791[/C][C]0.837582[/C][C]0.581209[/C][/ROW]
[ROW][C]82[/C][C]0.367081[/C][C]0.734161[/C][C]0.632919[/C][/ROW]
[ROW][C]83[/C][C]0.403424[/C][C]0.806848[/C][C]0.596576[/C][/ROW]
[ROW][C]84[/C][C]0.428981[/C][C]0.857963[/C][C]0.571019[/C][/ROW]
[ROW][C]85[/C][C]0.388328[/C][C]0.776656[/C][C]0.611672[/C][/ROW]
[ROW][C]86[/C][C]0.664727[/C][C]0.670545[/C][C]0.335273[/C][/ROW]
[ROW][C]87[/C][C]0.659105[/C][C]0.68179[/C][C]0.340895[/C][/ROW]
[ROW][C]88[/C][C]0.637435[/C][C]0.72513[/C][C]0.362565[/C][/ROW]
[ROW][C]89[/C][C]0.61902[/C][C]0.76196[/C][C]0.38098[/C][/ROW]
[ROW][C]90[/C][C]0.549736[/C][C]0.900529[/C][C]0.450264[/C][/ROW]
[ROW][C]91[/C][C]0.480071[/C][C]0.960143[/C][C]0.519929[/C][/ROW]
[ROW][C]92[/C][C]0.460368[/C][C]0.920736[/C][C]0.539632[/C][/ROW]
[ROW][C]93[/C][C]0.384574[/C][C]0.769148[/C][C]0.615426[/C][/ROW]
[ROW][C]94[/C][C]0.340321[/C][C]0.680643[/C][C]0.659679[/C][/ROW]
[ROW][C]95[/C][C]0.292607[/C][C]0.585214[/C][C]0.707393[/C][/ROW]
[ROW][C]96[/C][C]0.243313[/C][C]0.486627[/C][C]0.756687[/C][/ROW]
[ROW][C]97[/C][C]0.275585[/C][C]0.551169[/C][C]0.724415[/C][/ROW]
[ROW][C]98[/C][C]0.294357[/C][C]0.588713[/C][C]0.705643[/C][/ROW]
[ROW][C]99[/C][C]0.586798[/C][C]0.826405[/C][C]0.413202[/C][/ROW]
[ROW][C]100[/C][C]0.53974[/C][C]0.920521[/C][C]0.46026[/C][/ROW]
[ROW][C]101[/C][C]0.549431[/C][C]0.901138[/C][C]0.450569[/C][/ROW]
[ROW][C]102[/C][C]0.441863[/C][C]0.883726[/C][C]0.558137[/C][/ROW]
[ROW][C]103[/C][C]0.345529[/C][C]0.691058[/C][C]0.654471[/C][/ROW]
[ROW][C]104[/C][C]0.850453[/C][C]0.299094[/C][C]0.149547[/C][/ROW]
[ROW][C]105[/C][C]0.939269[/C][C]0.121462[/C][C]0.060731[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=266618&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=266618&T=5

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


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
80.5864490.8271020.413551
90.4496280.8992560.550372
100.3847240.7694480.615276
110.2996640.5993280.700336
120.2082710.4165410.791729
130.3137220.6274430.686278
140.3284710.6569430.671529
150.4464570.8929130.553543
160.5353760.9292490.464624
170.7323560.5352880.267644
180.6613490.6773010.338651
190.6067490.7865020.393251
200.5388770.9222470.461123
210.5190290.9619430.480971
220.4439750.887950.556025
230.5435670.9128660.456433
240.5292110.9415780.470789
250.4599850.9199690.540015
260.3986480.7972950.601352
270.3359890.6719770.664011
280.3177310.6354620.682269
290.3507980.7015960.649202
300.3109270.6218540.689073
310.3074640.6149280.692536
320.2735620.5471250.726438
330.2255370.4510740.774463
340.1978020.3956040.802198
350.4393760.8787520.560624
360.3913890.7827790.608611
370.3505460.7010920.649454
380.2983470.5966940.701653
390.2501390.5002780.749861
400.2178020.4356040.782198
410.2069550.4139110.793045
420.2278330.4556660.772167
430.1900470.3800950.809953
440.2203240.4406470.779676
450.2196630.4393250.780337
460.3622930.7245860.637707
470.383760.767520.61624
480.395140.7902810.60486
490.3913130.7826260.608687
500.3524610.7049220.647539
510.5220840.9558320.477916
520.519360.961280.48064
530.4735070.9470140.526493
540.4210030.8420050.578997
550.3776240.7552480.622376
560.3378660.6757320.662134
570.5282470.9435060.471753
580.5729540.8540920.427046
590.5447260.9105480.455274
600.4925280.9850560.507472
610.55290.89420.4471
620.5088830.9822340.491117
630.4743950.9487910.525605
640.4235560.8471120.576444
650.3756580.7513170.624342
660.3413820.6827650.658618
670.5580.8840.442
680.5036310.9927370.496369
690.4705760.9411510.529424
700.5089760.9820480.491024
710.5190330.9619330.480967
720.5481130.9037740.451887
730.577430.845140.42257
740.5382950.9234090.461705
750.5965850.8068290.403415
760.5449540.9100910.455046
770.4869630.9739250.513037
780.4299750.8599510.570025
790.4196590.8393180.580341
800.3977740.7955470.602226
810.4187910.8375820.581209
820.3670810.7341610.632919
830.4034240.8068480.596576
840.4289810.8579630.571019
850.3883280.7766560.611672
860.6647270.6705450.335273
870.6591050.681790.340895
880.6374350.725130.362565
890.619020.761960.38098
900.5497360.9005290.450264
910.4800710.9601430.519929
920.4603680.9207360.539632
930.3845740.7691480.615426
940.3403210.6806430.659679
950.2926070.5852140.707393
960.2433130.4866270.756687
970.2755850.5511690.724415
980.2943570.5887130.705643
990.5867980.8264050.413202
1000.539740.9205210.46026
1010.5494310.9011380.450569
1020.4418630.8837260.558137
1030.3455290.6910580.654471
1040.8504530.2990940.149547
1050.9392690.1214620.060731






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

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 0 & 0 & OK \tabularnewline
10% type I error level & 0 & 0 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=266618&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=266618&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=266618&T=6

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


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

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


Figures (Output of Computation)

Figure 1
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Figure 10
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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):
par3 <- 'No Linear Trend'
par2 <- 'Do not include Seasonal Dummies'
par1 <- '5'
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')
}