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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 computationThu, 19 Nov 2009 10:47:58 -0700
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2009/Nov/19/t1258652934tzxstiz0uybq3gy.htm/, Retrieved Sat, 20 Apr 2024 09:27:30 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=57849, Retrieved Sat, 20 Apr 2024 09:27:30 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordsws7l5
Estimated Impact205

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Q1 The Seatbeltlaw] [2007-11-14 19:27:43] [8cd6641b921d30ebe00b648d1481bba0]
- RM D  [Multiple Regression] [Seatbelt] [2009-11-12 14:14:11] [b98453cac15ba1066b407e146608df68]
-    D      [Multiple Regression] [] [2009-11-19 17:47:58] [42ed2e0ab6f351a3dce7cf3f388e378d] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
6,1	110,4	6,1	6,3
6,3	96,4	6,1	6,1
6,3	101,9	6,3	6,1
6	106,2	6,3	6,3
6,2	81	6	6,3
6,4	94,7	6,2	6
6,8	101	6,4	6,2
7,5	109,4	6,8	6,4
7,5	102,3	7,5	6,8
7,6	90,7	7,5	7,5
7,6	96,2	7,6	7,5
7,4	96,1	7,6	7,6
7,3	106	7,4	7,6
7,1	103,1	7,3	7,4
6,9	102	7,1	7,3
6,8	104,7	6,9	7,1
7,5	86	6,8	6,9
7,6	92,1	7,5	6,8
7,8	106,9	7,6	7,5
8	112,6	7,8	7,6
8,1	101,7	8	7,8
8,2	92	8,1	8
8,3	97,4	8,2	8,1
8,2	97	8,3	8,2
8	105,4	8,2	8,3
7,9	102,7	8	8,2
7,6	98,1	7,9	8
7,6	104,5	7,6	7,9
8,3	87,4	7,6	7,6
8,4	89,9	8,3	7,6
8,4	109,8	8,4	8,3
8,4	111,7	8,4	8,4
8,4	98,6	8,4	8,4
8,6	96,9	8,4	8,4
8,9	95,1	8,6	8,4
8,8	97	8,9	8,6
8,3	112,7	8,8	8,9
7,5	102,9	8,3	8,8
7,2	97,4	7,5	8,3
7,4	111,4	7,2	7,5
8,8	87,4	7,4	7,2
9,3	96,8	8,8	7,4
9,3	114,1	9,3	8,8
8,7	110,3	9,3	9,3
8,2	103,9	8,7	9,3
8,3	101,6	8,2	8,7
8,5	94,6	8,3	8,2
8,6	95,9	8,5	8,3
8,5	104,7	8,6	8,5
8,2	102,8	8,5	8,6
8,1	98,1	8,2	8,5
7,9	113,9	8,1	8,2
8,6	80,9	7,9	8,1
8,7	95,7	8,6	7,9
8,7	113,2	8,7	8,6
8,5	105,9	8,7	8,7
8,4	108,8	8,5	8,7
8,5	102,3	8,4	8,5
8,7	99	8,5	8,4
8,7	100,7	8,7	8,5
8,6	115,5	8,7	8,7
8,5	100,7	8,6	8,7
8,3	109,9	8,5	8,6
8	114,6	8,3	8,5
8,2	85,4	8	8,3
8,1	100,5	8,2	8
8,1	114,8	8,1	8,2
8	116,5	8,1	8,1
7,9	112,9	8	8,1
7,9	102	7,9	8
8	106	7,9	7,9
8	105,3	8	7,9
7,9	118,8	8	8
8	106,1	7,9	8
7,7	109,3	8	7,9
7,2	117,2	7,7	8
7,5	92,5	7,2	7,7
7,3	104,2	7,5	7,2
7	112,5	7,3	7,5
7	122,4	7	7,3
7	113,3	7	7
7,2	100	7	7
7,3	110,7	7,2	7
7,1	112,8	7,3	7,2
6,8	109,8	7,1	7,3
6,4	117,3	6,8	7,1
6,1	109,1	6,4	6,8
6,5	115,9	6,1	6,4
7,7	96	6,5	6,1
7,9	99,8	7,7	6,5
7,5	116,8	7,9	7,7
6,9	115,7	7,5	7,9
6,6	99,4	6,9	7,5
6,9	94,3	6,6	6,9
7,7	91	6,9	6,6

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 4 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57849&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]4 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57849&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57849&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 time4 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135






Multiple Linear Regression - Estimated Regression Equation
Y[t] = + 1.67058536444880 -0.00940480827711062X[t] + 1.4664080020596Y1[t] -0.578726593157617Y2[t] + 0.0892563503721176M1[t] + 0.0340631621848652M2[t] + 0.00541120381002455M3[t] + 0.174002531815138M4[t] + 0.646315973961958M5[t] -0.199110925542800M6[t] + 0.148855305434789M7[t] + 0.174528724554786M8[t] + 0.0891393379223372M9[t] + 0.27550884188137M10[t] + 0.244894451851841M11[t] + 0.000867678569418552t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Y[t] =  +  1.67058536444880 -0.00940480827711062X[t] +  1.4664080020596Y1[t] -0.578726593157617Y2[t] +  0.0892563503721176M1[t] +  0.0340631621848652M2[t] +  0.00541120381002455M3[t] +  0.174002531815138M4[t] +  0.646315973961958M5[t] -0.199110925542800M6[t] +  0.148855305434789M7[t] +  0.174528724554786M8[t] +  0.0891393379223372M9[t] +  0.27550884188137M10[t] +  0.244894451851841M11[t] +  0.000867678569418552t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57849&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Y[t] =  +  1.67058536444880 -0.00940480827711062X[t] +  1.4664080020596Y1[t] -0.578726593157617Y2[t] +  0.0892563503721176M1[t] +  0.0340631621848652M2[t] +  0.00541120381002455M3[t] +  0.174002531815138M4[t] +  0.646315973961958M5[t] -0.199110925542800M6[t] +  0.148855305434789M7[t] +  0.174528724554786M8[t] +  0.0891393379223372M9[t] +  0.27550884188137M10[t] +  0.244894451851841M11[t] +  0.000867678569418552t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57849&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57849&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
Y[t] = + 1.67058536444880 -0.00940480827711062X[t] + 1.4664080020596Y1[t] -0.578726593157617Y2[t] + 0.0892563503721176M1[t] + 0.0340631621848652M2[t] + 0.00541120381002455M3[t] + 0.174002531815138M4[t] + 0.646315973961958M5[t] -0.199110925542800M6[t] + 0.148855305434789M7[t] + 0.174528724554786M8[t] + 0.0891393379223372M9[t] + 0.27550884188137M10[t] + 0.244894451851841M11[t] + 0.000867678569418552t + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)1.670585364448800.6021832.77420.0069030.003451
X-0.009404808277110620.005569-1.68870.095230.047615
Y11.46640800205960.09448615.519800
Y2-0.5787265931576170.093488-6.190400
M10.08925635037211760.12610.70780.4811390.24057
M20.03406316218486520.11590.29390.7696050.384802
M30.005411203810024550.1162260.04660.9629830.481492
M40.1740025318151380.1309261.3290.1876680.093834
M50.6463159739619580.137044.71631e-055e-06
M6-0.1991109255428000.12375-1.6090.1116120.055806
M70.1488553054347890.1232931.20730.2309070.115453
M80.1745287245547860.1295591.34710.1818010.090901
M90.08913933792233720.1134980.78540.4345790.217289
M100.275508841881370.1134712.4280.0174550.008728
M110.2448944518518410.1103752.21870.0293740.014687
t0.0008676785694185520.0010640.81560.4171810.20859

\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) & 1.67058536444880 & 0.602183 & 2.7742 & 0.006903 & 0.003451 \tabularnewline
X & -0.00940480827711062 & 0.005569 & -1.6887 & 0.09523 & 0.047615 \tabularnewline
Y1 & 1.4664080020596 & 0.094486 & 15.5198 & 0 & 0 \tabularnewline
Y2 & -0.578726593157617 & 0.093488 & -6.1904 & 0 & 0 \tabularnewline
M1 & 0.0892563503721176 & 0.1261 & 0.7078 & 0.481139 & 0.24057 \tabularnewline
M2 & 0.0340631621848652 & 0.1159 & 0.2939 & 0.769605 & 0.384802 \tabularnewline
M3 & 0.00541120381002455 & 0.116226 & 0.0466 & 0.962983 & 0.481492 \tabularnewline
M4 & 0.174002531815138 & 0.130926 & 1.329 & 0.187668 & 0.093834 \tabularnewline
M5 & 0.646315973961958 & 0.13704 & 4.7163 & 1e-05 & 5e-06 \tabularnewline
M6 & -0.199110925542800 & 0.12375 & -1.609 & 0.111612 & 0.055806 \tabularnewline
M7 & 0.148855305434789 & 0.123293 & 1.2073 & 0.230907 & 0.115453 \tabularnewline
M8 & 0.174528724554786 & 0.129559 & 1.3471 & 0.181801 & 0.090901 \tabularnewline
M9 & 0.0891393379223372 & 0.113498 & 0.7854 & 0.434579 & 0.217289 \tabularnewline
M10 & 0.27550884188137 & 0.113471 & 2.428 & 0.017455 & 0.008728 \tabularnewline
M11 & 0.244894451851841 & 0.110375 & 2.2187 & 0.029374 & 0.014687 \tabularnewline
t & 0.000867678569418552 & 0.001064 & 0.8156 & 0.417181 & 0.20859 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57849&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]1.67058536444880[/C][C]0.602183[/C][C]2.7742[/C][C]0.006903[/C][C]0.003451[/C][/ROW]
[ROW][C]X[/C][C]-0.00940480827711062[/C][C]0.005569[/C][C]-1.6887[/C][C]0.09523[/C][C]0.047615[/C][/ROW]
[ROW][C]Y1[/C][C]1.4664080020596[/C][C]0.094486[/C][C]15.5198[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Y2[/C][C]-0.578726593157617[/C][C]0.093488[/C][C]-6.1904[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M1[/C][C]0.0892563503721176[/C][C]0.1261[/C][C]0.7078[/C][C]0.481139[/C][C]0.24057[/C][/ROW]
[ROW][C]M2[/C][C]0.0340631621848652[/C][C]0.1159[/C][C]0.2939[/C][C]0.769605[/C][C]0.384802[/C][/ROW]
[ROW][C]M3[/C][C]0.00541120381002455[/C][C]0.116226[/C][C]0.0466[/C][C]0.962983[/C][C]0.481492[/C][/ROW]
[ROW][C]M4[/C][C]0.174002531815138[/C][C]0.130926[/C][C]1.329[/C][C]0.187668[/C][C]0.093834[/C][/ROW]
[ROW][C]M5[/C][C]0.646315973961958[/C][C]0.13704[/C][C]4.7163[/C][C]1e-05[/C][C]5e-06[/C][/ROW]
[ROW][C]M6[/C][C]-0.199110925542800[/C][C]0.12375[/C][C]-1.609[/C][C]0.111612[/C][C]0.055806[/C][/ROW]
[ROW][C]M7[/C][C]0.148855305434789[/C][C]0.123293[/C][C]1.2073[/C][C]0.230907[/C][C]0.115453[/C][/ROW]
[ROW][C]M8[/C][C]0.174528724554786[/C][C]0.129559[/C][C]1.3471[/C][C]0.181801[/C][C]0.090901[/C][/ROW]
[ROW][C]M9[/C][C]0.0891393379223372[/C][C]0.113498[/C][C]0.7854[/C][C]0.434579[/C][C]0.217289[/C][/ROW]
[ROW][C]M10[/C][C]0.27550884188137[/C][C]0.113471[/C][C]2.428[/C][C]0.017455[/C][C]0.008728[/C][/ROW]
[ROW][C]M11[/C][C]0.244894451851841[/C][C]0.110375[/C][C]2.2187[/C][C]0.029374[/C][C]0.014687[/C][/ROW]
[ROW][C]t[/C][C]0.000867678569418552[/C][C]0.001064[/C][C]0.8156[/C][C]0.417181[/C][C]0.20859[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57849&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57849&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)1.670585364448800.6021832.77420.0069030.003451
X-0.009404808277110620.005569-1.68870.095230.047615
Y11.46640800205960.09448615.519800
Y2-0.5787265931576170.093488-6.190400
M10.08925635037211760.12610.70780.4811390.24057
M20.03406316218486520.11590.29390.7696050.384802
M30.005411203810024550.1162260.04660.9629830.481492
M40.1740025318151380.1309261.3290.1876680.093834
M50.6463159739619580.137044.71631e-055e-06
M6-0.1991109255428000.12375-1.6090.1116120.055806
M70.1488553054347890.1232931.20730.2309070.115453
M80.1745287245547860.1295591.34710.1818010.090901
M90.08913933792233720.1134980.78540.4345790.217289
M100.275508841881370.1134712.4280.0174550.008728
M110.2448944518518410.1103752.21870.0293740.014687
t0.0008676785694185520.0010640.81560.4171810.20859






Multiple Linear Regression - Regression Statistics
Multiple R0.96797577325124
R-squared0.936977097601335
Adjusted R-squared0.925010723728171
F-TEST (value)78.3008376248883
F-TEST (DF numerator)15
F-TEST (DF denominator)79
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.210508612002221
Sum Squared Residuals3.50079618244103

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.96797577325124 \tabularnewline
R-squared & 0.936977097601335 \tabularnewline
Adjusted R-squared & 0.925010723728171 \tabularnewline
F-TEST (value) & 78.3008376248883 \tabularnewline
F-TEST (DF numerator) & 15 \tabularnewline
F-TEST (DF denominator) & 79 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.210508612002221 \tabularnewline
Sum Squared Residuals & 3.50079618244103 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57849&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.96797577325124[/C][/ROW]
[ROW][C]R-squared[/C][C]0.936977097601335[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.925010723728171[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]78.3008376248883[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]15[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]79[/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]0.210508612002221[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]3.50079618244103[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57849&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57849&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 R0.96797577325124
R-squared0.936977097601335
Adjusted R-squared0.925010723728171
F-TEST (value)78.3008376248883
F-TEST (DF numerator)15
F-TEST (DF denominator)79
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.210508612002221
Sum Squared Residuals3.50079618244103






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
16.16.02152983526790.0784701647320955
26.36.214616960161140.0853830398388554
36.36.42838783524353-0.128387835243527
466.44166084759497-0.441660847594966
56.26.71192073627651-0.511920736276512
66.46.205415220303960.194584779696037
76.86.672535119485570.127464880514429
87.57.090893709839570.409106290160427
97.57.8681411047227-0.368141104722704
107.67.7593654480553-0.159365448055307
117.67.82453309127705-0.224533091277046
127.47.52357413950658-0.123574139506574
137.37.22730896609280.0726910339072044
147.17.16936191890414-0.0693619189041445
156.96.91651398710739-0.0165139871073851
166.86.88304372955332-0.0830437295533228
177.57.5011992834771-0.00119928347709286
187.67.68362899280886-0.0836289928088626
197.87.634803924850260.165196075149740
2087.84314655645630.156853443543697
218.18.038673540394180.0613264596058233
228.28.34803284478504-0.148032844785038
238.38.35626830951873-0.056268309518726
248.28.20477160043735-0.00477160043735051
2588.01138178032943-0.0113817803294315
267.97.747040311963640.152959688036361
277.67.73162266865849-0.13162266865849
287.67.45884116095740.141158839042606
298.38.266462481159510.0335375188404909
308.48.42487684097312-0.0248768409731164
318.48.328087250801250.0719127491987509
328.48.278886553448390.121113446551607
338.48.317567833815510.082432166184488
348.68.520793190415050.0792068095849475
358.98.801256734265660.0987432657343409
368.88.86353790724309-0.0635379072430851
378.38.48574766808074-0.185747668080739
387.57.84825793786455-0.348257937864551
397.26.988436998514360.211563001485637
407.47.049287563117560.350712436882440
418.88.215083660843660.584916339156341
429.39.21934512635540.0806548736446009
439.39.32846262331753-0.0284626233175294
448.79.10137869588116-0.401378695881158
458.28.197202959555870.00279704044412738
468.38.020103155986450.279896844013552
478.58.492194199250880.0078058007491174
488.68.471350116304370.128649883695627
498.58.50960731398177-0.0096073139817724
508.28.26863748056873-0.0686374805687286
518.17.90300605836360.196993941636394
527.97.95084627190112-0.0508462719011148
538.68.498977124665850.101022875334152
548.78.657457661302510.0425423386974851
558.78.583239610995710.116760389004286
568.58.62056314979227-0.120563149792274
578.48.21548589731370.184514102686296
588.58.432958852068940.0670411479310616
598.78.638761467445010.0612385325549858
608.78.614155461187660.085844538812338
618.68.449343008996440.150656991003563
628.58.387567861673880.112432138326119
638.38.184491204828840.115508795171158
6488.0743386714048-0.0743386714047969
658.28.49796311182631-0.297963111826309
668.17.97829086426580.121709135734197
678.17.930249896612650.169750103387354
6887.998675479546730.0013245204532663
697.97.801370281075340.0986297189246578
707.98.0023517329341-0.102351732934102
7187.992858447681310.00714155231869007
7287.902055840398830.097944159601175
737.97.80734229828360.0926577017163944
7487.725817053579120.274182946420882
757.77.87245084680866-0.172450846808662
767.27.46981680806038-0.269816808060378
777.57.61571067013873-0.115710670138734
787.37.39040088955789-0.0904008895578896
7977.19427531204567-0.194275312045673
8076.803531725805340.196468274194664
8176.97821175101130.0217882489887020
827.27.29053288362532-0.0905328836253206
837.37.45343632401205-0.153436324012047
847.17.22055493492213-0.120554934922129
856.86.98773912896731-0.187739128967314
866.46.5387004752848-0.138700475284793
876.16.17509040047512-0.0750904004751244
886.56.072164947410470.427835052589532
897.77.492682931612340.207317068387664
907.98.14058440443245-0.240584404432451
917.57.92834626189136-0.428346261891358
926.97.26292412923023-0.362924129230230
936.66.68334663211139-0.0833466321113905
946.96.82586189212980.0741381078702057
957.77.440691426549320.259308573450685

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 6.1 & 6.0215298352679 & 0.0784701647320955 \tabularnewline
2 & 6.3 & 6.21461696016114 & 0.0853830398388554 \tabularnewline
3 & 6.3 & 6.42838783524353 & -0.128387835243527 \tabularnewline
4 & 6 & 6.44166084759497 & -0.441660847594966 \tabularnewline
5 & 6.2 & 6.71192073627651 & -0.511920736276512 \tabularnewline
6 & 6.4 & 6.20541522030396 & 0.194584779696037 \tabularnewline
7 & 6.8 & 6.67253511948557 & 0.127464880514429 \tabularnewline
8 & 7.5 & 7.09089370983957 & 0.409106290160427 \tabularnewline
9 & 7.5 & 7.8681411047227 & -0.368141104722704 \tabularnewline
10 & 7.6 & 7.7593654480553 & -0.159365448055307 \tabularnewline
11 & 7.6 & 7.82453309127705 & -0.224533091277046 \tabularnewline
12 & 7.4 & 7.52357413950658 & -0.123574139506574 \tabularnewline
13 & 7.3 & 7.2273089660928 & 0.0726910339072044 \tabularnewline
14 & 7.1 & 7.16936191890414 & -0.0693619189041445 \tabularnewline
15 & 6.9 & 6.91651398710739 & -0.0165139871073851 \tabularnewline
16 & 6.8 & 6.88304372955332 & -0.0830437295533228 \tabularnewline
17 & 7.5 & 7.5011992834771 & -0.00119928347709286 \tabularnewline
18 & 7.6 & 7.68362899280886 & -0.0836289928088626 \tabularnewline
19 & 7.8 & 7.63480392485026 & 0.165196075149740 \tabularnewline
20 & 8 & 7.8431465564563 & 0.156853443543697 \tabularnewline
21 & 8.1 & 8.03867354039418 & 0.0613264596058233 \tabularnewline
22 & 8.2 & 8.34803284478504 & -0.148032844785038 \tabularnewline
23 & 8.3 & 8.35626830951873 & -0.056268309518726 \tabularnewline
24 & 8.2 & 8.20477160043735 & -0.00477160043735051 \tabularnewline
25 & 8 & 8.01138178032943 & -0.0113817803294315 \tabularnewline
26 & 7.9 & 7.74704031196364 & 0.152959688036361 \tabularnewline
27 & 7.6 & 7.73162266865849 & -0.13162266865849 \tabularnewline
28 & 7.6 & 7.4588411609574 & 0.141158839042606 \tabularnewline
29 & 8.3 & 8.26646248115951 & 0.0335375188404909 \tabularnewline
30 & 8.4 & 8.42487684097312 & -0.0248768409731164 \tabularnewline
31 & 8.4 & 8.32808725080125 & 0.0719127491987509 \tabularnewline
32 & 8.4 & 8.27888655344839 & 0.121113446551607 \tabularnewline
33 & 8.4 & 8.31756783381551 & 0.082432166184488 \tabularnewline
34 & 8.6 & 8.52079319041505 & 0.0792068095849475 \tabularnewline
35 & 8.9 & 8.80125673426566 & 0.0987432657343409 \tabularnewline
36 & 8.8 & 8.86353790724309 & -0.0635379072430851 \tabularnewline
37 & 8.3 & 8.48574766808074 & -0.185747668080739 \tabularnewline
38 & 7.5 & 7.84825793786455 & -0.348257937864551 \tabularnewline
39 & 7.2 & 6.98843699851436 & 0.211563001485637 \tabularnewline
40 & 7.4 & 7.04928756311756 & 0.350712436882440 \tabularnewline
41 & 8.8 & 8.21508366084366 & 0.584916339156341 \tabularnewline
42 & 9.3 & 9.2193451263554 & 0.0806548736446009 \tabularnewline
43 & 9.3 & 9.32846262331753 & -0.0284626233175294 \tabularnewline
44 & 8.7 & 9.10137869588116 & -0.401378695881158 \tabularnewline
45 & 8.2 & 8.19720295955587 & 0.00279704044412738 \tabularnewline
46 & 8.3 & 8.02010315598645 & 0.279896844013552 \tabularnewline
47 & 8.5 & 8.49219419925088 & 0.0078058007491174 \tabularnewline
48 & 8.6 & 8.47135011630437 & 0.128649883695627 \tabularnewline
49 & 8.5 & 8.50960731398177 & -0.0096073139817724 \tabularnewline
50 & 8.2 & 8.26863748056873 & -0.0686374805687286 \tabularnewline
51 & 8.1 & 7.9030060583636 & 0.196993941636394 \tabularnewline
52 & 7.9 & 7.95084627190112 & -0.0508462719011148 \tabularnewline
53 & 8.6 & 8.49897712466585 & 0.101022875334152 \tabularnewline
54 & 8.7 & 8.65745766130251 & 0.0425423386974851 \tabularnewline
55 & 8.7 & 8.58323961099571 & 0.116760389004286 \tabularnewline
56 & 8.5 & 8.62056314979227 & -0.120563149792274 \tabularnewline
57 & 8.4 & 8.2154858973137 & 0.184514102686296 \tabularnewline
58 & 8.5 & 8.43295885206894 & 0.0670411479310616 \tabularnewline
59 & 8.7 & 8.63876146744501 & 0.0612385325549858 \tabularnewline
60 & 8.7 & 8.61415546118766 & 0.085844538812338 \tabularnewline
61 & 8.6 & 8.44934300899644 & 0.150656991003563 \tabularnewline
62 & 8.5 & 8.38756786167388 & 0.112432138326119 \tabularnewline
63 & 8.3 & 8.18449120482884 & 0.115508795171158 \tabularnewline
64 & 8 & 8.0743386714048 & -0.0743386714047969 \tabularnewline
65 & 8.2 & 8.49796311182631 & -0.297963111826309 \tabularnewline
66 & 8.1 & 7.9782908642658 & 0.121709135734197 \tabularnewline
67 & 8.1 & 7.93024989661265 & 0.169750103387354 \tabularnewline
68 & 8 & 7.99867547954673 & 0.0013245204532663 \tabularnewline
69 & 7.9 & 7.80137028107534 & 0.0986297189246578 \tabularnewline
70 & 7.9 & 8.0023517329341 & -0.102351732934102 \tabularnewline
71 & 8 & 7.99285844768131 & 0.00714155231869007 \tabularnewline
72 & 8 & 7.90205584039883 & 0.097944159601175 \tabularnewline
73 & 7.9 & 7.8073422982836 & 0.0926577017163944 \tabularnewline
74 & 8 & 7.72581705357912 & 0.274182946420882 \tabularnewline
75 & 7.7 & 7.87245084680866 & -0.172450846808662 \tabularnewline
76 & 7.2 & 7.46981680806038 & -0.269816808060378 \tabularnewline
77 & 7.5 & 7.61571067013873 & -0.115710670138734 \tabularnewline
78 & 7.3 & 7.39040088955789 & -0.0904008895578896 \tabularnewline
79 & 7 & 7.19427531204567 & -0.194275312045673 \tabularnewline
80 & 7 & 6.80353172580534 & 0.196468274194664 \tabularnewline
81 & 7 & 6.9782117510113 & 0.0217882489887020 \tabularnewline
82 & 7.2 & 7.29053288362532 & -0.0905328836253206 \tabularnewline
83 & 7.3 & 7.45343632401205 & -0.153436324012047 \tabularnewline
84 & 7.1 & 7.22055493492213 & -0.120554934922129 \tabularnewline
85 & 6.8 & 6.98773912896731 & -0.187739128967314 \tabularnewline
86 & 6.4 & 6.5387004752848 & -0.138700475284793 \tabularnewline
87 & 6.1 & 6.17509040047512 & -0.0750904004751244 \tabularnewline
88 & 6.5 & 6.07216494741047 & 0.427835052589532 \tabularnewline
89 & 7.7 & 7.49268293161234 & 0.207317068387664 \tabularnewline
90 & 7.9 & 8.14058440443245 & -0.240584404432451 \tabularnewline
91 & 7.5 & 7.92834626189136 & -0.428346261891358 \tabularnewline
92 & 6.9 & 7.26292412923023 & -0.362924129230230 \tabularnewline
93 & 6.6 & 6.68334663211139 & -0.0833466321113905 \tabularnewline
94 & 6.9 & 6.8258618921298 & 0.0741381078702057 \tabularnewline
95 & 7.7 & 7.44069142654932 & 0.259308573450685 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57849&T=4

[TABLE]
[ROW][C]Multiple Linear Regression - Actuals, Interpolation, and Residuals[/C][/ROW]
[ROW][C]Time or Index[/C][C]Actuals[/C][C]InterpolationForecast[/C][C]ResidualsPrediction Error[/C][/ROW]
[ROW][C]1[/C][C]6.1[/C][C]6.0215298352679[/C][C]0.0784701647320955[/C][/ROW]
[ROW][C]2[/C][C]6.3[/C][C]6.21461696016114[/C][C]0.0853830398388554[/C][/ROW]
[ROW][C]3[/C][C]6.3[/C][C]6.42838783524353[/C][C]-0.128387835243527[/C][/ROW]
[ROW][C]4[/C][C]6[/C][C]6.44166084759497[/C][C]-0.441660847594966[/C][/ROW]
[ROW][C]5[/C][C]6.2[/C][C]6.71192073627651[/C][C]-0.511920736276512[/C][/ROW]
[ROW][C]6[/C][C]6.4[/C][C]6.20541522030396[/C][C]0.194584779696037[/C][/ROW]
[ROW][C]7[/C][C]6.8[/C][C]6.67253511948557[/C][C]0.127464880514429[/C][/ROW]
[ROW][C]8[/C][C]7.5[/C][C]7.09089370983957[/C][C]0.409106290160427[/C][/ROW]
[ROW][C]9[/C][C]7.5[/C][C]7.8681411047227[/C][C]-0.368141104722704[/C][/ROW]
[ROW][C]10[/C][C]7.6[/C][C]7.7593654480553[/C][C]-0.159365448055307[/C][/ROW]
[ROW][C]11[/C][C]7.6[/C][C]7.82453309127705[/C][C]-0.224533091277046[/C][/ROW]
[ROW][C]12[/C][C]7.4[/C][C]7.52357413950658[/C][C]-0.123574139506574[/C][/ROW]
[ROW][C]13[/C][C]7.3[/C][C]7.2273089660928[/C][C]0.0726910339072044[/C][/ROW]
[ROW][C]14[/C][C]7.1[/C][C]7.16936191890414[/C][C]-0.0693619189041445[/C][/ROW]
[ROW][C]15[/C][C]6.9[/C][C]6.91651398710739[/C][C]-0.0165139871073851[/C][/ROW]
[ROW][C]16[/C][C]6.8[/C][C]6.88304372955332[/C][C]-0.0830437295533228[/C][/ROW]
[ROW][C]17[/C][C]7.5[/C][C]7.5011992834771[/C][C]-0.00119928347709286[/C][/ROW]
[ROW][C]18[/C][C]7.6[/C][C]7.68362899280886[/C][C]-0.0836289928088626[/C][/ROW]
[ROW][C]19[/C][C]7.8[/C][C]7.63480392485026[/C][C]0.165196075149740[/C][/ROW]
[ROW][C]20[/C][C]8[/C][C]7.8431465564563[/C][C]0.156853443543697[/C][/ROW]
[ROW][C]21[/C][C]8.1[/C][C]8.03867354039418[/C][C]0.0613264596058233[/C][/ROW]
[ROW][C]22[/C][C]8.2[/C][C]8.34803284478504[/C][C]-0.148032844785038[/C][/ROW]
[ROW][C]23[/C][C]8.3[/C][C]8.35626830951873[/C][C]-0.056268309518726[/C][/ROW]
[ROW][C]24[/C][C]8.2[/C][C]8.20477160043735[/C][C]-0.00477160043735051[/C][/ROW]
[ROW][C]25[/C][C]8[/C][C]8.01138178032943[/C][C]-0.0113817803294315[/C][/ROW]
[ROW][C]26[/C][C]7.9[/C][C]7.74704031196364[/C][C]0.152959688036361[/C][/ROW]
[ROW][C]27[/C][C]7.6[/C][C]7.73162266865849[/C][C]-0.13162266865849[/C][/ROW]
[ROW][C]28[/C][C]7.6[/C][C]7.4588411609574[/C][C]0.141158839042606[/C][/ROW]
[ROW][C]29[/C][C]8.3[/C][C]8.26646248115951[/C][C]0.0335375188404909[/C][/ROW]
[ROW][C]30[/C][C]8.4[/C][C]8.42487684097312[/C][C]-0.0248768409731164[/C][/ROW]
[ROW][C]31[/C][C]8.4[/C][C]8.32808725080125[/C][C]0.0719127491987509[/C][/ROW]
[ROW][C]32[/C][C]8.4[/C][C]8.27888655344839[/C][C]0.121113446551607[/C][/ROW]
[ROW][C]33[/C][C]8.4[/C][C]8.31756783381551[/C][C]0.082432166184488[/C][/ROW]
[ROW][C]34[/C][C]8.6[/C][C]8.52079319041505[/C][C]0.0792068095849475[/C][/ROW]
[ROW][C]35[/C][C]8.9[/C][C]8.80125673426566[/C][C]0.0987432657343409[/C][/ROW]
[ROW][C]36[/C][C]8.8[/C][C]8.86353790724309[/C][C]-0.0635379072430851[/C][/ROW]
[ROW][C]37[/C][C]8.3[/C][C]8.48574766808074[/C][C]-0.185747668080739[/C][/ROW]
[ROW][C]38[/C][C]7.5[/C][C]7.84825793786455[/C][C]-0.348257937864551[/C][/ROW]
[ROW][C]39[/C][C]7.2[/C][C]6.98843699851436[/C][C]0.211563001485637[/C][/ROW]
[ROW][C]40[/C][C]7.4[/C][C]7.04928756311756[/C][C]0.350712436882440[/C][/ROW]
[ROW][C]41[/C][C]8.8[/C][C]8.21508366084366[/C][C]0.584916339156341[/C][/ROW]
[ROW][C]42[/C][C]9.3[/C][C]9.2193451263554[/C][C]0.0806548736446009[/C][/ROW]
[ROW][C]43[/C][C]9.3[/C][C]9.32846262331753[/C][C]-0.0284626233175294[/C][/ROW]
[ROW][C]44[/C][C]8.7[/C][C]9.10137869588116[/C][C]-0.401378695881158[/C][/ROW]
[ROW][C]45[/C][C]8.2[/C][C]8.19720295955587[/C][C]0.00279704044412738[/C][/ROW]
[ROW][C]46[/C][C]8.3[/C][C]8.02010315598645[/C][C]0.279896844013552[/C][/ROW]
[ROW][C]47[/C][C]8.5[/C][C]8.49219419925088[/C][C]0.0078058007491174[/C][/ROW]
[ROW][C]48[/C][C]8.6[/C][C]8.47135011630437[/C][C]0.128649883695627[/C][/ROW]
[ROW][C]49[/C][C]8.5[/C][C]8.50960731398177[/C][C]-0.0096073139817724[/C][/ROW]
[ROW][C]50[/C][C]8.2[/C][C]8.26863748056873[/C][C]-0.0686374805687286[/C][/ROW]
[ROW][C]51[/C][C]8.1[/C][C]7.9030060583636[/C][C]0.196993941636394[/C][/ROW]
[ROW][C]52[/C][C]7.9[/C][C]7.95084627190112[/C][C]-0.0508462719011148[/C][/ROW]
[ROW][C]53[/C][C]8.6[/C][C]8.49897712466585[/C][C]0.101022875334152[/C][/ROW]
[ROW][C]54[/C][C]8.7[/C][C]8.65745766130251[/C][C]0.0425423386974851[/C][/ROW]
[ROW][C]55[/C][C]8.7[/C][C]8.58323961099571[/C][C]0.116760389004286[/C][/ROW]
[ROW][C]56[/C][C]8.5[/C][C]8.62056314979227[/C][C]-0.120563149792274[/C][/ROW]
[ROW][C]57[/C][C]8.4[/C][C]8.2154858973137[/C][C]0.184514102686296[/C][/ROW]
[ROW][C]58[/C][C]8.5[/C][C]8.43295885206894[/C][C]0.0670411479310616[/C][/ROW]
[ROW][C]59[/C][C]8.7[/C][C]8.63876146744501[/C][C]0.0612385325549858[/C][/ROW]
[ROW][C]60[/C][C]8.7[/C][C]8.61415546118766[/C][C]0.085844538812338[/C][/ROW]
[ROW][C]61[/C][C]8.6[/C][C]8.44934300899644[/C][C]0.150656991003563[/C][/ROW]
[ROW][C]62[/C][C]8.5[/C][C]8.38756786167388[/C][C]0.112432138326119[/C][/ROW]
[ROW][C]63[/C][C]8.3[/C][C]8.18449120482884[/C][C]0.115508795171158[/C][/ROW]
[ROW][C]64[/C][C]8[/C][C]8.0743386714048[/C][C]-0.0743386714047969[/C][/ROW]
[ROW][C]65[/C][C]8.2[/C][C]8.49796311182631[/C][C]-0.297963111826309[/C][/ROW]
[ROW][C]66[/C][C]8.1[/C][C]7.9782908642658[/C][C]0.121709135734197[/C][/ROW]
[ROW][C]67[/C][C]8.1[/C][C]7.93024989661265[/C][C]0.169750103387354[/C][/ROW]
[ROW][C]68[/C][C]8[/C][C]7.99867547954673[/C][C]0.0013245204532663[/C][/ROW]
[ROW][C]69[/C][C]7.9[/C][C]7.80137028107534[/C][C]0.0986297189246578[/C][/ROW]
[ROW][C]70[/C][C]7.9[/C][C]8.0023517329341[/C][C]-0.102351732934102[/C][/ROW]
[ROW][C]71[/C][C]8[/C][C]7.99285844768131[/C][C]0.00714155231869007[/C][/ROW]
[ROW][C]72[/C][C]8[/C][C]7.90205584039883[/C][C]0.097944159601175[/C][/ROW]
[ROW][C]73[/C][C]7.9[/C][C]7.8073422982836[/C][C]0.0926577017163944[/C][/ROW]
[ROW][C]74[/C][C]8[/C][C]7.72581705357912[/C][C]0.274182946420882[/C][/ROW]
[ROW][C]75[/C][C]7.7[/C][C]7.87245084680866[/C][C]-0.172450846808662[/C][/ROW]
[ROW][C]76[/C][C]7.2[/C][C]7.46981680806038[/C][C]-0.269816808060378[/C][/ROW]
[ROW][C]77[/C][C]7.5[/C][C]7.61571067013873[/C][C]-0.115710670138734[/C][/ROW]
[ROW][C]78[/C][C]7.3[/C][C]7.39040088955789[/C][C]-0.0904008895578896[/C][/ROW]
[ROW][C]79[/C][C]7[/C][C]7.19427531204567[/C][C]-0.194275312045673[/C][/ROW]
[ROW][C]80[/C][C]7[/C][C]6.80353172580534[/C][C]0.196468274194664[/C][/ROW]
[ROW][C]81[/C][C]7[/C][C]6.9782117510113[/C][C]0.0217882489887020[/C][/ROW]
[ROW][C]82[/C][C]7.2[/C][C]7.29053288362532[/C][C]-0.0905328836253206[/C][/ROW]
[ROW][C]83[/C][C]7.3[/C][C]7.45343632401205[/C][C]-0.153436324012047[/C][/ROW]
[ROW][C]84[/C][C]7.1[/C][C]7.22055493492213[/C][C]-0.120554934922129[/C][/ROW]
[ROW][C]85[/C][C]6.8[/C][C]6.98773912896731[/C][C]-0.187739128967314[/C][/ROW]
[ROW][C]86[/C][C]6.4[/C][C]6.5387004752848[/C][C]-0.138700475284793[/C][/ROW]
[ROW][C]87[/C][C]6.1[/C][C]6.17509040047512[/C][C]-0.0750904004751244[/C][/ROW]
[ROW][C]88[/C][C]6.5[/C][C]6.07216494741047[/C][C]0.427835052589532[/C][/ROW]
[ROW][C]89[/C][C]7.7[/C][C]7.49268293161234[/C][C]0.207317068387664[/C][/ROW]
[ROW][C]90[/C][C]7.9[/C][C]8.14058440443245[/C][C]-0.240584404432451[/C][/ROW]
[ROW][C]91[/C][C]7.5[/C][C]7.92834626189136[/C][C]-0.428346261891358[/C][/ROW]
[ROW][C]92[/C][C]6.9[/C][C]7.26292412923023[/C][C]-0.362924129230230[/C][/ROW]
[ROW][C]93[/C][C]6.6[/C][C]6.68334663211139[/C][C]-0.0833466321113905[/C][/ROW]
[ROW][C]94[/C][C]6.9[/C][C]6.8258618921298[/C][C]0.0741381078702057[/C][/ROW]
[ROW][C]95[/C][C]7.7[/C][C]7.44069142654932[/C][C]0.259308573450685[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57849&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57849&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
16.16.02152983526790.0784701647320955
26.36.214616960161140.0853830398388554
36.36.42838783524353-0.128387835243527
466.44166084759497-0.441660847594966
56.26.71192073627651-0.511920736276512
66.46.205415220303960.194584779696037
76.86.672535119485570.127464880514429
87.57.090893709839570.409106290160427
97.57.8681411047227-0.368141104722704
107.67.7593654480553-0.159365448055307
117.67.82453309127705-0.224533091277046
127.47.52357413950658-0.123574139506574
137.37.22730896609280.0726910339072044
147.17.16936191890414-0.0693619189041445
156.96.91651398710739-0.0165139871073851
166.86.88304372955332-0.0830437295533228
177.57.5011992834771-0.00119928347709286
187.67.68362899280886-0.0836289928088626
197.87.634803924850260.165196075149740
2087.84314655645630.156853443543697
218.18.038673540394180.0613264596058233
228.28.34803284478504-0.148032844785038
238.38.35626830951873-0.056268309518726
248.28.20477160043735-0.00477160043735051
2588.01138178032943-0.0113817803294315
267.97.747040311963640.152959688036361
277.67.73162266865849-0.13162266865849
287.67.45884116095740.141158839042606
298.38.266462481159510.0335375188404909
308.48.42487684097312-0.0248768409731164
318.48.328087250801250.0719127491987509
328.48.278886553448390.121113446551607
338.48.317567833815510.082432166184488
348.68.520793190415050.0792068095849475
358.98.801256734265660.0987432657343409
368.88.86353790724309-0.0635379072430851
378.38.48574766808074-0.185747668080739
387.57.84825793786455-0.348257937864551
397.26.988436998514360.211563001485637
407.47.049287563117560.350712436882440
418.88.215083660843660.584916339156341
429.39.21934512635540.0806548736446009
439.39.32846262331753-0.0284626233175294
448.79.10137869588116-0.401378695881158
458.28.197202959555870.00279704044412738
468.38.020103155986450.279896844013552
478.58.492194199250880.0078058007491174
488.68.471350116304370.128649883695627
498.58.50960731398177-0.0096073139817724
508.28.26863748056873-0.0686374805687286
518.17.90300605836360.196993941636394
527.97.95084627190112-0.0508462719011148
538.68.498977124665850.101022875334152
548.78.657457661302510.0425423386974851
558.78.583239610995710.116760389004286
568.58.62056314979227-0.120563149792274
578.48.21548589731370.184514102686296
588.58.432958852068940.0670411479310616
598.78.638761467445010.0612385325549858
608.78.614155461187660.085844538812338
618.68.449343008996440.150656991003563
628.58.387567861673880.112432138326119
638.38.184491204828840.115508795171158
6488.0743386714048-0.0743386714047969
658.28.49796311182631-0.297963111826309
668.17.97829086426580.121709135734197
678.17.930249896612650.169750103387354
6887.998675479546730.0013245204532663
697.97.801370281075340.0986297189246578
707.98.0023517329341-0.102351732934102
7187.992858447681310.00714155231869007
7287.902055840398830.097944159601175
737.97.80734229828360.0926577017163944
7487.725817053579120.274182946420882
757.77.87245084680866-0.172450846808662
767.27.46981680806038-0.269816808060378
777.57.61571067013873-0.115710670138734
787.37.39040088955789-0.0904008895578896
7977.19427531204567-0.194275312045673
8076.803531725805340.196468274194664
8176.97821175101130.0217882489887020
827.27.29053288362532-0.0905328836253206
837.37.45343632401205-0.153436324012047
847.17.22055493492213-0.120554934922129
856.86.98773912896731-0.187739128967314
866.46.5387004752848-0.138700475284793
876.16.17509040047512-0.0750904004751244
886.56.072164947410470.427835052589532
897.77.492682931612340.207317068387664
907.98.14058440443245-0.240584404432451
917.57.92834626189136-0.428346261891358
926.97.26292412923023-0.362924129230230
936.66.68334663211139-0.0833466321113905
946.96.82586189212980.0741381078702057
957.77.440691426549320.259308573450685






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
190.3761528699361090.7523057398722180.623847130063891
200.4550515950335380.9101031900670770.544948404966462
210.314132011877330.628264023754660.68586798812267
220.4423996209218970.8847992418437940.557600379078103
230.3509294985325260.7018589970650520.649070501467474
240.2599057372824120.5198114745648250.740094262717588
250.1968049328924120.3936098657848250.803195067107587
260.129676275111420.259352550222840.87032372488858
270.1334734531484020.2669469062968040.866526546851598
280.1080572117642150.2161144235284310.891942788235785
290.08323575939773870.1664715187954770.916764240602261
300.05747711245770650.1149542249154130.942522887542294
310.05192364014736440.1038472802947290.948076359852636
320.1106729251218070.2213458502436150.889327074878193
330.08867059555941590.1773411911188320.911329404440584
340.08646869045515330.1729373809103070.913531309544847
350.05991407640975850.1198281528195170.940085923590241
360.05710927614392480.1142185522878500.942890723856075
370.1166489749013920.2332979498027840.883351025098608
380.5855835390699060.8288329218601880.414416460930094
390.5230946285955480.9538107428089040.476905371404452
400.4573961645815990.9147923291631980.542603835418401
410.5213732694864540.9572534610270920.478626730513546
420.481380394353030.962760788706060.51861960564697
430.4209542234760290.8419084469520580.579045776523971
440.714520236096840.5709595278063210.285479763903160
450.668812145694040.662375708611920.33118785430596
460.6264807126015960.7470385747968070.373519287398404
470.7211113278575740.5577773442848510.278888672142426
480.6917258262052210.6165483475895580.308274173794779
490.70123205232590.5975358953481990.298767947674099
500.7587090801760430.4825818396479130.241290919823957
510.6998473936416770.6003052127166470.300152606358323
520.7570289224311360.4859421551377280.242971077568864
530.7104168116129650.579166376774070.289583188387035
540.6947498197209980.6105003605580040.305250180279002
550.6510863055721360.6978273888557280.348913694427864
560.768993015567450.4620139688650990.231006984432549
570.7117799370167610.5764401259664780.288220062983239
580.6724176149115720.6551647701768550.327582385088428
590.6281129361311210.7437741277377590.371887063868879
600.5676378774000480.8647242451999030.432362122599952
610.5250130606736140.9499738786527710.474986939326386
620.4570709320807490.9141418641614980.542929067919251
630.4841035014861850.968207002972370.515896498513815
640.4284242816568450.856848563313690.571575718343155
650.6700758011187750.659848397762450.329924198881225
660.6391864105161070.7216271789677860.360813589483893
670.6585538229220880.6828923541558230.341446177077912
680.6629523235355410.6740953529289190.337047676464459
690.6052401698035330.7895196603929350.394759830196467
700.5342420214753990.9315159570492010.465757978524601
710.4368706089622280.8737412179244550.563129391037772
720.3311859479476500.6623718958952990.66881405205235
730.5659970770327930.8680058459344140.434002922967207
740.6070152141848240.7859695716303510.392984785815176
750.7763258940378770.4473482119242460.223674105962123
760.6437760357804440.7124479284391130.356223964219556

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
19 & 0.376152869936109 & 0.752305739872218 & 0.623847130063891 \tabularnewline
20 & 0.455051595033538 & 0.910103190067077 & 0.544948404966462 \tabularnewline
21 & 0.31413201187733 & 0.62826402375466 & 0.68586798812267 \tabularnewline
22 & 0.442399620921897 & 0.884799241843794 & 0.557600379078103 \tabularnewline
23 & 0.350929498532526 & 0.701858997065052 & 0.649070501467474 \tabularnewline
24 & 0.259905737282412 & 0.519811474564825 & 0.740094262717588 \tabularnewline
25 & 0.196804932892412 & 0.393609865784825 & 0.803195067107587 \tabularnewline
26 & 0.12967627511142 & 0.25935255022284 & 0.87032372488858 \tabularnewline
27 & 0.133473453148402 & 0.266946906296804 & 0.866526546851598 \tabularnewline
28 & 0.108057211764215 & 0.216114423528431 & 0.891942788235785 \tabularnewline
29 & 0.0832357593977387 & 0.166471518795477 & 0.916764240602261 \tabularnewline
30 & 0.0574771124577065 & 0.114954224915413 & 0.942522887542294 \tabularnewline
31 & 0.0519236401473644 & 0.103847280294729 & 0.948076359852636 \tabularnewline
32 & 0.110672925121807 & 0.221345850243615 & 0.889327074878193 \tabularnewline
33 & 0.0886705955594159 & 0.177341191118832 & 0.911329404440584 \tabularnewline
34 & 0.0864686904551533 & 0.172937380910307 & 0.913531309544847 \tabularnewline
35 & 0.0599140764097585 & 0.119828152819517 & 0.940085923590241 \tabularnewline
36 & 0.0571092761439248 & 0.114218552287850 & 0.942890723856075 \tabularnewline
37 & 0.116648974901392 & 0.233297949802784 & 0.883351025098608 \tabularnewline
38 & 0.585583539069906 & 0.828832921860188 & 0.414416460930094 \tabularnewline
39 & 0.523094628595548 & 0.953810742808904 & 0.476905371404452 \tabularnewline
40 & 0.457396164581599 & 0.914792329163198 & 0.542603835418401 \tabularnewline
41 & 0.521373269486454 & 0.957253461027092 & 0.478626730513546 \tabularnewline
42 & 0.48138039435303 & 0.96276078870606 & 0.51861960564697 \tabularnewline
43 & 0.420954223476029 & 0.841908446952058 & 0.579045776523971 \tabularnewline
44 & 0.71452023609684 & 0.570959527806321 & 0.285479763903160 \tabularnewline
45 & 0.66881214569404 & 0.66237570861192 & 0.33118785430596 \tabularnewline
46 & 0.626480712601596 & 0.747038574796807 & 0.373519287398404 \tabularnewline
47 & 0.721111327857574 & 0.557777344284851 & 0.278888672142426 \tabularnewline
48 & 0.691725826205221 & 0.616548347589558 & 0.308274173794779 \tabularnewline
49 & 0.7012320523259 & 0.597535895348199 & 0.298767947674099 \tabularnewline
50 & 0.758709080176043 & 0.482581839647913 & 0.241290919823957 \tabularnewline
51 & 0.699847393641677 & 0.600305212716647 & 0.300152606358323 \tabularnewline
52 & 0.757028922431136 & 0.485942155137728 & 0.242971077568864 \tabularnewline
53 & 0.710416811612965 & 0.57916637677407 & 0.289583188387035 \tabularnewline
54 & 0.694749819720998 & 0.610500360558004 & 0.305250180279002 \tabularnewline
55 & 0.651086305572136 & 0.697827388855728 & 0.348913694427864 \tabularnewline
56 & 0.76899301556745 & 0.462013968865099 & 0.231006984432549 \tabularnewline
57 & 0.711779937016761 & 0.576440125966478 & 0.288220062983239 \tabularnewline
58 & 0.672417614911572 & 0.655164770176855 & 0.327582385088428 \tabularnewline
59 & 0.628112936131121 & 0.743774127737759 & 0.371887063868879 \tabularnewline
60 & 0.567637877400048 & 0.864724245199903 & 0.432362122599952 \tabularnewline
61 & 0.525013060673614 & 0.949973878652771 & 0.474986939326386 \tabularnewline
62 & 0.457070932080749 & 0.914141864161498 & 0.542929067919251 \tabularnewline
63 & 0.484103501486185 & 0.96820700297237 & 0.515896498513815 \tabularnewline
64 & 0.428424281656845 & 0.85684856331369 & 0.571575718343155 \tabularnewline
65 & 0.670075801118775 & 0.65984839776245 & 0.329924198881225 \tabularnewline
66 & 0.639186410516107 & 0.721627178967786 & 0.360813589483893 \tabularnewline
67 & 0.658553822922088 & 0.682892354155823 & 0.341446177077912 \tabularnewline
68 & 0.662952323535541 & 0.674095352928919 & 0.337047676464459 \tabularnewline
69 & 0.605240169803533 & 0.789519660392935 & 0.394759830196467 \tabularnewline
70 & 0.534242021475399 & 0.931515957049201 & 0.465757978524601 \tabularnewline
71 & 0.436870608962228 & 0.873741217924455 & 0.563129391037772 \tabularnewline
72 & 0.331185947947650 & 0.662371895895299 & 0.66881405205235 \tabularnewline
73 & 0.565997077032793 & 0.868005845934414 & 0.434002922967207 \tabularnewline
74 & 0.607015214184824 & 0.785969571630351 & 0.392984785815176 \tabularnewline
75 & 0.776325894037877 & 0.447348211924246 & 0.223674105962123 \tabularnewline
76 & 0.643776035780444 & 0.712447928439113 & 0.356223964219556 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57849&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]19[/C][C]0.376152869936109[/C][C]0.752305739872218[/C][C]0.623847130063891[/C][/ROW]
[ROW][C]20[/C][C]0.455051595033538[/C][C]0.910103190067077[/C][C]0.544948404966462[/C][/ROW]
[ROW][C]21[/C][C]0.31413201187733[/C][C]0.62826402375466[/C][C]0.68586798812267[/C][/ROW]
[ROW][C]22[/C][C]0.442399620921897[/C][C]0.884799241843794[/C][C]0.557600379078103[/C][/ROW]
[ROW][C]23[/C][C]0.350929498532526[/C][C]0.701858997065052[/C][C]0.649070501467474[/C][/ROW]
[ROW][C]24[/C][C]0.259905737282412[/C][C]0.519811474564825[/C][C]0.740094262717588[/C][/ROW]
[ROW][C]25[/C][C]0.196804932892412[/C][C]0.393609865784825[/C][C]0.803195067107587[/C][/ROW]
[ROW][C]26[/C][C]0.12967627511142[/C][C]0.25935255022284[/C][C]0.87032372488858[/C][/ROW]
[ROW][C]27[/C][C]0.133473453148402[/C][C]0.266946906296804[/C][C]0.866526546851598[/C][/ROW]
[ROW][C]28[/C][C]0.108057211764215[/C][C]0.216114423528431[/C][C]0.891942788235785[/C][/ROW]
[ROW][C]29[/C][C]0.0832357593977387[/C][C]0.166471518795477[/C][C]0.916764240602261[/C][/ROW]
[ROW][C]30[/C][C]0.0574771124577065[/C][C]0.114954224915413[/C][C]0.942522887542294[/C][/ROW]
[ROW][C]31[/C][C]0.0519236401473644[/C][C]0.103847280294729[/C][C]0.948076359852636[/C][/ROW]
[ROW][C]32[/C][C]0.110672925121807[/C][C]0.221345850243615[/C][C]0.889327074878193[/C][/ROW]
[ROW][C]33[/C][C]0.0886705955594159[/C][C]0.177341191118832[/C][C]0.911329404440584[/C][/ROW]
[ROW][C]34[/C][C]0.0864686904551533[/C][C]0.172937380910307[/C][C]0.913531309544847[/C][/ROW]
[ROW][C]35[/C][C]0.0599140764097585[/C][C]0.119828152819517[/C][C]0.940085923590241[/C][/ROW]
[ROW][C]36[/C][C]0.0571092761439248[/C][C]0.114218552287850[/C][C]0.942890723856075[/C][/ROW]
[ROW][C]37[/C][C]0.116648974901392[/C][C]0.233297949802784[/C][C]0.883351025098608[/C][/ROW]
[ROW][C]38[/C][C]0.585583539069906[/C][C]0.828832921860188[/C][C]0.414416460930094[/C][/ROW]
[ROW][C]39[/C][C]0.523094628595548[/C][C]0.953810742808904[/C][C]0.476905371404452[/C][/ROW]
[ROW][C]40[/C][C]0.457396164581599[/C][C]0.914792329163198[/C][C]0.542603835418401[/C][/ROW]
[ROW][C]41[/C][C]0.521373269486454[/C][C]0.957253461027092[/C][C]0.478626730513546[/C][/ROW]
[ROW][C]42[/C][C]0.48138039435303[/C][C]0.96276078870606[/C][C]0.51861960564697[/C][/ROW]
[ROW][C]43[/C][C]0.420954223476029[/C][C]0.841908446952058[/C][C]0.579045776523971[/C][/ROW]
[ROW][C]44[/C][C]0.71452023609684[/C][C]0.570959527806321[/C][C]0.285479763903160[/C][/ROW]
[ROW][C]45[/C][C]0.66881214569404[/C][C]0.66237570861192[/C][C]0.33118785430596[/C][/ROW]
[ROW][C]46[/C][C]0.626480712601596[/C][C]0.747038574796807[/C][C]0.373519287398404[/C][/ROW]
[ROW][C]47[/C][C]0.721111327857574[/C][C]0.557777344284851[/C][C]0.278888672142426[/C][/ROW]
[ROW][C]48[/C][C]0.691725826205221[/C][C]0.616548347589558[/C][C]0.308274173794779[/C][/ROW]
[ROW][C]49[/C][C]0.7012320523259[/C][C]0.597535895348199[/C][C]0.298767947674099[/C][/ROW]
[ROW][C]50[/C][C]0.758709080176043[/C][C]0.482581839647913[/C][C]0.241290919823957[/C][/ROW]
[ROW][C]51[/C][C]0.699847393641677[/C][C]0.600305212716647[/C][C]0.300152606358323[/C][/ROW]
[ROW][C]52[/C][C]0.757028922431136[/C][C]0.485942155137728[/C][C]0.242971077568864[/C][/ROW]
[ROW][C]53[/C][C]0.710416811612965[/C][C]0.57916637677407[/C][C]0.289583188387035[/C][/ROW]
[ROW][C]54[/C][C]0.694749819720998[/C][C]0.610500360558004[/C][C]0.305250180279002[/C][/ROW]
[ROW][C]55[/C][C]0.651086305572136[/C][C]0.697827388855728[/C][C]0.348913694427864[/C][/ROW]
[ROW][C]56[/C][C]0.76899301556745[/C][C]0.462013968865099[/C][C]0.231006984432549[/C][/ROW]
[ROW][C]57[/C][C]0.711779937016761[/C][C]0.576440125966478[/C][C]0.288220062983239[/C][/ROW]
[ROW][C]58[/C][C]0.672417614911572[/C][C]0.655164770176855[/C][C]0.327582385088428[/C][/ROW]
[ROW][C]59[/C][C]0.628112936131121[/C][C]0.743774127737759[/C][C]0.371887063868879[/C][/ROW]
[ROW][C]60[/C][C]0.567637877400048[/C][C]0.864724245199903[/C][C]0.432362122599952[/C][/ROW]
[ROW][C]61[/C][C]0.525013060673614[/C][C]0.949973878652771[/C][C]0.474986939326386[/C][/ROW]
[ROW][C]62[/C][C]0.457070932080749[/C][C]0.914141864161498[/C][C]0.542929067919251[/C][/ROW]
[ROW][C]63[/C][C]0.484103501486185[/C][C]0.96820700297237[/C][C]0.515896498513815[/C][/ROW]
[ROW][C]64[/C][C]0.428424281656845[/C][C]0.85684856331369[/C][C]0.571575718343155[/C][/ROW]
[ROW][C]65[/C][C]0.670075801118775[/C][C]0.65984839776245[/C][C]0.329924198881225[/C][/ROW]
[ROW][C]66[/C][C]0.639186410516107[/C][C]0.721627178967786[/C][C]0.360813589483893[/C][/ROW]
[ROW][C]67[/C][C]0.658553822922088[/C][C]0.682892354155823[/C][C]0.341446177077912[/C][/ROW]
[ROW][C]68[/C][C]0.662952323535541[/C][C]0.674095352928919[/C][C]0.337047676464459[/C][/ROW]
[ROW][C]69[/C][C]0.605240169803533[/C][C]0.789519660392935[/C][C]0.394759830196467[/C][/ROW]
[ROW][C]70[/C][C]0.534242021475399[/C][C]0.931515957049201[/C][C]0.465757978524601[/C][/ROW]
[ROW][C]71[/C][C]0.436870608962228[/C][C]0.873741217924455[/C][C]0.563129391037772[/C][/ROW]
[ROW][C]72[/C][C]0.331185947947650[/C][C]0.662371895895299[/C][C]0.66881405205235[/C][/ROW]
[ROW][C]73[/C][C]0.565997077032793[/C][C]0.868005845934414[/C][C]0.434002922967207[/C][/ROW]
[ROW][C]74[/C][C]0.607015214184824[/C][C]0.785969571630351[/C][C]0.392984785815176[/C][/ROW]
[ROW][C]75[/C][C]0.776325894037877[/C][C]0.447348211924246[/C][C]0.223674105962123[/C][/ROW]
[ROW][C]76[/C][C]0.643776035780444[/C][C]0.712447928439113[/C][C]0.356223964219556[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57849&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57849&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
190.3761528699361090.7523057398722180.623847130063891
200.4550515950335380.9101031900670770.544948404966462
210.314132011877330.628264023754660.68586798812267
220.4423996209218970.8847992418437940.557600379078103
230.3509294985325260.7018589970650520.649070501467474
240.2599057372824120.5198114745648250.740094262717588
250.1968049328924120.3936098657848250.803195067107587
260.129676275111420.259352550222840.87032372488858
270.1334734531484020.2669469062968040.866526546851598
280.1080572117642150.2161144235284310.891942788235785
290.08323575939773870.1664715187954770.916764240602261
300.05747711245770650.1149542249154130.942522887542294
310.05192364014736440.1038472802947290.948076359852636
320.1106729251218070.2213458502436150.889327074878193
330.08867059555941590.1773411911188320.911329404440584
340.08646869045515330.1729373809103070.913531309544847
350.05991407640975850.1198281528195170.940085923590241
360.05710927614392480.1142185522878500.942890723856075
370.1166489749013920.2332979498027840.883351025098608
380.5855835390699060.8288329218601880.414416460930094
390.5230946285955480.9538107428089040.476905371404452
400.4573961645815990.9147923291631980.542603835418401
410.5213732694864540.9572534610270920.478626730513546
420.481380394353030.962760788706060.51861960564697
430.4209542234760290.8419084469520580.579045776523971
440.714520236096840.5709595278063210.285479763903160
450.668812145694040.662375708611920.33118785430596
460.6264807126015960.7470385747968070.373519287398404
470.7211113278575740.5577773442848510.278888672142426
480.6917258262052210.6165483475895580.308274173794779
490.70123205232590.5975358953481990.298767947674099
500.7587090801760430.4825818396479130.241290919823957
510.6998473936416770.6003052127166470.300152606358323
520.7570289224311360.4859421551377280.242971077568864
530.7104168116129650.579166376774070.289583188387035
540.6947498197209980.6105003605580040.305250180279002
550.6510863055721360.6978273888557280.348913694427864
560.768993015567450.4620139688650990.231006984432549
570.7117799370167610.5764401259664780.288220062983239
580.6724176149115720.6551647701768550.327582385088428
590.6281129361311210.7437741277377590.371887063868879
600.5676378774000480.8647242451999030.432362122599952
610.5250130606736140.9499738786527710.474986939326386
620.4570709320807490.9141418641614980.542929067919251
630.4841035014861850.968207002972370.515896498513815
640.4284242816568450.856848563313690.571575718343155
650.6700758011187750.659848397762450.329924198881225
660.6391864105161070.7216271789677860.360813589483893
670.6585538229220880.6828923541558230.341446177077912
680.6629523235355410.6740953529289190.337047676464459
690.6052401698035330.7895196603929350.394759830196467
700.5342420214753990.9315159570492010.465757978524601
710.4368706089622280.8737412179244550.563129391037772
720.3311859479476500.6623718958952990.66881405205235
730.5659970770327930.8680058459344140.434002922967207
740.6070152141848240.7859695716303510.392984785815176
750.7763258940378770.4473482119242460.223674105962123
760.6437760357804440.7124479284391130.356223964219556






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=57849&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=57849&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57849&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 6
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Figure 7
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Figure 9
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Figure 10
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Input Parameters & R Code

Parameters (Session):
par1 = 1 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;
Parameters (R input):
par1 = 1 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;
R code (references can be found in the software module):
library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,mysum$coefficients[i,1])
a<-table.element(a, round(mysum$coefficients[i,2],6))
a<-table.element(a, round(mysum$coefficients[i,3],4))
a<-table.element(a, round(mysum$coefficients[i,4],6))
a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, sqrt(mysum$r.squared))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, mysum$r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, mysum$adj.r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, mysum$fstatistic[1])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[3])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a, mysum$sigma)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, sum(myerror*myerror))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,x[i])
a<-table.element(a,x[i]-mysum$resid[i])
a<-table.element(a,mysum$resid[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
if (n > n25) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-values',header=TRUE)
a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'breakpoint index',header=TRUE)
a<-table.element(a,'greater',header=TRUE)
a<-table.element(a,'2-sided',header=TRUE)
a<-table.element(a,'less',header=TRUE)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
a<-table.element(a,mypoint,header=TRUE)
a<-table.element(a,gqarr[mypoint-kp3+1,1])
a<-table.element(a,gqarr[mypoint-kp3+1,2])
a<-table.element(a,gqarr[mypoint-kp3+1,3])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,numsignificant1)
a<-table.element(a,numsignificant1/numgqtests)
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,numsignificant5)
a<-table.element(a,numsignificant5/numgqtests)
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,numsignificant10)
a<-table.element(a,numsignificant10/numgqtests)
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}