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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 computationWed, 18 Nov 2009 14:53:28 -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/18/t1258581276rivurwtvlxkc6ae.htm/, Retrieved Sun, 05 May 2024 13:14:21 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=57630, Retrieved Sun, 05 May 2024 13:14:21 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact120

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 14:10:54] [b98453cac15ba1066b407e146608df68]
-    D      [Multiple Regression] [] [2009-11-18 21:53:28] [4f23cd6f600e6b4b5336072a0ca6bd10] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
7,3	20,9	7,4	8,1	8,3	8,2
7,7	20,9	7,3	7,4	8,1	8,3
8	22,3	7,7	7,3	7,4	8,1
8	22,3	8	7,7	7,3	7,4
7,7	22,3	8	8	7,7	7,3
6,9	19,9	7,7	8	8	7,7
6,6	19,9	6,9	7,7	8	8
6,9	19,9	6,6	6,9	7,7	8
7,5	24,1	6,9	6,6	6,9	7,7
7,9	24,1	7,5	6,9	6,6	6,9
7,7	24,1	7,9	7,5	6,9	6,6
6,5	13,8	7,7	7,9	7,5	6,9
6,1	13,8	6,5	7,7	7,9	7,5
6,4	13,8	6,1	6,5	7,7	7,9
6,8	16,2	6,4	6,1	6,5	7,7
7,1	16,2	6,8	6,4	6,1	6,5
7,3	16,2	7,1	6,8	6,4	6,1
7,2	18,6	7,3	7,1	6,8	6,4
7	18,6	7,2	7,3	7,1	6,8
7	18,6	7	7,2	7,3	7,1
7	22,4	7	7	7,2	7,3
7,3	22,4	7	7	7	7,2
7,5	22,4	7,3	7	7	7
7,2	22,6	7,5	7,3	7	7
7,7	22,6	7,2	7,5	7,3	7
8	22,6	7,7	7,2	7,5	7,3
7,9	20	8	7,7	7,2	7,5
8	20	7,9	8	7,7	7,2
8	20	8	7,9	8	7,7
7,9	21,8	8	8	7,9	8
7,9	21,8	7,9	8	8	7,9
8	21,8	7,9	7,9	8	8
8,1	28,7	8	7,9	7,9	8
8,1	28,7	8,1	8	7,9	7,9
8,2	28,7	8,1	8,1	8	7,9
8	19,5	8,2	8,1	8,1	8
8,3	19,5	8	8,2	8,1	8,1
8,5	19,5	8,3	8	8,2	8,1
8,6	19,4	8,5	8,3	8	8,2
8,7	19,4	8,6	8,5	8,3	8
8,7	19,4	8,7	8,6	8,5	8,3
8,5	21,7	8,7	8,7	8,6	8,5
8,4	21,7	8,5	8,7	8,7	8,6
8,5	21,7	8,4	8,5	8,7	8,7
8,7	26,2	8,5	8,4	8,5	8,7
8,7	26,2	8,7	8,5	8,4	8,5
8,6	26,2	8,7	8,7	8,5	8,4
7,9	19,1	8,6	8,7	8,7	8,5
8,1	19,1	7,9	8,6	8,7	8,7
8,2	19,1	8,1	7,9	8,6	8,7
8,5	21,3	8,2	8,1	7,9	8,6
8,6	21,3	8,5	8,2	8,1	7,9
8,5	21,3	8,6	8,5	8,2	8,1
8,3	24,1	8,5	8,6	8,5	8,2
8,2	24,1	8,3	8,5	8,6	8,5
8,7	24,1	8,2	8,3	8,5	8,6

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 3 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57630&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]3 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57630&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57630&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 time3 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135






Multiple Linear Regression - Estimated Regression Equation
Y[t] = + 0.550379527945818 + 0.03191199081617X[t] + 1.29906637784746Y1[t] -0.702880030402592Y2[t] -0.105447463900118Y3[t] + 0.276468222179575Y4[t] + 0.85590625548584M1[t] + 0.494902925375232M2[t] + 0.345194798134066M3[t] + 0.563227115239073M4[t] + 0.500826415002446M5[t] + 0.255445622080153M6[t] + 0.401570798521914M7[t] + 0.542381338318757M8[t] + 0.274628508917084M9[t] + 0.305463665399061M10[t] + 0.284067963059267M11[t] + 0.00685825937152281t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Y[t] =  +  0.550379527945818 +  0.03191199081617X[t] +  1.29906637784746Y1[t] -0.702880030402592Y2[t] -0.105447463900118Y3[t] +  0.276468222179575Y4[t] +  0.85590625548584M1[t] +  0.494902925375232M2[t] +  0.345194798134066M3[t] +  0.563227115239073M4[t] +  0.500826415002446M5[t] +  0.255445622080153M6[t] +  0.401570798521914M7[t] +  0.542381338318757M8[t] +  0.274628508917084M9[t] +  0.305463665399061M10[t] +  0.284067963059267M11[t] +  0.00685825937152281t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57630&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Y[t] =  +  0.550379527945818 +  0.03191199081617X[t] +  1.29906637784746Y1[t] -0.702880030402592Y2[t] -0.105447463900118Y3[t] +  0.276468222179575Y4[t] +  0.85590625548584M1[t] +  0.494902925375232M2[t] +  0.345194798134066M3[t] +  0.563227115239073M4[t] +  0.500826415002446M5[t] +  0.255445622080153M6[t] +  0.401570798521914M7[t] +  0.542381338318757M8[t] +  0.274628508917084M9[t] +  0.305463665399061M10[t] +  0.284067963059267M11[t] +  0.00685825937152281t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57630&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57630&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] = + 0.550379527945818 + 0.03191199081617X[t] + 1.29906637784746Y1[t] -0.702880030402592Y2[t] -0.105447463900118Y3[t] + 0.276468222179575Y4[t] + 0.85590625548584M1[t] + 0.494902925375232M2[t] + 0.345194798134066M3[t] + 0.563227115239073M4[t] + 0.500826415002446M5[t] + 0.255445622080153M6[t] + 0.401570798521914M7[t] + 0.542381338318757M8[t] + 0.274628508917084M9[t] + 0.305463665399061M10[t] + 0.284067963059267M11[t] + 0.00685825937152281t + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)0.5503795279458180.398631.38070.1754450.087723
X0.031911990816170.0117692.71150.0100010.005001
Y11.299066377847460.1542828.420100
Y2-0.7028800304025920.267159-2.63090.0122320.006116
Y3-0.1054474639001180.26533-0.39740.6932810.346641
Y40.2764682221795750.1380132.00320.0523260.026163
M10.855906255485840.1280636.683500
M20.4949029253752320.1502473.29390.0021440.001072
M30.3451947981340660.1376882.50710.0165680.008284
M40.5632271152390730.1039245.41964e-062e-06
M50.5008264150024460.1028894.86762e-051e-05
M60.2554456220801530.105052.43160.0198540.009927
M70.4015707985219140.113593.53530.001090.000545
M80.5423813383187570.1179944.59674.6e-052.3e-05
M90.2746285089170840.1420741.9330.0607130.030357
M100.3054636653990610.1347732.26650.0291970.014598
M110.2840679630592670.1301732.18220.0353430.017672
t0.006858259371522810.0020363.36930.0017390.00087

\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) & 0.550379527945818 & 0.39863 & 1.3807 & 0.175445 & 0.087723 \tabularnewline
X & 0.03191199081617 & 0.011769 & 2.7115 & 0.010001 & 0.005001 \tabularnewline
Y1 & 1.29906637784746 & 0.154282 & 8.4201 & 0 & 0 \tabularnewline
Y2 & -0.702880030402592 & 0.267159 & -2.6309 & 0.012232 & 0.006116 \tabularnewline
Y3 & -0.105447463900118 & 0.26533 & -0.3974 & 0.693281 & 0.346641 \tabularnewline
Y4 & 0.276468222179575 & 0.138013 & 2.0032 & 0.052326 & 0.026163 \tabularnewline
M1 & 0.85590625548584 & 0.128063 & 6.6835 & 0 & 0 \tabularnewline
M2 & 0.494902925375232 & 0.150247 & 3.2939 & 0.002144 & 0.001072 \tabularnewline
M3 & 0.345194798134066 & 0.137688 & 2.5071 & 0.016568 & 0.008284 \tabularnewline
M4 & 0.563227115239073 & 0.103924 & 5.4196 & 4e-06 & 2e-06 \tabularnewline
M5 & 0.500826415002446 & 0.102889 & 4.8676 & 2e-05 & 1e-05 \tabularnewline
M6 & 0.255445622080153 & 0.10505 & 2.4316 & 0.019854 & 0.009927 \tabularnewline
M7 & 0.401570798521914 & 0.11359 & 3.5353 & 0.00109 & 0.000545 \tabularnewline
M8 & 0.542381338318757 & 0.117994 & 4.5967 & 4.6e-05 & 2.3e-05 \tabularnewline
M9 & 0.274628508917084 & 0.142074 & 1.933 & 0.060713 & 0.030357 \tabularnewline
M10 & 0.305463665399061 & 0.134773 & 2.2665 & 0.029197 & 0.014598 \tabularnewline
M11 & 0.284067963059267 & 0.130173 & 2.1822 & 0.035343 & 0.017672 \tabularnewline
t & 0.00685825937152281 & 0.002036 & 3.3693 & 0.001739 & 0.00087 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57630&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]0.550379527945818[/C][C]0.39863[/C][C]1.3807[/C][C]0.175445[/C][C]0.087723[/C][/ROW]
[ROW][C]X[/C][C]0.03191199081617[/C][C]0.011769[/C][C]2.7115[/C][C]0.010001[/C][C]0.005001[/C][/ROW]
[ROW][C]Y1[/C][C]1.29906637784746[/C][C]0.154282[/C][C]8.4201[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Y2[/C][C]-0.702880030402592[/C][C]0.267159[/C][C]-2.6309[/C][C]0.012232[/C][C]0.006116[/C][/ROW]
[ROW][C]Y3[/C][C]-0.105447463900118[/C][C]0.26533[/C][C]-0.3974[/C][C]0.693281[/C][C]0.346641[/C][/ROW]
[ROW][C]Y4[/C][C]0.276468222179575[/C][C]0.138013[/C][C]2.0032[/C][C]0.052326[/C][C]0.026163[/C][/ROW]
[ROW][C]M1[/C][C]0.85590625548584[/C][C]0.128063[/C][C]6.6835[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M2[/C][C]0.494902925375232[/C][C]0.150247[/C][C]3.2939[/C][C]0.002144[/C][C]0.001072[/C][/ROW]
[ROW][C]M3[/C][C]0.345194798134066[/C][C]0.137688[/C][C]2.5071[/C][C]0.016568[/C][C]0.008284[/C][/ROW]
[ROW][C]M4[/C][C]0.563227115239073[/C][C]0.103924[/C][C]5.4196[/C][C]4e-06[/C][C]2e-06[/C][/ROW]
[ROW][C]M5[/C][C]0.500826415002446[/C][C]0.102889[/C][C]4.8676[/C][C]2e-05[/C][C]1e-05[/C][/ROW]
[ROW][C]M6[/C][C]0.255445622080153[/C][C]0.10505[/C][C]2.4316[/C][C]0.019854[/C][C]0.009927[/C][/ROW]
[ROW][C]M7[/C][C]0.401570798521914[/C][C]0.11359[/C][C]3.5353[/C][C]0.00109[/C][C]0.000545[/C][/ROW]
[ROW][C]M8[/C][C]0.542381338318757[/C][C]0.117994[/C][C]4.5967[/C][C]4.6e-05[/C][C]2.3e-05[/C][/ROW]
[ROW][C]M9[/C][C]0.274628508917084[/C][C]0.142074[/C][C]1.933[/C][C]0.060713[/C][C]0.030357[/C][/ROW]
[ROW][C]M10[/C][C]0.305463665399061[/C][C]0.134773[/C][C]2.2665[/C][C]0.029197[/C][C]0.014598[/C][/ROW]
[ROW][C]M11[/C][C]0.284067963059267[/C][C]0.130173[/C][C]2.1822[/C][C]0.035343[/C][C]0.017672[/C][/ROW]
[ROW][C]t[/C][C]0.00685825937152281[/C][C]0.002036[/C][C]3.3693[/C][C]0.001739[/C][C]0.00087[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57630&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57630&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)0.5503795279458180.398631.38070.1754450.087723
X0.031911990816170.0117692.71150.0100010.005001
Y11.299066377847460.1542828.420100
Y2-0.7028800304025920.267159-2.63090.0122320.006116
Y3-0.1054474639001180.26533-0.39740.6932810.346641
Y40.2764682221795750.1380132.00320.0523260.026163
M10.855906255485840.1280636.683500
M20.4949029253752320.1502473.29390.0021440.001072
M30.3451947981340660.1376882.50710.0165680.008284
M40.5632271152390730.1039245.41964e-062e-06
M50.5008264150024460.1028894.86762e-051e-05
M60.2554456220801530.105052.43160.0198540.009927
M70.4015707985219140.113593.53530.001090.000545
M80.5423813383187570.1179944.59674.6e-052.3e-05
M90.2746285089170840.1420741.9330.0607130.030357
M100.3054636653990610.1347732.26650.0291970.014598
M110.2840679630592670.1301732.18220.0353430.017672
t0.006858259371522810.0020363.36930.0017390.00087






Multiple Linear Regression - Regression Statistics
Multiple R0.982609821560171
R-squared0.965522061426511
Adjusted R-squared0.95009772048574
F-TEST (value)62.5972976825443
F-TEST (DF numerator)17
F-TEST (DF denominator)38
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.150754515256455
Sum Squared Residuals0.863623107067927

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.982609821560171 \tabularnewline
R-squared & 0.965522061426511 \tabularnewline
Adjusted R-squared & 0.95009772048574 \tabularnewline
F-TEST (value) & 62.5972976825443 \tabularnewline
F-TEST (DF numerator) & 17 \tabularnewline
F-TEST (DF denominator) & 38 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.150754515256455 \tabularnewline
Sum Squared Residuals & 0.863623107067927 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57630&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.982609821560171[/C][/ROW]
[ROW][C]R-squared[/C][C]0.965522061426511[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.95009772048574[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]62.5972976825443[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]17[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]38[/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]0.150754515256455[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]0.863623107067927[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57630&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57630&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 R0.982609821560171
R-squared0.965522061426511
Adjusted R-squared0.95009772048574
F-TEST (value)62.5972976825443
F-TEST (DF numerator)17
F-TEST (DF denominator)38
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.150754515256455
Sum Squared Residuals0.863623107067927






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
17.37.39169307217292-0.0916930721729179
27.77.448393699928860.251606300071140
387.958654753675270.0413452463247315
488.10913022220931-0.109130222209308
57.77.77289796444542-0.0728979644454228
66.97.1470197892834-0.247019789283400
76.66.554554598593360.0454454014066356
86.96.9064417478996-0.00644174789959902
97.57.38157896623860.118421033761397
107.97.798307861106180.101692138893823
117.77.76709424521143-0.0670942452114273
126.56.6398977367004-0.139897736700404
136.16.20806055196903-0.108060551969030
146.46.309421748225920.0905782517740782
156.86.98527589607459-0.185275896074591
167.17.22934613351388-0.129346133513884
177.37.140150065800120.159849934199884
187.27.06792705775070.132072942249306
1977.02938089940051-0.0293808994005084
2077.04937539991349-0.0493753999134895
2177.11616079189123-0.116160791891231
227.37.14729687830680.152703121693203
237.57.467185704256850.0328142957431514
247.27.24530766518105-0.0453076651810543
257.77.546142021433630.153857978566375
2688.1142451226129-0.114245122612897
277.98.0136318603801-0.113631860380104
2887.762087591347180.237912408652821
2988.01333966322683-0.0133396632268325
307.97.85545592314880.0445440768512077
317.97.840341152569360.0596588474306385
3288.08594477699594-0.0859447769959444
338.18.18569432777213-0.0856943277721255
348.18.25535955615215-0.155359556152155
358.28.159989363753610.0400106362463874
3687.73619805816980.263801941830205
378.38.296508116635370.00349188336463572
388.58.462114218941030.0378857810589745
398.68.413758733436460.186241266563541
408.78.541052058011270.158947941988733
418.78.60697922576450.0930207742355004
428.58.416315166096560.083684833903437
438.48.32658740216830.0734125978316991
448.58.5125723918504-0.012572391850397
458.78.616565914098040.083434085901959
468.78.79903570443487-0.0990357044348705
478.68.60573068677811-0.00573068677811138
487.97.97859653994875-0.0785965399487474
498.18.057596237789060.0424037622109373
508.28.4658252102913-0.265825210291295
518.58.428678756433580.0713212435664224
528.68.75838399491836-0.158383994918362
538.58.66663308076313-0.166633080763129
548.38.31328206372055-0.013282063720551
558.28.34913594726846-0.149135947268465
568.78.545665683340570.15433431665943

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 7.3 & 7.39169307217292 & -0.0916930721729179 \tabularnewline
2 & 7.7 & 7.44839369992886 & 0.251606300071140 \tabularnewline
3 & 8 & 7.95865475367527 & 0.0413452463247315 \tabularnewline
4 & 8 & 8.10913022220931 & -0.109130222209308 \tabularnewline
5 & 7.7 & 7.77289796444542 & -0.0728979644454228 \tabularnewline
6 & 6.9 & 7.1470197892834 & -0.247019789283400 \tabularnewline
7 & 6.6 & 6.55455459859336 & 0.0454454014066356 \tabularnewline
8 & 6.9 & 6.9064417478996 & -0.00644174789959902 \tabularnewline
9 & 7.5 & 7.3815789662386 & 0.118421033761397 \tabularnewline
10 & 7.9 & 7.79830786110618 & 0.101692138893823 \tabularnewline
11 & 7.7 & 7.76709424521143 & -0.0670942452114273 \tabularnewline
12 & 6.5 & 6.6398977367004 & -0.139897736700404 \tabularnewline
13 & 6.1 & 6.20806055196903 & -0.108060551969030 \tabularnewline
14 & 6.4 & 6.30942174822592 & 0.0905782517740782 \tabularnewline
15 & 6.8 & 6.98527589607459 & -0.185275896074591 \tabularnewline
16 & 7.1 & 7.22934613351388 & -0.129346133513884 \tabularnewline
17 & 7.3 & 7.14015006580012 & 0.159849934199884 \tabularnewline
18 & 7.2 & 7.0679270577507 & 0.132072942249306 \tabularnewline
19 & 7 & 7.02938089940051 & -0.0293808994005084 \tabularnewline
20 & 7 & 7.04937539991349 & -0.0493753999134895 \tabularnewline
21 & 7 & 7.11616079189123 & -0.116160791891231 \tabularnewline
22 & 7.3 & 7.1472968783068 & 0.152703121693203 \tabularnewline
23 & 7.5 & 7.46718570425685 & 0.0328142957431514 \tabularnewline
24 & 7.2 & 7.24530766518105 & -0.0453076651810543 \tabularnewline
25 & 7.7 & 7.54614202143363 & 0.153857978566375 \tabularnewline
26 & 8 & 8.1142451226129 & -0.114245122612897 \tabularnewline
27 & 7.9 & 8.0136318603801 & -0.113631860380104 \tabularnewline
28 & 8 & 7.76208759134718 & 0.237912408652821 \tabularnewline
29 & 8 & 8.01333966322683 & -0.0133396632268325 \tabularnewline
30 & 7.9 & 7.8554559231488 & 0.0445440768512077 \tabularnewline
31 & 7.9 & 7.84034115256936 & 0.0596588474306385 \tabularnewline
32 & 8 & 8.08594477699594 & -0.0859447769959444 \tabularnewline
33 & 8.1 & 8.18569432777213 & -0.0856943277721255 \tabularnewline
34 & 8.1 & 8.25535955615215 & -0.155359556152155 \tabularnewline
35 & 8.2 & 8.15998936375361 & 0.0400106362463874 \tabularnewline
36 & 8 & 7.7361980581698 & 0.263801941830205 \tabularnewline
37 & 8.3 & 8.29650811663537 & 0.00349188336463572 \tabularnewline
38 & 8.5 & 8.46211421894103 & 0.0378857810589745 \tabularnewline
39 & 8.6 & 8.41375873343646 & 0.186241266563541 \tabularnewline
40 & 8.7 & 8.54105205801127 & 0.158947941988733 \tabularnewline
41 & 8.7 & 8.6069792257645 & 0.0930207742355004 \tabularnewline
42 & 8.5 & 8.41631516609656 & 0.083684833903437 \tabularnewline
43 & 8.4 & 8.3265874021683 & 0.0734125978316991 \tabularnewline
44 & 8.5 & 8.5125723918504 & -0.012572391850397 \tabularnewline
45 & 8.7 & 8.61656591409804 & 0.083434085901959 \tabularnewline
46 & 8.7 & 8.79903570443487 & -0.0990357044348705 \tabularnewline
47 & 8.6 & 8.60573068677811 & -0.00573068677811138 \tabularnewline
48 & 7.9 & 7.97859653994875 & -0.0785965399487474 \tabularnewline
49 & 8.1 & 8.05759623778906 & 0.0424037622109373 \tabularnewline
50 & 8.2 & 8.4658252102913 & -0.265825210291295 \tabularnewline
51 & 8.5 & 8.42867875643358 & 0.0713212435664224 \tabularnewline
52 & 8.6 & 8.75838399491836 & -0.158383994918362 \tabularnewline
53 & 8.5 & 8.66663308076313 & -0.166633080763129 \tabularnewline
54 & 8.3 & 8.31328206372055 & -0.013282063720551 \tabularnewline
55 & 8.2 & 8.34913594726846 & -0.149135947268465 \tabularnewline
56 & 8.7 & 8.54566568334057 & 0.15433431665943 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57630&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]7.3[/C][C]7.39169307217292[/C][C]-0.0916930721729179[/C][/ROW]
[ROW][C]2[/C][C]7.7[/C][C]7.44839369992886[/C][C]0.251606300071140[/C][/ROW]
[ROW][C]3[/C][C]8[/C][C]7.95865475367527[/C][C]0.0413452463247315[/C][/ROW]
[ROW][C]4[/C][C]8[/C][C]8.10913022220931[/C][C]-0.109130222209308[/C][/ROW]
[ROW][C]5[/C][C]7.7[/C][C]7.77289796444542[/C][C]-0.0728979644454228[/C][/ROW]
[ROW][C]6[/C][C]6.9[/C][C]7.1470197892834[/C][C]-0.247019789283400[/C][/ROW]
[ROW][C]7[/C][C]6.6[/C][C]6.55455459859336[/C][C]0.0454454014066356[/C][/ROW]
[ROW][C]8[/C][C]6.9[/C][C]6.9064417478996[/C][C]-0.00644174789959902[/C][/ROW]
[ROW][C]9[/C][C]7.5[/C][C]7.3815789662386[/C][C]0.118421033761397[/C][/ROW]
[ROW][C]10[/C][C]7.9[/C][C]7.79830786110618[/C][C]0.101692138893823[/C][/ROW]
[ROW][C]11[/C][C]7.7[/C][C]7.76709424521143[/C][C]-0.0670942452114273[/C][/ROW]
[ROW][C]12[/C][C]6.5[/C][C]6.6398977367004[/C][C]-0.139897736700404[/C][/ROW]
[ROW][C]13[/C][C]6.1[/C][C]6.20806055196903[/C][C]-0.108060551969030[/C][/ROW]
[ROW][C]14[/C][C]6.4[/C][C]6.30942174822592[/C][C]0.0905782517740782[/C][/ROW]
[ROW][C]15[/C][C]6.8[/C][C]6.98527589607459[/C][C]-0.185275896074591[/C][/ROW]
[ROW][C]16[/C][C]7.1[/C][C]7.22934613351388[/C][C]-0.129346133513884[/C][/ROW]
[ROW][C]17[/C][C]7.3[/C][C]7.14015006580012[/C][C]0.159849934199884[/C][/ROW]
[ROW][C]18[/C][C]7.2[/C][C]7.0679270577507[/C][C]0.132072942249306[/C][/ROW]
[ROW][C]19[/C][C]7[/C][C]7.02938089940051[/C][C]-0.0293808994005084[/C][/ROW]
[ROW][C]20[/C][C]7[/C][C]7.04937539991349[/C][C]-0.0493753999134895[/C][/ROW]
[ROW][C]21[/C][C]7[/C][C]7.11616079189123[/C][C]-0.116160791891231[/C][/ROW]
[ROW][C]22[/C][C]7.3[/C][C]7.1472968783068[/C][C]0.152703121693203[/C][/ROW]
[ROW][C]23[/C][C]7.5[/C][C]7.46718570425685[/C][C]0.0328142957431514[/C][/ROW]
[ROW][C]24[/C][C]7.2[/C][C]7.24530766518105[/C][C]-0.0453076651810543[/C][/ROW]
[ROW][C]25[/C][C]7.7[/C][C]7.54614202143363[/C][C]0.153857978566375[/C][/ROW]
[ROW][C]26[/C][C]8[/C][C]8.1142451226129[/C][C]-0.114245122612897[/C][/ROW]
[ROW][C]27[/C][C]7.9[/C][C]8.0136318603801[/C][C]-0.113631860380104[/C][/ROW]
[ROW][C]28[/C][C]8[/C][C]7.76208759134718[/C][C]0.237912408652821[/C][/ROW]
[ROW][C]29[/C][C]8[/C][C]8.01333966322683[/C][C]-0.0133396632268325[/C][/ROW]
[ROW][C]30[/C][C]7.9[/C][C]7.8554559231488[/C][C]0.0445440768512077[/C][/ROW]
[ROW][C]31[/C][C]7.9[/C][C]7.84034115256936[/C][C]0.0596588474306385[/C][/ROW]
[ROW][C]32[/C][C]8[/C][C]8.08594477699594[/C][C]-0.0859447769959444[/C][/ROW]
[ROW][C]33[/C][C]8.1[/C][C]8.18569432777213[/C][C]-0.0856943277721255[/C][/ROW]
[ROW][C]34[/C][C]8.1[/C][C]8.25535955615215[/C][C]-0.155359556152155[/C][/ROW]
[ROW][C]35[/C][C]8.2[/C][C]8.15998936375361[/C][C]0.0400106362463874[/C][/ROW]
[ROW][C]36[/C][C]8[/C][C]7.7361980581698[/C][C]0.263801941830205[/C][/ROW]
[ROW][C]37[/C][C]8.3[/C][C]8.29650811663537[/C][C]0.00349188336463572[/C][/ROW]
[ROW][C]38[/C][C]8.5[/C][C]8.46211421894103[/C][C]0.0378857810589745[/C][/ROW]
[ROW][C]39[/C][C]8.6[/C][C]8.41375873343646[/C][C]0.186241266563541[/C][/ROW]
[ROW][C]40[/C][C]8.7[/C][C]8.54105205801127[/C][C]0.158947941988733[/C][/ROW]
[ROW][C]41[/C][C]8.7[/C][C]8.6069792257645[/C][C]0.0930207742355004[/C][/ROW]
[ROW][C]42[/C][C]8.5[/C][C]8.41631516609656[/C][C]0.083684833903437[/C][/ROW]
[ROW][C]43[/C][C]8.4[/C][C]8.3265874021683[/C][C]0.0734125978316991[/C][/ROW]
[ROW][C]44[/C][C]8.5[/C][C]8.5125723918504[/C][C]-0.012572391850397[/C][/ROW]
[ROW][C]45[/C][C]8.7[/C][C]8.61656591409804[/C][C]0.083434085901959[/C][/ROW]
[ROW][C]46[/C][C]8.7[/C][C]8.79903570443487[/C][C]-0.0990357044348705[/C][/ROW]
[ROW][C]47[/C][C]8.6[/C][C]8.60573068677811[/C][C]-0.00573068677811138[/C][/ROW]
[ROW][C]48[/C][C]7.9[/C][C]7.97859653994875[/C][C]-0.0785965399487474[/C][/ROW]
[ROW][C]49[/C][C]8.1[/C][C]8.05759623778906[/C][C]0.0424037622109373[/C][/ROW]
[ROW][C]50[/C][C]8.2[/C][C]8.4658252102913[/C][C]-0.265825210291295[/C][/ROW]
[ROW][C]51[/C][C]8.5[/C][C]8.42867875643358[/C][C]0.0713212435664224[/C][/ROW]
[ROW][C]52[/C][C]8.6[/C][C]8.75838399491836[/C][C]-0.158383994918362[/C][/ROW]
[ROW][C]53[/C][C]8.5[/C][C]8.66663308076313[/C][C]-0.166633080763129[/C][/ROW]
[ROW][C]54[/C][C]8.3[/C][C]8.31328206372055[/C][C]-0.013282063720551[/C][/ROW]
[ROW][C]55[/C][C]8.2[/C][C]8.34913594726846[/C][C]-0.149135947268465[/C][/ROW]
[ROW][C]56[/C][C]8.7[/C][C]8.54566568334057[/C][C]0.15433431665943[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57630&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57630&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
17.37.39169307217292-0.0916930721729179
27.77.448393699928860.251606300071140
387.958654753675270.0413452463247315
488.10913022220931-0.109130222209308
57.77.77289796444542-0.0728979644454228
66.97.1470197892834-0.247019789283400
76.66.554554598593360.0454454014066356
86.96.9064417478996-0.00644174789959902
97.57.38157896623860.118421033761397
107.97.798307861106180.101692138893823
117.77.76709424521143-0.0670942452114273
126.56.6398977367004-0.139897736700404
136.16.20806055196903-0.108060551969030
146.46.309421748225920.0905782517740782
156.86.98527589607459-0.185275896074591
167.17.22934613351388-0.129346133513884
177.37.140150065800120.159849934199884
187.27.06792705775070.132072942249306
1977.02938089940051-0.0293808994005084
2077.04937539991349-0.0493753999134895
2177.11616079189123-0.116160791891231
227.37.14729687830680.152703121693203
237.57.467185704256850.0328142957431514
247.27.24530766518105-0.0453076651810543
257.77.546142021433630.153857978566375
2688.1142451226129-0.114245122612897
277.98.0136318603801-0.113631860380104
2887.762087591347180.237912408652821
2988.01333966322683-0.0133396632268325
307.97.85545592314880.0445440768512077
317.97.840341152569360.0596588474306385
3288.08594477699594-0.0859447769959444
338.18.18569432777213-0.0856943277721255
348.18.25535955615215-0.155359556152155
358.28.159989363753610.0400106362463874
3687.73619805816980.263801941830205
378.38.296508116635370.00349188336463572
388.58.462114218941030.0378857810589745
398.68.413758733436460.186241266563541
408.78.541052058011270.158947941988733
418.78.60697922576450.0930207742355004
428.58.416315166096560.083684833903437
438.48.32658740216830.0734125978316991
448.58.5125723918504-0.012572391850397
458.78.616565914098040.083434085901959
468.78.79903570443487-0.0990357044348705
478.68.60573068677811-0.00573068677811138
487.97.97859653994875-0.0785965399487474
498.18.057596237789060.0424037622109373
508.28.4658252102913-0.265825210291295
518.58.428678756433580.0713212435664224
528.68.75838399491836-0.158383994918362
538.58.66663308076313-0.166633080763129
548.38.31328206372055-0.013282063720551
558.28.34913594726846-0.149135947268465
568.78.545665683340570.15433431665943






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
210.1038321795906660.2076643591813310.896167820409334
220.4311217484535150.862243496907030.568878251546485
230.5122365355599410.9755269288801180.487763464440059
240.5714273687923630.8571452624152740.428572631207637
250.4896937651762080.9793875303524170.510306234823792
260.5204650410853790.9590699178292410.479534958914621
270.6036198401069810.7927603197860390.396380159893019
280.742460141476640.5150797170467190.257539858523360
290.6334957228560390.7330085542879230.366504277143961
300.5469275974770490.9061448050459010.453072402522951
310.4440257330222780.8880514660445570.555974266977722
320.4635217098241910.9270434196483820.536478290175809
330.4495293895542590.8990587791085170.550470610445741
340.4855633298808630.9711266597617270.514436670119137
350.5491250122222080.9017499755555850.450874987777792

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
21 & 0.103832179590666 & 0.207664359181331 & 0.896167820409334 \tabularnewline
22 & 0.431121748453515 & 0.86224349690703 & 0.568878251546485 \tabularnewline
23 & 0.512236535559941 & 0.975526928880118 & 0.487763464440059 \tabularnewline
24 & 0.571427368792363 & 0.857145262415274 & 0.428572631207637 \tabularnewline
25 & 0.489693765176208 & 0.979387530352417 & 0.510306234823792 \tabularnewline
26 & 0.520465041085379 & 0.959069917829241 & 0.479534958914621 \tabularnewline
27 & 0.603619840106981 & 0.792760319786039 & 0.396380159893019 \tabularnewline
28 & 0.74246014147664 & 0.515079717046719 & 0.257539858523360 \tabularnewline
29 & 0.633495722856039 & 0.733008554287923 & 0.366504277143961 \tabularnewline
30 & 0.546927597477049 & 0.906144805045901 & 0.453072402522951 \tabularnewline
31 & 0.444025733022278 & 0.888051466044557 & 0.555974266977722 \tabularnewline
32 & 0.463521709824191 & 0.927043419648382 & 0.536478290175809 \tabularnewline
33 & 0.449529389554259 & 0.899058779108517 & 0.550470610445741 \tabularnewline
34 & 0.485563329880863 & 0.971126659761727 & 0.514436670119137 \tabularnewline
35 & 0.549125012222208 & 0.901749975555585 & 0.450874987777792 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57630&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]21[/C][C]0.103832179590666[/C][C]0.207664359181331[/C][C]0.896167820409334[/C][/ROW]
[ROW][C]22[/C][C]0.431121748453515[/C][C]0.86224349690703[/C][C]0.568878251546485[/C][/ROW]
[ROW][C]23[/C][C]0.512236535559941[/C][C]0.975526928880118[/C][C]0.487763464440059[/C][/ROW]
[ROW][C]24[/C][C]0.571427368792363[/C][C]0.857145262415274[/C][C]0.428572631207637[/C][/ROW]
[ROW][C]25[/C][C]0.489693765176208[/C][C]0.979387530352417[/C][C]0.510306234823792[/C][/ROW]
[ROW][C]26[/C][C]0.520465041085379[/C][C]0.959069917829241[/C][C]0.479534958914621[/C][/ROW]
[ROW][C]27[/C][C]0.603619840106981[/C][C]0.792760319786039[/C][C]0.396380159893019[/C][/ROW]
[ROW][C]28[/C][C]0.74246014147664[/C][C]0.515079717046719[/C][C]0.257539858523360[/C][/ROW]
[ROW][C]29[/C][C]0.633495722856039[/C][C]0.733008554287923[/C][C]0.366504277143961[/C][/ROW]
[ROW][C]30[/C][C]0.546927597477049[/C][C]0.906144805045901[/C][C]0.453072402522951[/C][/ROW]
[ROW][C]31[/C][C]0.444025733022278[/C][C]0.888051466044557[/C][C]0.555974266977722[/C][/ROW]
[ROW][C]32[/C][C]0.463521709824191[/C][C]0.927043419648382[/C][C]0.536478290175809[/C][/ROW]
[ROW][C]33[/C][C]0.449529389554259[/C][C]0.899058779108517[/C][C]0.550470610445741[/C][/ROW]
[ROW][C]34[/C][C]0.485563329880863[/C][C]0.971126659761727[/C][C]0.514436670119137[/C][/ROW]
[ROW][C]35[/C][C]0.549125012222208[/C][C]0.901749975555585[/C][C]0.450874987777792[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57630&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57630&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
210.1038321795906660.2076643591813310.896167820409334
220.4311217484535150.862243496907030.568878251546485
230.5122365355599410.9755269288801180.487763464440059
240.5714273687923630.8571452624152740.428572631207637
250.4896937651762080.9793875303524170.510306234823792
260.5204650410853790.9590699178292410.479534958914621
270.6036198401069810.7927603197860390.396380159893019
280.742460141476640.5150797170467190.257539858523360
290.6334957228560390.7330085542879230.366504277143961
300.5469275974770490.9061448050459010.453072402522951
310.4440257330222780.8880514660445570.555974266977722
320.4635217098241910.9270434196483820.536478290175809
330.4495293895542590.8990587791085170.550470610445741
340.4855633298808630.9711266597617270.514436670119137
350.5491250122222080.9017499755555850.450874987777792






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=57630&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=57630&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57630&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 = 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')
}