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Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationThu, 24 Nov 2011 09:44:56 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Nov/24/t1322146583k1xzude7av74h95.htm/, Retrieved Thu, 28 Mar 2024 14:28:51 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=146899, Retrieved Thu, 28 Mar 2024 14:28:51 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact74
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [] [2011-11-24 14:44:56] [c7041fab4904771a5085f5eb0f28763f] [Current]
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Dataseries X:
8.2	3.7	5.1	6.8	4.9	8.5	4.3	5
5.7	4.9	4.3	5.3	7.9	8.2	4	3.9
8.9	4.5	4	4.5	7.4	9.2	4.6	5.4
4.8	3	4.1	8.8	4.7	6.4	3.6	4.3
7.1	3.5	3.5	6.8	6	9	4.5	4.5
4.7	3.3	4.7	8.5	4.3	6.5	9.5	3.6
5.7	2	4.2	8.9	2.3	6.9	2.5	2.1
6.3	3.7	6.3	6.9	3.6	6.2	4.8	4.3
7	4.6	6.1	9.3	5.9	5.8	4.4	4.4
5.5	4.4	5.8	8.4	5.7	6.4	5.3	4.1
7.4	4	3.7	6.8	6.8	8.7	7.5	3.8
6	3.2	4.9	8.2	3.9	6.1	5.9	3
8.4	4.4	4.5	7.6	6.9	9.5	5.3	5.1
7.6	4.2	2.6	7.1	8.4	9.2	3	4.5
8	5.2	6.2	8.8	6.8	6.3	5.4	4.8
6.6	4.5	3.9	4.9	7.8	8.7	5	4.3
6.4	4.5	6.2	6.2	5.5	5.7	5.4	4.2
7.4	4.8	5.8	8.4	6.4	5.9	6.3	5.7
6.8	4.5	6	9.1	5.7	5.6	6.1	5
7.6	4.4	6.1	8.4	5.3	9.1	6.7	4.5
5.4	3.3	4.9	8.4	4.3	5.2	4.6	3.3
9.9	4.3	3	4.5	8.3	9.6	6.5	4.3
7	4	3.4	3.7	7.3	8.6	6	4.8
8.6	4.5	4.4	6.2	7.2	9.3	4.2	6.7
4.8	4	5.3	8	5.3	6	3.9	4.7
6.6	3.9	6.6	7.1	3.9	6.4	3.7	5.6
6.3	4.4	3.8	4.8	7.6	8.5	6.7	5.3
5.4	3.7	5.2	9	4.8	7	5.9	4.3
6.3	4.4	3.8	4.8	7.6	8.5	6	5.7
5.4	3.5	5.5	7.7	4.2	7.6	7.2	4.7
6.1	3.3	2.7	5.2	6.4	6.9	3.3	3.7
6.4	3	3.5	6.6	5.1	8.1	6.1	3
5.4	3.4	4.5	9.2	5.1	6.7	4.2	3.5
7.3	4.2	6.6	8.7	4.6	8	3.8	4.7
6.3	3.5	4.3	8.4	5.4	6.7	6	2.5
5.4	2.5	2.9	5.6	6.1	8.7	6.5	3.1
7.1	3.5	3.5	6.8	6	9	4.3	3.9
8.7	4.9	4.6	7.7	7.7	9.6	4.4	5.2
7.6	4.5	6.9	9	4.9	8.2	7.1	4.7
6	3.2	4.9	8.2	3.9	6.1	6.8	4.5
7	3.9	5.8	9.1	4.6	8.3	1.7	4.6
7.6	4.1	4.5	8.5	6.5	9.4	6.2	4.1
8.9	4.3	4.6	7.4	6.6	9.3	4.1	4.6
7.6	4.5	6.3	5.9	5.4	5.1	5.2	4.9
5.5	4.7	4.2	5.2	7.7	8	3.9	4.3
7.4	4.8	5.8	8.4	6.4	5.9	5.1	5.2
7.1	3.5	4	3.8	5.4	10	3.7	5
7.6	5.2	7.3	8.2	5.7	5.7	4.8	6.5
8.7	3.9	3.4	6.8	7	9.9	7.2	4.5
8.6	4.3	4.2	4.7	6.9	7.9	3.6	4.1
5.4	2.8	3.6	7.2	4.7	6.7	5.3	4
5.7	4.9	4.3	5.3	7.9	8.2	5	4.5
8.7	4.6	4.6	6.3	7.3	9.4	9.2	4.7
6.1	3.3	2.7	5.2	6.4	6.9	4.4	3.2
7.3	4.2	6.6	8.7	4.6	8	4.2	4.9
7.7	3.4	3.2	7.4	6.4	9.3	5.9	4.1
9	5.5	6.5	9.6	7.2	7.4	7.4	5.7
8.2	4	3.9	4.4	6.6	7.6	6.4	4.6
7.1	3.5	4	3.8	5.4	10	4.5	3.7
7.9	4	4.9	5.4	5.8	9.9	7	5.6
6.6	4.5	3.9	4.9	7.8	8.7	4.5	5.4
8	3.6	5	6.7	4.7	8.4	4.2	2.7
6.3	2.9	3.7	5.8	4.7	8.8	7.2	4.4
6	2.6	3.1	6.2	4.7	7.7	4.7	3.3
5.4	2.8	3.6	7.2	4.7	6.6	3.9	3.5
7.6	5.2	7.3	8.2	5.7	5.7	5	4.7
6.4	4.5	6.2	6.2	5.5	5.7	6.4	5
6.1	4.3	5.9	6	5.3	5.5	2.5	4.5
5.2	3.4	5.4	7.6	4.1	7.5	5.2	4
6.6	3.9	6.6	7.1	3.9	6.4	5.5	4.7
7.6	4.4	6.1	8.4	5.3	9.1	5.7	5.4
5.8	3.1	2.6	5	6.3	6.7	2.5	2.9
7.9	4.6	5.6	8.7	6.3	6.5	6.3	4.6
8.6	3.9	3.4	6.8	7	9.9	4.6	4.1
8.2	3.7	5.1	6.8	4.9	8.5	3.6	4.4
7.1	3.8	4.3	4.9	5.9	9.9	7.6	3.1
6.4	3.9	5.8	7.4	4.6	7.6	6.6	4.5
7.6	4.1	4.5	8.5	6.5	9.4	2.4	4.3
8.9	4.6	4.1	4.6	7.5	9.3	3.1	5.2
5.7	2.7	3.1	7.8	5	7.1	3.5	2.6
7.1	3.8	4.3	4.9	5.9	9.9	6.9	3.2
7.4	4	3.7	6.8	6.8	8.7	5.1	4.3
6.6	3	3	6.3	5.6	8.6	4	2.7
5	1.6	3.7	8.4	2.9	6.4	6.5	2
8.2	4.3	3.9	5.9	7.2	7.7	4.1	4.7
5.2	3.4	5.4	7.6	4.1	7.5	2.8	3.4
5.2	3.1	4.8	8.2	4.2	5	7.6	2.4
8.2	4.3	3.9	5.9	7.2	7.7	7.7	5.1
7.3	3.9	4.3	8.3	6.2	9.1	4.1	4.6
8.2	4.9	6.7	6.3	5.7	5.5	4.9	5.5
7.4	3.3	3	7.3	6.3	9.1	4.6	4.4
4.8	2.4	4	9.9	3.3	7.1	3.5	2
7.6	4.2	2.6	7.1	8.4	9.2	6.6	4.4
8.9	4.6	4.1	4.6	7.5	9.3	4.9	4.8
7.7	3.4	3.2	7.4	6.4	9.3	4.8	3.6
7.3	3.6	3.6	6.7	6	8.6	3.6	4.9
6.3	3.7	5.6	7.2	4.4	7.4	6.4	4.2
5.4	2.5	2.9	5.6	6.1	8.7	4.3	3.1
6.4	3.9	4.9	7.9	5.3	7.8	5.7	4.3
6.4	3.5	5.4	9.7	4.2	7.9	5.8	3.4
5.4	3.5	5.5	7.7	4.2	7.6	5.1	3.1
8.7	4.2	4.6	7.3	6.5	9.2	8.6	5.1
6.1	3.7	4.7	7.7	5.2	7.7	5.4	4
8.4	4.4	4.5	7.6	6.9	9.5	4.4	5.6
7.9	4.6	5.6	8.7	6.3	6.5	6.9	5
7	3.9	5.8	9.1	4.6	8.3	5.2	4.2
8.7	4.9	4.6	7.7	7.7	9.6	5.5	4.4
7.9	5.4	7.5	8.4	5.9	5.9	5.3	5.8
7.1	4.2	3.5	3.8	7.4	8.7	5.7	4.6
5.8	3.1	2.6	5	6.3	6.7	6.5	3.8
8.4	4.1	3.4	6.7	7.5	9.7	5.2	3.7
7.1	3.9	2.3	6.7	8.1	8.8	2.7	4
7.6	4.5	6.9	9	4.9	8.2	4.3	4.5
7.3	4.2	5.9	8.2	5.1	8.9	6.7	4.2
8	3.6	5	6.7	4.7	8.4	6.6	4
6.1	3.7	4.7	7.7	5.2	7.7	7.4	5.1
8.7	4.2	4.6	7.3	6.5	9.2	8.9	4.2
5.8	2.9	3.3	8	5.2	7.3	3.7	2.8
6.4	3.1	3.9	6	4.8	9	4.9	3.3
6.4	3	3.5	6.6	5.1	8.1	6.2	2.6
9	5.5	6.5	9.6	7.2	7.4	4.3	5.7
6.4	3.5	5.4	9.7	4.2	7.9	4.6	4.8
6	2.6	3.1	6.2	4.7	7.7	4.3	3.2
8.7	4.6	4.6	6.3	7.3	9.4	5.4	5.8
5	2.5	4.1	10	3.4	7.2	3.6	3.2
7.4	3.1	2.9	5.3	6.1	8.3	7.4	4.1
8.6	4.3	4.2	4.7	6.9	7.9	6.7	4.6
5.8	2.9	3.3	8	5.2	7.3	2.9	3.3
9.8	4.3	3	4.5	8.2	9.6	4.8	4.4
4.8	2.1	2.5	5.2	5.7	8.3	2.8	1.2
7	4	3.4	3.7	7.3	8.6	5.2	5
5.5	4.7	4.2	5.2	7.7	8	6.8	4.6
5	1.6	3.7	8.4	2.9	6.4	7	2.4
6	3.3	4.1	8.2	5.3	6.6	2.9	4.3
8	4.2	3.8	5.8	7.1	7.6	6.2	3.6
7.9	4.4	3.7	7.6	7.8	9.4	6	5.1
4.8	2.1	2.5	5.2	5.7	8.3	4.2	1.8
6.4	3.9	4.9	7.9	5.3	7.8	5.2	4.1
4.8	2.4	4	9.9	3.3	7.1	3.1	2.8
6.4	3.9	5.8	7.4	4.6	7.6	5.3	4.4
6.8	4.5	6	9.1	5.7	5.6	6	4.5
7.9	4	4.9	5.4	5.8	9.9	6.8	4
8.9	4.5	4	4.5	7.4	9.2	6.1	4.2
7.4	4.2	3.4	7.3	7.5	9.1	5.2	4.5
7	3.5	4	3.8	5.4	9.9	8	3.8
7	3.5	4	3.8	5.4	9.9	6.2	4.1
6	3.3	4.1	8.2	5.3	6.6	3.5	4.6
7.4	3.3	3	7.3	6.3	9.1	6.5	3.7
7.6	4.5	6.3	5.9	5.4	5.1	3.9	5.1
4.8	4	5.3	8	5.3	6	3.6	4.3
7.3	4.2	5.9	8.2	5.1	8.9	3.8	5
6.3	3.7	6.3	6.9	3.6	6.2	4.7	4
5	2.5	4.1	10	3.4	7.2	2.9	3
7.1	3.9	2.3	6.7	8.1	8.8	5.6	4.1
6.3	3.4	5.1	8.4	4.1	6.3	4.2	4.4
6.8	3.6	4.1	4.8	5.7	9.7	1.6	4
5.2	3.1	4.8	8.2	4.2	5	4	3.7
6.3	3.7	5.6	7.2	4.4	7.4	5.1	4
6.1	4.3	5.9	6	5.3	5.5	6	4.3
7.3	3.9	4.3	8.3	6.2	9.1	6.1	4.6
5.4	3.4	4.5	9.2	5.1	6.7	5.6	3.7
8	5.2	6.2	8.8	6.8	6.3	6.5	6.4
7.4	3.1	2.9	5.3	6.1	8.3	5.5	3.6
7.3	3	2.8	5.2	6	8.2	5.9	4.7
7.3	3	2.8	5.2	6	8.2	6.2	4
6.4	3.1	3.9	6	4.8	9	5.6	4.3
5.7	2.7	3.1	7.8	5	7.1	7.2	3.6
5.7	2	4.2	8.9	2.3	6.9	3.4	2.7
6.6	3	3	6.3	5.6	8.6	5.1	4
6.3	3.5	4.3	8.4	5.4	6.7	4	3.8
5.4	3.7	5.2	9	4.8	7	5.3	3.3
7.4	3.8	4.7	5.2	5.6	9.7	8.4	4.5
8.6	3.9	3.4	6.8	7	9.9	8	5
7.3	3.6	3.6	6.7	6	8.6	2.8	4.8
6.3	3.4	5.1	8.4	4.1	6.3	2.4	2.8
8.7	3.9	3.4	6.8	7	9.9	5.2	4.3
8.6	4.5	4.4	6.2	7.2	9.3	4.1	4
8.4	4.1	3.4	6.7	7.5	9.7	6.1	4.9
7.4	3.8	4.7	5.2	5.6	9.7	7.1	4.6
9.9	4.3	3	4.5	8.3	9.6	6.2	4
8	4.2	3.8	5.8	7.1	7.6	5.5	4.4
7.9	4.4	3.7	7.6	7.8	9.4	6.5	4.7
9.8	4.3	3	4.5	8.2	9.6	5.6	4.6
8.9	4.3	4.6	7.4	6.6	9.3	5.7	4.4
6.8	3.6	4.1	4.8	5.7	9.7	6.3	4.7
7.4	4.2	3.4	7.3	7.5	9.1	5.1	6
4.7	3.3	4.7	8.5	4.3	6.5	4.8	4.3
5.4	2.8	3.6	7.2	4.7	6.6	4.8	3.2
7	4.6	6.1	9.3	5.9	5.8	3.4	5.9
7.1	4.2	3.5	3.8	7.4	8.7	3.6	5.5
6.3	2.9	3.7	5.8	4.7	8.8	5.8	3.8
5.5	4.4	5.8	8.4	5.7	6.4	5	4
5.4	2.8	3.6	7.2	4.7	6.7	5	2.9
5.4	3.3	4.9	8.4	4.3	5.2	3.6	4.3
4.8	3	4.1	8.8	4.7	6.4	7	3.6
8.2	4	3.9	4.4	6.6	7.6	6.8	4.4
7.9	5.4	7.5	8.4	5.9	5.9	6.6	6
8.6	4.2	3.5	6.8	7.6	9.7	5.2	4.4
8.2	4.9	6.7	6.3	5.7	5.5	5.3	5.9
8.6	4.2	3.5	6.8	7.6	9.7	1.2	4.3




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time53 seconds
R Server'George Udny Yule' @ yule.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 53 seconds \tabularnewline
R Server & 'George Udny Yule' @ yule.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146899&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]53 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ yule.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146899&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146899&T=0

As an alternative you can also use a QR Code:  

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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time53 seconds
R Server'George Udny Yule' @ yule.wessa.net







Multiple Linear Regression - Estimated Regression Equation
Klantentevredenheid[t] = -0.0179745575457906 + 0.996389431447531Leveringssnelheid[t] -0.110000855351255Prijsflexibiliteit[t] -0.0073493263111357Prijszetting[t] -0.0215651387083096Productgamma[t] + 0.375691687523839Productkwaliteit[t] + 0.0328819746324995Productontwikkeling[t] + 0.126630372855167Facturatie[t] + 0.00160419889498828t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Klantentevredenheid[t] =  -0.0179745575457906 +  0.996389431447531Leveringssnelheid[t] -0.110000855351255Prijsflexibiliteit[t] -0.0073493263111357Prijszetting[t] -0.0215651387083096Productgamma[t] +  0.375691687523839Productkwaliteit[t] +  0.0328819746324995Productontwikkeling[t] +  0.126630372855167Facturatie[t] +  0.00160419889498828t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146899&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Klantentevredenheid[t] =  -0.0179745575457906 +  0.996389431447531Leveringssnelheid[t] -0.110000855351255Prijsflexibiliteit[t] -0.0073493263111357Prijszetting[t] -0.0215651387083096Productgamma[t] +  0.375691687523839Productkwaliteit[t] +  0.0328819746324995Productontwikkeling[t] +  0.126630372855167Facturatie[t] +  0.00160419889498828t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146899&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146899&T=1

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Estimated Regression Equation
Klantentevredenheid[t] = -0.0179745575457906 + 0.996389431447531Leveringssnelheid[t] -0.110000855351255Prijsflexibiliteit[t] -0.0073493263111357Prijszetting[t] -0.0215651387083096Productgamma[t] + 0.375691687523839Productkwaliteit[t] + 0.0328819746324995Productontwikkeling[t] + 0.126630372855167Facturatie[t] + 0.00160419889498828t + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-0.01797455754579060.966824-0.01860.9851870.492593
Leveringssnelheid0.9963894314475310.4793342.07870.0389820.019491
Prijsflexibiliteit-0.1100008553512550.251198-0.43790.6619510.330975
Prijszetting-0.00734932631113570.042494-0.1730.8628730.431437
Productgamma-0.02156513870830960.241464-0.08930.9289290.464465
Productkwaliteit0.3756916875238390.0500887.500700
Productontwikkeling0.03288197463249950.0369170.89070.3742070.187103
Facturatie0.1266303728551670.0940441.34650.1797350.089868
t0.001604198894988280.0009441.70010.0907380.045369

\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) & -0.0179745575457906 & 0.966824 & -0.0186 & 0.985187 & 0.492593 \tabularnewline
Leveringssnelheid & 0.996389431447531 & 0.479334 & 2.0787 & 0.038982 & 0.019491 \tabularnewline
Prijsflexibiliteit & -0.110000855351255 & 0.251198 & -0.4379 & 0.661951 & 0.330975 \tabularnewline
Prijszetting & -0.0073493263111357 & 0.042494 & -0.173 & 0.862873 & 0.431437 \tabularnewline
Productgamma & -0.0215651387083096 & 0.241464 & -0.0893 & 0.928929 & 0.464465 \tabularnewline
Productkwaliteit & 0.375691687523839 & 0.050088 & 7.5007 & 0 & 0 \tabularnewline
Productontwikkeling & 0.0328819746324995 & 0.036917 & 0.8907 & 0.374207 & 0.187103 \tabularnewline
Facturatie & 0.126630372855167 & 0.094044 & 1.3465 & 0.179735 & 0.089868 \tabularnewline
t & 0.00160419889498828 & 0.000944 & 1.7001 & 0.090738 & 0.045369 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146899&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]-0.0179745575457906[/C][C]0.966824[/C][C]-0.0186[/C][C]0.985187[/C][C]0.492593[/C][/ROW]
[ROW][C]Leveringssnelheid[/C][C]0.996389431447531[/C][C]0.479334[/C][C]2.0787[/C][C]0.038982[/C][C]0.019491[/C][/ROW]
[ROW][C]Prijsflexibiliteit[/C][C]-0.110000855351255[/C][C]0.251198[/C][C]-0.4379[/C][C]0.661951[/C][C]0.330975[/C][/ROW]
[ROW][C]Prijszetting[/C][C]-0.0073493263111357[/C][C]0.042494[/C][C]-0.173[/C][C]0.862873[/C][C]0.431437[/C][/ROW]
[ROW][C]Productgamma[/C][C]-0.0215651387083096[/C][C]0.241464[/C][C]-0.0893[/C][C]0.928929[/C][C]0.464465[/C][/ROW]
[ROW][C]Productkwaliteit[/C][C]0.375691687523839[/C][C]0.050088[/C][C]7.5007[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Productontwikkeling[/C][C]0.0328819746324995[/C][C]0.036917[/C][C]0.8907[/C][C]0.374207[/C][C]0.187103[/C][/ROW]
[ROW][C]Facturatie[/C][C]0.126630372855167[/C][C]0.094044[/C][C]1.3465[/C][C]0.179735[/C][C]0.089868[/C][/ROW]
[ROW][C]t[/C][C]0.00160419889498828[/C][C]0.000944[/C][C]1.7001[/C][C]0.090738[/C][C]0.045369[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146899&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146899&T=2

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-0.01797455754579060.966824-0.01860.9851870.492593
Leveringssnelheid0.9963894314475310.4793342.07870.0389820.019491
Prijsflexibiliteit-0.1100008553512550.251198-0.43790.6619510.330975
Prijszetting-0.00734932631113570.042494-0.1730.8628730.431437
Productgamma-0.02156513870830960.241464-0.08930.9289290.464465
Productkwaliteit0.3756916875238390.0500887.500700
Productontwikkeling0.03288197463249950.0369170.89070.3742070.187103
Facturatie0.1266303728551670.0940441.34650.1797350.089868
t0.001604198894988280.0009441.70010.0907380.045369







Multiple Linear Regression - Regression Statistics
Multiple R0.8016560470613
R-squared0.642652417789949
Adjusted R-squared0.627684979791622
F-TEST (value)42.93670151577
F-TEST (DF numerator)8
F-TEST (DF denominator)191
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.75730661475857
Sum Squared Residuals109.541041972603

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.8016560470613 \tabularnewline
R-squared & 0.642652417789949 \tabularnewline
Adjusted R-squared & 0.627684979791622 \tabularnewline
F-TEST (value) & 42.93670151577 \tabularnewline
F-TEST (DF numerator) & 8 \tabularnewline
F-TEST (DF denominator) & 191 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.75730661475857 \tabularnewline
Sum Squared Residuals & 109.541041972603 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146899&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.8016560470613[/C][/ROW]
[ROW][C]R-squared[/C][C]0.642652417789949[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.627684979791622[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]42.93670151577[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]8[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]191[/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]0.75730661475857[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]109.541041972603[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146899&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146899&T=3

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Regression Statistics
Multiple R0.8016560470613
R-squared0.642652417789949
Adjusted R-squared0.627684979791622
F-TEST (value)42.93670151577
F-TEST (DF numerator)8
F-TEST (DF denominator)191
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.75730661475857
Sum Squared Residuals109.541041972603







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
18.26.921545275975441.27845472402456
25.77.89128054144265-2.19128054144265
38.98.129357686353160.770642313646843
44.85.42788931407634-0.627889314076345
57.17.012070953509580.0879290464904158
64.75.817776439552-1.117776439552
75.74.349422645432291.35057735456771
86.35.891782234521340.40821776547866
976.59413246303650.4058675369635
105.56.65740613149605-1.15740613149605
117.47.377935737208110.0220642627918897
1265.371963364683960.628036635316037
138.48.076495738915650.323504261084355
147.67.79483636015661-0.194836360156615
1587.44623723559440.553762764405602
166.67.83566010746575-1.23566010746575
176.46.49772272387345-0.0977227238734543
187.47.1013446255780.298655374422006
196.86.584057730464230.215942269535774
207.67.7601283892658-0.160128389265806
215.45.133064203200910.266935796799095
229.98.12461082625281.77538917374719
2377.48192496491775-0.481924964917746
248.68.299900557631240.300099442368757
254.86.22914634024333-1.42914634024333
266.66.282583687561270.317416312438727
276.37.87710679029942-1.57710679029942
285.46.34027892371895-0.940278923718948
296.37.90794995498871-1.60794995498871
305.46.45251611472212-1.05251611472212
316.16.015920577742080.084079422257923
326.46.1026111794080.297388820591999
335.45.84853311921426-0.448533119214255
347.37.057907150893550.242092849106454
356.35.905347732138980.394652267861016
365.45.91384879973059-0.513848799730588
377.16.980850699509610.119149300490391
388.78.606446726749880.0935532732501167
397.67.506819232483550.093180767516447
4065.636420270195630.363579729804366
4176.886272989724140.113727010275859
427.67.69150657480105-0.0915065748010466
438.97.844010190468781.05598980953122
447.66.3910471738251.208952826175
455.57.74925686771959-2.24925686771959
467.47.043488638651080.356511361348917
477.17.47213523582728-0.37213523582728
487.67.376433543168430.223566456831566
498.77.89755027457410.802449725425899
508.67.306889028024571.29311097197543
515.45.50138587690058-0.101385876900583
525.78.08035068453767-2.38035068453767
538.78.3698879473630.330112052636992
546.16.025672137994970.0743278620050267
557.37.130074192112330.169925807887667
567.77.122300880945190.577699119054812
5798.358016786591430.641983213408572
588.27.105157595002861.09484240499714
597.17.35307171756142-0.253071717561422
607.98.01871836103659-0.118718361036586
616.68.03070148056466-1.43070148056466
6286.603703287550351.39629671244965
636.36.52164462282954-0.221644622829537
6465.652633571978410.34736642802159
655.45.376925541764950.0230744582350483
667.67.183950847065420.416049152934578
676.46.7121189415395-0.312118941539501
686.16.28653517875515-0.18653517875515
695.26.23735808150298-1.03735808150298
706.66.29838865770960.301611342290394
717.67.92302789384736-0.323027893847359
725.85.694293095320370.105706904679627
737.97.09837116793420.801628832065796
748.67.801509963762240.798490036237758
758.26.941260388248721.25873961175128
767.17.61577957220396-0.515779572203966
776.46.8419924618197-0.441992461819692
787.67.64963230598816-0.0496323059881598
798.98.299944345712550.600055654287449
805.75.406196590660750.293803409339254
817.17.61344622172167-0.513446221721674
827.47.47623230606186-0.0762323060618624
836.66.311652564444340.288347435555662
8454.051145212856140.948854787143859
858.27.398028386293260.801971613706736
865.26.10973449988669-0.909734499886686
875.24.963830159471890.23616984052811
888.27.57186824079730.628131759202706
897.37.47912114796314-0.179121147963143
908.27.026376787628911.17362321237109
917.47.023804724026750.376195275973247
924.84.97277830498786-0.172778304987857
937.68.02728014425217-0.42728014425217
948.98.332542734333810.567457265666193
957.77.08537927932640.614620720673602
967.37.118208540157540.18179145984246
976.36.58287777321627-0.282877773216275
985.45.94096878702836-0.540968787028361
996.46.97773383283866-0.577733832838664
1006.46.4631647273441-0.0631647273440926
1015.46.29475349296978-0.894753492969775
1028.78.015625332151730.684374667848268
1036.16.72507641933374-0.625076419333737
1048.48.256199247617910.143800752382088
1057.97.22008686649540.679913133504605
10677.05498067997006-0.05498067997006
1078.78.652002324315690.0479976756843097
1087.97.64711836290550.252881637094503
1097.17.80765092593077-0.707650925930775
1105.86.00074788742957-0.200747887429575
1118.47.944034270575940.455965729424063
1127.17.47208409734666-0.372084097346662
1137.67.508134347170650.0918656528293466
1147.37.62630081381802-0.32630081381802
11586.932262052814441.06773794718556
1166.16.95098836437427-0.850988364374267
1178.77.935585572396660.764414427603344
1185.85.745691777887970.0543082221120324
1196.46.64534748274441-0.245347482744412
1206.46.196416730488150.203583269511847
12198.358751394509930.641248605490071
1226.46.63628125547207-0.236281255472069
12365.72146547964420.278534520355798
1248.78.498127975444360.201872024555639
12555.30427809327199-0.304278093271988
1267.46.664212632056270.735787367943727
1278.67.5956616507271.004338349273
1285.85.798743373559430.00125662644056588
1299.88.255180302297651.54481969770235
1304.85.20911616185415-0.409116161854153
13177.65419894044151-0.654198940441514
1325.58.02216900987437-2.52216900987437
13354.196844095168880.803155904831117
13465.978943467682790.0210565323172062
13587.284680487770180.715319512229824
1367.98.3278524759331-0.427852475933095
1374.85.34235854231767-0.542358542317671
1386.46.99853052785592-0.598530527855924
1394.85.13632716148344-0.33632716148344
1406.46.88764738789619-0.487647387896188
1416.86.713166611761960.0868333882380376
1427.97.94107767893086-0.0410776789308582
1438.98.251312046174070.648687953825935
1447.47.96809146723845-0.568091467238453
14577.5812136022773-0.581213602277294
14677.56161935869033-0.561619358690333
14766.05751634995369-0.0575163499536912
1487.47.089078551844220.310921448155783
1497.66.542067565347551.05793243465245
1504.86.36915446058505-1.56915446058505
1517.37.69160274478247-0.391602744782471
1526.36.081509566079850.218490433920149
15355.30085220551788-0.300852205517876
1547.17.64748121465593-0.547481214655935
1556.35.96938013150940.330619868490595
1566.87.41342287922643-0.613422879226433
1575.25.122368458155790.0776315418442123
1586.36.61266126421728-0.312661264217277
1596.16.5222781148418-0.422278114841798
1607.37.65878321877231-0.35878321877231
1615.46.12523141682929-0.725231416829287
16287.920833241821690.079166758178309
1637.46.597777052941510.802222947058493
1647.36.628510871970110.671489128029888
1657.36.551338402256230.748661597743767
1666.46.87039258590678-0.470392585906778
1675.75.79405557352014-0.0940555735201409
1685.74.713270668407750.986729331592253
1696.66.6504033262208-0.0504033262207961
1706.36.220770118409120.0792298815908808
1715.46.42331980806761-1.02331980806761
1727.47.85849683189995-0.458496831899955
1738.68.186091703686230.41390829631377
1747.37.204367436975110.0956325630248906
1756.35.73966795850240.560332041497596
1768.78.010193510401580.68980648959842
1778.68.200152784392250.399847215607752
1788.48.23306582113560.166934178864399
1797.47.83964269442814-0.43964269442814
1809.98.330220547414651.56977945258535
18187.436760552981020.56323944701898
1827.98.36743446327674-0.467434463276738
1839.88.393438696904051.40656130309595
1848.98.09748731950310.802512680496903
1856.87.70313118895246-0.903131188952457
1867.48.22212518264746-0.82212518264746
1874.76.04223241977075-1.34223241977075
1885.45.56584667116121-0.165846671161209
18977.03995184878464-0.0399518487846402
1907.17.98250622526623-0.882506225266227
1916.36.60496909318944-0.304969093189437
1925.56.92684270070865-1.42684270070865
1935.45.58002411745848-0.180024117458484
1945.45.50433901025654-0.104339010256544
1954.85.75744875577099-0.957448755770988
1968.27.31436375779320.885636242206793
1977.97.857964706152740.0420352938472639
1988.68.257988246546220.342011753453782
1998.27.26503940617770.934960593822303
2008.68.117005708520680.48299429147932

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 8.2 & 6.92154527597544 & 1.27845472402456 \tabularnewline
2 & 5.7 & 7.89128054144265 & -2.19128054144265 \tabularnewline
3 & 8.9 & 8.12935768635316 & 0.770642313646843 \tabularnewline
4 & 4.8 & 5.42788931407634 & -0.627889314076345 \tabularnewline
5 & 7.1 & 7.01207095350958 & 0.0879290464904158 \tabularnewline
6 & 4.7 & 5.817776439552 & -1.117776439552 \tabularnewline
7 & 5.7 & 4.34942264543229 & 1.35057735456771 \tabularnewline
8 & 6.3 & 5.89178223452134 & 0.40821776547866 \tabularnewline
9 & 7 & 6.5941324630365 & 0.4058675369635 \tabularnewline
10 & 5.5 & 6.65740613149605 & -1.15740613149605 \tabularnewline
11 & 7.4 & 7.37793573720811 & 0.0220642627918897 \tabularnewline
12 & 6 & 5.37196336468396 & 0.628036635316037 \tabularnewline
13 & 8.4 & 8.07649573891565 & 0.323504261084355 \tabularnewline
14 & 7.6 & 7.79483636015661 & -0.194836360156615 \tabularnewline
15 & 8 & 7.4462372355944 & 0.553762764405602 \tabularnewline
16 & 6.6 & 7.83566010746575 & -1.23566010746575 \tabularnewline
17 & 6.4 & 6.49772272387345 & -0.0977227238734543 \tabularnewline
18 & 7.4 & 7.101344625578 & 0.298655374422006 \tabularnewline
19 & 6.8 & 6.58405773046423 & 0.215942269535774 \tabularnewline
20 & 7.6 & 7.7601283892658 & -0.160128389265806 \tabularnewline
21 & 5.4 & 5.13306420320091 & 0.266935796799095 \tabularnewline
22 & 9.9 & 8.1246108262528 & 1.77538917374719 \tabularnewline
23 & 7 & 7.48192496491775 & -0.481924964917746 \tabularnewline
24 & 8.6 & 8.29990055763124 & 0.300099442368757 \tabularnewline
25 & 4.8 & 6.22914634024333 & -1.42914634024333 \tabularnewline
26 & 6.6 & 6.28258368756127 & 0.317416312438727 \tabularnewline
27 & 6.3 & 7.87710679029942 & -1.57710679029942 \tabularnewline
28 & 5.4 & 6.34027892371895 & -0.940278923718948 \tabularnewline
29 & 6.3 & 7.90794995498871 & -1.60794995498871 \tabularnewline
30 & 5.4 & 6.45251611472212 & -1.05251611472212 \tabularnewline
31 & 6.1 & 6.01592057774208 & 0.084079422257923 \tabularnewline
32 & 6.4 & 6.102611179408 & 0.297388820591999 \tabularnewline
33 & 5.4 & 5.84853311921426 & -0.448533119214255 \tabularnewline
34 & 7.3 & 7.05790715089355 & 0.242092849106454 \tabularnewline
35 & 6.3 & 5.90534773213898 & 0.394652267861016 \tabularnewline
36 & 5.4 & 5.91384879973059 & -0.513848799730588 \tabularnewline
37 & 7.1 & 6.98085069950961 & 0.119149300490391 \tabularnewline
38 & 8.7 & 8.60644672674988 & 0.0935532732501167 \tabularnewline
39 & 7.6 & 7.50681923248355 & 0.093180767516447 \tabularnewline
40 & 6 & 5.63642027019563 & 0.363579729804366 \tabularnewline
41 & 7 & 6.88627298972414 & 0.113727010275859 \tabularnewline
42 & 7.6 & 7.69150657480105 & -0.0915065748010466 \tabularnewline
43 & 8.9 & 7.84401019046878 & 1.05598980953122 \tabularnewline
44 & 7.6 & 6.391047173825 & 1.208952826175 \tabularnewline
45 & 5.5 & 7.74925686771959 & -2.24925686771959 \tabularnewline
46 & 7.4 & 7.04348863865108 & 0.356511361348917 \tabularnewline
47 & 7.1 & 7.47213523582728 & -0.37213523582728 \tabularnewline
48 & 7.6 & 7.37643354316843 & 0.223566456831566 \tabularnewline
49 & 8.7 & 7.8975502745741 & 0.802449725425899 \tabularnewline
50 & 8.6 & 7.30688902802457 & 1.29311097197543 \tabularnewline
51 & 5.4 & 5.50138587690058 & -0.101385876900583 \tabularnewline
52 & 5.7 & 8.08035068453767 & -2.38035068453767 \tabularnewline
53 & 8.7 & 8.369887947363 & 0.330112052636992 \tabularnewline
54 & 6.1 & 6.02567213799497 & 0.0743278620050267 \tabularnewline
55 & 7.3 & 7.13007419211233 & 0.169925807887667 \tabularnewline
56 & 7.7 & 7.12230088094519 & 0.577699119054812 \tabularnewline
57 & 9 & 8.35801678659143 & 0.641983213408572 \tabularnewline
58 & 8.2 & 7.10515759500286 & 1.09484240499714 \tabularnewline
59 & 7.1 & 7.35307171756142 & -0.253071717561422 \tabularnewline
60 & 7.9 & 8.01871836103659 & -0.118718361036586 \tabularnewline
61 & 6.6 & 8.03070148056466 & -1.43070148056466 \tabularnewline
62 & 8 & 6.60370328755035 & 1.39629671244965 \tabularnewline
63 & 6.3 & 6.52164462282954 & -0.221644622829537 \tabularnewline
64 & 6 & 5.65263357197841 & 0.34736642802159 \tabularnewline
65 & 5.4 & 5.37692554176495 & 0.0230744582350483 \tabularnewline
66 & 7.6 & 7.18395084706542 & 0.416049152934578 \tabularnewline
67 & 6.4 & 6.7121189415395 & -0.312118941539501 \tabularnewline
68 & 6.1 & 6.28653517875515 & -0.18653517875515 \tabularnewline
69 & 5.2 & 6.23735808150298 & -1.03735808150298 \tabularnewline
70 & 6.6 & 6.2983886577096 & 0.301611342290394 \tabularnewline
71 & 7.6 & 7.92302789384736 & -0.323027893847359 \tabularnewline
72 & 5.8 & 5.69429309532037 & 0.105706904679627 \tabularnewline
73 & 7.9 & 7.0983711679342 & 0.801628832065796 \tabularnewline
74 & 8.6 & 7.80150996376224 & 0.798490036237758 \tabularnewline
75 & 8.2 & 6.94126038824872 & 1.25873961175128 \tabularnewline
76 & 7.1 & 7.61577957220396 & -0.515779572203966 \tabularnewline
77 & 6.4 & 6.8419924618197 & -0.441992461819692 \tabularnewline
78 & 7.6 & 7.64963230598816 & -0.0496323059881598 \tabularnewline
79 & 8.9 & 8.29994434571255 & 0.600055654287449 \tabularnewline
80 & 5.7 & 5.40619659066075 & 0.293803409339254 \tabularnewline
81 & 7.1 & 7.61344622172167 & -0.513446221721674 \tabularnewline
82 & 7.4 & 7.47623230606186 & -0.0762323060618624 \tabularnewline
83 & 6.6 & 6.31165256444434 & 0.288347435555662 \tabularnewline
84 & 5 & 4.05114521285614 & 0.948854787143859 \tabularnewline
85 & 8.2 & 7.39802838629326 & 0.801971613706736 \tabularnewline
86 & 5.2 & 6.10973449988669 & -0.909734499886686 \tabularnewline
87 & 5.2 & 4.96383015947189 & 0.23616984052811 \tabularnewline
88 & 8.2 & 7.5718682407973 & 0.628131759202706 \tabularnewline
89 & 7.3 & 7.47912114796314 & -0.179121147963143 \tabularnewline
90 & 8.2 & 7.02637678762891 & 1.17362321237109 \tabularnewline
91 & 7.4 & 7.02380472402675 & 0.376195275973247 \tabularnewline
92 & 4.8 & 4.97277830498786 & -0.172778304987857 \tabularnewline
93 & 7.6 & 8.02728014425217 & -0.42728014425217 \tabularnewline
94 & 8.9 & 8.33254273433381 & 0.567457265666193 \tabularnewline
95 & 7.7 & 7.0853792793264 & 0.614620720673602 \tabularnewline
96 & 7.3 & 7.11820854015754 & 0.18179145984246 \tabularnewline
97 & 6.3 & 6.58287777321627 & -0.282877773216275 \tabularnewline
98 & 5.4 & 5.94096878702836 & -0.540968787028361 \tabularnewline
99 & 6.4 & 6.97773383283866 & -0.577733832838664 \tabularnewline
100 & 6.4 & 6.4631647273441 & -0.0631647273440926 \tabularnewline
101 & 5.4 & 6.29475349296978 & -0.894753492969775 \tabularnewline
102 & 8.7 & 8.01562533215173 & 0.684374667848268 \tabularnewline
103 & 6.1 & 6.72507641933374 & -0.625076419333737 \tabularnewline
104 & 8.4 & 8.25619924761791 & 0.143800752382088 \tabularnewline
105 & 7.9 & 7.2200868664954 & 0.679913133504605 \tabularnewline
106 & 7 & 7.05498067997006 & -0.05498067997006 \tabularnewline
107 & 8.7 & 8.65200232431569 & 0.0479976756843097 \tabularnewline
108 & 7.9 & 7.6471183629055 & 0.252881637094503 \tabularnewline
109 & 7.1 & 7.80765092593077 & -0.707650925930775 \tabularnewline
110 & 5.8 & 6.00074788742957 & -0.200747887429575 \tabularnewline
111 & 8.4 & 7.94403427057594 & 0.455965729424063 \tabularnewline
112 & 7.1 & 7.47208409734666 & -0.372084097346662 \tabularnewline
113 & 7.6 & 7.50813434717065 & 0.0918656528293466 \tabularnewline
114 & 7.3 & 7.62630081381802 & -0.32630081381802 \tabularnewline
115 & 8 & 6.93226205281444 & 1.06773794718556 \tabularnewline
116 & 6.1 & 6.95098836437427 & -0.850988364374267 \tabularnewline
117 & 8.7 & 7.93558557239666 & 0.764414427603344 \tabularnewline
118 & 5.8 & 5.74569177788797 & 0.0543082221120324 \tabularnewline
119 & 6.4 & 6.64534748274441 & -0.245347482744412 \tabularnewline
120 & 6.4 & 6.19641673048815 & 0.203583269511847 \tabularnewline
121 & 9 & 8.35875139450993 & 0.641248605490071 \tabularnewline
122 & 6.4 & 6.63628125547207 & -0.236281255472069 \tabularnewline
123 & 6 & 5.7214654796442 & 0.278534520355798 \tabularnewline
124 & 8.7 & 8.49812797544436 & 0.201872024555639 \tabularnewline
125 & 5 & 5.30427809327199 & -0.304278093271988 \tabularnewline
126 & 7.4 & 6.66421263205627 & 0.735787367943727 \tabularnewline
127 & 8.6 & 7.595661650727 & 1.004338349273 \tabularnewline
128 & 5.8 & 5.79874337355943 & 0.00125662644056588 \tabularnewline
129 & 9.8 & 8.25518030229765 & 1.54481969770235 \tabularnewline
130 & 4.8 & 5.20911616185415 & -0.409116161854153 \tabularnewline
131 & 7 & 7.65419894044151 & -0.654198940441514 \tabularnewline
132 & 5.5 & 8.02216900987437 & -2.52216900987437 \tabularnewline
133 & 5 & 4.19684409516888 & 0.803155904831117 \tabularnewline
134 & 6 & 5.97894346768279 & 0.0210565323172062 \tabularnewline
135 & 8 & 7.28468048777018 & 0.715319512229824 \tabularnewline
136 & 7.9 & 8.3278524759331 & -0.427852475933095 \tabularnewline
137 & 4.8 & 5.34235854231767 & -0.542358542317671 \tabularnewline
138 & 6.4 & 6.99853052785592 & -0.598530527855924 \tabularnewline
139 & 4.8 & 5.13632716148344 & -0.33632716148344 \tabularnewline
140 & 6.4 & 6.88764738789619 & -0.487647387896188 \tabularnewline
141 & 6.8 & 6.71316661176196 & 0.0868333882380376 \tabularnewline
142 & 7.9 & 7.94107767893086 & -0.0410776789308582 \tabularnewline
143 & 8.9 & 8.25131204617407 & 0.648687953825935 \tabularnewline
144 & 7.4 & 7.96809146723845 & -0.568091467238453 \tabularnewline
145 & 7 & 7.5812136022773 & -0.581213602277294 \tabularnewline
146 & 7 & 7.56161935869033 & -0.561619358690333 \tabularnewline
147 & 6 & 6.05751634995369 & -0.0575163499536912 \tabularnewline
148 & 7.4 & 7.08907855184422 & 0.310921448155783 \tabularnewline
149 & 7.6 & 6.54206756534755 & 1.05793243465245 \tabularnewline
150 & 4.8 & 6.36915446058505 & -1.56915446058505 \tabularnewline
151 & 7.3 & 7.69160274478247 & -0.391602744782471 \tabularnewline
152 & 6.3 & 6.08150956607985 & 0.218490433920149 \tabularnewline
153 & 5 & 5.30085220551788 & -0.300852205517876 \tabularnewline
154 & 7.1 & 7.64748121465593 & -0.547481214655935 \tabularnewline
155 & 6.3 & 5.9693801315094 & 0.330619868490595 \tabularnewline
156 & 6.8 & 7.41342287922643 & -0.613422879226433 \tabularnewline
157 & 5.2 & 5.12236845815579 & 0.0776315418442123 \tabularnewline
158 & 6.3 & 6.61266126421728 & -0.312661264217277 \tabularnewline
159 & 6.1 & 6.5222781148418 & -0.422278114841798 \tabularnewline
160 & 7.3 & 7.65878321877231 & -0.35878321877231 \tabularnewline
161 & 5.4 & 6.12523141682929 & -0.725231416829287 \tabularnewline
162 & 8 & 7.92083324182169 & 0.079166758178309 \tabularnewline
163 & 7.4 & 6.59777705294151 & 0.802222947058493 \tabularnewline
164 & 7.3 & 6.62851087197011 & 0.671489128029888 \tabularnewline
165 & 7.3 & 6.55133840225623 & 0.748661597743767 \tabularnewline
166 & 6.4 & 6.87039258590678 & -0.470392585906778 \tabularnewline
167 & 5.7 & 5.79405557352014 & -0.0940555735201409 \tabularnewline
168 & 5.7 & 4.71327066840775 & 0.986729331592253 \tabularnewline
169 & 6.6 & 6.6504033262208 & -0.0504033262207961 \tabularnewline
170 & 6.3 & 6.22077011840912 & 0.0792298815908808 \tabularnewline
171 & 5.4 & 6.42331980806761 & -1.02331980806761 \tabularnewline
172 & 7.4 & 7.85849683189995 & -0.458496831899955 \tabularnewline
173 & 8.6 & 8.18609170368623 & 0.41390829631377 \tabularnewline
174 & 7.3 & 7.20436743697511 & 0.0956325630248906 \tabularnewline
175 & 6.3 & 5.7396679585024 & 0.560332041497596 \tabularnewline
176 & 8.7 & 8.01019351040158 & 0.68980648959842 \tabularnewline
177 & 8.6 & 8.20015278439225 & 0.399847215607752 \tabularnewline
178 & 8.4 & 8.2330658211356 & 0.166934178864399 \tabularnewline
179 & 7.4 & 7.83964269442814 & -0.43964269442814 \tabularnewline
180 & 9.9 & 8.33022054741465 & 1.56977945258535 \tabularnewline
181 & 8 & 7.43676055298102 & 0.56323944701898 \tabularnewline
182 & 7.9 & 8.36743446327674 & -0.467434463276738 \tabularnewline
183 & 9.8 & 8.39343869690405 & 1.40656130309595 \tabularnewline
184 & 8.9 & 8.0974873195031 & 0.802512680496903 \tabularnewline
185 & 6.8 & 7.70313118895246 & -0.903131188952457 \tabularnewline
186 & 7.4 & 8.22212518264746 & -0.82212518264746 \tabularnewline
187 & 4.7 & 6.04223241977075 & -1.34223241977075 \tabularnewline
188 & 5.4 & 5.56584667116121 & -0.165846671161209 \tabularnewline
189 & 7 & 7.03995184878464 & -0.0399518487846402 \tabularnewline
190 & 7.1 & 7.98250622526623 & -0.882506225266227 \tabularnewline
191 & 6.3 & 6.60496909318944 & -0.304969093189437 \tabularnewline
192 & 5.5 & 6.92684270070865 & -1.42684270070865 \tabularnewline
193 & 5.4 & 5.58002411745848 & -0.180024117458484 \tabularnewline
194 & 5.4 & 5.50433901025654 & -0.104339010256544 \tabularnewline
195 & 4.8 & 5.75744875577099 & -0.957448755770988 \tabularnewline
196 & 8.2 & 7.3143637577932 & 0.885636242206793 \tabularnewline
197 & 7.9 & 7.85796470615274 & 0.0420352938472639 \tabularnewline
198 & 8.6 & 8.25798824654622 & 0.342011753453782 \tabularnewline
199 & 8.2 & 7.2650394061777 & 0.934960593822303 \tabularnewline
200 & 8.6 & 8.11700570852068 & 0.48299429147932 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146899&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]8.2[/C][C]6.92154527597544[/C][C]1.27845472402456[/C][/ROW]
[ROW][C]2[/C][C]5.7[/C][C]7.89128054144265[/C][C]-2.19128054144265[/C][/ROW]
[ROW][C]3[/C][C]8.9[/C][C]8.12935768635316[/C][C]0.770642313646843[/C][/ROW]
[ROW][C]4[/C][C]4.8[/C][C]5.42788931407634[/C][C]-0.627889314076345[/C][/ROW]
[ROW][C]5[/C][C]7.1[/C][C]7.01207095350958[/C][C]0.0879290464904158[/C][/ROW]
[ROW][C]6[/C][C]4.7[/C][C]5.817776439552[/C][C]-1.117776439552[/C][/ROW]
[ROW][C]7[/C][C]5.7[/C][C]4.34942264543229[/C][C]1.35057735456771[/C][/ROW]
[ROW][C]8[/C][C]6.3[/C][C]5.89178223452134[/C][C]0.40821776547866[/C][/ROW]
[ROW][C]9[/C][C]7[/C][C]6.5941324630365[/C][C]0.4058675369635[/C][/ROW]
[ROW][C]10[/C][C]5.5[/C][C]6.65740613149605[/C][C]-1.15740613149605[/C][/ROW]
[ROW][C]11[/C][C]7.4[/C][C]7.37793573720811[/C][C]0.0220642627918897[/C][/ROW]
[ROW][C]12[/C][C]6[/C][C]5.37196336468396[/C][C]0.628036635316037[/C][/ROW]
[ROW][C]13[/C][C]8.4[/C][C]8.07649573891565[/C][C]0.323504261084355[/C][/ROW]
[ROW][C]14[/C][C]7.6[/C][C]7.79483636015661[/C][C]-0.194836360156615[/C][/ROW]
[ROW][C]15[/C][C]8[/C][C]7.4462372355944[/C][C]0.553762764405602[/C][/ROW]
[ROW][C]16[/C][C]6.6[/C][C]7.83566010746575[/C][C]-1.23566010746575[/C][/ROW]
[ROW][C]17[/C][C]6.4[/C][C]6.49772272387345[/C][C]-0.0977227238734543[/C][/ROW]
[ROW][C]18[/C][C]7.4[/C][C]7.101344625578[/C][C]0.298655374422006[/C][/ROW]
[ROW][C]19[/C][C]6.8[/C][C]6.58405773046423[/C][C]0.215942269535774[/C][/ROW]
[ROW][C]20[/C][C]7.6[/C][C]7.7601283892658[/C][C]-0.160128389265806[/C][/ROW]
[ROW][C]21[/C][C]5.4[/C][C]5.13306420320091[/C][C]0.266935796799095[/C][/ROW]
[ROW][C]22[/C][C]9.9[/C][C]8.1246108262528[/C][C]1.77538917374719[/C][/ROW]
[ROW][C]23[/C][C]7[/C][C]7.48192496491775[/C][C]-0.481924964917746[/C][/ROW]
[ROW][C]24[/C][C]8.6[/C][C]8.29990055763124[/C][C]0.300099442368757[/C][/ROW]
[ROW][C]25[/C][C]4.8[/C][C]6.22914634024333[/C][C]-1.42914634024333[/C][/ROW]
[ROW][C]26[/C][C]6.6[/C][C]6.28258368756127[/C][C]0.317416312438727[/C][/ROW]
[ROW][C]27[/C][C]6.3[/C][C]7.87710679029942[/C][C]-1.57710679029942[/C][/ROW]
[ROW][C]28[/C][C]5.4[/C][C]6.34027892371895[/C][C]-0.940278923718948[/C][/ROW]
[ROW][C]29[/C][C]6.3[/C][C]7.90794995498871[/C][C]-1.60794995498871[/C][/ROW]
[ROW][C]30[/C][C]5.4[/C][C]6.45251611472212[/C][C]-1.05251611472212[/C][/ROW]
[ROW][C]31[/C][C]6.1[/C][C]6.01592057774208[/C][C]0.084079422257923[/C][/ROW]
[ROW][C]32[/C][C]6.4[/C][C]6.102611179408[/C][C]0.297388820591999[/C][/ROW]
[ROW][C]33[/C][C]5.4[/C][C]5.84853311921426[/C][C]-0.448533119214255[/C][/ROW]
[ROW][C]34[/C][C]7.3[/C][C]7.05790715089355[/C][C]0.242092849106454[/C][/ROW]
[ROW][C]35[/C][C]6.3[/C][C]5.90534773213898[/C][C]0.394652267861016[/C][/ROW]
[ROW][C]36[/C][C]5.4[/C][C]5.91384879973059[/C][C]-0.513848799730588[/C][/ROW]
[ROW][C]37[/C][C]7.1[/C][C]6.98085069950961[/C][C]0.119149300490391[/C][/ROW]
[ROW][C]38[/C][C]8.7[/C][C]8.60644672674988[/C][C]0.0935532732501167[/C][/ROW]
[ROW][C]39[/C][C]7.6[/C][C]7.50681923248355[/C][C]0.093180767516447[/C][/ROW]
[ROW][C]40[/C][C]6[/C][C]5.63642027019563[/C][C]0.363579729804366[/C][/ROW]
[ROW][C]41[/C][C]7[/C][C]6.88627298972414[/C][C]0.113727010275859[/C][/ROW]
[ROW][C]42[/C][C]7.6[/C][C]7.69150657480105[/C][C]-0.0915065748010466[/C][/ROW]
[ROW][C]43[/C][C]8.9[/C][C]7.84401019046878[/C][C]1.05598980953122[/C][/ROW]
[ROW][C]44[/C][C]7.6[/C][C]6.391047173825[/C][C]1.208952826175[/C][/ROW]
[ROW][C]45[/C][C]5.5[/C][C]7.74925686771959[/C][C]-2.24925686771959[/C][/ROW]
[ROW][C]46[/C][C]7.4[/C][C]7.04348863865108[/C][C]0.356511361348917[/C][/ROW]
[ROW][C]47[/C][C]7.1[/C][C]7.47213523582728[/C][C]-0.37213523582728[/C][/ROW]
[ROW][C]48[/C][C]7.6[/C][C]7.37643354316843[/C][C]0.223566456831566[/C][/ROW]
[ROW][C]49[/C][C]8.7[/C][C]7.8975502745741[/C][C]0.802449725425899[/C][/ROW]
[ROW][C]50[/C][C]8.6[/C][C]7.30688902802457[/C][C]1.29311097197543[/C][/ROW]
[ROW][C]51[/C][C]5.4[/C][C]5.50138587690058[/C][C]-0.101385876900583[/C][/ROW]
[ROW][C]52[/C][C]5.7[/C][C]8.08035068453767[/C][C]-2.38035068453767[/C][/ROW]
[ROW][C]53[/C][C]8.7[/C][C]8.369887947363[/C][C]0.330112052636992[/C][/ROW]
[ROW][C]54[/C][C]6.1[/C][C]6.02567213799497[/C][C]0.0743278620050267[/C][/ROW]
[ROW][C]55[/C][C]7.3[/C][C]7.13007419211233[/C][C]0.169925807887667[/C][/ROW]
[ROW][C]56[/C][C]7.7[/C][C]7.12230088094519[/C][C]0.577699119054812[/C][/ROW]
[ROW][C]57[/C][C]9[/C][C]8.35801678659143[/C][C]0.641983213408572[/C][/ROW]
[ROW][C]58[/C][C]8.2[/C][C]7.10515759500286[/C][C]1.09484240499714[/C][/ROW]
[ROW][C]59[/C][C]7.1[/C][C]7.35307171756142[/C][C]-0.253071717561422[/C][/ROW]
[ROW][C]60[/C][C]7.9[/C][C]8.01871836103659[/C][C]-0.118718361036586[/C][/ROW]
[ROW][C]61[/C][C]6.6[/C][C]8.03070148056466[/C][C]-1.43070148056466[/C][/ROW]
[ROW][C]62[/C][C]8[/C][C]6.60370328755035[/C][C]1.39629671244965[/C][/ROW]
[ROW][C]63[/C][C]6.3[/C][C]6.52164462282954[/C][C]-0.221644622829537[/C][/ROW]
[ROW][C]64[/C][C]6[/C][C]5.65263357197841[/C][C]0.34736642802159[/C][/ROW]
[ROW][C]65[/C][C]5.4[/C][C]5.37692554176495[/C][C]0.0230744582350483[/C][/ROW]
[ROW][C]66[/C][C]7.6[/C][C]7.18395084706542[/C][C]0.416049152934578[/C][/ROW]
[ROW][C]67[/C][C]6.4[/C][C]6.7121189415395[/C][C]-0.312118941539501[/C][/ROW]
[ROW][C]68[/C][C]6.1[/C][C]6.28653517875515[/C][C]-0.18653517875515[/C][/ROW]
[ROW][C]69[/C][C]5.2[/C][C]6.23735808150298[/C][C]-1.03735808150298[/C][/ROW]
[ROW][C]70[/C][C]6.6[/C][C]6.2983886577096[/C][C]0.301611342290394[/C][/ROW]
[ROW][C]71[/C][C]7.6[/C][C]7.92302789384736[/C][C]-0.323027893847359[/C][/ROW]
[ROW][C]72[/C][C]5.8[/C][C]5.69429309532037[/C][C]0.105706904679627[/C][/ROW]
[ROW][C]73[/C][C]7.9[/C][C]7.0983711679342[/C][C]0.801628832065796[/C][/ROW]
[ROW][C]74[/C][C]8.6[/C][C]7.80150996376224[/C][C]0.798490036237758[/C][/ROW]
[ROW][C]75[/C][C]8.2[/C][C]6.94126038824872[/C][C]1.25873961175128[/C][/ROW]
[ROW][C]76[/C][C]7.1[/C][C]7.61577957220396[/C][C]-0.515779572203966[/C][/ROW]
[ROW][C]77[/C][C]6.4[/C][C]6.8419924618197[/C][C]-0.441992461819692[/C][/ROW]
[ROW][C]78[/C][C]7.6[/C][C]7.64963230598816[/C][C]-0.0496323059881598[/C][/ROW]
[ROW][C]79[/C][C]8.9[/C][C]8.29994434571255[/C][C]0.600055654287449[/C][/ROW]
[ROW][C]80[/C][C]5.7[/C][C]5.40619659066075[/C][C]0.293803409339254[/C][/ROW]
[ROW][C]81[/C][C]7.1[/C][C]7.61344622172167[/C][C]-0.513446221721674[/C][/ROW]
[ROW][C]82[/C][C]7.4[/C][C]7.47623230606186[/C][C]-0.0762323060618624[/C][/ROW]
[ROW][C]83[/C][C]6.6[/C][C]6.31165256444434[/C][C]0.288347435555662[/C][/ROW]
[ROW][C]84[/C][C]5[/C][C]4.05114521285614[/C][C]0.948854787143859[/C][/ROW]
[ROW][C]85[/C][C]8.2[/C][C]7.39802838629326[/C][C]0.801971613706736[/C][/ROW]
[ROW][C]86[/C][C]5.2[/C][C]6.10973449988669[/C][C]-0.909734499886686[/C][/ROW]
[ROW][C]87[/C][C]5.2[/C][C]4.96383015947189[/C][C]0.23616984052811[/C][/ROW]
[ROW][C]88[/C][C]8.2[/C][C]7.5718682407973[/C][C]0.628131759202706[/C][/ROW]
[ROW][C]89[/C][C]7.3[/C][C]7.47912114796314[/C][C]-0.179121147963143[/C][/ROW]
[ROW][C]90[/C][C]8.2[/C][C]7.02637678762891[/C][C]1.17362321237109[/C][/ROW]
[ROW][C]91[/C][C]7.4[/C][C]7.02380472402675[/C][C]0.376195275973247[/C][/ROW]
[ROW][C]92[/C][C]4.8[/C][C]4.97277830498786[/C][C]-0.172778304987857[/C][/ROW]
[ROW][C]93[/C][C]7.6[/C][C]8.02728014425217[/C][C]-0.42728014425217[/C][/ROW]
[ROW][C]94[/C][C]8.9[/C][C]8.33254273433381[/C][C]0.567457265666193[/C][/ROW]
[ROW][C]95[/C][C]7.7[/C][C]7.0853792793264[/C][C]0.614620720673602[/C][/ROW]
[ROW][C]96[/C][C]7.3[/C][C]7.11820854015754[/C][C]0.18179145984246[/C][/ROW]
[ROW][C]97[/C][C]6.3[/C][C]6.58287777321627[/C][C]-0.282877773216275[/C][/ROW]
[ROW][C]98[/C][C]5.4[/C][C]5.94096878702836[/C][C]-0.540968787028361[/C][/ROW]
[ROW][C]99[/C][C]6.4[/C][C]6.97773383283866[/C][C]-0.577733832838664[/C][/ROW]
[ROW][C]100[/C][C]6.4[/C][C]6.4631647273441[/C][C]-0.0631647273440926[/C][/ROW]
[ROW][C]101[/C][C]5.4[/C][C]6.29475349296978[/C][C]-0.894753492969775[/C][/ROW]
[ROW][C]102[/C][C]8.7[/C][C]8.01562533215173[/C][C]0.684374667848268[/C][/ROW]
[ROW][C]103[/C][C]6.1[/C][C]6.72507641933374[/C][C]-0.625076419333737[/C][/ROW]
[ROW][C]104[/C][C]8.4[/C][C]8.25619924761791[/C][C]0.143800752382088[/C][/ROW]
[ROW][C]105[/C][C]7.9[/C][C]7.2200868664954[/C][C]0.679913133504605[/C][/ROW]
[ROW][C]106[/C][C]7[/C][C]7.05498067997006[/C][C]-0.05498067997006[/C][/ROW]
[ROW][C]107[/C][C]8.7[/C][C]8.65200232431569[/C][C]0.0479976756843097[/C][/ROW]
[ROW][C]108[/C][C]7.9[/C][C]7.6471183629055[/C][C]0.252881637094503[/C][/ROW]
[ROW][C]109[/C][C]7.1[/C][C]7.80765092593077[/C][C]-0.707650925930775[/C][/ROW]
[ROW][C]110[/C][C]5.8[/C][C]6.00074788742957[/C][C]-0.200747887429575[/C][/ROW]
[ROW][C]111[/C][C]8.4[/C][C]7.94403427057594[/C][C]0.455965729424063[/C][/ROW]
[ROW][C]112[/C][C]7.1[/C][C]7.47208409734666[/C][C]-0.372084097346662[/C][/ROW]
[ROW][C]113[/C][C]7.6[/C][C]7.50813434717065[/C][C]0.0918656528293466[/C][/ROW]
[ROW][C]114[/C][C]7.3[/C][C]7.62630081381802[/C][C]-0.32630081381802[/C][/ROW]
[ROW][C]115[/C][C]8[/C][C]6.93226205281444[/C][C]1.06773794718556[/C][/ROW]
[ROW][C]116[/C][C]6.1[/C][C]6.95098836437427[/C][C]-0.850988364374267[/C][/ROW]
[ROW][C]117[/C][C]8.7[/C][C]7.93558557239666[/C][C]0.764414427603344[/C][/ROW]
[ROW][C]118[/C][C]5.8[/C][C]5.74569177788797[/C][C]0.0543082221120324[/C][/ROW]
[ROW][C]119[/C][C]6.4[/C][C]6.64534748274441[/C][C]-0.245347482744412[/C][/ROW]
[ROW][C]120[/C][C]6.4[/C][C]6.19641673048815[/C][C]0.203583269511847[/C][/ROW]
[ROW][C]121[/C][C]9[/C][C]8.35875139450993[/C][C]0.641248605490071[/C][/ROW]
[ROW][C]122[/C][C]6.4[/C][C]6.63628125547207[/C][C]-0.236281255472069[/C][/ROW]
[ROW][C]123[/C][C]6[/C][C]5.7214654796442[/C][C]0.278534520355798[/C][/ROW]
[ROW][C]124[/C][C]8.7[/C][C]8.49812797544436[/C][C]0.201872024555639[/C][/ROW]
[ROW][C]125[/C][C]5[/C][C]5.30427809327199[/C][C]-0.304278093271988[/C][/ROW]
[ROW][C]126[/C][C]7.4[/C][C]6.66421263205627[/C][C]0.735787367943727[/C][/ROW]
[ROW][C]127[/C][C]8.6[/C][C]7.595661650727[/C][C]1.004338349273[/C][/ROW]
[ROW][C]128[/C][C]5.8[/C][C]5.79874337355943[/C][C]0.00125662644056588[/C][/ROW]
[ROW][C]129[/C][C]9.8[/C][C]8.25518030229765[/C][C]1.54481969770235[/C][/ROW]
[ROW][C]130[/C][C]4.8[/C][C]5.20911616185415[/C][C]-0.409116161854153[/C][/ROW]
[ROW][C]131[/C][C]7[/C][C]7.65419894044151[/C][C]-0.654198940441514[/C][/ROW]
[ROW][C]132[/C][C]5.5[/C][C]8.02216900987437[/C][C]-2.52216900987437[/C][/ROW]
[ROW][C]133[/C][C]5[/C][C]4.19684409516888[/C][C]0.803155904831117[/C][/ROW]
[ROW][C]134[/C][C]6[/C][C]5.97894346768279[/C][C]0.0210565323172062[/C][/ROW]
[ROW][C]135[/C][C]8[/C][C]7.28468048777018[/C][C]0.715319512229824[/C][/ROW]
[ROW][C]136[/C][C]7.9[/C][C]8.3278524759331[/C][C]-0.427852475933095[/C][/ROW]
[ROW][C]137[/C][C]4.8[/C][C]5.34235854231767[/C][C]-0.542358542317671[/C][/ROW]
[ROW][C]138[/C][C]6.4[/C][C]6.99853052785592[/C][C]-0.598530527855924[/C][/ROW]
[ROW][C]139[/C][C]4.8[/C][C]5.13632716148344[/C][C]-0.33632716148344[/C][/ROW]
[ROW][C]140[/C][C]6.4[/C][C]6.88764738789619[/C][C]-0.487647387896188[/C][/ROW]
[ROW][C]141[/C][C]6.8[/C][C]6.71316661176196[/C][C]0.0868333882380376[/C][/ROW]
[ROW][C]142[/C][C]7.9[/C][C]7.94107767893086[/C][C]-0.0410776789308582[/C][/ROW]
[ROW][C]143[/C][C]8.9[/C][C]8.25131204617407[/C][C]0.648687953825935[/C][/ROW]
[ROW][C]144[/C][C]7.4[/C][C]7.96809146723845[/C][C]-0.568091467238453[/C][/ROW]
[ROW][C]145[/C][C]7[/C][C]7.5812136022773[/C][C]-0.581213602277294[/C][/ROW]
[ROW][C]146[/C][C]7[/C][C]7.56161935869033[/C][C]-0.561619358690333[/C][/ROW]
[ROW][C]147[/C][C]6[/C][C]6.05751634995369[/C][C]-0.0575163499536912[/C][/ROW]
[ROW][C]148[/C][C]7.4[/C][C]7.08907855184422[/C][C]0.310921448155783[/C][/ROW]
[ROW][C]149[/C][C]7.6[/C][C]6.54206756534755[/C][C]1.05793243465245[/C][/ROW]
[ROW][C]150[/C][C]4.8[/C][C]6.36915446058505[/C][C]-1.56915446058505[/C][/ROW]
[ROW][C]151[/C][C]7.3[/C][C]7.69160274478247[/C][C]-0.391602744782471[/C][/ROW]
[ROW][C]152[/C][C]6.3[/C][C]6.08150956607985[/C][C]0.218490433920149[/C][/ROW]
[ROW][C]153[/C][C]5[/C][C]5.30085220551788[/C][C]-0.300852205517876[/C][/ROW]
[ROW][C]154[/C][C]7.1[/C][C]7.64748121465593[/C][C]-0.547481214655935[/C][/ROW]
[ROW][C]155[/C][C]6.3[/C][C]5.9693801315094[/C][C]0.330619868490595[/C][/ROW]
[ROW][C]156[/C][C]6.8[/C][C]7.41342287922643[/C][C]-0.613422879226433[/C][/ROW]
[ROW][C]157[/C][C]5.2[/C][C]5.12236845815579[/C][C]0.0776315418442123[/C][/ROW]
[ROW][C]158[/C][C]6.3[/C][C]6.61266126421728[/C][C]-0.312661264217277[/C][/ROW]
[ROW][C]159[/C][C]6.1[/C][C]6.5222781148418[/C][C]-0.422278114841798[/C][/ROW]
[ROW][C]160[/C][C]7.3[/C][C]7.65878321877231[/C][C]-0.35878321877231[/C][/ROW]
[ROW][C]161[/C][C]5.4[/C][C]6.12523141682929[/C][C]-0.725231416829287[/C][/ROW]
[ROW][C]162[/C][C]8[/C][C]7.92083324182169[/C][C]0.079166758178309[/C][/ROW]
[ROW][C]163[/C][C]7.4[/C][C]6.59777705294151[/C][C]0.802222947058493[/C][/ROW]
[ROW][C]164[/C][C]7.3[/C][C]6.62851087197011[/C][C]0.671489128029888[/C][/ROW]
[ROW][C]165[/C][C]7.3[/C][C]6.55133840225623[/C][C]0.748661597743767[/C][/ROW]
[ROW][C]166[/C][C]6.4[/C][C]6.87039258590678[/C][C]-0.470392585906778[/C][/ROW]
[ROW][C]167[/C][C]5.7[/C][C]5.79405557352014[/C][C]-0.0940555735201409[/C][/ROW]
[ROW][C]168[/C][C]5.7[/C][C]4.71327066840775[/C][C]0.986729331592253[/C][/ROW]
[ROW][C]169[/C][C]6.6[/C][C]6.6504033262208[/C][C]-0.0504033262207961[/C][/ROW]
[ROW][C]170[/C][C]6.3[/C][C]6.22077011840912[/C][C]0.0792298815908808[/C][/ROW]
[ROW][C]171[/C][C]5.4[/C][C]6.42331980806761[/C][C]-1.02331980806761[/C][/ROW]
[ROW][C]172[/C][C]7.4[/C][C]7.85849683189995[/C][C]-0.458496831899955[/C][/ROW]
[ROW][C]173[/C][C]8.6[/C][C]8.18609170368623[/C][C]0.41390829631377[/C][/ROW]
[ROW][C]174[/C][C]7.3[/C][C]7.20436743697511[/C][C]0.0956325630248906[/C][/ROW]
[ROW][C]175[/C][C]6.3[/C][C]5.7396679585024[/C][C]0.560332041497596[/C][/ROW]
[ROW][C]176[/C][C]8.7[/C][C]8.01019351040158[/C][C]0.68980648959842[/C][/ROW]
[ROW][C]177[/C][C]8.6[/C][C]8.20015278439225[/C][C]0.399847215607752[/C][/ROW]
[ROW][C]178[/C][C]8.4[/C][C]8.2330658211356[/C][C]0.166934178864399[/C][/ROW]
[ROW][C]179[/C][C]7.4[/C][C]7.83964269442814[/C][C]-0.43964269442814[/C][/ROW]
[ROW][C]180[/C][C]9.9[/C][C]8.33022054741465[/C][C]1.56977945258535[/C][/ROW]
[ROW][C]181[/C][C]8[/C][C]7.43676055298102[/C][C]0.56323944701898[/C][/ROW]
[ROW][C]182[/C][C]7.9[/C][C]8.36743446327674[/C][C]-0.467434463276738[/C][/ROW]
[ROW][C]183[/C][C]9.8[/C][C]8.39343869690405[/C][C]1.40656130309595[/C][/ROW]
[ROW][C]184[/C][C]8.9[/C][C]8.0974873195031[/C][C]0.802512680496903[/C][/ROW]
[ROW][C]185[/C][C]6.8[/C][C]7.70313118895246[/C][C]-0.903131188952457[/C][/ROW]
[ROW][C]186[/C][C]7.4[/C][C]8.22212518264746[/C][C]-0.82212518264746[/C][/ROW]
[ROW][C]187[/C][C]4.7[/C][C]6.04223241977075[/C][C]-1.34223241977075[/C][/ROW]
[ROW][C]188[/C][C]5.4[/C][C]5.56584667116121[/C][C]-0.165846671161209[/C][/ROW]
[ROW][C]189[/C][C]7[/C][C]7.03995184878464[/C][C]-0.0399518487846402[/C][/ROW]
[ROW][C]190[/C][C]7.1[/C][C]7.98250622526623[/C][C]-0.882506225266227[/C][/ROW]
[ROW][C]191[/C][C]6.3[/C][C]6.60496909318944[/C][C]-0.304969093189437[/C][/ROW]
[ROW][C]192[/C][C]5.5[/C][C]6.92684270070865[/C][C]-1.42684270070865[/C][/ROW]
[ROW][C]193[/C][C]5.4[/C][C]5.58002411745848[/C][C]-0.180024117458484[/C][/ROW]
[ROW][C]194[/C][C]5.4[/C][C]5.50433901025654[/C][C]-0.104339010256544[/C][/ROW]
[ROW][C]195[/C][C]4.8[/C][C]5.75744875577099[/C][C]-0.957448755770988[/C][/ROW]
[ROW][C]196[/C][C]8.2[/C][C]7.3143637577932[/C][C]0.885636242206793[/C][/ROW]
[ROW][C]197[/C][C]7.9[/C][C]7.85796470615274[/C][C]0.0420352938472639[/C][/ROW]
[ROW][C]198[/C][C]8.6[/C][C]8.25798824654622[/C][C]0.342011753453782[/C][/ROW]
[ROW][C]199[/C][C]8.2[/C][C]7.2650394061777[/C][C]0.934960593822303[/C][/ROW]
[ROW][C]200[/C][C]8.6[/C][C]8.11700570852068[/C][C]0.48299429147932[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146899&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146899&T=4

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
18.26.921545275975441.27845472402456
25.77.89128054144265-2.19128054144265
38.98.129357686353160.770642313646843
44.85.42788931407634-0.627889314076345
57.17.012070953509580.0879290464904158
64.75.817776439552-1.117776439552
75.74.349422645432291.35057735456771
86.35.891782234521340.40821776547866
976.59413246303650.4058675369635
105.56.65740613149605-1.15740613149605
117.47.377935737208110.0220642627918897
1265.371963364683960.628036635316037
138.48.076495738915650.323504261084355
147.67.79483636015661-0.194836360156615
1587.44623723559440.553762764405602
166.67.83566010746575-1.23566010746575
176.46.49772272387345-0.0977227238734543
187.47.1013446255780.298655374422006
196.86.584057730464230.215942269535774
207.67.7601283892658-0.160128389265806
215.45.133064203200910.266935796799095
229.98.12461082625281.77538917374719
2377.48192496491775-0.481924964917746
248.68.299900557631240.300099442368757
254.86.22914634024333-1.42914634024333
266.66.282583687561270.317416312438727
276.37.87710679029942-1.57710679029942
285.46.34027892371895-0.940278923718948
296.37.90794995498871-1.60794995498871
305.46.45251611472212-1.05251611472212
316.16.015920577742080.084079422257923
326.46.1026111794080.297388820591999
335.45.84853311921426-0.448533119214255
347.37.057907150893550.242092849106454
356.35.905347732138980.394652267861016
365.45.91384879973059-0.513848799730588
377.16.980850699509610.119149300490391
388.78.606446726749880.0935532732501167
397.67.506819232483550.093180767516447
4065.636420270195630.363579729804366
4176.886272989724140.113727010275859
427.67.69150657480105-0.0915065748010466
438.97.844010190468781.05598980953122
447.66.3910471738251.208952826175
455.57.74925686771959-2.24925686771959
467.47.043488638651080.356511361348917
477.17.47213523582728-0.37213523582728
487.67.376433543168430.223566456831566
498.77.89755027457410.802449725425899
508.67.306889028024571.29311097197543
515.45.50138587690058-0.101385876900583
525.78.08035068453767-2.38035068453767
538.78.3698879473630.330112052636992
546.16.025672137994970.0743278620050267
557.37.130074192112330.169925807887667
567.77.122300880945190.577699119054812
5798.358016786591430.641983213408572
588.27.105157595002861.09484240499714
597.17.35307171756142-0.253071717561422
607.98.01871836103659-0.118718361036586
616.68.03070148056466-1.43070148056466
6286.603703287550351.39629671244965
636.36.52164462282954-0.221644622829537
6465.652633571978410.34736642802159
655.45.376925541764950.0230744582350483
667.67.183950847065420.416049152934578
676.46.7121189415395-0.312118941539501
686.16.28653517875515-0.18653517875515
695.26.23735808150298-1.03735808150298
706.66.29838865770960.301611342290394
717.67.92302789384736-0.323027893847359
725.85.694293095320370.105706904679627
737.97.09837116793420.801628832065796
748.67.801509963762240.798490036237758
758.26.941260388248721.25873961175128
767.17.61577957220396-0.515779572203966
776.46.8419924618197-0.441992461819692
787.67.64963230598816-0.0496323059881598
798.98.299944345712550.600055654287449
805.75.406196590660750.293803409339254
817.17.61344622172167-0.513446221721674
827.47.47623230606186-0.0762323060618624
836.66.311652564444340.288347435555662
8454.051145212856140.948854787143859
858.27.398028386293260.801971613706736
865.26.10973449988669-0.909734499886686
875.24.963830159471890.23616984052811
888.27.57186824079730.628131759202706
897.37.47912114796314-0.179121147963143
908.27.026376787628911.17362321237109
917.47.023804724026750.376195275973247
924.84.97277830498786-0.172778304987857
937.68.02728014425217-0.42728014425217
948.98.332542734333810.567457265666193
957.77.08537927932640.614620720673602
967.37.118208540157540.18179145984246
976.36.58287777321627-0.282877773216275
985.45.94096878702836-0.540968787028361
996.46.97773383283866-0.577733832838664
1006.46.4631647273441-0.0631647273440926
1015.46.29475349296978-0.894753492969775
1028.78.015625332151730.684374667848268
1036.16.72507641933374-0.625076419333737
1048.48.256199247617910.143800752382088
1057.97.22008686649540.679913133504605
10677.05498067997006-0.05498067997006
1078.78.652002324315690.0479976756843097
1087.97.64711836290550.252881637094503
1097.17.80765092593077-0.707650925930775
1105.86.00074788742957-0.200747887429575
1118.47.944034270575940.455965729424063
1127.17.47208409734666-0.372084097346662
1137.67.508134347170650.0918656528293466
1147.37.62630081381802-0.32630081381802
11586.932262052814441.06773794718556
1166.16.95098836437427-0.850988364374267
1178.77.935585572396660.764414427603344
1185.85.745691777887970.0543082221120324
1196.46.64534748274441-0.245347482744412
1206.46.196416730488150.203583269511847
12198.358751394509930.641248605490071
1226.46.63628125547207-0.236281255472069
12365.72146547964420.278534520355798
1248.78.498127975444360.201872024555639
12555.30427809327199-0.304278093271988
1267.46.664212632056270.735787367943727
1278.67.5956616507271.004338349273
1285.85.798743373559430.00125662644056588
1299.88.255180302297651.54481969770235
1304.85.20911616185415-0.409116161854153
13177.65419894044151-0.654198940441514
1325.58.02216900987437-2.52216900987437
13354.196844095168880.803155904831117
13465.978943467682790.0210565323172062
13587.284680487770180.715319512229824
1367.98.3278524759331-0.427852475933095
1374.85.34235854231767-0.542358542317671
1386.46.99853052785592-0.598530527855924
1394.85.13632716148344-0.33632716148344
1406.46.88764738789619-0.487647387896188
1416.86.713166611761960.0868333882380376
1427.97.94107767893086-0.0410776789308582
1438.98.251312046174070.648687953825935
1447.47.96809146723845-0.568091467238453
14577.5812136022773-0.581213602277294
14677.56161935869033-0.561619358690333
14766.05751634995369-0.0575163499536912
1487.47.089078551844220.310921448155783
1497.66.542067565347551.05793243465245
1504.86.36915446058505-1.56915446058505
1517.37.69160274478247-0.391602744782471
1526.36.081509566079850.218490433920149
15355.30085220551788-0.300852205517876
1547.17.64748121465593-0.547481214655935
1556.35.96938013150940.330619868490595
1566.87.41342287922643-0.613422879226433
1575.25.122368458155790.0776315418442123
1586.36.61266126421728-0.312661264217277
1596.16.5222781148418-0.422278114841798
1607.37.65878321877231-0.35878321877231
1615.46.12523141682929-0.725231416829287
16287.920833241821690.079166758178309
1637.46.597777052941510.802222947058493
1647.36.628510871970110.671489128029888
1657.36.551338402256230.748661597743767
1666.46.87039258590678-0.470392585906778
1675.75.79405557352014-0.0940555735201409
1685.74.713270668407750.986729331592253
1696.66.6504033262208-0.0504033262207961
1706.36.220770118409120.0792298815908808
1715.46.42331980806761-1.02331980806761
1727.47.85849683189995-0.458496831899955
1738.68.186091703686230.41390829631377
1747.37.204367436975110.0956325630248906
1756.35.73966795850240.560332041497596
1768.78.010193510401580.68980648959842
1778.68.200152784392250.399847215607752
1788.48.23306582113560.166934178864399
1797.47.83964269442814-0.43964269442814
1809.98.330220547414651.56977945258535
18187.436760552981020.56323944701898
1827.98.36743446327674-0.467434463276738
1839.88.393438696904051.40656130309595
1848.98.09748731950310.802512680496903
1856.87.70313118895246-0.903131188952457
1867.48.22212518264746-0.82212518264746
1874.76.04223241977075-1.34223241977075
1885.45.56584667116121-0.165846671161209
18977.03995184878464-0.0399518487846402
1907.17.98250622526623-0.882506225266227
1916.36.60496909318944-0.304969093189437
1925.56.92684270070865-1.42684270070865
1935.45.58002411745848-0.180024117458484
1945.45.50433901025654-0.104339010256544
1954.85.75744875577099-0.957448755770988
1968.27.31436375779320.885636242206793
1977.97.857964706152740.0420352938472639
1988.68.257988246546220.342011753453782
1998.27.26503940617770.934960593822303
2008.68.117005708520680.48299429147932







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
120.861221639642780.2775567207144390.138778360357219
130.9235967772814240.1528064454371510.0764032227185756
140.8669193686711610.2661612626576770.133080631328839
150.8317195011220520.3365609977558960.168280498877948
160.7715421803837060.4569156392325870.228457819616294
170.6987949228528880.6024101542942250.301205077147112
180.6303412502518240.7393174994963530.369658749748176
190.5429430812447260.9141138375105490.457056918755274
200.678203558907610.6435928821847810.32179644109239
210.6016758156243640.7966483687512730.398324184375636
220.9188945286755180.1622109426489640.081105471324482
230.9393893390911470.1212213218177070.0606106609088535
240.9479073161453220.1041853677093570.0520926838546783
250.9841767796271310.03164644074573770.0158232203728688
260.977934952408510.04413009518297780.0220650475914889
270.9884671104366760.02306577912664710.0115328895633236
280.9899661338546730.02006773229065440.0100338661453272
290.993260785095720.01347842980856030.00673921490428014
300.9947490059395690.01050198812086240.00525099406043121
310.9941143585987660.01177128280246910.00588564140123454
320.9922286296141170.01554274077176570.00777137038588285
330.9895370090641360.02092598187172850.0104629909358643
340.9849217373747080.03015652525058320.0150782626252916
350.9829270958462780.03414580830744420.0170729041537221
360.978253101803580.04349379639283830.0217468981964191
370.969974786575790.06005042684842010.0300252134242101
380.9597324151846670.0805351696306660.040267584815333
390.9466238480617410.1067523038765180.0533761519382588
400.9383417193556420.1233165612887150.0616582806443577
410.9253413562469080.1493172875061840.0746586437530919
420.9050749855700740.1898500288598520.0949250144299258
430.9156624247935020.1686751504129960.084337575206498
440.9492112330866960.1015775338266080.050788766913304
450.9919576333263820.01608473334723660.00804236667361832
460.9905624645031930.01887507099361310.00943753549680655
470.9883287835828180.02334243283436320.0116712164171816
480.984375557855340.03124888428931850.0156244421446592
490.9856636691727660.02867266165446770.0143363308272338
500.9919326243069620.01613475138607550.00806737569303773
510.9890666116436890.02186677671262250.0109333883563112
520.9991374291019970.001725141796005680.000862570898002841
530.998945795652440.002108408695120880.00105420434756044
540.9985798360250190.002840327949962040.00142016397498102
550.9980099526816250.003980094636749580.00199004731837479
560.9975121783037450.004975643392510270.00248782169625514
570.9971691777219670.005661644556065360.00283082227803268
580.9979965941862820.004006811627436330.00200340581371817
590.9973947054093870.005210589181225530.00260529459061277
600.9964747136625690.007050572674862870.00352528633743143
610.9984669546396280.003066090720743890.00153304536037195
620.9990844530105930.001831093978813930.000915546989406967
630.9987965981570310.002406803685937750.00120340184296887
640.9983056821786970.003388635642605910.00169431782130296
650.9976300588379850.004739882324030460.00236994116201523
660.9968112407980260.006377518403947190.0031887592019736
670.9959649121386470.008070175722705960.00403508786135298
680.9947635742669860.01047285146602830.00523642573301413
690.9969044211559410.006191157688117310.00309557884405866
700.9957762543310170.008447491337965340.00422374566898267
710.9949238661822290.01015226763554240.00507613381777118
720.9933588933401650.01328221331966960.00664110665983482
730.9929826400863030.01403471982739390.00701735991369695
740.9924247715901360.01515045681972840.00757522840986421
750.9945403103431650.01091937931366970.00545968965683487
760.9938642805532590.01227143889348240.0061357194467412
770.9930286911814730.01394261763705410.00697130881852707
780.9911103596983140.01777928060337240.0088896403016862
790.9896416660176430.02071666796471480.0103583339823574
800.986518222907890.02696355418422160.0134817770921108
810.984827837572030.03034432485593860.0151721624279693
820.9806495203834010.03870095923319810.019350479616599
830.9753227688056730.0493544623886540.024677231194327
840.977049405726440.04590118854711970.0229505942735599
850.9758304520260660.04833909594786840.0241695479739342
860.9817058983894220.03658820322115680.0182941016105784
870.9765486524187820.04690269516243670.0234513475812184
880.973254258004550.05349148399089890.0267457419954494
890.9679549260922440.06409014781551160.0320450739077558
900.9746081184301080.05078376313978480.0253918815698924
910.968879657502970.06224068499406180.0311203424970309
920.964925605839530.07014878832094230.0350743941604711
930.961170299526210.07765940094757980.0388297004737899
940.9554715869033680.08905682619326470.0445284130966324
950.9504118203594660.09917635928106850.0495881796405343
960.9398395522859290.1203208954281420.060160447714071
970.9304252499768480.1391495000463040.0695747500231518
980.92272543943440.1545491211312010.0772745605656005
990.9203720653132520.1592558693734960.0796279346867482
1000.9062580654108090.1874838691783830.0937419345891914
1010.9148856194016340.1702287611967330.0851143805983663
1020.9097904955930340.1804190088139320.090209504406966
1030.9071747231238320.1856505537523360.0928252768761678
1040.8892886598680480.2214226802639040.110711340131952
1050.881985969245630.2360280615087390.118014030754369
1060.8620156308427130.2759687383145730.137984369157286
1070.8373209619873150.325358076025370.162679038012685
1080.8132190836621250.373561832675750.186780916337875
1090.818093951601050.3638120967978990.18190604839895
1100.7961860442371690.4076279115256620.203813955762831
1110.7733664900307960.4532670199384070.226633509969203
1120.7579946746837130.4840106506325730.242005325316287
1130.7292777197525460.5414445604949080.270722280247454
1140.6989780512305690.6020438975388620.301021948769431
1150.7384954136478010.5230091727043980.261504586352199
1160.7502159517020260.4995680965959480.249784048297974
1170.7565923918117840.4868152163764310.243407608188216
1180.7219655110619520.5560689778760960.278034488938048
1190.6890284053326420.6219431893347160.310971594667358
1200.6523032364000490.6953935271999020.347696763599951
1210.6553968340948250.689206331810350.344603165905175
1220.6283369287761530.7433261424476940.371663071223847
1230.5893115133914620.8213769732170760.410688486608538
1240.5546250293482730.8907499413034550.445374970651727
1250.5229984966945430.9540030066109140.477001503305457
1260.5152824806586540.969435038682690.484717519341346
1270.5406480663440230.9187038673119550.459351933655977
1280.4982366409973770.9964732819947540.501763359002623
1290.6302803297904260.7394393404191490.369719670209574
1300.6077342055349450.784531588930110.392265794465055
1310.600677975058930.798644049882140.39932202494107
1320.939443078467160.1211138430656810.0605569215328404
1330.957737846286170.08452430742766090.0422621537138304
1340.9470241480561420.1059517038877150.0529758519438577
1350.9389235953201450.1221528093597090.0610764046798547
1360.9270897187442020.1458205625115970.0729102812557983
1370.9297266008336770.1405467983326450.0702733991663226
1380.9189923452790280.1620153094419430.0810076547209716
1390.9041318679908540.1917362640182930.0958681320091465
1400.8866675396091990.2266649207816030.113332460390801
1410.8639330182341480.2721339635317040.136066981765852
1420.8359428838628620.3281142322742770.164057116137138
1430.8193281177302660.3613437645394690.180671882269734
1440.8043289848133390.3913420303733210.195671015186661
1450.7880803966663280.4238392066673430.211919603333672
1460.7814998851157290.4370002297685420.218500114884271
1470.7434537855880530.5130924288238940.256546214411947
1480.709340083330290.5813198333394210.29065991666971
1490.7283001814614820.5433996370770360.271699818538518
1500.856696022510630.2866079549787390.14330397748937
1510.8276605017401230.3446789965197550.172339498259877
1520.798996904840240.4020061903195190.20100309515976
1530.763543155099850.4729136898002990.23645684490015
1540.8141192900679470.3717614198641050.185880709932053
1550.7979332221144580.4041335557710840.202066777885542
1560.8220105865850980.3559788268298040.177989413414902
1570.7836827079392060.4326345841215880.216317292060794
1580.744914940620420.5101701187591580.255085059379579
1590.7756493681966310.4487012636067380.224350631803369
1600.7350064070268580.5299871859462830.264993592973142
1610.7415802166903290.5168395666193430.258419783309671
1620.6921410310908630.6157179378182730.307858968909137
1630.6495517159036470.7008965681927070.350448284096353
1640.6068052945471270.7863894109057460.393194705452873
1650.5649161357046850.870167728590630.435083864295315
1660.5194296369002580.9611407261994830.480570363099742
1670.4582931397473490.9165862794946980.541706860252651
1680.7180338403619750.563932319276050.281966159638025
1690.6617313321154340.6765373357691320.338268667884566
1700.6007072020626230.7985855958747530.399292797937377
1710.6415740066960090.7168519866079830.358425993303991
1720.5909024783773760.8181950432452480.409097521622624
1730.572404262029610.8551914759407790.427595737970389
1740.5138569180865830.9722861638268340.486143081913417
1750.4888868065775930.9777736131551860.511113193422407
1760.508965766515740.982068466968520.49103423348426
1770.4340203119603880.8680406239207750.565979688039612
1780.3641351584321620.7282703168643250.635864841567838
1790.2933434503573670.5866869007147340.706656549642633
1800.2726203375463450.545240675092690.727379662453655
1810.2122939779755120.4245879559510250.787706022024488
1820.1716623273598730.3433246547197470.828337672640127
1830.2155979345961720.4311958691923440.784402065403828
1840.6356612591871030.7286774816257950.364338740812897
1850.5229070020342790.9541859959314430.477092997965721
1860.4129576588114050.825915317622810.587042341188595
1870.2955855677162970.5911711354325940.704414432283703
1880.269717920678650.53943584135730.73028207932135

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
12 & 0.86122163964278 & 0.277556720714439 & 0.138778360357219 \tabularnewline
13 & 0.923596777281424 & 0.152806445437151 & 0.0764032227185756 \tabularnewline
14 & 0.866919368671161 & 0.266161262657677 & 0.133080631328839 \tabularnewline
15 & 0.831719501122052 & 0.336560997755896 & 0.168280498877948 \tabularnewline
16 & 0.771542180383706 & 0.456915639232587 & 0.228457819616294 \tabularnewline
17 & 0.698794922852888 & 0.602410154294225 & 0.301205077147112 \tabularnewline
18 & 0.630341250251824 & 0.739317499496353 & 0.369658749748176 \tabularnewline
19 & 0.542943081244726 & 0.914113837510549 & 0.457056918755274 \tabularnewline
20 & 0.67820355890761 & 0.643592882184781 & 0.32179644109239 \tabularnewline
21 & 0.601675815624364 & 0.796648368751273 & 0.398324184375636 \tabularnewline
22 & 0.918894528675518 & 0.162210942648964 & 0.081105471324482 \tabularnewline
23 & 0.939389339091147 & 0.121221321817707 & 0.0606106609088535 \tabularnewline
24 & 0.947907316145322 & 0.104185367709357 & 0.0520926838546783 \tabularnewline
25 & 0.984176779627131 & 0.0316464407457377 & 0.0158232203728688 \tabularnewline
26 & 0.97793495240851 & 0.0441300951829778 & 0.0220650475914889 \tabularnewline
27 & 0.988467110436676 & 0.0230657791266471 & 0.0115328895633236 \tabularnewline
28 & 0.989966133854673 & 0.0200677322906544 & 0.0100338661453272 \tabularnewline
29 & 0.99326078509572 & 0.0134784298085603 & 0.00673921490428014 \tabularnewline
30 & 0.994749005939569 & 0.0105019881208624 & 0.00525099406043121 \tabularnewline
31 & 0.994114358598766 & 0.0117712828024691 & 0.00588564140123454 \tabularnewline
32 & 0.992228629614117 & 0.0155427407717657 & 0.00777137038588285 \tabularnewline
33 & 0.989537009064136 & 0.0209259818717285 & 0.0104629909358643 \tabularnewline
34 & 0.984921737374708 & 0.0301565252505832 & 0.0150782626252916 \tabularnewline
35 & 0.982927095846278 & 0.0341458083074442 & 0.0170729041537221 \tabularnewline
36 & 0.97825310180358 & 0.0434937963928383 & 0.0217468981964191 \tabularnewline
37 & 0.96997478657579 & 0.0600504268484201 & 0.0300252134242101 \tabularnewline
38 & 0.959732415184667 & 0.080535169630666 & 0.040267584815333 \tabularnewline
39 & 0.946623848061741 & 0.106752303876518 & 0.0533761519382588 \tabularnewline
40 & 0.938341719355642 & 0.123316561288715 & 0.0616582806443577 \tabularnewline
41 & 0.925341356246908 & 0.149317287506184 & 0.0746586437530919 \tabularnewline
42 & 0.905074985570074 & 0.189850028859852 & 0.0949250144299258 \tabularnewline
43 & 0.915662424793502 & 0.168675150412996 & 0.084337575206498 \tabularnewline
44 & 0.949211233086696 & 0.101577533826608 & 0.050788766913304 \tabularnewline
45 & 0.991957633326382 & 0.0160847333472366 & 0.00804236667361832 \tabularnewline
46 & 0.990562464503193 & 0.0188750709936131 & 0.00943753549680655 \tabularnewline
47 & 0.988328783582818 & 0.0233424328343632 & 0.0116712164171816 \tabularnewline
48 & 0.98437555785534 & 0.0312488842893185 & 0.0156244421446592 \tabularnewline
49 & 0.985663669172766 & 0.0286726616544677 & 0.0143363308272338 \tabularnewline
50 & 0.991932624306962 & 0.0161347513860755 & 0.00806737569303773 \tabularnewline
51 & 0.989066611643689 & 0.0218667767126225 & 0.0109333883563112 \tabularnewline
52 & 0.999137429101997 & 0.00172514179600568 & 0.000862570898002841 \tabularnewline
53 & 0.99894579565244 & 0.00210840869512088 & 0.00105420434756044 \tabularnewline
54 & 0.998579836025019 & 0.00284032794996204 & 0.00142016397498102 \tabularnewline
55 & 0.998009952681625 & 0.00398009463674958 & 0.00199004731837479 \tabularnewline
56 & 0.997512178303745 & 0.00497564339251027 & 0.00248782169625514 \tabularnewline
57 & 0.997169177721967 & 0.00566164455606536 & 0.00283082227803268 \tabularnewline
58 & 0.997996594186282 & 0.00400681162743633 & 0.00200340581371817 \tabularnewline
59 & 0.997394705409387 & 0.00521058918122553 & 0.00260529459061277 \tabularnewline
60 & 0.996474713662569 & 0.00705057267486287 & 0.00352528633743143 \tabularnewline
61 & 0.998466954639628 & 0.00306609072074389 & 0.00153304536037195 \tabularnewline
62 & 0.999084453010593 & 0.00183109397881393 & 0.000915546989406967 \tabularnewline
63 & 0.998796598157031 & 0.00240680368593775 & 0.00120340184296887 \tabularnewline
64 & 0.998305682178697 & 0.00338863564260591 & 0.00169431782130296 \tabularnewline
65 & 0.997630058837985 & 0.00473988232403046 & 0.00236994116201523 \tabularnewline
66 & 0.996811240798026 & 0.00637751840394719 & 0.0031887592019736 \tabularnewline
67 & 0.995964912138647 & 0.00807017572270596 & 0.00403508786135298 \tabularnewline
68 & 0.994763574266986 & 0.0104728514660283 & 0.00523642573301413 \tabularnewline
69 & 0.996904421155941 & 0.00619115768811731 & 0.00309557884405866 \tabularnewline
70 & 0.995776254331017 & 0.00844749133796534 & 0.00422374566898267 \tabularnewline
71 & 0.994923866182229 & 0.0101522676355424 & 0.00507613381777118 \tabularnewline
72 & 0.993358893340165 & 0.0132822133196696 & 0.00664110665983482 \tabularnewline
73 & 0.992982640086303 & 0.0140347198273939 & 0.00701735991369695 \tabularnewline
74 & 0.992424771590136 & 0.0151504568197284 & 0.00757522840986421 \tabularnewline
75 & 0.994540310343165 & 0.0109193793136697 & 0.00545968965683487 \tabularnewline
76 & 0.993864280553259 & 0.0122714388934824 & 0.0061357194467412 \tabularnewline
77 & 0.993028691181473 & 0.0139426176370541 & 0.00697130881852707 \tabularnewline
78 & 0.991110359698314 & 0.0177792806033724 & 0.0088896403016862 \tabularnewline
79 & 0.989641666017643 & 0.0207166679647148 & 0.0103583339823574 \tabularnewline
80 & 0.98651822290789 & 0.0269635541842216 & 0.0134817770921108 \tabularnewline
81 & 0.98482783757203 & 0.0303443248559386 & 0.0151721624279693 \tabularnewline
82 & 0.980649520383401 & 0.0387009592331981 & 0.019350479616599 \tabularnewline
83 & 0.975322768805673 & 0.049354462388654 & 0.024677231194327 \tabularnewline
84 & 0.97704940572644 & 0.0459011885471197 & 0.0229505942735599 \tabularnewline
85 & 0.975830452026066 & 0.0483390959478684 & 0.0241695479739342 \tabularnewline
86 & 0.981705898389422 & 0.0365882032211568 & 0.0182941016105784 \tabularnewline
87 & 0.976548652418782 & 0.0469026951624367 & 0.0234513475812184 \tabularnewline
88 & 0.97325425800455 & 0.0534914839908989 & 0.0267457419954494 \tabularnewline
89 & 0.967954926092244 & 0.0640901478155116 & 0.0320450739077558 \tabularnewline
90 & 0.974608118430108 & 0.0507837631397848 & 0.0253918815698924 \tabularnewline
91 & 0.96887965750297 & 0.0622406849940618 & 0.0311203424970309 \tabularnewline
92 & 0.96492560583953 & 0.0701487883209423 & 0.0350743941604711 \tabularnewline
93 & 0.96117029952621 & 0.0776594009475798 & 0.0388297004737899 \tabularnewline
94 & 0.955471586903368 & 0.0890568261932647 & 0.0445284130966324 \tabularnewline
95 & 0.950411820359466 & 0.0991763592810685 & 0.0495881796405343 \tabularnewline
96 & 0.939839552285929 & 0.120320895428142 & 0.060160447714071 \tabularnewline
97 & 0.930425249976848 & 0.139149500046304 & 0.0695747500231518 \tabularnewline
98 & 0.9227254394344 & 0.154549121131201 & 0.0772745605656005 \tabularnewline
99 & 0.920372065313252 & 0.159255869373496 & 0.0796279346867482 \tabularnewline
100 & 0.906258065410809 & 0.187483869178383 & 0.0937419345891914 \tabularnewline
101 & 0.914885619401634 & 0.170228761196733 & 0.0851143805983663 \tabularnewline
102 & 0.909790495593034 & 0.180419008813932 & 0.090209504406966 \tabularnewline
103 & 0.907174723123832 & 0.185650553752336 & 0.0928252768761678 \tabularnewline
104 & 0.889288659868048 & 0.221422680263904 & 0.110711340131952 \tabularnewline
105 & 0.88198596924563 & 0.236028061508739 & 0.118014030754369 \tabularnewline
106 & 0.862015630842713 & 0.275968738314573 & 0.137984369157286 \tabularnewline
107 & 0.837320961987315 & 0.32535807602537 & 0.162679038012685 \tabularnewline
108 & 0.813219083662125 & 0.37356183267575 & 0.186780916337875 \tabularnewline
109 & 0.81809395160105 & 0.363812096797899 & 0.18190604839895 \tabularnewline
110 & 0.796186044237169 & 0.407627911525662 & 0.203813955762831 \tabularnewline
111 & 0.773366490030796 & 0.453267019938407 & 0.226633509969203 \tabularnewline
112 & 0.757994674683713 & 0.484010650632573 & 0.242005325316287 \tabularnewline
113 & 0.729277719752546 & 0.541444560494908 & 0.270722280247454 \tabularnewline
114 & 0.698978051230569 & 0.602043897538862 & 0.301021948769431 \tabularnewline
115 & 0.738495413647801 & 0.523009172704398 & 0.261504586352199 \tabularnewline
116 & 0.750215951702026 & 0.499568096595948 & 0.249784048297974 \tabularnewline
117 & 0.756592391811784 & 0.486815216376431 & 0.243407608188216 \tabularnewline
118 & 0.721965511061952 & 0.556068977876096 & 0.278034488938048 \tabularnewline
119 & 0.689028405332642 & 0.621943189334716 & 0.310971594667358 \tabularnewline
120 & 0.652303236400049 & 0.695393527199902 & 0.347696763599951 \tabularnewline
121 & 0.655396834094825 & 0.68920633181035 & 0.344603165905175 \tabularnewline
122 & 0.628336928776153 & 0.743326142447694 & 0.371663071223847 \tabularnewline
123 & 0.589311513391462 & 0.821376973217076 & 0.410688486608538 \tabularnewline
124 & 0.554625029348273 & 0.890749941303455 & 0.445374970651727 \tabularnewline
125 & 0.522998496694543 & 0.954003006610914 & 0.477001503305457 \tabularnewline
126 & 0.515282480658654 & 0.96943503868269 & 0.484717519341346 \tabularnewline
127 & 0.540648066344023 & 0.918703867311955 & 0.459351933655977 \tabularnewline
128 & 0.498236640997377 & 0.996473281994754 & 0.501763359002623 \tabularnewline
129 & 0.630280329790426 & 0.739439340419149 & 0.369719670209574 \tabularnewline
130 & 0.607734205534945 & 0.78453158893011 & 0.392265794465055 \tabularnewline
131 & 0.60067797505893 & 0.79864404988214 & 0.39932202494107 \tabularnewline
132 & 0.93944307846716 & 0.121113843065681 & 0.0605569215328404 \tabularnewline
133 & 0.95773784628617 & 0.0845243074276609 & 0.0422621537138304 \tabularnewline
134 & 0.947024148056142 & 0.105951703887715 & 0.0529758519438577 \tabularnewline
135 & 0.938923595320145 & 0.122152809359709 & 0.0610764046798547 \tabularnewline
136 & 0.927089718744202 & 0.145820562511597 & 0.0729102812557983 \tabularnewline
137 & 0.929726600833677 & 0.140546798332645 & 0.0702733991663226 \tabularnewline
138 & 0.918992345279028 & 0.162015309441943 & 0.0810076547209716 \tabularnewline
139 & 0.904131867990854 & 0.191736264018293 & 0.0958681320091465 \tabularnewline
140 & 0.886667539609199 & 0.226664920781603 & 0.113332460390801 \tabularnewline
141 & 0.863933018234148 & 0.272133963531704 & 0.136066981765852 \tabularnewline
142 & 0.835942883862862 & 0.328114232274277 & 0.164057116137138 \tabularnewline
143 & 0.819328117730266 & 0.361343764539469 & 0.180671882269734 \tabularnewline
144 & 0.804328984813339 & 0.391342030373321 & 0.195671015186661 \tabularnewline
145 & 0.788080396666328 & 0.423839206667343 & 0.211919603333672 \tabularnewline
146 & 0.781499885115729 & 0.437000229768542 & 0.218500114884271 \tabularnewline
147 & 0.743453785588053 & 0.513092428823894 & 0.256546214411947 \tabularnewline
148 & 0.70934008333029 & 0.581319833339421 & 0.29065991666971 \tabularnewline
149 & 0.728300181461482 & 0.543399637077036 & 0.271699818538518 \tabularnewline
150 & 0.85669602251063 & 0.286607954978739 & 0.14330397748937 \tabularnewline
151 & 0.827660501740123 & 0.344678996519755 & 0.172339498259877 \tabularnewline
152 & 0.79899690484024 & 0.402006190319519 & 0.20100309515976 \tabularnewline
153 & 0.76354315509985 & 0.472913689800299 & 0.23645684490015 \tabularnewline
154 & 0.814119290067947 & 0.371761419864105 & 0.185880709932053 \tabularnewline
155 & 0.797933222114458 & 0.404133555771084 & 0.202066777885542 \tabularnewline
156 & 0.822010586585098 & 0.355978826829804 & 0.177989413414902 \tabularnewline
157 & 0.783682707939206 & 0.432634584121588 & 0.216317292060794 \tabularnewline
158 & 0.74491494062042 & 0.510170118759158 & 0.255085059379579 \tabularnewline
159 & 0.775649368196631 & 0.448701263606738 & 0.224350631803369 \tabularnewline
160 & 0.735006407026858 & 0.529987185946283 & 0.264993592973142 \tabularnewline
161 & 0.741580216690329 & 0.516839566619343 & 0.258419783309671 \tabularnewline
162 & 0.692141031090863 & 0.615717937818273 & 0.307858968909137 \tabularnewline
163 & 0.649551715903647 & 0.700896568192707 & 0.350448284096353 \tabularnewline
164 & 0.606805294547127 & 0.786389410905746 & 0.393194705452873 \tabularnewline
165 & 0.564916135704685 & 0.87016772859063 & 0.435083864295315 \tabularnewline
166 & 0.519429636900258 & 0.961140726199483 & 0.480570363099742 \tabularnewline
167 & 0.458293139747349 & 0.916586279494698 & 0.541706860252651 \tabularnewline
168 & 0.718033840361975 & 0.56393231927605 & 0.281966159638025 \tabularnewline
169 & 0.661731332115434 & 0.676537335769132 & 0.338268667884566 \tabularnewline
170 & 0.600707202062623 & 0.798585595874753 & 0.399292797937377 \tabularnewline
171 & 0.641574006696009 & 0.716851986607983 & 0.358425993303991 \tabularnewline
172 & 0.590902478377376 & 0.818195043245248 & 0.409097521622624 \tabularnewline
173 & 0.57240426202961 & 0.855191475940779 & 0.427595737970389 \tabularnewline
174 & 0.513856918086583 & 0.972286163826834 & 0.486143081913417 \tabularnewline
175 & 0.488886806577593 & 0.977773613155186 & 0.511113193422407 \tabularnewline
176 & 0.50896576651574 & 0.98206846696852 & 0.49103423348426 \tabularnewline
177 & 0.434020311960388 & 0.868040623920775 & 0.565979688039612 \tabularnewline
178 & 0.364135158432162 & 0.728270316864325 & 0.635864841567838 \tabularnewline
179 & 0.293343450357367 & 0.586686900714734 & 0.706656549642633 \tabularnewline
180 & 0.272620337546345 & 0.54524067509269 & 0.727379662453655 \tabularnewline
181 & 0.212293977975512 & 0.424587955951025 & 0.787706022024488 \tabularnewline
182 & 0.171662327359873 & 0.343324654719747 & 0.828337672640127 \tabularnewline
183 & 0.215597934596172 & 0.431195869192344 & 0.784402065403828 \tabularnewline
184 & 0.635661259187103 & 0.728677481625795 & 0.364338740812897 \tabularnewline
185 & 0.522907002034279 & 0.954185995931443 & 0.477092997965721 \tabularnewline
186 & 0.412957658811405 & 0.82591531762281 & 0.587042341188595 \tabularnewline
187 & 0.295585567716297 & 0.591171135432594 & 0.704414432283703 \tabularnewline
188 & 0.26971792067865 & 0.5394358413573 & 0.73028207932135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146899&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]12[/C][C]0.86122163964278[/C][C]0.277556720714439[/C][C]0.138778360357219[/C][/ROW]
[ROW][C]13[/C][C]0.923596777281424[/C][C]0.152806445437151[/C][C]0.0764032227185756[/C][/ROW]
[ROW][C]14[/C][C]0.866919368671161[/C][C]0.266161262657677[/C][C]0.133080631328839[/C][/ROW]
[ROW][C]15[/C][C]0.831719501122052[/C][C]0.336560997755896[/C][C]0.168280498877948[/C][/ROW]
[ROW][C]16[/C][C]0.771542180383706[/C][C]0.456915639232587[/C][C]0.228457819616294[/C][/ROW]
[ROW][C]17[/C][C]0.698794922852888[/C][C]0.602410154294225[/C][C]0.301205077147112[/C][/ROW]
[ROW][C]18[/C][C]0.630341250251824[/C][C]0.739317499496353[/C][C]0.369658749748176[/C][/ROW]
[ROW][C]19[/C][C]0.542943081244726[/C][C]0.914113837510549[/C][C]0.457056918755274[/C][/ROW]
[ROW][C]20[/C][C]0.67820355890761[/C][C]0.643592882184781[/C][C]0.32179644109239[/C][/ROW]
[ROW][C]21[/C][C]0.601675815624364[/C][C]0.796648368751273[/C][C]0.398324184375636[/C][/ROW]
[ROW][C]22[/C][C]0.918894528675518[/C][C]0.162210942648964[/C][C]0.081105471324482[/C][/ROW]
[ROW][C]23[/C][C]0.939389339091147[/C][C]0.121221321817707[/C][C]0.0606106609088535[/C][/ROW]
[ROW][C]24[/C][C]0.947907316145322[/C][C]0.104185367709357[/C][C]0.0520926838546783[/C][/ROW]
[ROW][C]25[/C][C]0.984176779627131[/C][C]0.0316464407457377[/C][C]0.0158232203728688[/C][/ROW]
[ROW][C]26[/C][C]0.97793495240851[/C][C]0.0441300951829778[/C][C]0.0220650475914889[/C][/ROW]
[ROW][C]27[/C][C]0.988467110436676[/C][C]0.0230657791266471[/C][C]0.0115328895633236[/C][/ROW]
[ROW][C]28[/C][C]0.989966133854673[/C][C]0.0200677322906544[/C][C]0.0100338661453272[/C][/ROW]
[ROW][C]29[/C][C]0.99326078509572[/C][C]0.0134784298085603[/C][C]0.00673921490428014[/C][/ROW]
[ROW][C]30[/C][C]0.994749005939569[/C][C]0.0105019881208624[/C][C]0.00525099406043121[/C][/ROW]
[ROW][C]31[/C][C]0.994114358598766[/C][C]0.0117712828024691[/C][C]0.00588564140123454[/C][/ROW]
[ROW][C]32[/C][C]0.992228629614117[/C][C]0.0155427407717657[/C][C]0.00777137038588285[/C][/ROW]
[ROW][C]33[/C][C]0.989537009064136[/C][C]0.0209259818717285[/C][C]0.0104629909358643[/C][/ROW]
[ROW][C]34[/C][C]0.984921737374708[/C][C]0.0301565252505832[/C][C]0.0150782626252916[/C][/ROW]
[ROW][C]35[/C][C]0.982927095846278[/C][C]0.0341458083074442[/C][C]0.0170729041537221[/C][/ROW]
[ROW][C]36[/C][C]0.97825310180358[/C][C]0.0434937963928383[/C][C]0.0217468981964191[/C][/ROW]
[ROW][C]37[/C][C]0.96997478657579[/C][C]0.0600504268484201[/C][C]0.0300252134242101[/C][/ROW]
[ROW][C]38[/C][C]0.959732415184667[/C][C]0.080535169630666[/C][C]0.040267584815333[/C][/ROW]
[ROW][C]39[/C][C]0.946623848061741[/C][C]0.106752303876518[/C][C]0.0533761519382588[/C][/ROW]
[ROW][C]40[/C][C]0.938341719355642[/C][C]0.123316561288715[/C][C]0.0616582806443577[/C][/ROW]
[ROW][C]41[/C][C]0.925341356246908[/C][C]0.149317287506184[/C][C]0.0746586437530919[/C][/ROW]
[ROW][C]42[/C][C]0.905074985570074[/C][C]0.189850028859852[/C][C]0.0949250144299258[/C][/ROW]
[ROW][C]43[/C][C]0.915662424793502[/C][C]0.168675150412996[/C][C]0.084337575206498[/C][/ROW]
[ROW][C]44[/C][C]0.949211233086696[/C][C]0.101577533826608[/C][C]0.050788766913304[/C][/ROW]
[ROW][C]45[/C][C]0.991957633326382[/C][C]0.0160847333472366[/C][C]0.00804236667361832[/C][/ROW]
[ROW][C]46[/C][C]0.990562464503193[/C][C]0.0188750709936131[/C][C]0.00943753549680655[/C][/ROW]
[ROW][C]47[/C][C]0.988328783582818[/C][C]0.0233424328343632[/C][C]0.0116712164171816[/C][/ROW]
[ROW][C]48[/C][C]0.98437555785534[/C][C]0.0312488842893185[/C][C]0.0156244421446592[/C][/ROW]
[ROW][C]49[/C][C]0.985663669172766[/C][C]0.0286726616544677[/C][C]0.0143363308272338[/C][/ROW]
[ROW][C]50[/C][C]0.991932624306962[/C][C]0.0161347513860755[/C][C]0.00806737569303773[/C][/ROW]
[ROW][C]51[/C][C]0.989066611643689[/C][C]0.0218667767126225[/C][C]0.0109333883563112[/C][/ROW]
[ROW][C]52[/C][C]0.999137429101997[/C][C]0.00172514179600568[/C][C]0.000862570898002841[/C][/ROW]
[ROW][C]53[/C][C]0.99894579565244[/C][C]0.00210840869512088[/C][C]0.00105420434756044[/C][/ROW]
[ROW][C]54[/C][C]0.998579836025019[/C][C]0.00284032794996204[/C][C]0.00142016397498102[/C][/ROW]
[ROW][C]55[/C][C]0.998009952681625[/C][C]0.00398009463674958[/C][C]0.00199004731837479[/C][/ROW]
[ROW][C]56[/C][C]0.997512178303745[/C][C]0.00497564339251027[/C][C]0.00248782169625514[/C][/ROW]
[ROW][C]57[/C][C]0.997169177721967[/C][C]0.00566164455606536[/C][C]0.00283082227803268[/C][/ROW]
[ROW][C]58[/C][C]0.997996594186282[/C][C]0.00400681162743633[/C][C]0.00200340581371817[/C][/ROW]
[ROW][C]59[/C][C]0.997394705409387[/C][C]0.00521058918122553[/C][C]0.00260529459061277[/C][/ROW]
[ROW][C]60[/C][C]0.996474713662569[/C][C]0.00705057267486287[/C][C]0.00352528633743143[/C][/ROW]
[ROW][C]61[/C][C]0.998466954639628[/C][C]0.00306609072074389[/C][C]0.00153304536037195[/C][/ROW]
[ROW][C]62[/C][C]0.999084453010593[/C][C]0.00183109397881393[/C][C]0.000915546989406967[/C][/ROW]
[ROW][C]63[/C][C]0.998796598157031[/C][C]0.00240680368593775[/C][C]0.00120340184296887[/C][/ROW]
[ROW][C]64[/C][C]0.998305682178697[/C][C]0.00338863564260591[/C][C]0.00169431782130296[/C][/ROW]
[ROW][C]65[/C][C]0.997630058837985[/C][C]0.00473988232403046[/C][C]0.00236994116201523[/C][/ROW]
[ROW][C]66[/C][C]0.996811240798026[/C][C]0.00637751840394719[/C][C]0.0031887592019736[/C][/ROW]
[ROW][C]67[/C][C]0.995964912138647[/C][C]0.00807017572270596[/C][C]0.00403508786135298[/C][/ROW]
[ROW][C]68[/C][C]0.994763574266986[/C][C]0.0104728514660283[/C][C]0.00523642573301413[/C][/ROW]
[ROW][C]69[/C][C]0.996904421155941[/C][C]0.00619115768811731[/C][C]0.00309557884405866[/C][/ROW]
[ROW][C]70[/C][C]0.995776254331017[/C][C]0.00844749133796534[/C][C]0.00422374566898267[/C][/ROW]
[ROW][C]71[/C][C]0.994923866182229[/C][C]0.0101522676355424[/C][C]0.00507613381777118[/C][/ROW]
[ROW][C]72[/C][C]0.993358893340165[/C][C]0.0132822133196696[/C][C]0.00664110665983482[/C][/ROW]
[ROW][C]73[/C][C]0.992982640086303[/C][C]0.0140347198273939[/C][C]0.00701735991369695[/C][/ROW]
[ROW][C]74[/C][C]0.992424771590136[/C][C]0.0151504568197284[/C][C]0.00757522840986421[/C][/ROW]
[ROW][C]75[/C][C]0.994540310343165[/C][C]0.0109193793136697[/C][C]0.00545968965683487[/C][/ROW]
[ROW][C]76[/C][C]0.993864280553259[/C][C]0.0122714388934824[/C][C]0.0061357194467412[/C][/ROW]
[ROW][C]77[/C][C]0.993028691181473[/C][C]0.0139426176370541[/C][C]0.00697130881852707[/C][/ROW]
[ROW][C]78[/C][C]0.991110359698314[/C][C]0.0177792806033724[/C][C]0.0088896403016862[/C][/ROW]
[ROW][C]79[/C][C]0.989641666017643[/C][C]0.0207166679647148[/C][C]0.0103583339823574[/C][/ROW]
[ROW][C]80[/C][C]0.98651822290789[/C][C]0.0269635541842216[/C][C]0.0134817770921108[/C][/ROW]
[ROW][C]81[/C][C]0.98482783757203[/C][C]0.0303443248559386[/C][C]0.0151721624279693[/C][/ROW]
[ROW][C]82[/C][C]0.980649520383401[/C][C]0.0387009592331981[/C][C]0.019350479616599[/C][/ROW]
[ROW][C]83[/C][C]0.975322768805673[/C][C]0.049354462388654[/C][C]0.024677231194327[/C][/ROW]
[ROW][C]84[/C][C]0.97704940572644[/C][C]0.0459011885471197[/C][C]0.0229505942735599[/C][/ROW]
[ROW][C]85[/C][C]0.975830452026066[/C][C]0.0483390959478684[/C][C]0.0241695479739342[/C][/ROW]
[ROW][C]86[/C][C]0.981705898389422[/C][C]0.0365882032211568[/C][C]0.0182941016105784[/C][/ROW]
[ROW][C]87[/C][C]0.976548652418782[/C][C]0.0469026951624367[/C][C]0.0234513475812184[/C][/ROW]
[ROW][C]88[/C][C]0.97325425800455[/C][C]0.0534914839908989[/C][C]0.0267457419954494[/C][/ROW]
[ROW][C]89[/C][C]0.967954926092244[/C][C]0.0640901478155116[/C][C]0.0320450739077558[/C][/ROW]
[ROW][C]90[/C][C]0.974608118430108[/C][C]0.0507837631397848[/C][C]0.0253918815698924[/C][/ROW]
[ROW][C]91[/C][C]0.96887965750297[/C][C]0.0622406849940618[/C][C]0.0311203424970309[/C][/ROW]
[ROW][C]92[/C][C]0.96492560583953[/C][C]0.0701487883209423[/C][C]0.0350743941604711[/C][/ROW]
[ROW][C]93[/C][C]0.96117029952621[/C][C]0.0776594009475798[/C][C]0.0388297004737899[/C][/ROW]
[ROW][C]94[/C][C]0.955471586903368[/C][C]0.0890568261932647[/C][C]0.0445284130966324[/C][/ROW]
[ROW][C]95[/C][C]0.950411820359466[/C][C]0.0991763592810685[/C][C]0.0495881796405343[/C][/ROW]
[ROW][C]96[/C][C]0.939839552285929[/C][C]0.120320895428142[/C][C]0.060160447714071[/C][/ROW]
[ROW][C]97[/C][C]0.930425249976848[/C][C]0.139149500046304[/C][C]0.0695747500231518[/C][/ROW]
[ROW][C]98[/C][C]0.9227254394344[/C][C]0.154549121131201[/C][C]0.0772745605656005[/C][/ROW]
[ROW][C]99[/C][C]0.920372065313252[/C][C]0.159255869373496[/C][C]0.0796279346867482[/C][/ROW]
[ROW][C]100[/C][C]0.906258065410809[/C][C]0.187483869178383[/C][C]0.0937419345891914[/C][/ROW]
[ROW][C]101[/C][C]0.914885619401634[/C][C]0.170228761196733[/C][C]0.0851143805983663[/C][/ROW]
[ROW][C]102[/C][C]0.909790495593034[/C][C]0.180419008813932[/C][C]0.090209504406966[/C][/ROW]
[ROW][C]103[/C][C]0.907174723123832[/C][C]0.185650553752336[/C][C]0.0928252768761678[/C][/ROW]
[ROW][C]104[/C][C]0.889288659868048[/C][C]0.221422680263904[/C][C]0.110711340131952[/C][/ROW]
[ROW][C]105[/C][C]0.88198596924563[/C][C]0.236028061508739[/C][C]0.118014030754369[/C][/ROW]
[ROW][C]106[/C][C]0.862015630842713[/C][C]0.275968738314573[/C][C]0.137984369157286[/C][/ROW]
[ROW][C]107[/C][C]0.837320961987315[/C][C]0.32535807602537[/C][C]0.162679038012685[/C][/ROW]
[ROW][C]108[/C][C]0.813219083662125[/C][C]0.37356183267575[/C][C]0.186780916337875[/C][/ROW]
[ROW][C]109[/C][C]0.81809395160105[/C][C]0.363812096797899[/C][C]0.18190604839895[/C][/ROW]
[ROW][C]110[/C][C]0.796186044237169[/C][C]0.407627911525662[/C][C]0.203813955762831[/C][/ROW]
[ROW][C]111[/C][C]0.773366490030796[/C][C]0.453267019938407[/C][C]0.226633509969203[/C][/ROW]
[ROW][C]112[/C][C]0.757994674683713[/C][C]0.484010650632573[/C][C]0.242005325316287[/C][/ROW]
[ROW][C]113[/C][C]0.729277719752546[/C][C]0.541444560494908[/C][C]0.270722280247454[/C][/ROW]
[ROW][C]114[/C][C]0.698978051230569[/C][C]0.602043897538862[/C][C]0.301021948769431[/C][/ROW]
[ROW][C]115[/C][C]0.738495413647801[/C][C]0.523009172704398[/C][C]0.261504586352199[/C][/ROW]
[ROW][C]116[/C][C]0.750215951702026[/C][C]0.499568096595948[/C][C]0.249784048297974[/C][/ROW]
[ROW][C]117[/C][C]0.756592391811784[/C][C]0.486815216376431[/C][C]0.243407608188216[/C][/ROW]
[ROW][C]118[/C][C]0.721965511061952[/C][C]0.556068977876096[/C][C]0.278034488938048[/C][/ROW]
[ROW][C]119[/C][C]0.689028405332642[/C][C]0.621943189334716[/C][C]0.310971594667358[/C][/ROW]
[ROW][C]120[/C][C]0.652303236400049[/C][C]0.695393527199902[/C][C]0.347696763599951[/C][/ROW]
[ROW][C]121[/C][C]0.655396834094825[/C][C]0.68920633181035[/C][C]0.344603165905175[/C][/ROW]
[ROW][C]122[/C][C]0.628336928776153[/C][C]0.743326142447694[/C][C]0.371663071223847[/C][/ROW]
[ROW][C]123[/C][C]0.589311513391462[/C][C]0.821376973217076[/C][C]0.410688486608538[/C][/ROW]
[ROW][C]124[/C][C]0.554625029348273[/C][C]0.890749941303455[/C][C]0.445374970651727[/C][/ROW]
[ROW][C]125[/C][C]0.522998496694543[/C][C]0.954003006610914[/C][C]0.477001503305457[/C][/ROW]
[ROW][C]126[/C][C]0.515282480658654[/C][C]0.96943503868269[/C][C]0.484717519341346[/C][/ROW]
[ROW][C]127[/C][C]0.540648066344023[/C][C]0.918703867311955[/C][C]0.459351933655977[/C][/ROW]
[ROW][C]128[/C][C]0.498236640997377[/C][C]0.996473281994754[/C][C]0.501763359002623[/C][/ROW]
[ROW][C]129[/C][C]0.630280329790426[/C][C]0.739439340419149[/C][C]0.369719670209574[/C][/ROW]
[ROW][C]130[/C][C]0.607734205534945[/C][C]0.78453158893011[/C][C]0.392265794465055[/C][/ROW]
[ROW][C]131[/C][C]0.60067797505893[/C][C]0.79864404988214[/C][C]0.39932202494107[/C][/ROW]
[ROW][C]132[/C][C]0.93944307846716[/C][C]0.121113843065681[/C][C]0.0605569215328404[/C][/ROW]
[ROW][C]133[/C][C]0.95773784628617[/C][C]0.0845243074276609[/C][C]0.0422621537138304[/C][/ROW]
[ROW][C]134[/C][C]0.947024148056142[/C][C]0.105951703887715[/C][C]0.0529758519438577[/C][/ROW]
[ROW][C]135[/C][C]0.938923595320145[/C][C]0.122152809359709[/C][C]0.0610764046798547[/C][/ROW]
[ROW][C]136[/C][C]0.927089718744202[/C][C]0.145820562511597[/C][C]0.0729102812557983[/C][/ROW]
[ROW][C]137[/C][C]0.929726600833677[/C][C]0.140546798332645[/C][C]0.0702733991663226[/C][/ROW]
[ROW][C]138[/C][C]0.918992345279028[/C][C]0.162015309441943[/C][C]0.0810076547209716[/C][/ROW]
[ROW][C]139[/C][C]0.904131867990854[/C][C]0.191736264018293[/C][C]0.0958681320091465[/C][/ROW]
[ROW][C]140[/C][C]0.886667539609199[/C][C]0.226664920781603[/C][C]0.113332460390801[/C][/ROW]
[ROW][C]141[/C][C]0.863933018234148[/C][C]0.272133963531704[/C][C]0.136066981765852[/C][/ROW]
[ROW][C]142[/C][C]0.835942883862862[/C][C]0.328114232274277[/C][C]0.164057116137138[/C][/ROW]
[ROW][C]143[/C][C]0.819328117730266[/C][C]0.361343764539469[/C][C]0.180671882269734[/C][/ROW]
[ROW][C]144[/C][C]0.804328984813339[/C][C]0.391342030373321[/C][C]0.195671015186661[/C][/ROW]
[ROW][C]145[/C][C]0.788080396666328[/C][C]0.423839206667343[/C][C]0.211919603333672[/C][/ROW]
[ROW][C]146[/C][C]0.781499885115729[/C][C]0.437000229768542[/C][C]0.218500114884271[/C][/ROW]
[ROW][C]147[/C][C]0.743453785588053[/C][C]0.513092428823894[/C][C]0.256546214411947[/C][/ROW]
[ROW][C]148[/C][C]0.70934008333029[/C][C]0.581319833339421[/C][C]0.29065991666971[/C][/ROW]
[ROW][C]149[/C][C]0.728300181461482[/C][C]0.543399637077036[/C][C]0.271699818538518[/C][/ROW]
[ROW][C]150[/C][C]0.85669602251063[/C][C]0.286607954978739[/C][C]0.14330397748937[/C][/ROW]
[ROW][C]151[/C][C]0.827660501740123[/C][C]0.344678996519755[/C][C]0.172339498259877[/C][/ROW]
[ROW][C]152[/C][C]0.79899690484024[/C][C]0.402006190319519[/C][C]0.20100309515976[/C][/ROW]
[ROW][C]153[/C][C]0.76354315509985[/C][C]0.472913689800299[/C][C]0.23645684490015[/C][/ROW]
[ROW][C]154[/C][C]0.814119290067947[/C][C]0.371761419864105[/C][C]0.185880709932053[/C][/ROW]
[ROW][C]155[/C][C]0.797933222114458[/C][C]0.404133555771084[/C][C]0.202066777885542[/C][/ROW]
[ROW][C]156[/C][C]0.822010586585098[/C][C]0.355978826829804[/C][C]0.177989413414902[/C][/ROW]
[ROW][C]157[/C][C]0.783682707939206[/C][C]0.432634584121588[/C][C]0.216317292060794[/C][/ROW]
[ROW][C]158[/C][C]0.74491494062042[/C][C]0.510170118759158[/C][C]0.255085059379579[/C][/ROW]
[ROW][C]159[/C][C]0.775649368196631[/C][C]0.448701263606738[/C][C]0.224350631803369[/C][/ROW]
[ROW][C]160[/C][C]0.735006407026858[/C][C]0.529987185946283[/C][C]0.264993592973142[/C][/ROW]
[ROW][C]161[/C][C]0.741580216690329[/C][C]0.516839566619343[/C][C]0.258419783309671[/C][/ROW]
[ROW][C]162[/C][C]0.692141031090863[/C][C]0.615717937818273[/C][C]0.307858968909137[/C][/ROW]
[ROW][C]163[/C][C]0.649551715903647[/C][C]0.700896568192707[/C][C]0.350448284096353[/C][/ROW]
[ROW][C]164[/C][C]0.606805294547127[/C][C]0.786389410905746[/C][C]0.393194705452873[/C][/ROW]
[ROW][C]165[/C][C]0.564916135704685[/C][C]0.87016772859063[/C][C]0.435083864295315[/C][/ROW]
[ROW][C]166[/C][C]0.519429636900258[/C][C]0.961140726199483[/C][C]0.480570363099742[/C][/ROW]
[ROW][C]167[/C][C]0.458293139747349[/C][C]0.916586279494698[/C][C]0.541706860252651[/C][/ROW]
[ROW][C]168[/C][C]0.718033840361975[/C][C]0.56393231927605[/C][C]0.281966159638025[/C][/ROW]
[ROW][C]169[/C][C]0.661731332115434[/C][C]0.676537335769132[/C][C]0.338268667884566[/C][/ROW]
[ROW][C]170[/C][C]0.600707202062623[/C][C]0.798585595874753[/C][C]0.399292797937377[/C][/ROW]
[ROW][C]171[/C][C]0.641574006696009[/C][C]0.716851986607983[/C][C]0.358425993303991[/C][/ROW]
[ROW][C]172[/C][C]0.590902478377376[/C][C]0.818195043245248[/C][C]0.409097521622624[/C][/ROW]
[ROW][C]173[/C][C]0.57240426202961[/C][C]0.855191475940779[/C][C]0.427595737970389[/C][/ROW]
[ROW][C]174[/C][C]0.513856918086583[/C][C]0.972286163826834[/C][C]0.486143081913417[/C][/ROW]
[ROW][C]175[/C][C]0.488886806577593[/C][C]0.977773613155186[/C][C]0.511113193422407[/C][/ROW]
[ROW][C]176[/C][C]0.50896576651574[/C][C]0.98206846696852[/C][C]0.49103423348426[/C][/ROW]
[ROW][C]177[/C][C]0.434020311960388[/C][C]0.868040623920775[/C][C]0.565979688039612[/C][/ROW]
[ROW][C]178[/C][C]0.364135158432162[/C][C]0.728270316864325[/C][C]0.635864841567838[/C][/ROW]
[ROW][C]179[/C][C]0.293343450357367[/C][C]0.586686900714734[/C][C]0.706656549642633[/C][/ROW]
[ROW][C]180[/C][C]0.272620337546345[/C][C]0.54524067509269[/C][C]0.727379662453655[/C][/ROW]
[ROW][C]181[/C][C]0.212293977975512[/C][C]0.424587955951025[/C][C]0.787706022024488[/C][/ROW]
[ROW][C]182[/C][C]0.171662327359873[/C][C]0.343324654719747[/C][C]0.828337672640127[/C][/ROW]
[ROW][C]183[/C][C]0.215597934596172[/C][C]0.431195869192344[/C][C]0.784402065403828[/C][/ROW]
[ROW][C]184[/C][C]0.635661259187103[/C][C]0.728677481625795[/C][C]0.364338740812897[/C][/ROW]
[ROW][C]185[/C][C]0.522907002034279[/C][C]0.954185995931443[/C][C]0.477092997965721[/C][/ROW]
[ROW][C]186[/C][C]0.412957658811405[/C][C]0.82591531762281[/C][C]0.587042341188595[/C][/ROW]
[ROW][C]187[/C][C]0.295585567716297[/C][C]0.591171135432594[/C][C]0.704414432283703[/C][/ROW]
[ROW][C]188[/C][C]0.26971792067865[/C][C]0.5394358413573[/C][C]0.73028207932135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146899&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146899&T=5

As an alternative you can also use a QR Code:  

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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
120.861221639642780.2775567207144390.138778360357219
130.9235967772814240.1528064454371510.0764032227185756
140.8669193686711610.2661612626576770.133080631328839
150.8317195011220520.3365609977558960.168280498877948
160.7715421803837060.4569156392325870.228457819616294
170.6987949228528880.6024101542942250.301205077147112
180.6303412502518240.7393174994963530.369658749748176
190.5429430812447260.9141138375105490.457056918755274
200.678203558907610.6435928821847810.32179644109239
210.6016758156243640.7966483687512730.398324184375636
220.9188945286755180.1622109426489640.081105471324482
230.9393893390911470.1212213218177070.0606106609088535
240.9479073161453220.1041853677093570.0520926838546783
250.9841767796271310.03164644074573770.0158232203728688
260.977934952408510.04413009518297780.0220650475914889
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280.9899661338546730.02006773229065440.0100338661453272
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670.9959649121386470.008070175722705960.00403508786135298
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1880.269717920678650.53943584135730.73028207932135







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level180.101694915254237NOK
5% type I error level550.310734463276836NOK
10% type I error level660.372881355932203NOK

\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 & 18 & 0.101694915254237 & NOK \tabularnewline
5% type I error level & 55 & 0.310734463276836 & NOK \tabularnewline
10% type I error level & 66 & 0.372881355932203 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146899&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]18[/C][C]0.101694915254237[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]55[/C][C]0.310734463276836[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]66[/C][C]0.372881355932203[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146899&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146899&T=6

As an alternative you can also use a QR Code:  

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level180.101694915254237NOK
5% type I error level550.310734463276836NOK
10% type I error level660.372881355932203NOK



Parameters (Session):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;
Parameters (R input):
par1 = 1 ; par2 = Do not include Seasonal 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')
}