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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Mon, 10 Dec 2018 17:26:24 +0100

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2018/Dec/10/t1544459692ik9y6jdxtujmhux.htm/, Retrieved Wed, 22 May 2024 09:53:21 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=315828, Retrieved Wed, 22 May 2024 09:53:21 +0000

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Estimated Impact

103

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)

-       [Multiple Regression] [2 2.0] [2018-12-10 16:26:24] [2798cb2ba7c98983ee6ef359c139afa6] [Current]

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Dataseries X:

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5 3 4 5 4 5 5 4 5 4 4 4 4 2 4 3 5 4 13
2 2 5 2 5 5 5 4 5 NA 4 4 5 3 3 4 5 4 16
3 3 4 2 5 5 4 4 4 3 3 2 4 4 5 4 5 4 17
3 3 4 2 3 4 4 4 4 3 3 3 3 4 3 3 4 4 NA
3 2 4 4 5 5 5 4 5 4 4 3 4 4 5 4 5 4 NA
4 4 5 4 5 5 5 4 5 3 4 3 3 4 4 4 5 5 16
4 3 5 NA 5 4 5 5 5 4 2 3 3 4 4 3 3 4 NA
2 2 5 3 4 NA 4 4 5 4 2 4 3 4 5 4 4 4 NA
5 4 5 2 5 5 4 4 5 2 2 4 4 5 4 4 5 5 NA
4 2 5 4 5 5 5 5 5 1 2 4 4 5 5 4 5 5 17
2 2 5 2 4 3 4 3 4 4 3 2 4 4 2 4 5 4 17
4 4 4 4 3 5 4 3 5 4 3 2 4 4 5 3 5 4 15
3 5 4 3 4 5 5 4 5 4 5 4 4 4 4 3 4 5 16
3 5 5 3 5 5 5 4 5 5 4 5 3 3 5 4 4 5 14
4 2 5 4 4 4 4 4 4 4 3 4 4 4 5 4 2 5 16
2 2 4 3 5 4 5 4 5 1 4 4 3 4 5 4 4 5 17
1 1 4 2 4 5 5 4 3 4 4 2 3 4 5 4 4 5 NA
NA 5 NA NA NA NA NA NA 5 4 NA NA NA NA 5 NA 5 5 NA
2 2 4 2 5 4 4 4 5 2 NA 2 5 5 4 3 4 4 NA
3 4 5 2 5 4 5 5 5 3 4 5 4 4 4 4 5 4 NA
5 4 5 2 5 5 5 4 5 3 NA 4 3 4 5 3 4 5 16
4 4 4 3 3 5 5 4 NA 2 3 1 4 4 4 4 5 5 NA
5 4 4 2 4 5 5 4 3 1 3 5 4 4 5 4 4 5 16
3 3 4 2 4 4 4 4 4 3 2 3 4 4 5 4 4 4 NA
5 5 5 3 5 5 5 5 4 2 2 4 4 4 5 4 4 5 NA
2 2 4 2 3 4 3 3 4 NA 3 4 3 4 4 4 4 4 NA
4 5 5 3 5 5 4 5 5 4 3 2 3 4 4 3 5 5 16
4 2 4 2 4 4 4 3 4 4 3 4 4 4 4 4 4 4 15
3 3 5 2 4 5 4 4 5 2 4 2 2 4 5 4 5 5 16
2 1 4 2 4 5 4 4 4 3 4 3 5 4 4 4 4 4 16
1 1 4 5 4 3 5 4 5 4 3 4 4 3 5 4 4 4 13
2 2 3 3 5 4 5 3 4 4 4 4 4 5 5 4 5 5 15
5 1 5 4 5 5 5 4 4 4 3 4 5 4 5 4 4 5 17
4 4 4 3 4 4 5 5 4 3 4 4 4 3 5 4 NA 5 NA
3 3 4 3 5 5 5 4 5 4 3 4 2 3 5 4 5 4 13
2 3 5 3 5 5 5 5 5 4 3 4 4 5 2 4 4 4 17
1 2 4 2 4 4 4 4 5 4 3 5 3 4 5 4 4 4 NA
3 2 5 4 5 4 4 4 5 4 3 4 4 3 5 3 4 5 14
3 3 5 3 4 4 4 4 2 3 2 4 4 3 3 4 4 4 14
3 1 5 2 4 5 4 3 4 3 5 3 4 4 5 4 4 4 18
5 3 4 3 4 4 4 4 4 4 3 4 5 4 4 4 4 4 NA
2 2 4 4 4 4 4 4 4 2 1 4 4 5 5 4 5 5 17
2 2 4 3 4 3 4 3 5 3 2 3 3 3 4 4 4 4 13
1 2 5 4 5 5 4 3 5 4 2 2 5 5 5 3 5 5 16
4 4 4 3 5 4 5 4 5 4 3 5 5 4 5 3 4 4 15
4 1 4 4 4 4 4 4 4 3 2 4 4 4 4 3 4 5 15
2 2 4 3 4 4 4 4 4 2 3 3 4 4 4 4 4 4 NA
1 5 2 2 4 NA 4 1 5 3 5 4 3 5 5 3 3 4 15
5 4 4 3 4 4 4 4 5 3 4 4 4 4 4 4 5 4 13
4 4 4 1 4 4 4 3 5 4 5 4 2 3 4 2 NA 4 NA
4 4 5 2 5 5 5 4 4 3 2 3 4 5 5 4 4 4 17
4 2 5 3 4 4 4 4 4 3 4 4 5 5 2 4 5 4 NA
2 2 5 3 4 5 4 4 5 3 3 4 5 5 5 4 4 4 NA
2 2 4 2 5 5 5 4 5 3 3 4 4 3 5 4 5 5 11
3 2 4 3 4 5 4 4 5 3 2 4 4 3 4 3 4 5 14
2 1 4 2 4 5 4 4 4 5 3 5 4 4 5 4 4 4 13
3 5 5 2 4 4 4 3 5 4 2 4 3 4 4 3 3 4 NA
4 5 5 2 5 4 3 4 5 NA 4 2 3 4 4 4 4 3 17
3 3 4 2 4 4 4 4 4 3 NA 4 4 4 4 3 5 4 16
2 2 5 2 5 4 4 3 4 4 3 5 4 4 4 4 5 4 NA
2 2 5 2 4 5 4 4 5 4 1 2 5 5 3 4 5 5 17
1 2 4 2 4 5 5 4 5 1 1 3 2 4 4 4 5 5 16
3 2 5 3 4 5 5 4 4 4 3 4 4 4 4 4 5 5 16
4 5 5 3 5 5 5 3 4 3 NA 3 3 4 4 4 2 4 16
4 5 5 4 5 5 5 4 5 3 2 4 4 4 5 4 5 5 15
4 3 5 3 4 4 3 3 3 4 3 4 4 2 4 4 4 4 12
3 3 3 3 4 2 4 3 3 2 4 4 4 4 4 3 5 3 17
5 4 5 4 4 5 5 4 5 4 3 5 4 4 4 3 5 4 14
4 1 4 2 4 4 4 4 4 5 4 3 5 4 5 3 3 5 14
1 1 3 1 4 4 4 3 4 4 4 4 3 4 4 3 5 5 16
1 1 5 3 4 5 5 4 5 4 3 4 3 4 4 3 4 5 NA
5 5 5 4 4 5 5 4 5 4 4 4 4 5 5 5 5 4 NA
5 4 3 4 2 5 4 5 4 NA 4 4 4 4 3 4 NA 4 NA
3 1 4 4 5 5 5 4 5 4 3 4 4 4 4 4 4 4 NA
2 2 4 2 4 5 4 4 4 2 3 4 4 4 4 5 5 4 NA
4 3 5 2 5 5 4 3 4 4 5 4 3 4 3 4 4 4 15
4 2 5 1 5 5 5 4 4 2 2 4 4 4 4 4 5 4 16
4 2 5 2 4 5 5 5 5 5 4 4 3 4 5 3 5 5 14
4 5 5 2 5 5 5 5 4 5 3 3 3 3 5 4 4 5 15
5 5 5 3 5 5 5 4 4 2 3 3 4 3 5 4 4 4 17
4 2 5 2 4 5 5 4 4 4 3 2 4 4 5 4 4 5 NA
4 4 4 3 4 4 4 4 4 3 4 2 3 3 3 4 4 4 10
4 4 4 4 4 4 4 4 4 3 4 2 4 4 4 4 5 4 NA
2 1 4 2 4 3 4 4 2 3 NA 3 4 4 3 4 5 5 17
1 1 5 2 5 5 5 5 4 4 5 4 4 4 4 4 5 5 NA
1 2 4 1 4 5 4 3 4 4 3 4 5 4 4 4 4 4 20
5 4 5 4 4 4 4 4 5 3 4 4 5 4 3 5 4 5 17
5 5 5 3 5 5 5 5 4 3 3 4 4 4 5 4 5 5 18
3 2 5 4 5 5 5 5 5 4 5 4 3 4 5 4 4 5 NA
2 2 2 2 4 5 5 4 4 4 4 4 3 NA 4 4 4 4 17
4 3 4 3 5 4 2 4 4 2 4 4 4 2 3 3 4 4 14
2 1 5 5 4 3 4 3 3 3 4 2 4 4 5 4 4 3 NA
3 4 4 3 4 4 4 4 4 3 4 3 4 4 5 4 4 5 17
1 1 4 1 3 4 3 4 2 3 2 2 4 4 4 4 5 4 NA
5 5 5 3 4 5 5 4 4 4 3 3 4 5 4 4 5 3 17
4 4 5 3 5 5 5 5 5 4 4 4 3 4 4 3 5 5 NA
2 1 4 2 5 5 5 5 3 4 3 5 4 4 5 4 4 5 16
2 3 5 1 4 5 5 4 4 4 3 4 5 4 3 4 4 5 18
1 1 5 3 5 5 5 5 5 5 5 5 5 4 5 5 4 5 18
4 2 5 2 3 4 4 3 2 4 3 3 4 5 4 4 5 5 16
2 1 5 2 5 5 5 5 5 3 1 5 3 4 5 4 4 5 NA
3 1 5 3 4 5 4 4 5 4 3 4 5 3 4 4 5 5 NA
1 3 4 3 5 5 5 5 5 4 4 5 4 4 5 4 4 5 15
2 2 5 3 3 4 4 3 4 2 2 2 5 4 4 4 4 5 13
3 2 4 3 4 4 4 4 4 3 3 3 3 4 4 3 NA 4 NA
1 2 5 2 5 5 5 5 5 3 4 4 5 4 4 5 5 5 NA
5 5 5 NA 5 5 5 4 5 3 4 5 4 4 5 3 NA 5 NA
4 3 4 1 4 5 4 5 4 4 4 4 4 4 3 3 4 3 NA
1 2 5 4 4 5 4 4 4 4 4 5 4 4 5 4 4 4 NA
4 4 5 3 4 5 4 4 5 4 NA 5 4 4 5 4 4 4 16
1 3 5 2 5 4 5 5 5 4 4 5 3 4 5 4 5 3 NA
4 2 3 3 4 4 4 3 5 3 3 4 4 4 4 4 4 4 NA
2 2 5 3 5 4 5 4 4 3 3 4 4 4 4 3 4 5 NA
3 4 3 3 4 3 4 4 5 3 3 4 3 3 4 3 5 5 12
3 1 4 2 4 4 4 4 4 2 NA 4 4 4 4 3 4 4 NA
3 4 4 3 4 4 4 4 5 3 4 4 3 4 5 4 4 4 16
3 3 5 2 5 5 5 5 4 2 2 4 4 4 5 4 3 4 16
3 5 4 3 5 5 4 4 5 4 5 5 5 4 5 1 5 5 NA
2 4 5 2 5 5 5 5 5 5 2 5 5 4 5 4 5 5 16
2 3 5 3 5 5 5 3 4 3 2 5 4 4 4 4 4 3 14
4 4 5 4 4 5 4 4 4 3 2 4 4 4 5 3 4 4 15
2 3 4 3 5 4 5 5 4 3 3 4 3 4 4 3 4 5 14
5 5 4 3 4 5 5 4 5 2 3 4 4 4 4 4 4 4 NA
1 1 5 2 5 5 5 4 5 3 4 5 4 4 4 4 5 4 15
3 2 4 3 5 4 3 5 4 3 NA 4 4 5 3 4 4 4 NA
3 4 5 2 5 5 4 4 4 3 4 4 3 4 4 4 4 4 15
3 4 5 2 4 5 4 4 5 4 3 4 4 4 4 3 4 4 16
4 5 3 2 4 4 4 4 5 4 4 4 4 4 4 4 4 5 NA
3 2 5 2 5 5 5 4 4 3 4 2 3 4 3 3 4 4 NA
3 3 4 NA 5 5 4 4 4 4 3 4 4 4 4 3 4 3 NA
2 4 4 3 4 5 4 4 4 1 3 2 3 2 4 2 4 4 11
4 5 4 2 5 5 4 4 4 5 5 4 4 4 4 3 5 4 NA
5 5 3 3 4 4 4 4 5 4 4 3 5 4 4 3 5 4 18
4 2 5 2 5 5 5 5 5 3 3 5 2 4 4 3 3 5 NA
4 4 4 2 4 3 4 3 4 5 3 2 3 3 4 4 4 4 11
4 4 4 2 4 5 4 4 NA 4 3 4 4 4 4 3 4 4 NA
3 5 4 5 3 3 2 5 4 3 3 3 5 5 4 4 5 4 18
4 2 4 3 2 3 4 4 4 NA NA NA NA NA 2 NA NA NA NA
3 4 5 3 4 5 4 4 3 4 3 3 4 5 5 4 4 4 15
NA 1 5 1 4 5 5 4 4 4 2 4 5 5 5 5 5 4 19
1 2 5 3 4 4 4 4 5 3 4 5 4 5 5 4 5 5 17
2 2 5 2 4 5 NA 4 4 2 4 3 4 4 4 3 4 5 NA
1 1 4 3 5 5 5 4 4 4 4 2 3 4 5 4 5 4 14
4 4 4 3 5 5 4 NA 5 3 5 5 4 4 5 4 4 4 NA
5 3 5 3 3 5 5 4 3 3 2 4 4 4 2 4 4 4 13
4 4 5 3 4 5 4 3 4 4 2 4 4 4 3 4 5 5 17
3 1 4 2 4 5 4 4 1 2 3 2 4 4 4 4 5 5 14
2 4 5 4 5 5 4 3 5 3 3 5 5 4 5 3 5 4 19
1 2 5 2 4 5 4 4 4 4 2 3 4 3 5 4 4 4 14
3 3 5 1 5 5 5 5 5 4 4 3 4 4 5 4 4 4 NA
4 3 5 2 3 4 4 3 3 3 2 3 3 3 2 3 4 4 NA
4 5 5 4 5 5 5 5 4 4 3 4 4 5 5 4 4 3 16
1 5 5 4 5 5 5 4 4 4 NA 4 4 4 4 3 4 4 16
5 5 5 4 3 5 5 3 4 3 3 4 4 4 4 4 4 5 15
3 4 3 3 5 5 5 4 4 2 3 4 3 4 5 3 5 5 12
NA 2 4 2 4 5 4 4 5 4 4 4 4 4 5 4 4 5 NA
4 2 5 4 5 5 5 4 5 2 2 4 5 4 5 4 5 4 17
1 1 3 2 5 5 5 5 5 3 5 5 4 4 5 4 3 4 NA
3 2 4 5 5 4 5 5 5 4 4 3 2 3 5 4 4 4 NA
3 4 NA 2 5 5 5 4 4 3 3 NA 4 4 4 4 4 5 18
4 2 5 3 4 5 4 3 5 2 5 4 4 3 4 3 5 5 15
4 3 2 2 5 4 5 4 5 4 2 4 4 4 4 4 4 3 18
5 5 5 3 5 4 2 5 4 1 4 5 4 5 5 5 4 4 15
1 1 3 3 4 5 4 4 3 5 4 3 5 4 3 4 4 4 NA
NA 5 5 4 4 5 5 4 4 4 4 4 5 4 4 3 4 4 NA
1 1 1 2 4 4 5 3 4 3 3 2 3 3 1 4 5 5 NA
5 3 5 4 4 5 4 4 5 4 5 5 4 4 4 4 4 5 16
3 4 5 2 4 4 4 3 4 4 3 4 4 4 4 4 5 4 NA
4 3 5 5 5 5 5 3 4 3 3 3 2 3 4 5 5 4 16

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	12 seconds
R Server	Big Analytics Cloud Computing Center

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input view raw input (R code)  \tabularnewline
Raw Outputview raw output of R engine  \tabularnewline
Computing time12 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=315828&T=0

[TABLE]
[ROW]

Summary of computational transaction[/C][/ROW] [ROW]

Raw Input[/C]

view raw input (R code) [/C][/ROW] [ROW]

Raw Output[/C]

view raw output of R engine [/C][/ROW] [ROW]

Computing time[/C]

12 seconds[/C][/ROW] [ROW]

R Server[/C]

Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=315828&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=315828&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 Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	12 seconds
R Server	Big Analytics Cloud Computing Center

Multiple Linear Regression - Estimated Regression Equation
TVDCSUM[t] = + 2.54172 -0.0172262EC1[t] + 0.115027EC2[t] + 0.0971547EC3[t] -0.17406EC4[t] + 0.411308IK1[t] + 0.138152IK2[t] + 0.017728IK3[t] -0.253064IK4[t] + 0.27868KVDD1[t] -0.189681KVDD2[t] + 0.207551KVDD3[t] -0.0369484KVDD4[t] + 0.847968`SK/EOU1`[t] + 1.18938`SK/EOU2`[t] -0.0579306`SK/EOU3`[t] + 0.422787`SK/EOU4`[t] + 0.140789`SK/EOU5`[t] + 0.0220796`SK/EOU6`[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
TVDCSUM[t] =  +  2.54172 -0.0172262EC1[t] +  0.115027EC2[t] +  0.0971547EC3[t] -0.17406EC4[t] +  0.411308IK1[t] +  0.138152IK2[t] +  0.017728IK3[t] -0.253064IK4[t] +  0.27868KVDD1[t] -0.189681KVDD2[t] +  0.207551KVDD3[t] -0.0369484KVDD4[t] +  0.847968`SK/EOU1`[t] +  1.18938`SK/EOU2`[t] -0.0579306`SK/EOU3`[t] +  0.422787`SK/EOU4`[t] +  0.140789`SK/EOU5`[t] +  0.0220796`SK/EOU6`[t]  + e[t] \tabularnewline
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=315828&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]TVDCSUM[t] =  +  2.54172 -0.0172262EC1[t] +  0.115027EC2[t] +  0.0971547EC3[t] -0.17406EC4[t] +  0.411308IK1[t] +  0.138152IK2[t] +  0.017728IK3[t] -0.253064IK4[t] +  0.27868KVDD1[t] -0.189681KVDD2[t] +  0.207551KVDD3[t] -0.0369484KVDD4[t] +  0.847968`SK/EOU1`[t] +  1.18938`SK/EOU2`[t] -0.0579306`SK/EOU3`[t] +  0.422787`SK/EOU4`[t] +  0.140789`SK/EOU5`[t] +  0.0220796`SK/EOU6`[t]  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=315828&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=315828&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
TVDCSUM[t] = + 2.54172 -0.0172262EC1[t] + 0.115027EC2[t] + 0.0971547EC3[t] -0.17406EC4[t] + 0.411308IK1[t] + 0.138152IK2[t] + 0.017728IK3[t] -0.253064IK4[t] + 0.27868KVDD1[t] -0.189681KVDD2[t] + 0.207551KVDD3[t] -0.0369484KVDD4[t] + 0.847968`SK/EOU1`[t] + 1.18938`SK/EOU2`[t] -0.0579306`SK/EOU3`[t] + 0.422787`SK/EOU4`[t] + 0.140789`SK/EOU5`[t] + 0.0220796`SK/EOU6`[t] + e[t]

Multiple Linear Regression - Ordinary Least Squares
Variable	Parameter	S.D.	T-STATH0: parameter = 0	2-tail p-value	1-tail p-value
(Intercept)	+2.542	3.088	+8.2300e-01	0.4132	0.2066
EC1	-0.01723	0.1687	-1.0210e-01	0.9189	0.4595
EC2	+0.115	0.1643	+7.0020e-01	0.4861	0.243
EC3	+0.09716	0.3231	+3.0070e-01	0.7645	0.3823
EC4	-0.1741	0.2145	-8.1140e-01	0.4198	0.2099
IK1	+0.4113	0.3504	+1.1740e+00	0.2444	0.1222
IK2	+0.1381	0.339	+4.0760e-01	0.6848	0.3424
IK3	+0.01773	0.3122	+5.6780e-02	0.9549	0.4774
IK4	-0.2531	0.3142	-8.0540e-01	0.4232	0.2116
KVDD1	+0.2787	0.2533	+1.1000e+00	0.275	0.1375
KVDD2	-0.1897	0.1768	-1.0730e+00	0.2869	0.1434
KVDD3	+0.2076	0.1983	+1.0470e+00	0.2986	0.1493
KVDD4	-0.03695	0.2076	-1.7800e-01	0.8592	0.4296
`SK/EOU1`	+0.848	0.2685	+3.1590e+00	0.002316	0.001158
`SK/EOU2`	+1.189	0.278	+4.2780e+00	5.712e-05	2.856e-05
`SK/EOU3`	-0.05793	0.2464	-2.3510e-01	0.8148	0.4074
`SK/EOU4`	+0.4228	0.3575	+1.1820e+00	0.2409	0.1205
`SK/EOU5`	+0.1408	0.3171	+4.4400e-01	0.6584	0.3292
`SK/EOU6`	+0.02208	0.3078	+7.1740e-02	0.943	0.4715

\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) & +2.542 &  3.088 & +8.2300e-01 &  0.4132 &  0.2066 \tabularnewline
EC1 & -0.01723 &  0.1687 & -1.0210e-01 &  0.9189 &  0.4595 \tabularnewline
EC2 & +0.115 &  0.1643 & +7.0020e-01 &  0.4861 &  0.243 \tabularnewline
EC3 & +0.09716 &  0.3231 & +3.0070e-01 &  0.7645 &  0.3823 \tabularnewline
EC4 & -0.1741 &  0.2145 & -8.1140e-01 &  0.4198 &  0.2099 \tabularnewline
IK1 & +0.4113 &  0.3504 & +1.1740e+00 &  0.2444 &  0.1222 \tabularnewline
IK2 & +0.1381 &  0.339 & +4.0760e-01 &  0.6848 &  0.3424 \tabularnewline
IK3 & +0.01773 &  0.3122 & +5.6780e-02 &  0.9549 &  0.4774 \tabularnewline
IK4 & -0.2531 &  0.3142 & -8.0540e-01 &  0.4232 &  0.2116 \tabularnewline
KVDD1 & +0.2787 &  0.2533 & +1.1000e+00 &  0.275 &  0.1375 \tabularnewline
KVDD2 & -0.1897 &  0.1768 & -1.0730e+00 &  0.2869 &  0.1434 \tabularnewline
KVDD3 & +0.2076 &  0.1983 & +1.0470e+00 &  0.2986 &  0.1493 \tabularnewline
KVDD4 & -0.03695 &  0.2076 & -1.7800e-01 &  0.8592 &  0.4296 \tabularnewline
`SK/EOU1` & +0.848 &  0.2685 & +3.1590e+00 &  0.002316 &  0.001158 \tabularnewline
`SK/EOU2` & +1.189 &  0.278 & +4.2780e+00 &  5.712e-05 &  2.856e-05 \tabularnewline
`SK/EOU3` & -0.05793 &  0.2464 & -2.3510e-01 &  0.8148 &  0.4074 \tabularnewline
`SK/EOU4` & +0.4228 &  0.3575 & +1.1820e+00 &  0.2409 &  0.1205 \tabularnewline
`SK/EOU5` & +0.1408 &  0.3171 & +4.4400e-01 &  0.6584 &  0.3292 \tabularnewline
`SK/EOU6` & +0.02208 &  0.3078 & +7.1740e-02 &  0.943 &  0.4715 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=315828&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]+2.542[/C][C] 3.088[/C][C]+8.2300e-01[/C][C] 0.4132[/C][C] 0.2066[/C][/ROW]
[ROW][C]EC1[/C][C]-0.01723[/C][C] 0.1687[/C][C]-1.0210e-01[/C][C] 0.9189[/C][C] 0.4595[/C][/ROW]
[ROW][C]EC2[/C][C]+0.115[/C][C] 0.1643[/C][C]+7.0020e-01[/C][C] 0.4861[/C][C] 0.243[/C][/ROW]
[ROW][C]EC3[/C][C]+0.09716[/C][C] 0.3231[/C][C]+3.0070e-01[/C][C] 0.7645[/C][C] 0.3823[/C][/ROW]
[ROW][C]EC4[/C][C]-0.1741[/C][C] 0.2145[/C][C]-8.1140e-01[/C][C] 0.4198[/C][C] 0.2099[/C][/ROW]
[ROW][C]IK1[/C][C]+0.4113[/C][C] 0.3504[/C][C]+1.1740e+00[/C][C] 0.2444[/C][C] 0.1222[/C][/ROW]
[ROW][C]IK2[/C][C]+0.1381[/C][C] 0.339[/C][C]+4.0760e-01[/C][C] 0.6848[/C][C] 0.3424[/C][/ROW]
[ROW][C]IK3[/C][C]+0.01773[/C][C] 0.3122[/C][C]+5.6780e-02[/C][C] 0.9549[/C][C] 0.4774[/C][/ROW]
[ROW][C]IK4[/C][C]-0.2531[/C][C] 0.3142[/C][C]-8.0540e-01[/C][C] 0.4232[/C][C] 0.2116[/C][/ROW]
[ROW][C]KVDD1[/C][C]+0.2787[/C][C] 0.2533[/C][C]+1.1000e+00[/C][C] 0.275[/C][C] 0.1375[/C][/ROW]
[ROW][C]KVDD2[/C][C]-0.1897[/C][C] 0.1768[/C][C]-1.0730e+00[/C][C] 0.2869[/C][C] 0.1434[/C][/ROW]
[ROW][C]KVDD3[/C][C]+0.2076[/C][C] 0.1983[/C][C]+1.0470e+00[/C][C] 0.2986[/C][C] 0.1493[/C][/ROW]
[ROW][C]KVDD4[/C][C]-0.03695[/C][C] 0.2076[/C][C]-1.7800e-01[/C][C] 0.8592[/C][C] 0.4296[/C][/ROW]
[ROW][C]`SK/EOU1`[/C][C]+0.848[/C][C] 0.2685[/C][C]+3.1590e+00[/C][C] 0.002316[/C][C] 0.001158[/C][/ROW]
[ROW][C]`SK/EOU2`[/C][C]+1.189[/C][C] 0.278[/C][C]+4.2780e+00[/C][C] 5.712e-05[/C][C] 2.856e-05[/C][/ROW]
[ROW][C]`SK/EOU3`[/C][C]-0.05793[/C][C] 0.2464[/C][C]-2.3510e-01[/C][C] 0.8148[/C][C] 0.4074[/C][/ROW]
[ROW][C]`SK/EOU4`[/C][C]+0.4228[/C][C] 0.3575[/C][C]+1.1820e+00[/C][C] 0.2409[/C][C] 0.1205[/C][/ROW]
[ROW][C]`SK/EOU5`[/C][C]+0.1408[/C][C] 0.3171[/C][C]+4.4400e-01[/C][C] 0.6584[/C][C] 0.3292[/C][/ROW]
[ROW][C]`SK/EOU6`[/C][C]+0.02208[/C][C] 0.3078[/C][C]+7.1740e-02[/C][C] 0.943[/C][C] 0.4715[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=315828&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=315828&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
Variable	Parameter	S.D.	T-STATH0: parameter = 0	2-tail p-value	1-tail p-value
(Intercept)	+2.542	3.088	+8.2300e-01	0.4132	0.2066
EC1	-0.01723	0.1687	-1.0210e-01	0.9189	0.4595
EC2	+0.115	0.1643	+7.0020e-01	0.4861	0.243
EC3	+0.09716	0.3231	+3.0070e-01	0.7645	0.3823
EC4	-0.1741	0.2145	-8.1140e-01	0.4198	0.2099
IK1	+0.4113	0.3504	+1.1740e+00	0.2444	0.1222
IK2	+0.1381	0.339	+4.0760e-01	0.6848	0.3424
IK3	+0.01773	0.3122	+5.6780e-02	0.9549	0.4774
IK4	-0.2531	0.3142	-8.0540e-01	0.4232	0.2116
KVDD1	+0.2787	0.2533	+1.1000e+00	0.275	0.1375
KVDD2	-0.1897	0.1768	-1.0730e+00	0.2869	0.1434
KVDD3	+0.2076	0.1983	+1.0470e+00	0.2986	0.1493
KVDD4	-0.03695	0.2076	-1.7800e-01	0.8592	0.4296
`SK/EOU1`	+0.848	0.2685	+3.1590e+00	0.002316	0.001158
`SK/EOU2`	+1.189	0.278	+4.2780e+00	5.712e-05	2.856e-05
`SK/EOU3`	-0.05793	0.2464	-2.3510e-01	0.8148	0.4074
`SK/EOU4`	+0.4228	0.3575	+1.1820e+00	0.2409	0.1205
`SK/EOU5`	+0.1408	0.3171	+4.4400e-01	0.6584	0.3292
`SK/EOU6`	+0.02208	0.3078	+7.1740e-02	0.943	0.4715

Multiple Linear Regression - Regression Statistics
Multiple R	0.6644
R-squared	0.4414
Adjusted R-squared	0.3018
F-TEST (value)	3.161
F-TEST (DF numerator)	18
F-TEST (DF denominator)	72
p-value	0.0002714
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.595
Sum Squared Residuals	183.3

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.6644 \tabularnewline
R-squared &  0.4414 \tabularnewline
Adjusted R-squared &  0.3018 \tabularnewline
F-TEST (value) &  3.161 \tabularnewline
F-TEST (DF numerator) & 18 \tabularnewline
F-TEST (DF denominator) & 72 \tabularnewline
p-value &  0.0002714 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  1.595 \tabularnewline
Sum Squared Residuals &  183.3 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=315828&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.6644[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.4414[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.3018[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 3.161[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]18[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]72[/C][/ROW]
[ROW][C]p-value[/C][C] 0.0002714[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C] 1.595[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 183.3[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=315828&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=315828&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 R	0.6644
R-squared	0.4414
Adjusted R-squared	0.3018
F-TEST (value)	3.161
F-TEST (DF numerator)	18
F-TEST (DF denominator)	72
p-value	0.0002714
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.595
Sum Squared Residuals	183.3

Menu of Residual Diagnostics
Description	Link
Histogram	Compute
Central Tendency	Compute
QQ Plot	Compute
Kernel Density Plot	Compute
Skewness/Kurtosis Test	Compute
Skewness-Kurtosis Plot	Compute
Harrell-Davis Plot	Compute
Bootstrap Plot -- Central Tendency	Compute
Blocked Bootstrap Plot -- Central Tendency	Compute
(Partial) Autocorrelation Plot	Compute
Spectral Analysis	Compute
Tukey lambda PPCC Plot	Compute
Box-Cox Normality Plot	Compute
Summary Statistics	Compute

\begin{tabular}{lllllllll}
\hline
Menu of Residual Diagnostics \tabularnewline
Description & Link \tabularnewline
Histogram & Compute \tabularnewline
Central Tendency & Compute \tabularnewline
QQ Plot & Compute \tabularnewline
Kernel Density Plot & Compute \tabularnewline
Skewness/Kurtosis Test & Compute \tabularnewline
Skewness-Kurtosis Plot & Compute \tabularnewline
Harrell-Davis Plot & Compute \tabularnewline
Bootstrap Plot -- Central Tendency & Compute \tabularnewline
Blocked Bootstrap Plot -- Central Tendency & Compute \tabularnewline
(Partial) Autocorrelation Plot & Compute \tabularnewline
Spectral Analysis & Compute \tabularnewline
Tukey lambda PPCC Plot & Compute \tabularnewline
Box-Cox Normality Plot & Compute \tabularnewline
Summary Statistics & Compute \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=315828&T=4

[TABLE]
[ROW][C]Menu of Residual Diagnostics[/C][/ROW]
[ROW][C]Description[/C][C]Link[/C][/ROW]
[ROW][C]Histogram[/C][C]Compute[/C][/ROW]
[ROW][C]Central Tendency[/C][C]Compute[/C][/ROW]
[ROW][C]QQ Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Kernel Density Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Skewness/Kurtosis Test[/C][C]Compute[/C][/ROW]
[ROW][C]Skewness-Kurtosis Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Harrell-Davis Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Bootstrap Plot -- Central Tendency[/C][C]Compute[/C][/ROW]
[ROW][C]Blocked Bootstrap Plot -- Central Tendency[/C][C]Compute[/C][/ROW]
[ROW][C](Partial) Autocorrelation Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Spectral Analysis[/C][C]Compute[/C][/ROW]
[ROW][C]Tukey lambda PPCC Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Box-Cox Normality Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Summary Statistics[/C][C]Compute[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=315828&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=315828&T=4

As an alternative you can also use a QR Code:

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

Menu of Residual Diagnostics
Description	Link
Histogram	Compute
Central Tendency	Compute
QQ Plot	Compute
Kernel Density Plot	Compute
Skewness/Kurtosis Test	Compute
Skewness-Kurtosis Plot	Compute
Harrell-Davis Plot	Compute
Bootstrap Plot -- Central Tendency	Compute
Blocked Bootstrap Plot -- Central Tendency	Compute
(Partial) Autocorrelation Plot	Compute
Spectral Analysis	Compute
Tukey lambda PPCC Plot	Compute
Box-Cox Normality Plot	Compute
Summary Statistics	Compute

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	13	12.65	0.352
2	17	16.12	0.8809
3	16	15.66	0.335
4	17	17.09	-0.08863
5	17	15.67	1.332
6	15	14.97	0.03452
7	16	15.73	0.2718
8	14	14.13	-0.13
9	16	14.52	1.477
10	17	15.55	1.448
11	16	15.68	0.3227
12	16	14.9	1.1
13	15	15.34	-0.3441
14	16	14.81	1.193
15	16	16.43	-0.4307
16	13	13.42	-0.4164
17	15	17.04	-2.038
18	17	16.09	0.9129
19	13	13.09	-0.0926
20	17	17.06	-0.0616
21	14	13.9	0.1008
22	14	13.44	0.5603
23	18	16.07	1.935
24	17	16.04	0.9641
25	13	13.33	-0.3267
26	16	17.56	-1.559
27	15	16.18	-1.185
28	15	14.21	0.7907
29	13	15.95	-2.946
30	17	17.14	-0.1359
31	11	15.08	-4.077
32	14	13.74	0.257
33	13	14.86	-1.864
34	17	17.56	-0.5564
35	16	14.24	1.765
36	16	15.35	0.6499
37	15	16.12	-1.118
38	12	12.71	-0.707
39	17	14.93	2.067
40	14	15.17	-1.168
41	14	15.28	-1.279
42	16	14.46	1.543
43	15	15.73	-0.7308
44	16	16.24	-0.242
45	14	14.22	-0.2217
46	15	13.62	1.379
47	17	15.08	1.922
48	10	13.64	-3.639
49	20	16.56	3.444
50	17	17.08	-0.07953
51	18	15.95	2.049
52	14	13.25	0.7489
53	17	15.56	1.437
54	17	16.84	0.1571
55	16	14.97	1.027
56	18	16.6	1.404
57	18	16.97	1.033
58	16	15.86	0.1383
59	15	15.81	-0.8112
60	13	16.01	-3.006
61	12	13.1	-1.1
62	16	14.93	1.066
63	16	15.61	0.3924
64	16	16.56	-0.5642
65	14	15.91	-1.907
66	15	14.71	0.2904
67	14	14.18	-0.1834
68	15	16.47	-1.472
69	15	15.53	-0.5341
70	16	15.43	0.5705
71	11	11.89	-0.8902
72	18	16.24	1.763
73	11	13.28	-2.283
74	18	16.5	1.502
75	15	16.29	-1.289
76	17	17	0.0009587
77	14	14.94	-0.9371
78	13	14.68	-1.676
79	17	15.65	1.351
80	14	14.91	-0.9114
81	19	16.85	2.153
82	14	13.96	0.03993
83	16	16.61	-0.6086
84	15	15.38	-0.3773
85	12	14.85	-2.848
86	17	16.79	0.2115
87	15	15.03	-0.02915
88	18	15.49	2.51
89	15	17.76	-2.759
90	16	15.75	0.2451
91	16	13.51	2.494

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 &  13 &  12.65 &  0.352 \tabularnewline
2 &  17 &  16.12 &  0.8809 \tabularnewline
3 &  16 &  15.66 &  0.335 \tabularnewline
4 &  17 &  17.09 & -0.08863 \tabularnewline
5 &  17 &  15.67 &  1.332 \tabularnewline
6 &  15 &  14.97 &  0.03452 \tabularnewline
7 &  16 &  15.73 &  0.2718 \tabularnewline
8 &  14 &  14.13 & -0.13 \tabularnewline
9 &  16 &  14.52 &  1.477 \tabularnewline
10 &  17 &  15.55 &  1.448 \tabularnewline
11 &  16 &  15.68 &  0.3227 \tabularnewline
12 &  16 &  14.9 &  1.1 \tabularnewline
13 &  15 &  15.34 & -0.3441 \tabularnewline
14 &  16 &  14.81 &  1.193 \tabularnewline
15 &  16 &  16.43 & -0.4307 \tabularnewline
16 &  13 &  13.42 & -0.4164 \tabularnewline
17 &  15 &  17.04 & -2.038 \tabularnewline
18 &  17 &  16.09 &  0.9129 \tabularnewline
19 &  13 &  13.09 & -0.0926 \tabularnewline
20 &  17 &  17.06 & -0.0616 \tabularnewline
21 &  14 &  13.9 &  0.1008 \tabularnewline
22 &  14 &  13.44 &  0.5603 \tabularnewline
23 &  18 &  16.07 &  1.935 \tabularnewline
24 &  17 &  16.04 &  0.9641 \tabularnewline
25 &  13 &  13.33 & -0.3267 \tabularnewline
26 &  16 &  17.56 & -1.559 \tabularnewline
27 &  15 &  16.18 & -1.185 \tabularnewline
28 &  15 &  14.21 &  0.7907 \tabularnewline
29 &  13 &  15.95 & -2.946 \tabularnewline
30 &  17 &  17.14 & -0.1359 \tabularnewline
31 &  11 &  15.08 & -4.077 \tabularnewline
32 &  14 &  13.74 &  0.257 \tabularnewline
33 &  13 &  14.86 & -1.864 \tabularnewline
34 &  17 &  17.56 & -0.5564 \tabularnewline
35 &  16 &  14.24 &  1.765 \tabularnewline
36 &  16 &  15.35 &  0.6499 \tabularnewline
37 &  15 &  16.12 & -1.118 \tabularnewline
38 &  12 &  12.71 & -0.707 \tabularnewline
39 &  17 &  14.93 &  2.067 \tabularnewline
40 &  14 &  15.17 & -1.168 \tabularnewline
41 &  14 &  15.28 & -1.279 \tabularnewline
42 &  16 &  14.46 &  1.543 \tabularnewline
43 &  15 &  15.73 & -0.7308 \tabularnewline
44 &  16 &  16.24 & -0.242 \tabularnewline
45 &  14 &  14.22 & -0.2217 \tabularnewline
46 &  15 &  13.62 &  1.379 \tabularnewline
47 &  17 &  15.08 &  1.922 \tabularnewline
48 &  10 &  13.64 & -3.639 \tabularnewline
49 &  20 &  16.56 &  3.444 \tabularnewline
50 &  17 &  17.08 & -0.07953 \tabularnewline
51 &  18 &  15.95 &  2.049 \tabularnewline
52 &  14 &  13.25 &  0.7489 \tabularnewline
53 &  17 &  15.56 &  1.437 \tabularnewline
54 &  17 &  16.84 &  0.1571 \tabularnewline
55 &  16 &  14.97 &  1.027 \tabularnewline
56 &  18 &  16.6 &  1.404 \tabularnewline
57 &  18 &  16.97 &  1.033 \tabularnewline
58 &  16 &  15.86 &  0.1383 \tabularnewline
59 &  15 &  15.81 & -0.8112 \tabularnewline
60 &  13 &  16.01 & -3.006 \tabularnewline
61 &  12 &  13.1 & -1.1 \tabularnewline
62 &  16 &  14.93 &  1.066 \tabularnewline
63 &  16 &  15.61 &  0.3924 \tabularnewline
64 &  16 &  16.56 & -0.5642 \tabularnewline
65 &  14 &  15.91 & -1.907 \tabularnewline
66 &  15 &  14.71 &  0.2904 \tabularnewline
67 &  14 &  14.18 & -0.1834 \tabularnewline
68 &  15 &  16.47 & -1.472 \tabularnewline
69 &  15 &  15.53 & -0.5341 \tabularnewline
70 &  16 &  15.43 &  0.5705 \tabularnewline
71 &  11 &  11.89 & -0.8902 \tabularnewline
72 &  18 &  16.24 &  1.763 \tabularnewline
73 &  11 &  13.28 & -2.283 \tabularnewline
74 &  18 &  16.5 &  1.502 \tabularnewline
75 &  15 &  16.29 & -1.289 \tabularnewline
76 &  17 &  17 &  0.0009587 \tabularnewline
77 &  14 &  14.94 & -0.9371 \tabularnewline
78 &  13 &  14.68 & -1.676 \tabularnewline
79 &  17 &  15.65 &  1.351 \tabularnewline
80 &  14 &  14.91 & -0.9114 \tabularnewline
81 &  19 &  16.85 &  2.153 \tabularnewline
82 &  14 &  13.96 &  0.03993 \tabularnewline
83 &  16 &  16.61 & -0.6086 \tabularnewline
84 &  15 &  15.38 & -0.3773 \tabularnewline
85 &  12 &  14.85 & -2.848 \tabularnewline
86 &  17 &  16.79 &  0.2115 \tabularnewline
87 &  15 &  15.03 & -0.02915 \tabularnewline
88 &  18 &  15.49 &  2.51 \tabularnewline
89 &  15 &  17.76 & -2.759 \tabularnewline
90 &  16 &  15.75 &  0.2451 \tabularnewline
91 &  16 &  13.51 &  2.494 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=315828&T=5

[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] 13[/C][C] 12.65[/C][C] 0.352[/C][/ROW]
[ROW][C]2[/C][C] 17[/C][C] 16.12[/C][C] 0.8809[/C][/ROW]
[ROW][C]3[/C][C] 16[/C][C] 15.66[/C][C] 0.335[/C][/ROW]
[ROW][C]4[/C][C] 17[/C][C] 17.09[/C][C]-0.08863[/C][/ROW]
[ROW][C]5[/C][C] 17[/C][C] 15.67[/C][C] 1.332[/C][/ROW]
[ROW][C]6[/C][C] 15[/C][C] 14.97[/C][C] 0.03452[/C][/ROW]
[ROW][C]7[/C][C] 16[/C][C] 15.73[/C][C] 0.2718[/C][/ROW]
[ROW][C]8[/C][C] 14[/C][C] 14.13[/C][C]-0.13[/C][/ROW]
[ROW][C]9[/C][C] 16[/C][C] 14.52[/C][C] 1.477[/C][/ROW]
[ROW][C]10[/C][C] 17[/C][C] 15.55[/C][C] 1.448[/C][/ROW]
[ROW][C]11[/C][C] 16[/C][C] 15.68[/C][C] 0.3227[/C][/ROW]
[ROW][C]12[/C][C] 16[/C][C] 14.9[/C][C] 1.1[/C][/ROW]
[ROW][C]13[/C][C] 15[/C][C] 15.34[/C][C]-0.3441[/C][/ROW]
[ROW][C]14[/C][C] 16[/C][C] 14.81[/C][C] 1.193[/C][/ROW]
[ROW][C]15[/C][C] 16[/C][C] 16.43[/C][C]-0.4307[/C][/ROW]
[ROW][C]16[/C][C] 13[/C][C] 13.42[/C][C]-0.4164[/C][/ROW]
[ROW][C]17[/C][C] 15[/C][C] 17.04[/C][C]-2.038[/C][/ROW]
[ROW][C]18[/C][C] 17[/C][C] 16.09[/C][C] 0.9129[/C][/ROW]
[ROW][C]19[/C][C] 13[/C][C] 13.09[/C][C]-0.0926[/C][/ROW]
[ROW][C]20[/C][C] 17[/C][C] 17.06[/C][C]-0.0616[/C][/ROW]
[ROW][C]21[/C][C] 14[/C][C] 13.9[/C][C] 0.1008[/C][/ROW]
[ROW][C]22[/C][C] 14[/C][C] 13.44[/C][C] 0.5603[/C][/ROW]
[ROW][C]23[/C][C] 18[/C][C] 16.07[/C][C] 1.935[/C][/ROW]
[ROW][C]24[/C][C] 17[/C][C] 16.04[/C][C] 0.9641[/C][/ROW]
[ROW][C]25[/C][C] 13[/C][C] 13.33[/C][C]-0.3267[/C][/ROW]
[ROW][C]26[/C][C] 16[/C][C] 17.56[/C][C]-1.559[/C][/ROW]
[ROW][C]27[/C][C] 15[/C][C] 16.18[/C][C]-1.185[/C][/ROW]
[ROW][C]28[/C][C] 15[/C][C] 14.21[/C][C] 0.7907[/C][/ROW]
[ROW][C]29[/C][C] 13[/C][C] 15.95[/C][C]-2.946[/C][/ROW]
[ROW][C]30[/C][C] 17[/C][C] 17.14[/C][C]-0.1359[/C][/ROW]
[ROW][C]31[/C][C] 11[/C][C] 15.08[/C][C]-4.077[/C][/ROW]
[ROW][C]32[/C][C] 14[/C][C] 13.74[/C][C] 0.257[/C][/ROW]
[ROW][C]33[/C][C] 13[/C][C] 14.86[/C][C]-1.864[/C][/ROW]
[ROW][C]34[/C][C] 17[/C][C] 17.56[/C][C]-0.5564[/C][/ROW]
[ROW][C]35[/C][C] 16[/C][C] 14.24[/C][C] 1.765[/C][/ROW]
[ROW][C]36[/C][C] 16[/C][C] 15.35[/C][C] 0.6499[/C][/ROW]
[ROW][C]37[/C][C] 15[/C][C] 16.12[/C][C]-1.118[/C][/ROW]
[ROW][C]38[/C][C] 12[/C][C] 12.71[/C][C]-0.707[/C][/ROW]
[ROW][C]39[/C][C] 17[/C][C] 14.93[/C][C] 2.067[/C][/ROW]
[ROW][C]40[/C][C] 14[/C][C] 15.17[/C][C]-1.168[/C][/ROW]
[ROW][C]41[/C][C] 14[/C][C] 15.28[/C][C]-1.279[/C][/ROW]
[ROW][C]42[/C][C] 16[/C][C] 14.46[/C][C] 1.543[/C][/ROW]
[ROW][C]43[/C][C] 15[/C][C] 15.73[/C][C]-0.7308[/C][/ROW]
[ROW][C]44[/C][C] 16[/C][C] 16.24[/C][C]-0.242[/C][/ROW]
[ROW][C]45[/C][C] 14[/C][C] 14.22[/C][C]-0.2217[/C][/ROW]
[ROW][C]46[/C][C] 15[/C][C] 13.62[/C][C] 1.379[/C][/ROW]
[ROW][C]47[/C][C] 17[/C][C] 15.08[/C][C] 1.922[/C][/ROW]
[ROW][C]48[/C][C] 10[/C][C] 13.64[/C][C]-3.639[/C][/ROW]
[ROW][C]49[/C][C] 20[/C][C] 16.56[/C][C] 3.444[/C][/ROW]
[ROW][C]50[/C][C] 17[/C][C] 17.08[/C][C]-0.07953[/C][/ROW]
[ROW][C]51[/C][C] 18[/C][C] 15.95[/C][C] 2.049[/C][/ROW]
[ROW][C]52[/C][C] 14[/C][C] 13.25[/C][C] 0.7489[/C][/ROW]
[ROW][C]53[/C][C] 17[/C][C] 15.56[/C][C] 1.437[/C][/ROW]
[ROW][C]54[/C][C] 17[/C][C] 16.84[/C][C] 0.1571[/C][/ROW]
[ROW][C]55[/C][C] 16[/C][C] 14.97[/C][C] 1.027[/C][/ROW]
[ROW][C]56[/C][C] 18[/C][C] 16.6[/C][C] 1.404[/C][/ROW]
[ROW][C]57[/C][C] 18[/C][C] 16.97[/C][C] 1.033[/C][/ROW]
[ROW][C]58[/C][C] 16[/C][C] 15.86[/C][C] 0.1383[/C][/ROW]
[ROW][C]59[/C][C] 15[/C][C] 15.81[/C][C]-0.8112[/C][/ROW]
[ROW][C]60[/C][C] 13[/C][C] 16.01[/C][C]-3.006[/C][/ROW]
[ROW][C]61[/C][C] 12[/C][C] 13.1[/C][C]-1.1[/C][/ROW]
[ROW][C]62[/C][C] 16[/C][C] 14.93[/C][C] 1.066[/C][/ROW]
[ROW][C]63[/C][C] 16[/C][C] 15.61[/C][C] 0.3924[/C][/ROW]
[ROW][C]64[/C][C] 16[/C][C] 16.56[/C][C]-0.5642[/C][/ROW]
[ROW][C]65[/C][C] 14[/C][C] 15.91[/C][C]-1.907[/C][/ROW]
[ROW][C]66[/C][C] 15[/C][C] 14.71[/C][C] 0.2904[/C][/ROW]
[ROW][C]67[/C][C] 14[/C][C] 14.18[/C][C]-0.1834[/C][/ROW]
[ROW][C]68[/C][C] 15[/C][C] 16.47[/C][C]-1.472[/C][/ROW]
[ROW][C]69[/C][C] 15[/C][C] 15.53[/C][C]-0.5341[/C][/ROW]
[ROW][C]70[/C][C] 16[/C][C] 15.43[/C][C] 0.5705[/C][/ROW]
[ROW][C]71[/C][C] 11[/C][C] 11.89[/C][C]-0.8902[/C][/ROW]
[ROW][C]72[/C][C] 18[/C][C] 16.24[/C][C] 1.763[/C][/ROW]
[ROW][C]73[/C][C] 11[/C][C] 13.28[/C][C]-2.283[/C][/ROW]
[ROW][C]74[/C][C] 18[/C][C] 16.5[/C][C] 1.502[/C][/ROW]
[ROW][C]75[/C][C] 15[/C][C] 16.29[/C][C]-1.289[/C][/ROW]
[ROW][C]76[/C][C] 17[/C][C] 17[/C][C] 0.0009587[/C][/ROW]
[ROW][C]77[/C][C] 14[/C][C] 14.94[/C][C]-0.9371[/C][/ROW]
[ROW][C]78[/C][C] 13[/C][C] 14.68[/C][C]-1.676[/C][/ROW]
[ROW][C]79[/C][C] 17[/C][C] 15.65[/C][C] 1.351[/C][/ROW]
[ROW][C]80[/C][C] 14[/C][C] 14.91[/C][C]-0.9114[/C][/ROW]
[ROW][C]81[/C][C] 19[/C][C] 16.85[/C][C] 2.153[/C][/ROW]
[ROW][C]82[/C][C] 14[/C][C] 13.96[/C][C] 0.03993[/C][/ROW]
[ROW][C]83[/C][C] 16[/C][C] 16.61[/C][C]-0.6086[/C][/ROW]
[ROW][C]84[/C][C] 15[/C][C] 15.38[/C][C]-0.3773[/C][/ROW]
[ROW][C]85[/C][C] 12[/C][C] 14.85[/C][C]-2.848[/C][/ROW]
[ROW][C]86[/C][C] 17[/C][C] 16.79[/C][C] 0.2115[/C][/ROW]
[ROW][C]87[/C][C] 15[/C][C] 15.03[/C][C]-0.02915[/C][/ROW]
[ROW][C]88[/C][C] 18[/C][C] 15.49[/C][C] 2.51[/C][/ROW]
[ROW][C]89[/C][C] 15[/C][C] 17.76[/C][C]-2.759[/C][/ROW]
[ROW][C]90[/C][C] 16[/C][C] 15.75[/C][C] 0.2451[/C][/ROW]
[ROW][C]91[/C][C] 16[/C][C] 13.51[/C][C] 2.494[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=315828&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=315828&T=5

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 Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	13	12.65	0.352
2	17	16.12	0.8809
3	16	15.66	0.335
4	17	17.09	-0.08863
5	17	15.67	1.332
6	15	14.97	0.03452
7	16	15.73	0.2718
8	14	14.13	-0.13
9	16	14.52	1.477
10	17	15.55	1.448
11	16	15.68	0.3227
12	16	14.9	1.1
13	15	15.34	-0.3441
14	16	14.81	1.193
15	16	16.43	-0.4307
16	13	13.42	-0.4164
17	15	17.04	-2.038
18	17	16.09	0.9129
19	13	13.09	-0.0926
20	17	17.06	-0.0616
21	14	13.9	0.1008
22	14	13.44	0.5603
23	18	16.07	1.935
24	17	16.04	0.9641
25	13	13.33	-0.3267
26	16	17.56	-1.559
27	15	16.18	-1.185
28	15	14.21	0.7907
29	13	15.95	-2.946
30	17	17.14	-0.1359
31	11	15.08	-4.077
32	14	13.74	0.257
33	13	14.86	-1.864
34	17	17.56	-0.5564
35	16	14.24	1.765
36	16	15.35	0.6499
37	15	16.12	-1.118
38	12	12.71	-0.707
39	17	14.93	2.067
40	14	15.17	-1.168
41	14	15.28	-1.279
42	16	14.46	1.543
43	15	15.73	-0.7308
44	16	16.24	-0.242
45	14	14.22	-0.2217
46	15	13.62	1.379
47	17	15.08	1.922
48	10	13.64	-3.639
49	20	16.56	3.444
50	17	17.08	-0.07953
51	18	15.95	2.049
52	14	13.25	0.7489
53	17	15.56	1.437
54	17	16.84	0.1571
55	16	14.97	1.027
56	18	16.6	1.404
57	18	16.97	1.033
58	16	15.86	0.1383
59	15	15.81	-0.8112
60	13	16.01	-3.006
61	12	13.1	-1.1
62	16	14.93	1.066
63	16	15.61	0.3924
64	16	16.56	-0.5642
65	14	15.91	-1.907
66	15	14.71	0.2904
67	14	14.18	-0.1834
68	15	16.47	-1.472
69	15	15.53	-0.5341
70	16	15.43	0.5705
71	11	11.89	-0.8902
72	18	16.24	1.763
73	11	13.28	-2.283
74	18	16.5	1.502
75	15	16.29	-1.289
76	17	17	0.0009587
77	14	14.94	-0.9371
78	13	14.68	-1.676
79	17	15.65	1.351
80	14	14.91	-0.9114
81	19	16.85	2.153
82	14	13.96	0.03993
83	16	16.61	-0.6086
84	15	15.38	-0.3773
85	12	14.85	-2.848
86	17	16.79	0.2115
87	15	15.03	-0.02915
88	18	15.49	2.51
89	15	17.76	-2.759
90	16	15.75	0.2451
91	16	13.51	2.494

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
22	0.02378	0.04756	0.9762
23	0.005589	0.01118	0.9944
24	0.1412	0.2824	0.8588
25	0.09456	0.1891	0.9054
26	0.0602	0.1204	0.9398
27	0.03375	0.0675	0.9663
28	0.01699	0.03398	0.983
29	0.03909	0.07818	0.9609
30	0.02681	0.05362	0.9732
31	0.1062	0.2125	0.8938
32	0.08409	0.1682	0.9159
33	0.07286	0.1457	0.9271
34	0.0576	0.1152	0.9424
35	0.05329	0.1066	0.9467
36	0.0348	0.06961	0.9652
37	0.02338	0.04677	0.9766
38	0.01741	0.03483	0.9826
39	0.01753	0.03506	0.9825
40	0.01576	0.03151	0.9842
41	0.02095	0.04189	0.9791
42	0.02186	0.04372	0.9781
43	0.01927	0.03854	0.9807
44	0.0117	0.02339	0.9883
45	0.007881	0.01576	0.9921
46	0.005955	0.01191	0.994
47	0.006077	0.01215	0.9939
48	0.1169	0.2338	0.8831
49	0.3497	0.6993	0.6503
50	0.3417	0.6834	0.6583
51	0.3882	0.7764	0.6118
52	0.3254	0.6508	0.6746
53	0.3377	0.6755	0.6623
54	0.269	0.5381	0.731
55	0.2238	0.4477	0.7762
56	0.2653	0.5306	0.7347
57	0.2287	0.4574	0.7713
58	0.2375	0.475	0.7625
59	0.206	0.412	0.794
60	0.4255	0.8511	0.5745
61	0.3393	0.6785	0.6607
62	0.3678	0.7355	0.6322
63	0.392	0.7839	0.608
64	0.3007	0.6014	0.6993
65	0.3593	0.7186	0.6407
66	0.2608	0.5216	0.7392
67	0.2975	0.5951	0.7025
68	0.3005	0.6009	0.6995
69	0.1984	0.3968	0.8016

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
22 &  0.02378 &  0.04756 &  0.9762 \tabularnewline
23 &  0.005589 &  0.01118 &  0.9944 \tabularnewline
24 &  0.1412 &  0.2824 &  0.8588 \tabularnewline
25 &  0.09456 &  0.1891 &  0.9054 \tabularnewline
26 &  0.0602 &  0.1204 &  0.9398 \tabularnewline
27 &  0.03375 &  0.0675 &  0.9663 \tabularnewline
28 &  0.01699 &  0.03398 &  0.983 \tabularnewline
29 &  0.03909 &  0.07818 &  0.9609 \tabularnewline
30 &  0.02681 &  0.05362 &  0.9732 \tabularnewline
31 &  0.1062 &  0.2125 &  0.8938 \tabularnewline
32 &  0.08409 &  0.1682 &  0.9159 \tabularnewline
33 &  0.07286 &  0.1457 &  0.9271 \tabularnewline
34 &  0.0576 &  0.1152 &  0.9424 \tabularnewline
35 &  0.05329 &  0.1066 &  0.9467 \tabularnewline
36 &  0.0348 &  0.06961 &  0.9652 \tabularnewline
37 &  0.02338 &  0.04677 &  0.9766 \tabularnewline
38 &  0.01741 &  0.03483 &  0.9826 \tabularnewline
39 &  0.01753 &  0.03506 &  0.9825 \tabularnewline
40 &  0.01576 &  0.03151 &  0.9842 \tabularnewline
41 &  0.02095 &  0.04189 &  0.9791 \tabularnewline
42 &  0.02186 &  0.04372 &  0.9781 \tabularnewline
43 &  0.01927 &  0.03854 &  0.9807 \tabularnewline
44 &  0.0117 &  0.02339 &  0.9883 \tabularnewline
45 &  0.007881 &  0.01576 &  0.9921 \tabularnewline
46 &  0.005955 &  0.01191 &  0.994 \tabularnewline
47 &  0.006077 &  0.01215 &  0.9939 \tabularnewline
48 &  0.1169 &  0.2338 &  0.8831 \tabularnewline
49 &  0.3497 &  0.6993 &  0.6503 \tabularnewline
50 &  0.3417 &  0.6834 &  0.6583 \tabularnewline
51 &  0.3882 &  0.7764 &  0.6118 \tabularnewline
52 &  0.3254 &  0.6508 &  0.6746 \tabularnewline
53 &  0.3377 &  0.6755 &  0.6623 \tabularnewline
54 &  0.269 &  0.5381 &  0.731 \tabularnewline
55 &  0.2238 &  0.4477 &  0.7762 \tabularnewline
56 &  0.2653 &  0.5306 &  0.7347 \tabularnewline
57 &  0.2287 &  0.4574 &  0.7713 \tabularnewline
58 &  0.2375 &  0.475 &  0.7625 \tabularnewline
59 &  0.206 &  0.412 &  0.794 \tabularnewline
60 &  0.4255 &  0.8511 &  0.5745 \tabularnewline
61 &  0.3393 &  0.6785 &  0.6607 \tabularnewline
62 &  0.3678 &  0.7355 &  0.6322 \tabularnewline
63 &  0.392 &  0.7839 &  0.608 \tabularnewline
64 &  0.3007 &  0.6014 &  0.6993 \tabularnewline
65 &  0.3593 &  0.7186 &  0.6407 \tabularnewline
66 &  0.2608 &  0.5216 &  0.7392 \tabularnewline
67 &  0.2975 &  0.5951 &  0.7025 \tabularnewline
68 &  0.3005 &  0.6009 &  0.6995 \tabularnewline
69 &  0.1984 &  0.3968 &  0.8016 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=315828&T=6

[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]22[/C][C] 0.02378[/C][C] 0.04756[/C][C] 0.9762[/C][/ROW]
[ROW][C]23[/C][C] 0.005589[/C][C] 0.01118[/C][C] 0.9944[/C][/ROW]
[ROW][C]24[/C][C] 0.1412[/C][C] 0.2824[/C][C] 0.8588[/C][/ROW]
[ROW][C]25[/C][C] 0.09456[/C][C] 0.1891[/C][C] 0.9054[/C][/ROW]
[ROW][C]26[/C][C] 0.0602[/C][C] 0.1204[/C][C] 0.9398[/C][/ROW]
[ROW][C]27[/C][C] 0.03375[/C][C] 0.0675[/C][C] 0.9663[/C][/ROW]
[ROW][C]28[/C][C] 0.01699[/C][C] 0.03398[/C][C] 0.983[/C][/ROW]
[ROW][C]29[/C][C] 0.03909[/C][C] 0.07818[/C][C] 0.9609[/C][/ROW]
[ROW][C]30[/C][C] 0.02681[/C][C] 0.05362[/C][C] 0.9732[/C][/ROW]
[ROW][C]31[/C][C] 0.1062[/C][C] 0.2125[/C][C] 0.8938[/C][/ROW]
[ROW][C]32[/C][C] 0.08409[/C][C] 0.1682[/C][C] 0.9159[/C][/ROW]
[ROW][C]33[/C][C] 0.07286[/C][C] 0.1457[/C][C] 0.9271[/C][/ROW]
[ROW][C]34[/C][C] 0.0576[/C][C] 0.1152[/C][C] 0.9424[/C][/ROW]
[ROW][C]35[/C][C] 0.05329[/C][C] 0.1066[/C][C] 0.9467[/C][/ROW]
[ROW][C]36[/C][C] 0.0348[/C][C] 0.06961[/C][C] 0.9652[/C][/ROW]
[ROW][C]37[/C][C] 0.02338[/C][C] 0.04677[/C][C] 0.9766[/C][/ROW]
[ROW][C]38[/C][C] 0.01741[/C][C] 0.03483[/C][C] 0.9826[/C][/ROW]
[ROW][C]39[/C][C] 0.01753[/C][C] 0.03506[/C][C] 0.9825[/C][/ROW]
[ROW][C]40[/C][C] 0.01576[/C][C] 0.03151[/C][C] 0.9842[/C][/ROW]
[ROW][C]41[/C][C] 0.02095[/C][C] 0.04189[/C][C] 0.9791[/C][/ROW]
[ROW][C]42[/C][C] 0.02186[/C][C] 0.04372[/C][C] 0.9781[/C][/ROW]
[ROW][C]43[/C][C] 0.01927[/C][C] 0.03854[/C][C] 0.9807[/C][/ROW]
[ROW][C]44[/C][C] 0.0117[/C][C] 0.02339[/C][C] 0.9883[/C][/ROW]
[ROW][C]45[/C][C] 0.007881[/C][C] 0.01576[/C][C] 0.9921[/C][/ROW]
[ROW][C]46[/C][C] 0.005955[/C][C] 0.01191[/C][C] 0.994[/C][/ROW]
[ROW][C]47[/C][C] 0.006077[/C][C] 0.01215[/C][C] 0.9939[/C][/ROW]
[ROW][C]48[/C][C] 0.1169[/C][C] 0.2338[/C][C] 0.8831[/C][/ROW]
[ROW][C]49[/C][C] 0.3497[/C][C] 0.6993[/C][C] 0.6503[/C][/ROW]
[ROW][C]50[/C][C] 0.3417[/C][C] 0.6834[/C][C] 0.6583[/C][/ROW]
[ROW][C]51[/C][C] 0.3882[/C][C] 0.7764[/C][C] 0.6118[/C][/ROW]
[ROW][C]52[/C][C] 0.3254[/C][C] 0.6508[/C][C] 0.6746[/C][/ROW]
[ROW][C]53[/C][C] 0.3377[/C][C] 0.6755[/C][C] 0.6623[/C][/ROW]
[ROW][C]54[/C][C] 0.269[/C][C] 0.5381[/C][C] 0.731[/C][/ROW]
[ROW][C]55[/C][C] 0.2238[/C][C] 0.4477[/C][C] 0.7762[/C][/ROW]
[ROW][C]56[/C][C] 0.2653[/C][C] 0.5306[/C][C] 0.7347[/C][/ROW]
[ROW][C]57[/C][C] 0.2287[/C][C] 0.4574[/C][C] 0.7713[/C][/ROW]
[ROW][C]58[/C][C] 0.2375[/C][C] 0.475[/C][C] 0.7625[/C][/ROW]
[ROW][C]59[/C][C] 0.206[/C][C] 0.412[/C][C] 0.794[/C][/ROW]
[ROW][C]60[/C][C] 0.4255[/C][C] 0.8511[/C][C] 0.5745[/C][/ROW]
[ROW][C]61[/C][C] 0.3393[/C][C] 0.6785[/C][C] 0.6607[/C][/ROW]
[ROW][C]62[/C][C] 0.3678[/C][C] 0.7355[/C][C] 0.6322[/C][/ROW]
[ROW][C]63[/C][C] 0.392[/C][C] 0.7839[/C][C] 0.608[/C][/ROW]
[ROW][C]64[/C][C] 0.3007[/C][C] 0.6014[/C][C] 0.6993[/C][/ROW]
[ROW][C]65[/C][C] 0.3593[/C][C] 0.7186[/C][C] 0.6407[/C][/ROW]
[ROW][C]66[/C][C] 0.2608[/C][C] 0.5216[/C][C] 0.7392[/C][/ROW]
[ROW][C]67[/C][C] 0.2975[/C][C] 0.5951[/C][C] 0.7025[/C][/ROW]
[ROW][C]68[/C][C] 0.3005[/C][C] 0.6009[/C][C] 0.6995[/C][/ROW]
[ROW][C]69[/C][C] 0.1984[/C][C] 0.3968[/C][C] 0.8016[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=315828&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=315828&T=6

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-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
22	0.02378	0.04756	0.9762
23	0.005589	0.01118	0.9944
24	0.1412	0.2824	0.8588
25	0.09456	0.1891	0.9054
26	0.0602	0.1204	0.9398
27	0.03375	0.0675	0.9663
28	0.01699	0.03398	0.983
29	0.03909	0.07818	0.9609
30	0.02681	0.05362	0.9732
31	0.1062	0.2125	0.8938
32	0.08409	0.1682	0.9159
33	0.07286	0.1457	0.9271
34	0.0576	0.1152	0.9424
35	0.05329	0.1066	0.9467
36	0.0348	0.06961	0.9652
37	0.02338	0.04677	0.9766
38	0.01741	0.03483	0.9826
39	0.01753	0.03506	0.9825
40	0.01576	0.03151	0.9842
41	0.02095	0.04189	0.9791
42	0.02186	0.04372	0.9781
43	0.01927	0.03854	0.9807
44	0.0117	0.02339	0.9883
45	0.007881	0.01576	0.9921
46	0.005955	0.01191	0.994
47	0.006077	0.01215	0.9939
48	0.1169	0.2338	0.8831
49	0.3497	0.6993	0.6503
50	0.3417	0.6834	0.6583
51	0.3882	0.7764	0.6118
52	0.3254	0.6508	0.6746
53	0.3377	0.6755	0.6623
54	0.269	0.5381	0.731
55	0.2238	0.4477	0.7762
56	0.2653	0.5306	0.7347
57	0.2287	0.4574	0.7713
58	0.2375	0.475	0.7625
59	0.206	0.412	0.794
60	0.4255	0.8511	0.5745
61	0.3393	0.6785	0.6607
62	0.3678	0.7355	0.6322
63	0.392	0.7839	0.608
64	0.3007	0.6014	0.6993
65	0.3593	0.7186	0.6407
66	0.2608	0.5216	0.7392
67	0.2975	0.5951	0.7025
68	0.3005	0.6009	0.6995
69	0.1984	0.3968	0.8016

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	0	0	OK
5% type I error level	14	0.291667	NOK
10% type I error level	18	0.375	NOK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 &  0 & OK \tabularnewline
5% type I error level & 14 & 0.291667 & NOK \tabularnewline
10% type I error level & 18 & 0.375 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=315828&T=7

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C] 0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]14[/C][C]0.291667[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]18[/C][C]0.375[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=315828&T=7

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=315828&T=7

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 tests	OK/NOK
1% type I error level	0	0	OK
5% type I error level	14	0.291667	NOK
10% type I error level	18	0.375	NOK

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted RESET test data: mylm RESET = 1.7812, df1 = 2, df2 = 70, p-value = 0.176
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 1.449, df1 = 36, df2 = 36, p-value = 0.1353
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 0.35462, df1 = 2, df2 = 70, p-value = 0.7027

\begin{tabular}{lllllllll}
\hline
Ramsey RESET F-Test for powers (2 and 3) of fitted values \tabularnewline
> reset_test_fitted
	RESET test
data:  mylm
RESET = 1.7812, df1 = 2, df2 = 70, p-value = 0.176
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of regressors \tabularnewline
> reset_test_regressors
	RESET test
data:  mylm
RESET = 1.449, df1 = 36, df2 = 36, p-value = 0.1353
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of principal components \tabularnewline
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 0.35462, df1 = 2, df2 = 70, p-value = 0.7027
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=315828&T=8

[TABLE]
[ROW][C]Ramsey RESET F-Test for powers (2 and 3) of fitted values[/C][/ROW]
[ROW][C]> reset_test_fitted
	RESET test
data:  mylm
RESET = 1.7812, df1 = 2, df2 = 70, p-value = 0.176
[/C][/ROW]
[ROW][C]Ramsey RESET F-Test for powers (2 and 3) of regressors[/C][/ROW]
[ROW][C]> reset_test_regressors
	RESET test
data:  mylm
RESET = 1.449, df1 = 36, df2 = 36, p-value = 0.1353
[/C][/ROW]
[ROW][C]Ramsey RESET F-Test for powers (2 and 3) of principal components[/C][/ROW]
[ROW][C]> reset_test_principal_components
	RESET test
data:  mylm
RESET = 0.35462, df1 = 2, df2 = 70, p-value = 0.7027
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=315828&T=8

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=315828&T=8

As an alternative you can also use a QR Code:

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

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted RESET test data: mylm RESET = 1.7812, df1 = 2, df2 = 70, p-value = 0.176
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 1.449, df1 = 36, df2 = 36, p-value = 0.1353
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 0.35462, df1 = 2, df2 = 70, p-value = 0.7027

Variance Inflation Factors (Multicollinearity)

> vif
      EC1       EC2       EC3       EC4       IK1       IK2       IK3       IK4 
 1.593218  1.589847  1.668398  1.375893  1.593469  1.824401  1.590695  1.380800 
    KVDD1     KVDD2     KVDD3     KVDD4 `SK/EOU1` `SK/EOU2` `SK/EOU3` `SK/EOU4` 
 1.375960  1.171124  1.199187  1.276512  1.384436  1.363493  1.282181  1.313468 
`SK/EOU5` `SK/EOU6` 
 1.254353  1.201570

\begin{tabular}{lllllllll}
\hline
Variance Inflation Factors (Multicollinearity) \tabularnewline
> vif
      EC1       EC2       EC3       EC4       IK1       IK2       IK3       IK4 
 1.593218  1.589847  1.668398  1.375893  1.593469  1.824401  1.590695  1.380800 
    KVDD1     KVDD2     KVDD3     KVDD4 `SK/EOU1` `SK/EOU2` `SK/EOU3` `SK/EOU4` 
 1.375960  1.171124  1.199187  1.276512  1.384436  1.363493  1.282181  1.313468 
`SK/EOU5` `SK/EOU6` 
 1.254353  1.201570 
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=315828&T=9

[TABLE]
[ROW][C]Variance Inflation Factors (Multicollinearity)[/C][/ROW]
[ROW][C]> vif
      EC1       EC2       EC3       EC4       IK1       IK2       IK3       IK4 
 1.593218  1.589847  1.668398  1.375893  1.593469  1.824401  1.590695  1.380800 
    KVDD1     KVDD2     KVDD3     KVDD4 `SK/EOU1` `SK/EOU2` `SK/EOU3` `SK/EOU4` 
 1.375960  1.171124  1.199187  1.276512  1.384436  1.363493  1.282181  1.313468 
`SK/EOU5` `SK/EOU6` 
 1.254353  1.201570 
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=315828&T=9

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=315828&T=9

As an alternative you can also use a QR Code:

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

Variance Inflation Factors (Multicollinearity)

> vif
      EC1       EC2       EC3       EC4       IK1       IK2       IK3       IK4 
 1.593218  1.589847  1.668398  1.375893  1.593469  1.824401  1.590695  1.380800 
    KVDD1     KVDD2     KVDD3     KVDD4 `SK/EOU1` `SK/EOU2` `SK/EOU3` `SK/EOU4` 
 1.375960  1.171124  1.199187  1.276512  1.384436  1.363493  1.282181  1.313468 
`SK/EOU5` `SK/EOU6` 
 1.254353  1.201570

Figure 1

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Figure 2

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Figure 3

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Figure 4

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Figure 6

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Figure 7

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Figure 8

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Figure 9

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Figure 10

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Parameters (Session):

Parameters (R input):

par1 = 19 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ; par4 = ; par5 = ; par6 = 12 ;

R code (references can be found in the software module):

library(lattice)
library(lmtest)
library(car)
library(MASS)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
mywarning <- ''
par6 <- as.numeric(par6)
if(is.na(par6)) {
par6 <- 12
mywarning = 'Warning: you did not specify the seasonality. The seasonal period was set to s = 12.'
}
par1 <- as.numeric(par1)
if(is.na(par1)) {
par1 <- 1
mywarning = 'Warning: you did not specify the column number of the endogenous series! The first column was selected by default.'
}
if (par4=='') par4 <- 0
par4 <- as.numeric(par4)
if (!is.numeric(par4)) par4 <- 0
if (par5=='') par5 <- 0
par5 <- as.numeric(par5)
if (!is.numeric(par5)) par5 <- 0
x <- na.omit(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'){
(n <- n -1)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par3 == 'Seasonal Differences (s)'){
(n <- n - par6)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-Bs)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+par6,j] - x[i,j]
}
}
x <- x2
}
if (par3 == 'First and Seasonal Differences (s)'){
(n <- n -1)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
(n <- n - par6)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-Bs)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+par6,j] - x[i,j]
}
}
x <- x2
}
if(par4 > 0) {
x2 <- array(0, dim=c(n-par4,par4), dimnames=list(1:(n-par4), paste(colnames(x)[par1],'(t-',1:par4,')',sep='')))
for (i in 1:(n-par4)) {
for (j in 1:par4) {
x2[i,j] <- x[i+par4-j,par1]
}
}
x <- cbind(x[(par4+1):n,], x2)
n <- n - par4
}
if(par5 > 0) {
x2 <- array(0, dim=c(n-par5*par6,par5), dimnames=list(1:(n-par5*par6), paste(colnames(x)[par1],'(t-',1:par5,'s)',sep='')))
for (i in 1:(n-par5*par6)) {
for (j in 1:par5) {
x2[i,j] <- x[i+par5*par6-j*par6,par1]
}
}
x <- cbind(x[(par5*par6+1):n,], x2)
n <- n - par5*par6
}
if (par2 == 'Include Seasonal Dummies'){
x2 <- array(0, dim=c(n,par6-1), dimnames=list(1:n, paste('M', seq(1:(par6-1)), sep ='')))
for (i in 1:(par6-1)){
x2[seq(i,n,par6),i] <- 1
}
x <- cbind(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[n,]))
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
print(x)
(k <- length(x[n,]))
head(x)
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$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
sresid <- studres(mylm)
hist(sresid, freq=FALSE, main='Distribution of Studentized Residuals')
xfit<-seq(min(sresid),max(sresid),length=40)
yfit<-dnorm(xfit)
lines(xfit, yfit)
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqPlot(mylm, main='QQ Plot')
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
print(z)
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, signif(mysum$coefficients[i,1],6), sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, mywarning)
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,'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$coefficients)[i],header=TRUE)
a<-table.element(a,formatC(signif(mysum$coefficients[i,1],5),format='g',flag='+'))
a<-table.element(a,formatC(signif(mysum$coefficients[i,2],5),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$coefficients[i,3],4),format='e',flag='+'))
a<-table.element(a,formatC(signif(mysum$coefficients[i,4],4),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$coefficients[i,4]/2,4),format='g',flag=' '))
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,formatC(signif(sqrt(mysum$r.squared),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a,formatC(signif(mysum$r.squared,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a,formatC(signif(mysum$adj.r.squared,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a,formatC(signif(mysum$fstatistic[1],6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[2],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[3],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a,formatC(signif(1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a,formatC(signif(mysum$sigma,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a,formatC(signif(sum(myerror*myerror),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
myr <- as.numeric(mysum$resid)
myr
a <-table.start()
a <- table.row.start(a)
a <- table.element(a,'Menu of Residual Diagnostics',2,TRUE)
a <- table.row.end(a)
a <- table.row.start(a)
a <- table.element(a,'Description',1,TRUE)
a <- table.element(a,'Link',1,TRUE)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Histogram',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_histogram.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Central Tendency',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_centraltendency.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'QQ Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_fitdistrnorm.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Kernel Density Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_density.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Skewness/Kurtosis Test',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_skewness_kurtosis.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Skewness-Kurtosis Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_skewness_kurtosis_plot.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Harrell-Davis Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_harrell_davis.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Bootstrap Plot -- Central Tendency',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_bootstrapplot1.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Blocked Bootstrap Plot -- Central Tendency',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_bootstrapplot.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'(Partial) Autocorrelation Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_autocorrelation.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Spectral Analysis',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_spectrum.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Tukey lambda PPCC Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_tukeylambda.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Box-Cox Normality Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_boxcoxnorm.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <- table.element(a,'Summary Statistics',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_summary1.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable7.tab')
if(n < 200) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,formatC(signif(x[i],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(x[i]-mysum$resid[i],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$resid[i],6),format='g',flag=' '))
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,formatC(signif(gqarr[mypoint-kp3+1,1],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,2],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,3],6),format='g',flag=' '))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,signif(numsignificant1,6))
a<-table.element(a,formatC(signif(numsignificant1/numgqtests,6),format='g',flag=' '))
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,signif(numsignificant5,6))
a<-table.element(a,signif(numsignificant5/numgqtests,6))
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,signif(numsignificant10,6))
a<-table.element(a,signif(numsignificant10/numgqtests,6))
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}
}
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Ramsey RESET F-Test for powers (2 and 3) of fitted values',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
reset_test_fitted <- resettest(mylm,power=2:3,type='fitted')
a<-table.element(a,paste('',RC.texteval('reset_test_fitted'),'
',sep=''))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Ramsey RESET F-Test for powers (2 and 3) of regressors',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
reset_test_regressors <- resettest(mylm,power=2:3,type='regressor')
a<-table.element(a,paste('',RC.texteval('reset_test_regressors'),'
',sep=''))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Ramsey RESET F-Test for powers (2 and 3) of principal components',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
reset_test_principal_components <- resettest(mylm,power=2:3,type='princomp')
a<-table.element(a,paste('',RC.texteval('reset_test_principal_components'),'
',sep=''))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable8.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Variance Inflation Factors (Multicollinearity)',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
vif <- vif(mylm)
a<-table.element(a,paste('',RC.texteval('vif'),'
',sep=''))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable9.tab')

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code