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R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationSat, 11 Apr 2020 22:30:26 +0200
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2020/Apr/11/t15866412104jcizwq6oz445z6.htm/, Retrieved Fri, 24 Sep 2021 21:05:28 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=319127, Retrieved Fri, 24 Sep 2021 21:05:28 +0000
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IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact53
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
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Dataseries X:
6.0 -.10 0.57 1.01 1.68 1.1 0.0 -.5 0.5
7.5 -.99 -.10 0.57 1.01 2.2 1.1 0.0 -.5
7.0 .11 -.99 -.10 0.57 1.7 2.2 1.1 0.0
1.4 -.26 .11 -.99 -.10 2.1 1.7 2.2 1.1
-5.4 -.66 -.26 .11 -.99 0.1 2.1 1.7 2.2
-3.5 .05 -.66 -.26 .11 0.9 0.1 2.1 1.7
1.9 -.27 .05 -.66 -.26 0.0 0.9 0.1 2.1
4.4 .00 -.27 .05 -.66 -.2 0.0 0.9 0.1
4.4 .99 .00 -.27 .05 -1.9 -.2 0.0 0.9
9.5 1.61 .99 .00 -.27 -1.2 -1.9 -.2 0.0
17.7 1.26 1.61 .99 .00 0.6 -1.2 -1.9 -.2
11.5 1.04 1.26 1.61 .99 2.9 0.6 -1.2 -1.9
14.1 1.23 1.04 1.26 1.61 7.8 2.9 0.6 -1.2
7.9 0.94 1.23 1.04 1.26 6.4 7.8 2.9 0.6
6.7 1.45 0.94 1.23 1.04 4.2 6.4 7.8 2.9
4.2 2.39 1.45 0.94 1.23 4.9 4.2 6.4 7.8
2.7 1.63 2.39 1.45 0.94 1.1 4.9 4.2 6.4
7.2 0.91 1.63 2.39 1.45 3.9 1.1 4.9 4.2
9.7 1.24 0.91 1.63 2.39 2.1 3.9 1.1 4.9
9.2 1.39 1.24 0.91 1.63 0.3 2.1 3.9 1.1
6.1 0.50 1.39 1.24 0.91 2.6 0.3 2.1 3.9
3.3 0.75 0.50 1.39 1.24 0.9 2.6 0.3 2.1
-1.0 0.23 0.75 0.50 1.39 -.9 0.9 2.6 0.3
-5.3 0.19 0.23 0.75 0.50 0.4 -.9 0.9 2.6
-.6 0.40 0.19 0.23 0.75 -4.8 0.4 -.9 0.9
-.2 0.15 0.40 0.19 0.23 -4.8 -4.8 0.4 -.9
4.4 1.25 0.15 0.40 0.19 -1.8 -4.8 -4.8 0.4
8.9 1.42 1.25 0.15 0.40 -1.4 -1.8 -4.8 -4.8
12.6 1.51 1.42 1.25 0.15 -.3 -1.4 -1.8 -4.8
8.00 0.72 1.51 1.42 1.25 -.8 -.3 -1.4 -1.8
8.6 0.59 0.72 1.51 1.42 1.0 -.8 -.3 -1.4
6.2 0.32 0.59 0.72 1.51 0.2 1.0 -.8 -.3
1.8 0.54 0.32 0.59 0.72 0.0 0.2 1.0 -.8
5.6 0.22 0.54 0.32 0.59 1.3 0.0 0.2 1.0
5.1 0.06 0.22 0.54 0.32 -.4 1.3 0.0 0.2
8.6 0.61 0.06 0.22 0.54 0.9 -.4 1.3 0.0
8.1 0.31 0.61 0.06 0.22 3.6 0.9 -.4 1.3
2.1 0.03 0.31 0.61 0.06 -.4 3.6 0.9 -.4
7.1 -.01 0.03 0.31 0.61 0.2 -.4 3.6 0.9
-5.4 -.63 -.01 0.03 0.31 -.5 0.2 -.4 3.6
-7.2 -.20 -.63 -.01 0.03 2.0 -.5 0.2 -.4
3.9 1.47 -.20 -.63 -.01 2.0 2.0 -.5 0.2
13.2 1.46 1.47 -.20 -.63 0.8 2.0 2.0 -.5
13.1 1.78 1.46 1.47 -.20 1.5 0.8 2.0 2.0
10.0 1.86 1.78 1.46 1.47 -1.6 1.5 0.8 2.0
10.0 1.20 1.86 1.78 1.46 0.0 -1.6 1.5 0.8
5.0 1.00 1.20 1.86 1.78 -.6 0.0 -1.6 1.5
5.0 -1.26 1.00 1.20 1.86 -.4 -.6 0.0 -1.6
5.0 -.37 -1.26 1.00 1.20 -1.0 -.4 -.6 0.0
4.3 -.30 -.37 -1.26 1.00 0.8 -1.0 -.4 -.6
1.7 1.33 -.30 -.37 -1.26 1.5 0.8 -1.0 -.4
-3.2 -0.10 1.33 -.30 -.37 0.2 1.5 0.8 -1.0
3.4 0.70 -.10 1.33 -.30 0.5 0.2 1.5 0.8
11.0 1.03 0.70 -.10 1.33 1.6 0.5 0.2 1.5
9.0 0.84 1.03 0.70 -.10 0.8 1.6 0.5 0.2
14.4 1.30 0.84 1.03 0.70 1.9 0.8 1.6 0.5
11.6 0.93 1.30 0.84 1.03 3.4 1.9 0.8 1.6
8.5 0.97 0.93 1.30 0.84 0.0 3.4 1.9 0.8
6.2 -.13 0.97 0.93 1.30 1.1 0.0 3.4 1.9
5.4 0.80 -.13 0.97 0.93 0.7 1.1 0.0 3.4
7.7 1.53 0.80 -.13 0.97 0.1 0.7 1.1 0.0
8.7 1.37 1.53 0.80 -.13 -1.6 0.1 0.7 1.1
11.1 1.53 1.37 1.53 0.80 1.2 -1.6 0.1 0.7
10.6 1.47 1.53 1.37 1.53 1.4 1.2 -1.6 0.1
12.9 1.00 1.47 1.53 1.37 0.0 1.4 1.2 -1.6
8.7 1.06 1.00 1.47 1.53 -.1 0.0 1.4 1.2
8.8 2.54 1.06 1.00 1.47 -1.1 -.1 0.0 1.4
6.0 2.66 2.54 1.06 1.00 -.2 -1.1 -.1 0.0
20.0 1.20 2.66 2.54 1.06 -.6 -.2 -1.1 -.1
12.9 0.94 1.20 2.66 2.54 1.9 -.6 -.2 -1.1
14.7 1.86 0.94 1.20 2.66 1.8 1.9 -.6 -.2
20.8 3.00 1.86 0.94 1.20 4.2 1.8 1.9 -.6
21.3 2.90 3.00 1.86 0.94 1.8 4.2 1.8 1.9
11.5 1.84 2.90 3.00 1.86 3.2 1.8 4.2 1.8
10.6 -.54 1.84 2.90 3.00 4.7 3.2 1.8 4.2
14.3 0.50 -.54 1.84 2.90 2.0 4.7 3.2 1.8
5.8 1.70 0.50 -.54 1.84 5.1 2.0 4.7 3.2
7.9 2.40 1.70 0.50 -.54 0.8 5.1 2.0 4.7
17.1 3.87 2.40 1.70 .50 2.7 0.8 5.1 2.0
17.6 2.93 3.87 2.40 1.70 1.6 2.7 0.8 5.1
17.9 2.40 2.93 3.87 2.40 2.3 1.6 2.7 0.8
26.0 3.17 2.40 2.93 3.87 2.8 2.3 1.6 2.7
17.7 3.67 3.17 2.40 2.93 -.2 2.8 2.3 1.6
15.4 4.13 3.67 3.17 2.40 0.0 -.2 2.8 2.3
20.9 3.53 4.13 3.67 3.17 -1.6 0.0 -.2 2.8
16.2 1.60 3.53 4.13 3.67 0.2 -1.6 0.0 -.2
17.9 0.90 1.60 3.53 4.13 1.1 0.2 -1.6 0.0
6.7 1.64 0.90 1.60 3.53 -.5 1.1 0.2 -1.6
10.0 2.16 1.64 0.90 1.60 0.2 -.5 1.1 0.2
14.3 1.54 2.16 1.64 0.90 -2.2 0.2 -.5 1.1
17.3 2.73 1.54 2.16 1.64 -1.6 -2.2 0.2 -.5
22.9 3.77 2.73 1.54 2.16 0.8 -1.6 -2.2 0.2
22.8 3.56 3.77 2.73 1.54 0.8 0.8 -1.6 -2.2
19.6 4.60 3.56 3.77 2.73 -.8 0.8 0.8 -1.6
17.7 3.84 4.60 3.56 3.77 1.6 -.8 0.8 0.8
19.2 2.10 3.84 4.60 3.56 0.8 1.6 -.8 0.8
36.6 4.46 2.10 3.84 4.60 5.3 0.8 1.6 -.8
29.3 3.80 4.46 2.10 3.84 0.1 5.3 0.8 1.6
24.4 4.94 3.80 4.46 2.10 -2.4 0.1 5.3 0.8
37.4 5.90 4.94 3.80 4.46 0.4 -2.4 0.1 5.3




Summary of computational transaction
Raw Input view raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R ServerBig 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 time3 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=319127&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]3 seconds[/C][/ROW] [ROW]R Server[/C]Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=319127&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=319127&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 Outputview raw output of R engine
Computing time3 seconds
R ServerBig Analytics Cloud Computing Center







Multiple Linear Regression - Estimated Regression Equation
^QP[t] = + 2.70262 + 2.48764`^M`[t] + 0.856761`^M-1`[t] + 1.46887`^M-2`[t] + 0.600318`^M-3`[t] + 0.70018`^Gf`[t] + 0.160499`^Gf-1`[t] -0.472344`^Gf-2`[t] -0.650205`^Gf-3`[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
^QP[t] =  +  2.70262 +  2.48764`^M`[t] +  0.856761`^M-1`[t] +  1.46887`^M-2`[t] +  0.600318`^M-3`[t] +  0.70018`^Gf`[t] +  0.160499`^Gf-1`[t] -0.472344`^Gf-2`[t] -0.650205`^Gf-3`[t]  + e[t] \tabularnewline
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=319127&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]^QP[t] =  +  2.70262 +  2.48764`^M`[t] +  0.856761`^M-1`[t] +  1.46887`^M-2`[t] +  0.600318`^M-3`[t] +  0.70018`^Gf`[t] +  0.160499`^Gf-1`[t] -0.472344`^Gf-2`[t] -0.650205`^Gf-3`[t]  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=319127&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=319127&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
^QP[t] = + 2.70262 + 2.48764`^M`[t] + 0.856761`^M-1`[t] + 1.46887`^M-2`[t] + 0.600318`^M-3`[t] + 0.70018`^Gf`[t] + 0.160499`^Gf-1`[t] -0.472344`^Gf-2`[t] -0.650205`^Gf-3`[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)+2.703 0.6306+4.2860e+00 4.689e-05 2.344e-05
`^M`+2.488 0.5122+4.8560e+00 5.226e-06 2.613e-06
`^M-1`+0.8568 0.6882+1.2450e+00 0.2165 0.1082
`^M-2`+1.469 0.6918+2.1230e+00 0.03657 0.01828
`^M-3`+0.6003 0.5432+1.1050e+00 0.2722 0.1361
`^Gf`+0.7002 0.2448+2.8600e+00 0.0053 0.00265
`^Gf-1`+0.1605 0.2663+6.0270e-01 0.5483 0.2741
`^Gf-2`-0.4723 0.2685-1.7590e+00 0.08201 0.04101
`^Gf-3`-0.6502 0.2441-2.6630e+00 0.009219 0.00461

\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) & +2.703 &  0.6306 & +4.2860e+00 &  4.689e-05 &  2.344e-05 \tabularnewline
`^M` & +2.488 &  0.5122 & +4.8560e+00 &  5.226e-06 &  2.613e-06 \tabularnewline
`^M-1` & +0.8568 &  0.6882 & +1.2450e+00 &  0.2165 &  0.1082 \tabularnewline
`^M-2` & +1.469 &  0.6918 & +2.1230e+00 &  0.03657 &  0.01828 \tabularnewline
`^M-3` & +0.6003 &  0.5432 & +1.1050e+00 &  0.2722 &  0.1361 \tabularnewline
`^Gf` & +0.7002 &  0.2448 & +2.8600e+00 &  0.0053 &  0.00265 \tabularnewline
`^Gf-1` & +0.1605 &  0.2663 & +6.0270e-01 &  0.5483 &  0.2741 \tabularnewline
`^Gf-2` & -0.4723 &  0.2685 & -1.7590e+00 &  0.08201 &  0.04101 \tabularnewline
`^Gf-3` & -0.6502 &  0.2441 & -2.6630e+00 &  0.009219 &  0.00461 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=319127&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]+2.703[/C][C] 0.6306[/C][C]+4.2860e+00[/C][C] 4.689e-05[/C][C] 2.344e-05[/C][/ROW]
[ROW][C]`^M`[/C][C]+2.488[/C][C] 0.5122[/C][C]+4.8560e+00[/C][C] 5.226e-06[/C][C] 2.613e-06[/C][/ROW]
[ROW][C]`^M-1`[/C][C]+0.8568[/C][C] 0.6882[/C][C]+1.2450e+00[/C][C] 0.2165[/C][C] 0.1082[/C][/ROW]
[ROW][C]`^M-2`[/C][C]+1.469[/C][C] 0.6918[/C][C]+2.1230e+00[/C][C] 0.03657[/C][C] 0.01828[/C][/ROW]
[ROW][C]`^M-3`[/C][C]+0.6003[/C][C] 0.5432[/C][C]+1.1050e+00[/C][C] 0.2722[/C][C] 0.1361[/C][/ROW]
[ROW][C]`^Gf`[/C][C]+0.7002[/C][C] 0.2448[/C][C]+2.8600e+00[/C][C] 0.0053[/C][C] 0.00265[/C][/ROW]
[ROW][C]`^Gf-1`[/C][C]+0.1605[/C][C] 0.2663[/C][C]+6.0270e-01[/C][C] 0.5483[/C][C] 0.2741[/C][/ROW]
[ROW][C]`^Gf-2`[/C][C]-0.4723[/C][C] 0.2685[/C][C]-1.7590e+00[/C][C] 0.08201[/C][C] 0.04101[/C][/ROW]
[ROW][C]`^Gf-3`[/C][C]-0.6502[/C][C] 0.2441[/C][C]-2.6630e+00[/C][C] 0.009219[/C][C] 0.00461[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=319127&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=319127&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)+2.703 0.6306+4.2860e+00 4.689e-05 2.344e-05
`^M`+2.488 0.5122+4.8560e+00 5.226e-06 2.613e-06
`^M-1`+0.8568 0.6882+1.2450e+00 0.2165 0.1082
`^M-2`+1.469 0.6918+2.1230e+00 0.03657 0.01828
`^M-3`+0.6003 0.5432+1.1050e+00 0.2722 0.1361
`^Gf`+0.7002 0.2448+2.8600e+00 0.0053 0.00265
`^Gf-1`+0.1605 0.2663+6.0270e-01 0.5483 0.2741
`^Gf-2`-0.4723 0.2685-1.7590e+00 0.08201 0.04101
`^Gf-3`-0.6502 0.2441-2.6630e+00 0.009219 0.00461







Multiple Linear Regression - Regression Statistics
Multiple R 0.8504
R-squared 0.7232
Adjusted R-squared 0.6977
F-TEST (value) 28.41
F-TEST (DF numerator)8
F-TEST (DF denominator)87
p-value 0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation 3.792
Sum Squared Residuals 1251

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.8504 \tabularnewline
R-squared &  0.7232 \tabularnewline
Adjusted R-squared &  0.6977 \tabularnewline
F-TEST (value) &  28.41 \tabularnewline
F-TEST (DF numerator) & 8 \tabularnewline
F-TEST (DF denominator) & 87 \tabularnewline
p-value &  0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  3.792 \tabularnewline
Sum Squared Residuals &  1251 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=319127&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.8504[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.7232[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.6977[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 28.41[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]8[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]87[/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] 3.792[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 1251[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=319127&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=319127&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 0.8504
R-squared 0.7232
Adjusted R-squared 0.6977
F-TEST (value) 28.41
F-TEST (DF numerator)8
F-TEST (DF denominator)87
p-value 0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation 3.792
Sum Squared Residuals 1251







Menu of Residual Diagnostics
DescriptionLink
HistogramCompute
Central TendencyCompute
QQ PlotCompute
Kernel Density PlotCompute
Skewness/Kurtosis TestCompute
Skewness-Kurtosis PlotCompute
Harrell-Davis PlotCompute
Bootstrap Plot -- Central TendencyCompute
Blocked Bootstrap Plot -- Central TendencyCompute
(Partial) Autocorrelation PlotCompute
Spectral AnalysisCompute
Tukey lambda PPCC PlotCompute
Box-Cox Normality PlotCompute
Summary StatisticsCompute

\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=319127&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=319127&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=319127&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
DescriptionLink
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Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1 6 6.116-0.1156
2 7.5 3.64 3.86
3 7 3.347 3.653
4 1.4 0.6247 0.7753
5-5.4-1.421-3.979
6-3.5 0.4946-3.995
7 1.9-0.32 2.22
8 4.4 1.518 2.882
9 4.4 2.851 1.549
10 9.5 6.343 3.157
11 17.7 9.926 7.774
12 11.5 13.26-1.758
13 14.1 15.89-1.794
14 7.9 12.35-4.452
15 6.7 7.944-1.244
16 4.2 8.02-3.82
17 2.7 6.911-4.211
18 7.2 8.606-1.406
19 9.7 8.787 0.9133
20 9.2 7.528 1.672
21 6.1 5.846 0.2541
22 3.3 7.323-4.023
23-1 3.577-4.577
24-5.3 2.794-8.094
25-0.6 1.192-1.792
26-0.2 0.1006-0.3006
27 4.4 6.619-2.219
28 8.9 11.89-2.986
29 12.6 13.14-0.5381
30 8 9.847-1.847
31 8.6 9.481-0.8814
32 6.2 6.842-0.6417
33 1.8 5.699-3.899
34 5.6 4.702 0.8977
35 5.1 3.824 1.276
36 8.6 4.871 3.729
37 8.1 6.225 1.875
38 2.1 4.108-2.008
39 7.1 1.315 5.785
40-5.4-1.113-4.287
41-7.2 3.154-10.35
42 3.9 7.084-3.184
43 13.2 7.184 6.016
44 13.1 9.354 3.746
45 10 9.324 0.6761
46 10 9.287 0.7131
47 5 9.379-4.379
48 5 3.968 1.032
49 5 2.411 2.589
50 4.3 1.368 2.932
51 1.7 6.365-4.665
52-3.2 3.584-6.784
53 3.4 5.285-1.885
54 11 6.647 4.353
55 9 7.094 1.906
56 14.4 8.967 5.433
57 11.6 9.249 2.351
58 8.5 7.454 1.046
59 6.2 3.286 2.914
60 5.4 5.02 0.3796
61 7.7 7.248 0.4517
62 8.7 6.368 2.332
63 11.1 10.49 0.6089
64 10.6 12.46-1.865
65 12.9 10.22 2.682
66 8.7 7.762 0.9375
67 8.8 10.58-1.784
68 6 13.38-7.384
69 20 12.47 7.534
70 12.9 13.54-0.6449
71 14.7 13.47 1.227
72 20.8 16.58 4.217
73 21.3 15.63 5.668
74 11.5 14.66-3.163
75 10.6 9.22 1.38
76 14.3 7.4 6.9
77 5.8 7.263-1.463
78 7.9 7.918-0.01776
79 17.1 15.49 1.607
80 17.6 15.71 1.887
81 17.9 18.38-0.4803
82 26 19.09 6.91
83 17.7 18.02-0.3151
84 15.4 19.37-3.968
85 20.9 19.47 1.43
86 16.2 17.99-1.79
87 17.9 15.53 2.365
88 6.7 12.8-6.095
89 10 11.17-1.174
90 14.3 9.346 4.954
91 17.3 13.73 3.572
92 22.9 19.19 3.709
93 22.8 22.6 0.2021
94 19.6 24.6-5.003
95 17.7 23.78-6.082
96 19.2 20.79-1.585

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 &  6 &  6.116 & -0.1156 \tabularnewline
2 &  7.5 &  3.64 &  3.86 \tabularnewline
3 &  7 &  3.347 &  3.653 \tabularnewline
4 &  1.4 &  0.6247 &  0.7753 \tabularnewline
5 & -5.4 & -1.421 & -3.979 \tabularnewline
6 & -3.5 &  0.4946 & -3.995 \tabularnewline
7 &  1.9 & -0.32 &  2.22 \tabularnewline
8 &  4.4 &  1.518 &  2.882 \tabularnewline
9 &  4.4 &  2.851 &  1.549 \tabularnewline
10 &  9.5 &  6.343 &  3.157 \tabularnewline
11 &  17.7 &  9.926 &  7.774 \tabularnewline
12 &  11.5 &  13.26 & -1.758 \tabularnewline
13 &  14.1 &  15.89 & -1.794 \tabularnewline
14 &  7.9 &  12.35 & -4.452 \tabularnewline
15 &  6.7 &  7.944 & -1.244 \tabularnewline
16 &  4.2 &  8.02 & -3.82 \tabularnewline
17 &  2.7 &  6.911 & -4.211 \tabularnewline
18 &  7.2 &  8.606 & -1.406 \tabularnewline
19 &  9.7 &  8.787 &  0.9133 \tabularnewline
20 &  9.2 &  7.528 &  1.672 \tabularnewline
21 &  6.1 &  5.846 &  0.2541 \tabularnewline
22 &  3.3 &  7.323 & -4.023 \tabularnewline
23 & -1 &  3.577 & -4.577 \tabularnewline
24 & -5.3 &  2.794 & -8.094 \tabularnewline
25 & -0.6 &  1.192 & -1.792 \tabularnewline
26 & -0.2 &  0.1006 & -0.3006 \tabularnewline
27 &  4.4 &  6.619 & -2.219 \tabularnewline
28 &  8.9 &  11.89 & -2.986 \tabularnewline
29 &  12.6 &  13.14 & -0.5381 \tabularnewline
30 &  8 &  9.847 & -1.847 \tabularnewline
31 &  8.6 &  9.481 & -0.8814 \tabularnewline
32 &  6.2 &  6.842 & -0.6417 \tabularnewline
33 &  1.8 &  5.699 & -3.899 \tabularnewline
34 &  5.6 &  4.702 &  0.8977 \tabularnewline
35 &  5.1 &  3.824 &  1.276 \tabularnewline
36 &  8.6 &  4.871 &  3.729 \tabularnewline
37 &  8.1 &  6.225 &  1.875 \tabularnewline
38 &  2.1 &  4.108 & -2.008 \tabularnewline
39 &  7.1 &  1.315 &  5.785 \tabularnewline
40 & -5.4 & -1.113 & -4.287 \tabularnewline
41 & -7.2 &  3.154 & -10.35 \tabularnewline
42 &  3.9 &  7.084 & -3.184 \tabularnewline
43 &  13.2 &  7.184 &  6.016 \tabularnewline
44 &  13.1 &  9.354 &  3.746 \tabularnewline
45 &  10 &  9.324 &  0.6761 \tabularnewline
46 &  10 &  9.287 &  0.7131 \tabularnewline
47 &  5 &  9.379 & -4.379 \tabularnewline
48 &  5 &  3.968 &  1.032 \tabularnewline
49 &  5 &  2.411 &  2.589 \tabularnewline
50 &  4.3 &  1.368 &  2.932 \tabularnewline
51 &  1.7 &  6.365 & -4.665 \tabularnewline
52 & -3.2 &  3.584 & -6.784 \tabularnewline
53 &  3.4 &  5.285 & -1.885 \tabularnewline
54 &  11 &  6.647 &  4.353 \tabularnewline
55 &  9 &  7.094 &  1.906 \tabularnewline
56 &  14.4 &  8.967 &  5.433 \tabularnewline
57 &  11.6 &  9.249 &  2.351 \tabularnewline
58 &  8.5 &  7.454 &  1.046 \tabularnewline
59 &  6.2 &  3.286 &  2.914 \tabularnewline
60 &  5.4 &  5.02 &  0.3796 \tabularnewline
61 &  7.7 &  7.248 &  0.4517 \tabularnewline
62 &  8.7 &  6.368 &  2.332 \tabularnewline
63 &  11.1 &  10.49 &  0.6089 \tabularnewline
64 &  10.6 &  12.46 & -1.865 \tabularnewline
65 &  12.9 &  10.22 &  2.682 \tabularnewline
66 &  8.7 &  7.762 &  0.9375 \tabularnewline
67 &  8.8 &  10.58 & -1.784 \tabularnewline
68 &  6 &  13.38 & -7.384 \tabularnewline
69 &  20 &  12.47 &  7.534 \tabularnewline
70 &  12.9 &  13.54 & -0.6449 \tabularnewline
71 &  14.7 &  13.47 &  1.227 \tabularnewline
72 &  20.8 &  16.58 &  4.217 \tabularnewline
73 &  21.3 &  15.63 &  5.668 \tabularnewline
74 &  11.5 &  14.66 & -3.163 \tabularnewline
75 &  10.6 &  9.22 &  1.38 \tabularnewline
76 &  14.3 &  7.4 &  6.9 \tabularnewline
77 &  5.8 &  7.263 & -1.463 \tabularnewline
78 &  7.9 &  7.918 & -0.01776 \tabularnewline
79 &  17.1 &  15.49 &  1.607 \tabularnewline
80 &  17.6 &  15.71 &  1.887 \tabularnewline
81 &  17.9 &  18.38 & -0.4803 \tabularnewline
82 &  26 &  19.09 &  6.91 \tabularnewline
83 &  17.7 &  18.02 & -0.3151 \tabularnewline
84 &  15.4 &  19.37 & -3.968 \tabularnewline
85 &  20.9 &  19.47 &  1.43 \tabularnewline
86 &  16.2 &  17.99 & -1.79 \tabularnewline
87 &  17.9 &  15.53 &  2.365 \tabularnewline
88 &  6.7 &  12.8 & -6.095 \tabularnewline
89 &  10 &  11.17 & -1.174 \tabularnewline
90 &  14.3 &  9.346 &  4.954 \tabularnewline
91 &  17.3 &  13.73 &  3.572 \tabularnewline
92 &  22.9 &  19.19 &  3.709 \tabularnewline
93 &  22.8 &  22.6 &  0.2021 \tabularnewline
94 &  19.6 &  24.6 & -5.003 \tabularnewline
95 &  17.7 &  23.78 & -6.082 \tabularnewline
96 &  19.2 &  20.79 & -1.585 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=319127&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] 6[/C][C] 6.116[/C][C]-0.1156[/C][/ROW]
[ROW][C]2[/C][C] 7.5[/C][C] 3.64[/C][C] 3.86[/C][/ROW]
[ROW][C]3[/C][C] 7[/C][C] 3.347[/C][C] 3.653[/C][/ROW]
[ROW][C]4[/C][C] 1.4[/C][C] 0.6247[/C][C] 0.7753[/C][/ROW]
[ROW][C]5[/C][C]-5.4[/C][C]-1.421[/C][C]-3.979[/C][/ROW]
[ROW][C]6[/C][C]-3.5[/C][C] 0.4946[/C][C]-3.995[/C][/ROW]
[ROW][C]7[/C][C] 1.9[/C][C]-0.32[/C][C] 2.22[/C][/ROW]
[ROW][C]8[/C][C] 4.4[/C][C] 1.518[/C][C] 2.882[/C][/ROW]
[ROW][C]9[/C][C] 4.4[/C][C] 2.851[/C][C] 1.549[/C][/ROW]
[ROW][C]10[/C][C] 9.5[/C][C] 6.343[/C][C] 3.157[/C][/ROW]
[ROW][C]11[/C][C] 17.7[/C][C] 9.926[/C][C] 7.774[/C][/ROW]
[ROW][C]12[/C][C] 11.5[/C][C] 13.26[/C][C]-1.758[/C][/ROW]
[ROW][C]13[/C][C] 14.1[/C][C] 15.89[/C][C]-1.794[/C][/ROW]
[ROW][C]14[/C][C] 7.9[/C][C] 12.35[/C][C]-4.452[/C][/ROW]
[ROW][C]15[/C][C] 6.7[/C][C] 7.944[/C][C]-1.244[/C][/ROW]
[ROW][C]16[/C][C] 4.2[/C][C] 8.02[/C][C]-3.82[/C][/ROW]
[ROW][C]17[/C][C] 2.7[/C][C] 6.911[/C][C]-4.211[/C][/ROW]
[ROW][C]18[/C][C] 7.2[/C][C] 8.606[/C][C]-1.406[/C][/ROW]
[ROW][C]19[/C][C] 9.7[/C][C] 8.787[/C][C] 0.9133[/C][/ROW]
[ROW][C]20[/C][C] 9.2[/C][C] 7.528[/C][C] 1.672[/C][/ROW]
[ROW][C]21[/C][C] 6.1[/C][C] 5.846[/C][C] 0.2541[/C][/ROW]
[ROW][C]22[/C][C] 3.3[/C][C] 7.323[/C][C]-4.023[/C][/ROW]
[ROW][C]23[/C][C]-1[/C][C] 3.577[/C][C]-4.577[/C][/ROW]
[ROW][C]24[/C][C]-5.3[/C][C] 2.794[/C][C]-8.094[/C][/ROW]
[ROW][C]25[/C][C]-0.6[/C][C] 1.192[/C][C]-1.792[/C][/ROW]
[ROW][C]26[/C][C]-0.2[/C][C] 0.1006[/C][C]-0.3006[/C][/ROW]
[ROW][C]27[/C][C] 4.4[/C][C] 6.619[/C][C]-2.219[/C][/ROW]
[ROW][C]28[/C][C] 8.9[/C][C] 11.89[/C][C]-2.986[/C][/ROW]
[ROW][C]29[/C][C] 12.6[/C][C] 13.14[/C][C]-0.5381[/C][/ROW]
[ROW][C]30[/C][C] 8[/C][C] 9.847[/C][C]-1.847[/C][/ROW]
[ROW][C]31[/C][C] 8.6[/C][C] 9.481[/C][C]-0.8814[/C][/ROW]
[ROW][C]32[/C][C] 6.2[/C][C] 6.842[/C][C]-0.6417[/C][/ROW]
[ROW][C]33[/C][C] 1.8[/C][C] 5.699[/C][C]-3.899[/C][/ROW]
[ROW][C]34[/C][C] 5.6[/C][C] 4.702[/C][C] 0.8977[/C][/ROW]
[ROW][C]35[/C][C] 5.1[/C][C] 3.824[/C][C] 1.276[/C][/ROW]
[ROW][C]36[/C][C] 8.6[/C][C] 4.871[/C][C] 3.729[/C][/ROW]
[ROW][C]37[/C][C] 8.1[/C][C] 6.225[/C][C] 1.875[/C][/ROW]
[ROW][C]38[/C][C] 2.1[/C][C] 4.108[/C][C]-2.008[/C][/ROW]
[ROW][C]39[/C][C] 7.1[/C][C] 1.315[/C][C] 5.785[/C][/ROW]
[ROW][C]40[/C][C]-5.4[/C][C]-1.113[/C][C]-4.287[/C][/ROW]
[ROW][C]41[/C][C]-7.2[/C][C] 3.154[/C][C]-10.35[/C][/ROW]
[ROW][C]42[/C][C] 3.9[/C][C] 7.084[/C][C]-3.184[/C][/ROW]
[ROW][C]43[/C][C] 13.2[/C][C] 7.184[/C][C] 6.016[/C][/ROW]
[ROW][C]44[/C][C] 13.1[/C][C] 9.354[/C][C] 3.746[/C][/ROW]
[ROW][C]45[/C][C] 10[/C][C] 9.324[/C][C] 0.6761[/C][/ROW]
[ROW][C]46[/C][C] 10[/C][C] 9.287[/C][C] 0.7131[/C][/ROW]
[ROW][C]47[/C][C] 5[/C][C] 9.379[/C][C]-4.379[/C][/ROW]
[ROW][C]48[/C][C] 5[/C][C] 3.968[/C][C] 1.032[/C][/ROW]
[ROW][C]49[/C][C] 5[/C][C] 2.411[/C][C] 2.589[/C][/ROW]
[ROW][C]50[/C][C] 4.3[/C][C] 1.368[/C][C] 2.932[/C][/ROW]
[ROW][C]51[/C][C] 1.7[/C][C] 6.365[/C][C]-4.665[/C][/ROW]
[ROW][C]52[/C][C]-3.2[/C][C] 3.584[/C][C]-6.784[/C][/ROW]
[ROW][C]53[/C][C] 3.4[/C][C] 5.285[/C][C]-1.885[/C][/ROW]
[ROW][C]54[/C][C] 11[/C][C] 6.647[/C][C] 4.353[/C][/ROW]
[ROW][C]55[/C][C] 9[/C][C] 7.094[/C][C] 1.906[/C][/ROW]
[ROW][C]56[/C][C] 14.4[/C][C] 8.967[/C][C] 5.433[/C][/ROW]
[ROW][C]57[/C][C] 11.6[/C][C] 9.249[/C][C] 2.351[/C][/ROW]
[ROW][C]58[/C][C] 8.5[/C][C] 7.454[/C][C] 1.046[/C][/ROW]
[ROW][C]59[/C][C] 6.2[/C][C] 3.286[/C][C] 2.914[/C][/ROW]
[ROW][C]60[/C][C] 5.4[/C][C] 5.02[/C][C] 0.3796[/C][/ROW]
[ROW][C]61[/C][C] 7.7[/C][C] 7.248[/C][C] 0.4517[/C][/ROW]
[ROW][C]62[/C][C] 8.7[/C][C] 6.368[/C][C] 2.332[/C][/ROW]
[ROW][C]63[/C][C] 11.1[/C][C] 10.49[/C][C] 0.6089[/C][/ROW]
[ROW][C]64[/C][C] 10.6[/C][C] 12.46[/C][C]-1.865[/C][/ROW]
[ROW][C]65[/C][C] 12.9[/C][C] 10.22[/C][C] 2.682[/C][/ROW]
[ROW][C]66[/C][C] 8.7[/C][C] 7.762[/C][C] 0.9375[/C][/ROW]
[ROW][C]67[/C][C] 8.8[/C][C] 10.58[/C][C]-1.784[/C][/ROW]
[ROW][C]68[/C][C] 6[/C][C] 13.38[/C][C]-7.384[/C][/ROW]
[ROW][C]69[/C][C] 20[/C][C] 12.47[/C][C] 7.534[/C][/ROW]
[ROW][C]70[/C][C] 12.9[/C][C] 13.54[/C][C]-0.6449[/C][/ROW]
[ROW][C]71[/C][C] 14.7[/C][C] 13.47[/C][C] 1.227[/C][/ROW]
[ROW][C]72[/C][C] 20.8[/C][C] 16.58[/C][C] 4.217[/C][/ROW]
[ROW][C]73[/C][C] 21.3[/C][C] 15.63[/C][C] 5.668[/C][/ROW]
[ROW][C]74[/C][C] 11.5[/C][C] 14.66[/C][C]-3.163[/C][/ROW]
[ROW][C]75[/C][C] 10.6[/C][C] 9.22[/C][C] 1.38[/C][/ROW]
[ROW][C]76[/C][C] 14.3[/C][C] 7.4[/C][C] 6.9[/C][/ROW]
[ROW][C]77[/C][C] 5.8[/C][C] 7.263[/C][C]-1.463[/C][/ROW]
[ROW][C]78[/C][C] 7.9[/C][C] 7.918[/C][C]-0.01776[/C][/ROW]
[ROW][C]79[/C][C] 17.1[/C][C] 15.49[/C][C] 1.607[/C][/ROW]
[ROW][C]80[/C][C] 17.6[/C][C] 15.71[/C][C] 1.887[/C][/ROW]
[ROW][C]81[/C][C] 17.9[/C][C] 18.38[/C][C]-0.4803[/C][/ROW]
[ROW][C]82[/C][C] 26[/C][C] 19.09[/C][C] 6.91[/C][/ROW]
[ROW][C]83[/C][C] 17.7[/C][C] 18.02[/C][C]-0.3151[/C][/ROW]
[ROW][C]84[/C][C] 15.4[/C][C] 19.37[/C][C]-3.968[/C][/ROW]
[ROW][C]85[/C][C] 20.9[/C][C] 19.47[/C][C] 1.43[/C][/ROW]
[ROW][C]86[/C][C] 16.2[/C][C] 17.99[/C][C]-1.79[/C][/ROW]
[ROW][C]87[/C][C] 17.9[/C][C] 15.53[/C][C] 2.365[/C][/ROW]
[ROW][C]88[/C][C] 6.7[/C][C] 12.8[/C][C]-6.095[/C][/ROW]
[ROW][C]89[/C][C] 10[/C][C] 11.17[/C][C]-1.174[/C][/ROW]
[ROW][C]90[/C][C] 14.3[/C][C] 9.346[/C][C] 4.954[/C][/ROW]
[ROW][C]91[/C][C] 17.3[/C][C] 13.73[/C][C] 3.572[/C][/ROW]
[ROW][C]92[/C][C] 22.9[/C][C] 19.19[/C][C] 3.709[/C][/ROW]
[ROW][C]93[/C][C] 22.8[/C][C] 22.6[/C][C] 0.2021[/C][/ROW]
[ROW][C]94[/C][C] 19.6[/C][C] 24.6[/C][C]-5.003[/C][/ROW]
[ROW][C]95[/C][C] 17.7[/C][C] 23.78[/C][C]-6.082[/C][/ROW]
[ROW][C]96[/C][C] 19.2[/C][C] 20.79[/C][C]-1.585[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=319127&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=319127&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 IndexActualsInterpolationForecastResidualsPrediction Error
1 6 6.116-0.1156
2 7.5 3.64 3.86
3 7 3.347 3.653
4 1.4 0.6247 0.7753
5-5.4-1.421-3.979
6-3.5 0.4946-3.995
7 1.9-0.32 2.22
8 4.4 1.518 2.882
9 4.4 2.851 1.549
10 9.5 6.343 3.157
11 17.7 9.926 7.774
12 11.5 13.26-1.758
13 14.1 15.89-1.794
14 7.9 12.35-4.452
15 6.7 7.944-1.244
16 4.2 8.02-3.82
17 2.7 6.911-4.211
18 7.2 8.606-1.406
19 9.7 8.787 0.9133
20 9.2 7.528 1.672
21 6.1 5.846 0.2541
22 3.3 7.323-4.023
23-1 3.577-4.577
24-5.3 2.794-8.094
25-0.6 1.192-1.792
26-0.2 0.1006-0.3006
27 4.4 6.619-2.219
28 8.9 11.89-2.986
29 12.6 13.14-0.5381
30 8 9.847-1.847
31 8.6 9.481-0.8814
32 6.2 6.842-0.6417
33 1.8 5.699-3.899
34 5.6 4.702 0.8977
35 5.1 3.824 1.276
36 8.6 4.871 3.729
37 8.1 6.225 1.875
38 2.1 4.108-2.008
39 7.1 1.315 5.785
40-5.4-1.113-4.287
41-7.2 3.154-10.35
42 3.9 7.084-3.184
43 13.2 7.184 6.016
44 13.1 9.354 3.746
45 10 9.324 0.6761
46 10 9.287 0.7131
47 5 9.379-4.379
48 5 3.968 1.032
49 5 2.411 2.589
50 4.3 1.368 2.932
51 1.7 6.365-4.665
52-3.2 3.584-6.784
53 3.4 5.285-1.885
54 11 6.647 4.353
55 9 7.094 1.906
56 14.4 8.967 5.433
57 11.6 9.249 2.351
58 8.5 7.454 1.046
59 6.2 3.286 2.914
60 5.4 5.02 0.3796
61 7.7 7.248 0.4517
62 8.7 6.368 2.332
63 11.1 10.49 0.6089
64 10.6 12.46-1.865
65 12.9 10.22 2.682
66 8.7 7.762 0.9375
67 8.8 10.58-1.784
68 6 13.38-7.384
69 20 12.47 7.534
70 12.9 13.54-0.6449
71 14.7 13.47 1.227
72 20.8 16.58 4.217
73 21.3 15.63 5.668
74 11.5 14.66-3.163
75 10.6 9.22 1.38
76 14.3 7.4 6.9
77 5.8 7.263-1.463
78 7.9 7.918-0.01776
79 17.1 15.49 1.607
80 17.6 15.71 1.887
81 17.9 18.38-0.4803
82 26 19.09 6.91
83 17.7 18.02-0.3151
84 15.4 19.37-3.968
85 20.9 19.47 1.43
86 16.2 17.99-1.79
87 17.9 15.53 2.365
88 6.7 12.8-6.095
89 10 11.17-1.174
90 14.3 9.346 4.954
91 17.3 13.73 3.572
92 22.9 19.19 3.709
93 22.8 22.6 0.2021
94 19.6 24.6-5.003
95 17.7 23.78-6.082
96 19.2 20.79-1.585







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
12 0.4649 0.9297 0.5351
13 0.3123 0.6245 0.6877
14 0.3677 0.7354 0.6323
15 0.6501 0.6999 0.3499
16 0.5775 0.8449 0.4225
17 0.5052 0.9896 0.4948
18 0.4088 0.8177 0.5912
19 0.3585 0.717 0.6415
20 0.2706 0.5413 0.7294
21 0.1997 0.3993 0.8003
22 0.2107 0.4214 0.7893
23 0.3205 0.6411 0.6795
24 0.6233 0.7535 0.3767
25 0.5686 0.8628 0.4314
26 0.5021 0.9958 0.4979
27 0.4992 0.9984 0.5008
28 0.5458 0.9084 0.4542
29 0.4764 0.9529 0.5236
30 0.408 0.816 0.592
31 0.3387 0.6775 0.6613
32 0.2756 0.5513 0.7244
33 0.2654 0.5308 0.7346
34 0.2134 0.4269 0.7866
35 0.1775 0.355 0.8225
36 0.1784 0.3569 0.8216
37 0.1416 0.2831 0.8584
38 0.1149 0.2297 0.8851
39 0.1563 0.3127 0.8437
40 0.1694 0.3389 0.8306
41 0.5723 0.8554 0.4277
42 0.5625 0.875 0.4375
43 0.6129 0.7742 0.3871
44 0.6064 0.7872 0.3936
45 0.5486 0.9028 0.4514
46 0.4866 0.9731 0.5134
47 0.5111 0.9778 0.4889
48 0.453 0.9061 0.547
49 0.4682 0.9363 0.5318
50 0.4359 0.8718 0.5641
51 0.529 0.9419 0.471
52 0.6964 0.6072 0.3036
53 0.7105 0.5789 0.2895
54 0.707 0.5859 0.293
55 0.667 0.6661 0.333
56 0.6878 0.6245 0.3122
57 0.6399 0.7203 0.3601
58 0.5933 0.8133 0.4067
59 0.5697 0.8606 0.4303
60 0.5876 0.8249 0.4124
61 0.5246 0.9508 0.4754
62 0.4691 0.9382 0.5309
63 0.4091 0.8182 0.5909
64 0.4324 0.8648 0.5676
65 0.4263 0.8525 0.5737
66 0.3578 0.7156 0.6422
67 0.3767 0.7535 0.6233
68 0.5946 0.8108 0.4054
69 0.7488 0.5025 0.2512
70 0.721 0.5581 0.279
71 0.6729 0.6542 0.3271
72 0.6634 0.6731 0.3366
73 0.8049 0.3903 0.1951
74 0.7519 0.4963 0.2481
75 0.6881 0.6237 0.3119
76 0.7221 0.5558 0.2779
77 0.6419 0.7162 0.3581
78 0.7992 0.4016 0.2008
79 0.7134 0.5733 0.2866
80 0.8102 0.3796 0.1898
81 0.7253 0.5494 0.2747
82 0.8205 0.359 0.1795
83 0.9969 0.006174 0.003087
84 0.9842 0.03163 0.01581

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
12 &  0.4649 &  0.9297 &  0.5351 \tabularnewline
13 &  0.3123 &  0.6245 &  0.6877 \tabularnewline
14 &  0.3677 &  0.7354 &  0.6323 \tabularnewline
15 &  0.6501 &  0.6999 &  0.3499 \tabularnewline
16 &  0.5775 &  0.8449 &  0.4225 \tabularnewline
17 &  0.5052 &  0.9896 &  0.4948 \tabularnewline
18 &  0.4088 &  0.8177 &  0.5912 \tabularnewline
19 &  0.3585 &  0.717 &  0.6415 \tabularnewline
20 &  0.2706 &  0.5413 &  0.7294 \tabularnewline
21 &  0.1997 &  0.3993 &  0.8003 \tabularnewline
22 &  0.2107 &  0.4214 &  0.7893 \tabularnewline
23 &  0.3205 &  0.6411 &  0.6795 \tabularnewline
24 &  0.6233 &  0.7535 &  0.3767 \tabularnewline
25 &  0.5686 &  0.8628 &  0.4314 \tabularnewline
26 &  0.5021 &  0.9958 &  0.4979 \tabularnewline
27 &  0.4992 &  0.9984 &  0.5008 \tabularnewline
28 &  0.5458 &  0.9084 &  0.4542 \tabularnewline
29 &  0.4764 &  0.9529 &  0.5236 \tabularnewline
30 &  0.408 &  0.816 &  0.592 \tabularnewline
31 &  0.3387 &  0.6775 &  0.6613 \tabularnewline
32 &  0.2756 &  0.5513 &  0.7244 \tabularnewline
33 &  0.2654 &  0.5308 &  0.7346 \tabularnewline
34 &  0.2134 &  0.4269 &  0.7866 \tabularnewline
35 &  0.1775 &  0.355 &  0.8225 \tabularnewline
36 &  0.1784 &  0.3569 &  0.8216 \tabularnewline
37 &  0.1416 &  0.2831 &  0.8584 \tabularnewline
38 &  0.1149 &  0.2297 &  0.8851 \tabularnewline
39 &  0.1563 &  0.3127 &  0.8437 \tabularnewline
40 &  0.1694 &  0.3389 &  0.8306 \tabularnewline
41 &  0.5723 &  0.8554 &  0.4277 \tabularnewline
42 &  0.5625 &  0.875 &  0.4375 \tabularnewline
43 &  0.6129 &  0.7742 &  0.3871 \tabularnewline
44 &  0.6064 &  0.7872 &  0.3936 \tabularnewline
45 &  0.5486 &  0.9028 &  0.4514 \tabularnewline
46 &  0.4866 &  0.9731 &  0.5134 \tabularnewline
47 &  0.5111 &  0.9778 &  0.4889 \tabularnewline
48 &  0.453 &  0.9061 &  0.547 \tabularnewline
49 &  0.4682 &  0.9363 &  0.5318 \tabularnewline
50 &  0.4359 &  0.8718 &  0.5641 \tabularnewline
51 &  0.529 &  0.9419 &  0.471 \tabularnewline
52 &  0.6964 &  0.6072 &  0.3036 \tabularnewline
53 &  0.7105 &  0.5789 &  0.2895 \tabularnewline
54 &  0.707 &  0.5859 &  0.293 \tabularnewline
55 &  0.667 &  0.6661 &  0.333 \tabularnewline
56 &  0.6878 &  0.6245 &  0.3122 \tabularnewline
57 &  0.6399 &  0.7203 &  0.3601 \tabularnewline
58 &  0.5933 &  0.8133 &  0.4067 \tabularnewline
59 &  0.5697 &  0.8606 &  0.4303 \tabularnewline
60 &  0.5876 &  0.8249 &  0.4124 \tabularnewline
61 &  0.5246 &  0.9508 &  0.4754 \tabularnewline
62 &  0.4691 &  0.9382 &  0.5309 \tabularnewline
63 &  0.4091 &  0.8182 &  0.5909 \tabularnewline
64 &  0.4324 &  0.8648 &  0.5676 \tabularnewline
65 &  0.4263 &  0.8525 &  0.5737 \tabularnewline
66 &  0.3578 &  0.7156 &  0.6422 \tabularnewline
67 &  0.3767 &  0.7535 &  0.6233 \tabularnewline
68 &  0.5946 &  0.8108 &  0.4054 \tabularnewline
69 &  0.7488 &  0.5025 &  0.2512 \tabularnewline
70 &  0.721 &  0.5581 &  0.279 \tabularnewline
71 &  0.6729 &  0.6542 &  0.3271 \tabularnewline
72 &  0.6634 &  0.6731 &  0.3366 \tabularnewline
73 &  0.8049 &  0.3903 &  0.1951 \tabularnewline
74 &  0.7519 &  0.4963 &  0.2481 \tabularnewline
75 &  0.6881 &  0.6237 &  0.3119 \tabularnewline
76 &  0.7221 &  0.5558 &  0.2779 \tabularnewline
77 &  0.6419 &  0.7162 &  0.3581 \tabularnewline
78 &  0.7992 &  0.4016 &  0.2008 \tabularnewline
79 &  0.7134 &  0.5733 &  0.2866 \tabularnewline
80 &  0.8102 &  0.3796 &  0.1898 \tabularnewline
81 &  0.7253 &  0.5494 &  0.2747 \tabularnewline
82 &  0.8205 &  0.359 &  0.1795 \tabularnewline
83 &  0.9969 &  0.006174 &  0.003087 \tabularnewline
84 &  0.9842 &  0.03163 &  0.01581 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=319127&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]12[/C][C] 0.4649[/C][C] 0.9297[/C][C] 0.5351[/C][/ROW]
[ROW][C]13[/C][C] 0.3123[/C][C] 0.6245[/C][C] 0.6877[/C][/ROW]
[ROW][C]14[/C][C] 0.3677[/C][C] 0.7354[/C][C] 0.6323[/C][/ROW]
[ROW][C]15[/C][C] 0.6501[/C][C] 0.6999[/C][C] 0.3499[/C][/ROW]
[ROW][C]16[/C][C] 0.5775[/C][C] 0.8449[/C][C] 0.4225[/C][/ROW]
[ROW][C]17[/C][C] 0.5052[/C][C] 0.9896[/C][C] 0.4948[/C][/ROW]
[ROW][C]18[/C][C] 0.4088[/C][C] 0.8177[/C][C] 0.5912[/C][/ROW]
[ROW][C]19[/C][C] 0.3585[/C][C] 0.717[/C][C] 0.6415[/C][/ROW]
[ROW][C]20[/C][C] 0.2706[/C][C] 0.5413[/C][C] 0.7294[/C][/ROW]
[ROW][C]21[/C][C] 0.1997[/C][C] 0.3993[/C][C] 0.8003[/C][/ROW]
[ROW][C]22[/C][C] 0.2107[/C][C] 0.4214[/C][C] 0.7893[/C][/ROW]
[ROW][C]23[/C][C] 0.3205[/C][C] 0.6411[/C][C] 0.6795[/C][/ROW]
[ROW][C]24[/C][C] 0.6233[/C][C] 0.7535[/C][C] 0.3767[/C][/ROW]
[ROW][C]25[/C][C] 0.5686[/C][C] 0.8628[/C][C] 0.4314[/C][/ROW]
[ROW][C]26[/C][C] 0.5021[/C][C] 0.9958[/C][C] 0.4979[/C][/ROW]
[ROW][C]27[/C][C] 0.4992[/C][C] 0.9984[/C][C] 0.5008[/C][/ROW]
[ROW][C]28[/C][C] 0.5458[/C][C] 0.9084[/C][C] 0.4542[/C][/ROW]
[ROW][C]29[/C][C] 0.4764[/C][C] 0.9529[/C][C] 0.5236[/C][/ROW]
[ROW][C]30[/C][C] 0.408[/C][C] 0.816[/C][C] 0.592[/C][/ROW]
[ROW][C]31[/C][C] 0.3387[/C][C] 0.6775[/C][C] 0.6613[/C][/ROW]
[ROW][C]32[/C][C] 0.2756[/C][C] 0.5513[/C][C] 0.7244[/C][/ROW]
[ROW][C]33[/C][C] 0.2654[/C][C] 0.5308[/C][C] 0.7346[/C][/ROW]
[ROW][C]34[/C][C] 0.2134[/C][C] 0.4269[/C][C] 0.7866[/C][/ROW]
[ROW][C]35[/C][C] 0.1775[/C][C] 0.355[/C][C] 0.8225[/C][/ROW]
[ROW][C]36[/C][C] 0.1784[/C][C] 0.3569[/C][C] 0.8216[/C][/ROW]
[ROW][C]37[/C][C] 0.1416[/C][C] 0.2831[/C][C] 0.8584[/C][/ROW]
[ROW][C]38[/C][C] 0.1149[/C][C] 0.2297[/C][C] 0.8851[/C][/ROW]
[ROW][C]39[/C][C] 0.1563[/C][C] 0.3127[/C][C] 0.8437[/C][/ROW]
[ROW][C]40[/C][C] 0.1694[/C][C] 0.3389[/C][C] 0.8306[/C][/ROW]
[ROW][C]41[/C][C] 0.5723[/C][C] 0.8554[/C][C] 0.4277[/C][/ROW]
[ROW][C]42[/C][C] 0.5625[/C][C] 0.875[/C][C] 0.4375[/C][/ROW]
[ROW][C]43[/C][C] 0.6129[/C][C] 0.7742[/C][C] 0.3871[/C][/ROW]
[ROW][C]44[/C][C] 0.6064[/C][C] 0.7872[/C][C] 0.3936[/C][/ROW]
[ROW][C]45[/C][C] 0.5486[/C][C] 0.9028[/C][C] 0.4514[/C][/ROW]
[ROW][C]46[/C][C] 0.4866[/C][C] 0.9731[/C][C] 0.5134[/C][/ROW]
[ROW][C]47[/C][C] 0.5111[/C][C] 0.9778[/C][C] 0.4889[/C][/ROW]
[ROW][C]48[/C][C] 0.453[/C][C] 0.9061[/C][C] 0.547[/C][/ROW]
[ROW][C]49[/C][C] 0.4682[/C][C] 0.9363[/C][C] 0.5318[/C][/ROW]
[ROW][C]50[/C][C] 0.4359[/C][C] 0.8718[/C][C] 0.5641[/C][/ROW]
[ROW][C]51[/C][C] 0.529[/C][C] 0.9419[/C][C] 0.471[/C][/ROW]
[ROW][C]52[/C][C] 0.6964[/C][C] 0.6072[/C][C] 0.3036[/C][/ROW]
[ROW][C]53[/C][C] 0.7105[/C][C] 0.5789[/C][C] 0.2895[/C][/ROW]
[ROW][C]54[/C][C] 0.707[/C][C] 0.5859[/C][C] 0.293[/C][/ROW]
[ROW][C]55[/C][C] 0.667[/C][C] 0.6661[/C][C] 0.333[/C][/ROW]
[ROW][C]56[/C][C] 0.6878[/C][C] 0.6245[/C][C] 0.3122[/C][/ROW]
[ROW][C]57[/C][C] 0.6399[/C][C] 0.7203[/C][C] 0.3601[/C][/ROW]
[ROW][C]58[/C][C] 0.5933[/C][C] 0.8133[/C][C] 0.4067[/C][/ROW]
[ROW][C]59[/C][C] 0.5697[/C][C] 0.8606[/C][C] 0.4303[/C][/ROW]
[ROW][C]60[/C][C] 0.5876[/C][C] 0.8249[/C][C] 0.4124[/C][/ROW]
[ROW][C]61[/C][C] 0.5246[/C][C] 0.9508[/C][C] 0.4754[/C][/ROW]
[ROW][C]62[/C][C] 0.4691[/C][C] 0.9382[/C][C] 0.5309[/C][/ROW]
[ROW][C]63[/C][C] 0.4091[/C][C] 0.8182[/C][C] 0.5909[/C][/ROW]
[ROW][C]64[/C][C] 0.4324[/C][C] 0.8648[/C][C] 0.5676[/C][/ROW]
[ROW][C]65[/C][C] 0.4263[/C][C] 0.8525[/C][C] 0.5737[/C][/ROW]
[ROW][C]66[/C][C] 0.3578[/C][C] 0.7156[/C][C] 0.6422[/C][/ROW]
[ROW][C]67[/C][C] 0.3767[/C][C] 0.7535[/C][C] 0.6233[/C][/ROW]
[ROW][C]68[/C][C] 0.5946[/C][C] 0.8108[/C][C] 0.4054[/C][/ROW]
[ROW][C]69[/C][C] 0.7488[/C][C] 0.5025[/C][C] 0.2512[/C][/ROW]
[ROW][C]70[/C][C] 0.721[/C][C] 0.5581[/C][C] 0.279[/C][/ROW]
[ROW][C]71[/C][C] 0.6729[/C][C] 0.6542[/C][C] 0.3271[/C][/ROW]
[ROW][C]72[/C][C] 0.6634[/C][C] 0.6731[/C][C] 0.3366[/C][/ROW]
[ROW][C]73[/C][C] 0.8049[/C][C] 0.3903[/C][C] 0.1951[/C][/ROW]
[ROW][C]74[/C][C] 0.7519[/C][C] 0.4963[/C][C] 0.2481[/C][/ROW]
[ROW][C]75[/C][C] 0.6881[/C][C] 0.6237[/C][C] 0.3119[/C][/ROW]
[ROW][C]76[/C][C] 0.7221[/C][C] 0.5558[/C][C] 0.2779[/C][/ROW]
[ROW][C]77[/C][C] 0.6419[/C][C] 0.7162[/C][C] 0.3581[/C][/ROW]
[ROW][C]78[/C][C] 0.7992[/C][C] 0.4016[/C][C] 0.2008[/C][/ROW]
[ROW][C]79[/C][C] 0.7134[/C][C] 0.5733[/C][C] 0.2866[/C][/ROW]
[ROW][C]80[/C][C] 0.8102[/C][C] 0.3796[/C][C] 0.1898[/C][/ROW]
[ROW][C]81[/C][C] 0.7253[/C][C] 0.5494[/C][C] 0.2747[/C][/ROW]
[ROW][C]82[/C][C] 0.8205[/C][C] 0.359[/C][C] 0.1795[/C][/ROW]
[ROW][C]83[/C][C] 0.9969[/C][C] 0.006174[/C][C] 0.003087[/C][/ROW]
[ROW][C]84[/C][C] 0.9842[/C][C] 0.03163[/C][C] 0.01581[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=319127&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=319127&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-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
12 0.4649 0.9297 0.5351
13 0.3123 0.6245 0.6877
14 0.3677 0.7354 0.6323
15 0.6501 0.6999 0.3499
16 0.5775 0.8449 0.4225
17 0.5052 0.9896 0.4948
18 0.4088 0.8177 0.5912
19 0.3585 0.717 0.6415
20 0.2706 0.5413 0.7294
21 0.1997 0.3993 0.8003
22 0.2107 0.4214 0.7893
23 0.3205 0.6411 0.6795
24 0.6233 0.7535 0.3767
25 0.5686 0.8628 0.4314
26 0.5021 0.9958 0.4979
27 0.4992 0.9984 0.5008
28 0.5458 0.9084 0.4542
29 0.4764 0.9529 0.5236
30 0.408 0.816 0.592
31 0.3387 0.6775 0.6613
32 0.2756 0.5513 0.7244
33 0.2654 0.5308 0.7346
34 0.2134 0.4269 0.7866
35 0.1775 0.355 0.8225
36 0.1784 0.3569 0.8216
37 0.1416 0.2831 0.8584
38 0.1149 0.2297 0.8851
39 0.1563 0.3127 0.8437
40 0.1694 0.3389 0.8306
41 0.5723 0.8554 0.4277
42 0.5625 0.875 0.4375
43 0.6129 0.7742 0.3871
44 0.6064 0.7872 0.3936
45 0.5486 0.9028 0.4514
46 0.4866 0.9731 0.5134
47 0.5111 0.9778 0.4889
48 0.453 0.9061 0.547
49 0.4682 0.9363 0.5318
50 0.4359 0.8718 0.5641
51 0.529 0.9419 0.471
52 0.6964 0.6072 0.3036
53 0.7105 0.5789 0.2895
54 0.707 0.5859 0.293
55 0.667 0.6661 0.333
56 0.6878 0.6245 0.3122
57 0.6399 0.7203 0.3601
58 0.5933 0.8133 0.4067
59 0.5697 0.8606 0.4303
60 0.5876 0.8249 0.4124
61 0.5246 0.9508 0.4754
62 0.4691 0.9382 0.5309
63 0.4091 0.8182 0.5909
64 0.4324 0.8648 0.5676
65 0.4263 0.8525 0.5737
66 0.3578 0.7156 0.6422
67 0.3767 0.7535 0.6233
68 0.5946 0.8108 0.4054
69 0.7488 0.5025 0.2512
70 0.721 0.5581 0.279
71 0.6729 0.6542 0.3271
72 0.6634 0.6731 0.3366
73 0.8049 0.3903 0.1951
74 0.7519 0.4963 0.2481
75 0.6881 0.6237 0.3119
76 0.7221 0.5558 0.2779
77 0.6419 0.7162 0.3581
78 0.7992 0.4016 0.2008
79 0.7134 0.5733 0.2866
80 0.8102 0.3796 0.1898
81 0.7253 0.5494 0.2747
82 0.8205 0.359 0.1795
83 0.9969 0.006174 0.003087
84 0.9842 0.03163 0.01581







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level1 0.0137NOK
5% type I error level20.0273973OK
10% type I error level20.0273973OK

\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 & 1 &  0.0137 & NOK \tabularnewline
5% type I error level & 2 & 0.0273973 & OK \tabularnewline
10% type I error level & 2 & 0.0273973 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=319127&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]1[/C][C] 0.0137[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]2[/C][C]0.0273973[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]2[/C][C]0.0273973[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=319127&T=7

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=319127&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 testsOK/NOK
1% type I error level1 0.0137NOK
5% type I error level20.0273973OK
10% type I error level20.0273973OK







Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted
	RESET test
data:  mylm
RESET = 1.7749, df1 = 2, df2 = 85, p-value = 0.1757
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors
	RESET test
data:  mylm
RESET = 1.1593, df1 = 16, df2 = 71, p-value = 0.3215
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 1.3781, df1 = 2, df2 = 85, p-value = 0.2576

\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 = 1.7749, df1 = 2, df2 = 85, p-value = 0.1757
\tabularnewline Ramsey RESET F-Test for powers (2 and 3) of regressors \tabularnewline
> reset_test_regressors
	RESET test
data:  mylm
RESET = 1.1593, df1 = 16, df2 = 71, p-value = 0.3215
\tabularnewline Ramsey RESET F-Test for powers (2 and 3) of principal components \tabularnewline
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 1.3781, df1 = 2, df2 = 85, p-value = 0.2576
\tabularnewline \hline \end{tabular} %Source: https://freestatistics.org/blog/index.php?pk=319127&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 = 1.7749, df1 = 2, df2 = 85, p-value = 0.1757
[/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 = 1.1593, df1 = 16, df2 = 71, p-value = 0.3215
[/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 = 1.3781, df1 = 2, df2 = 85, p-value = 0.2576
[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=319127&T=8

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=319127&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 = 1.7749, df1 = 2, df2 = 85, p-value = 0.1757
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors
	RESET test
data:  mylm
RESET = 1.1593, df1 = 16, df2 = 71, p-value = 0.3215
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 1.3781, df1 = 2, df2 = 85, p-value = 0.2576







Variance Inflation Factors (Multicollinearity)
> vif
    `^M`   `^M-1`   `^M-2`   `^M-3`    `^Gf`  `^Gf-1`  `^Gf-2`  `^Gf-3` 
2.579924 4.645671 4.464674 2.517397 1.568928 1.860056 1.896317 1.557756 

\begin{tabular}{lllllllll}
\hline
Variance Inflation Factors (Multicollinearity) \tabularnewline
> vif
    `^M`   `^M-1`   `^M-2`   `^M-3`    `^Gf`  `^Gf-1`  `^Gf-2`  `^Gf-3` 
2.579924 4.645671 4.464674 2.517397 1.568928 1.860056 1.896317 1.557756 
\tabularnewline \hline \end{tabular} %Source: https://freestatistics.org/blog/index.php?pk=319127&T=9

[TABLE]
[ROW][C]Variance Inflation Factors (Multicollinearity)[/C][/ROW]
[ROW][C]
> vif
    `^M`   `^M-1`   `^M-2`   `^M-3`    `^Gf`  `^Gf-1`  `^Gf-2`  `^Gf-3` 
2.579924 4.645671 4.464674 2.517397 1.568928 1.860056 1.896317 1.557756 
[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=319127&T=9

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=319127&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
    `^M`   `^M-1`   `^M-2`   `^M-3`    `^Gf`  `^Gf-1`  `^Gf-2`  `^Gf-3` 
2.579924 4.645671 4.464674 2.517397 1.568928 1.860056 1.896317 1.557756 



Parameters (Session):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ; par6 = 12 ;
Parameters (R input):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ; par4 = ; par5 = ; par6 = 12 ;
R code (references can be found in the software module):
par6 <- '12'
par5 <- ''
par4 <- ''
par3 <- 'No Linear Trend'
par2 <- 'Do not include Seasonal Dummies'
par1 <- '1'
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')