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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 21:09:15 +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 21:09:15 +0000
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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 Output view raw output of R engine Computing time 3 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 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 Output view raw output of R engine Computing time 3 seconds R Server Big 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 Variable Parameter S.D. T-STATH0: parameter = 0 2-tail p-value 1-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 Variable Parameter S.D. T-STATH0: parameter = 0 2-tail p-value 1-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 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=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 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 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 Index Actuals InterpolationForecast ResidualsPrediction 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-values Alternative Hypothesis breakpoint index greater 2-sided less 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-values Alternative Hypothesis breakpoint index greater 2-sided less 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 tests OK/NOK 1% type I error level 1 0.0137 NOK 5% type I error level 2 0.0273973 OK 10% type I error level 2 0.0273973 OK

\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 tests OK/NOK 1% type I error level 1 0.0137 NOK 5% type I error level 2 0.0273973 OK 10% type I error level 2 0.0273973 OK

 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 

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 PNG link Postscript link PDF link

 PNG link Postscript link PDF link

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 testmywarning <- ''par6 <- as.numeric(par6)if(is.na(par6)) {par6 <- 12mywarning = 'Warning: you did not specify the seasonality. The seasonal period was set to s = 12.'}par1 <- as.numeric(par1)if(is.na(par1)) {par1 <- 1mywarning = 'Warning: you did not specify the column number of the endogenous series! The first column was selected by default.'}if (par4=='') par4 <- 0par4 <- as.numeric(par4)if (!is.numeric(par4)) par4 <- 0if (par5=='') par5 <- 0par5 <- as.numeric(par5)if (!is.numeric(par5)) par5 <- 0x <- 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 <- x1if (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 + 3nmkm3 <- n - k - 3gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))numgqtests <- 0numsignificant1 <- 0numsignificant5 <- 0numsignificant10 <- 0for (mypoint in kp3:nmkm3) {j <- 0numgqtests <- numgqtests + 1for (myalt in c('greater', 'two.sided', 'less')) {j <- j + 1gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value}if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1}gqarr}bitmap(file='test0.png')plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')points(x[,1]-mysum$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$resid))bitmap(file='test5.png')dum <- cbind(lag(myerror,k=1),myerror)dumdum1 <- dum[2:length(myerror),]dum1z <- as.data.frame(dum1)print(z)plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')lines(lowess(z))abline(lm(z))grid()dev.off()bitmap(file='test6.png')acf(mysum$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-STATH0: 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$resid)myra <-table.start()a <- table.row.start(a)a <- table.element(a,'Menu of Residual Diagnostics',2,TRUE)a <- table.row.end(a)a <- table.row.start(a)a <- table.element(a,'Description',1,TRUE)a <- table.element(a,'Link',1,TRUE)a <- table.row.end(a)a <- table.row.start(a)a <-table.element(a,'Histogram',1,header=TRUE)a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_histogram.wasp?convertgetintopost=1&data=',paste(as.character(mysum$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$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)a <- table.row.end(a)a<-table.end(a)table.save(a,file='mytable7.tab')if(n < 200) {a<-table.start()a<-table.row.start(a)a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)a<-table.row.end(a)a<-table.row.start(a)a<-table.element(a, 'Time or Index', 1, TRUE)a<-table.element(a, 'Actuals', 1, TRUE)a<-table.element(a, 'InterpolationForecast', 1, TRUE)a<-table.element(a, 'ResidualsPrediction Error', 1, TRUE)a<-table.row.end(a)for (i in 1:n) {a<-table.row.start(a)a<-table.element(a,i, 1, TRUE)a<-table.element(a,formatC(signif(x[i],6),format='g',flag=' '))a<-table.element(a,formatC(signif(x[i]-mysum$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')