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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Thu, 20 Dec 2018 20:32:37 +0100

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2018/Dec/20/t1545334419f6urastxdmffgtx.htm/, Retrieved Fri, 17 May 2024 04:41:58 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=316144, Retrieved Fri, 17 May 2024 04:41:58 +0000

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

-       [Multiple Regression] [] [2018-12-20 19:32:37] [593aecdf71e2387dd5acdd2c766144c5] [Current]

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0.79 0.3 0.2 0.08 0.18 0
2.21 0.78 0.22 0.25 0.87 0.02
2.12 0.6 0.16 0.17 1.14 0.01
0.93 0.33 0.15 0.12 0.2 0.09
5.38 NA NA NA NA NA
3.14 0.78 0.79 0.29 1.08 0.1
2.23 0.74 0.18 0.34 0.89 0.01
11.88 NA NA NA NA NA
9.31 2.68 0.63 0.89 4.85 0.11
6.06 0.82 0.27 0.63 4.14 0.06
2.31 0.66 0.22 0.11 1.25 0.01
6.84 0.97 1.05 0.19 4.46 0.14
7.49 0.52 0.45 0.16 6.19 0.07
0.72 0.29 0 0.08 0.26 0.02
4.48 0.56 0.24 0.14 3.28 0.22
5.09 1.32 0.12 0.91 2.57 0.08
7.44 1.15 0.48 0.99 4.43 0.12
1.41 0.49 0.04 0.26 0.51 0.08
5.77 NA NA NA NA NA
4.84 0.5 0.42 3.03 0.63 0.02
2.96 0.37 1.69 0.17 0.67 0.01
3.12 0.63 0.27 0.44 1.74 0.02
3.83 0.3 0.89 0.24 2.36 0.01
3.11 0.62 0.85 0.6 0.91 0.04
2.86 NA NA NA NA NA
4.06 0.31 0.13 0.26 3.24 0.09
3.32 0.6 0.13 0.35 2.08 0.03
1.21 0.47 0.17 0.36 0.12 0.03
0.8 0.21 0.07 0.45 0.04 0
2.52 NA NA NA NA NA
1.21 NA NA NA NA NA
1.17 0.54 0.1 0.21 0.19 0.08
8.17 1.46 0.33 1.2 5 0.12
5.65 0.36 0.45 1.23 3.56 0.05
1.24 0.3 0.55 0.26 0.08 0.02
1.46 0.36 0.75 0.27 0.01 0.01
4.36 0.61 0.33 0.99 2.04 0.25
3.38 0.55 0.13 0.19 2.32 0.08
1.87 0.35 0.58 0.16 0.67 0.03
1.03 0.33 0.03 0.18 0.25 0.25
1.29 0.22 0.09 0.38 0.47 0.1
0.82 0.15 0.01 0.51 0.07 0.01
2.84 0.4 0.21 0.68 1.37 0.08
1.27 0.51 0.06 0.22 0.26 0.14
3.92 0.74 0.13 0.72 2.21 0.06
1.95 0.48 0.11 0.09 1.23 0.02
4.21 0.77 0.08 0.23 2.94 0.14
5.19 0.62 0.24 0.74 3.42 0.03
5.51 1.18 0.47 0.77 2.6 0.24
2.19 NA NA NA NA NA
2.57 0.64 0.15 0.14 1.47 0.17
1.53 0.35 0.12 0.12 0.86 0.04
2.17 0.3 0.3 0.24 1.08 0.18
2.15 0.68 0.08 0.17 1.02 0.04
2.07 0.44 0.21 0.4 0.84 0.14
3.97 0.27 0.02 0.26 3.17 0.22
0.42 0.1 0.18 0.06 0.03 0.01
6.86 NA NA NA NA NA
1.02 0.31 0.12 0.46 0.07 0
2.9 0.55 0.2 0.42 1.06 0.62
5.87 NA NA NA NA NA
5.14 1.23 0.27 0.53 2.71 0.19
2.34 0.07 0.06 0.46 1.58 0.17
4.73 0.75 0.65 0.12 2.39 0.82
2.02 0.53 0.13 0.79 0.43 0.11
1.03 0.46 0.05 0.2 0.21 0.08
1.58 0.39 0.2 0.1 0.83 0.02
5.3 1.1 0.14 0.48 3.28 0.05
1.97 0.56 0.07 0.65 0.43 0.19
4.38 1.07 0.35 0.24 2.58 0.08
2.98 NA NA NA NA NA
3.23 0.11 0.03 0.16 2.61 0.31
1.89 0.37 0.16 0.58 0.7 0.01
1.41 0.39 0.32 0.45 0.16 0.05
1.53 0.35 0.36 0.67 0.09 0.01
3.07 0.7 0.22 0.77 1.25 0.06
0.61 0.27 0.04 0.1 0.15 0.01
1.68 0.28 0.25 0.5 0.6 0
2.92 0.42 0.08 0.36 1.9 0.02
1.16 0.34 0.01 0.14 0.61 0.02
1.58 0.44 0.03 0.2 0.64 0.21
2.79 0.69 0.09 0.07 1.72 0.12
1.88 0.43 0.03 0.01 1.36 0.01
5.57 1.08 0.49 0.46 3.22 0.18
6.22 0.89 0.22 0.36 4.59 0.07
4.61 0.91 0.32 0.42 2.77 0.12
1.89 0.41 0.09 0.18 1.09 0.07
5.02 0.53 0.1 0.27 3.69 0.33
2.1 0.54 0.18 0.17 1.09 0.03
5.55 0.58 0.19 0.12 4.59 0.03
1.03 0.25 0.23 0.27 0.2 0.05
1.17 0.28 0 0.14 0.68 0.02
5.69 0.71 0.12 0.21 4.17 0.41
8.13 0.55 0.24 0.21 6.89 0.09
1.91 0.59 0.18 0.09 0.95 0.01
1.22 0.57 0.08 0.36 0.09 0.01
6.29 2.28 0.09 2.02 1.66 0.11
3.84 0.67 0.3 0.25 2.52 0.05
1.66 0.22 0.49 0.42 0.51 0
1.21 0.23 0.03 0.75 0.14 0.03
3.69 0.79 0.32 0.14 2.33 0.1
5.83 1.89 0.21 1.28 2.15 0.16
15.82 1.1 0.76 1.03 12.65 0.13
3.26 0.62 0.21 0.31 2.06 0.03
0.99 0.27 0.34 0.24 0.07 0.02
0.81 0.43 0.05 0.2 0.07 0.01
3.71 0.67 0.12 0.38 2.1 0.36
1.53 0.52 0.66 0.17 0.1 0.03
2.08 0.13 0.02 0.12 1.73 0.04
2.54 0.39 1.2 0.21 0.55 0.15
3.46 0.52 0.22 0.18 1.99 0.55
2.89 0.55 0.24 0.25 1.74 0.06
1.78 0.43 0.08 0.15 1.03 0.06
6.08 0.29 3.47 0.17 2.09 0
3.78 0.64 0.33 0.62 2.13 0.05
7.78 NA NA NA NA NA
1.68 0.6 0.17 0.14 0.67 0.06
0.87 0.31 0.03 0.29 0.17 0.03
1.43 0.8 0.01 0.32 0.09 0.1
2.48 0.33 0.23 0.17 1.02 0.72
2.94 NA NA NA NA NA
0.98 0.43 0.07 0.21 0.16 0
5.28 0.76 0.58 0.38 3.23 0.16
3.58 0.68 0.62 0.18 1.78 0.32
5.6 0.63 0.23 1.08 2.84 0.7
1.39 0.34 0.12 0.42 0.45 0.03
1.56 0.67 0.48 0.26 0.1 0.02
1.16 0.53 0.1 0.19 0.21 0.08
4.98 NA NA NA NA NA
7.52 0.57 0.4 0.15 5.8 0.4
0.79 0.27 0.01 0.08 0.38 0.02
2.79 0.36 0.4 0.19 1.44 0.37
1.91 0.3 0.05 0.36 0.35 0.73
4.16 1.11 1.1 0.83 0.97 0.01
2.28 0.5 0.51 0.19 0.67 0.33
1.1 0.36 0.03 0.09 0.34 0.23
4.44 0.84 0.04 0.78 2.64 0.05
3.88 1.03 0.25 0.09 2.15 0.31
10.8 0.57 0.27 0.15 9.57 0.19
3.65 0.14 0.01 0.15 3.27 0.09
2.71 0.72 0.05 0.33 1.46 0.03
5.69 0.77 0.15 0.67 3.87 0.19
0.87 0.43 0.05 0.25 0.07 0.01
4.94 0.51 0.18 0.09 3.34 0.81
2.45 0.38 0.1 0.17 1.56 0.23
3.11 NA NA NA NA NA
2.77 0.97 0.16 0.27 0.96 0.42
1.49 0.36 0.02 0.27 0.37 0.47
5.61 0.74 0.27 0.27 4.21 0.08
1.21 0.34 0.21 0.21 0.3 0.12
2.7 0.49 0.02 0.46 1.66 0.02
1.24 0.47 0.11 0.38 0.07 0.15
7.97 0.67 0.24 0.91 5.91 0.22
4.06 0.31 0.08 0.72 2.82 0.03
5.81 0.64 0.17 0.65 4.27 0.05
1.29 0.47 0.03 0.09 0 0.46
1.24 0.16 0.43 0.52 0.07 0.01
3.31 0.44 0.11 0.29 2.34 0.08
3.67 0.78 0.15 0.17 2.22 0.32
1.32 0.31 0.01 0.16 0.52 0.27
4.25 0.43 0.06 0.52 3.01 0.14
2.01 0.35 0.41 0.52 0.67 0.01
7.25 1.47 0.27 1.3 3.88 0.09
5.79 0.75 0.22 0.38 4.26 0.07
1.51 0.52 0.09 0.04 0.81 0.01
0.91 0.46 0.14 0.1 0.13 0
1.32 0.44 0.34 0.23 0.17 0.09
2.66 0.67 0.02 0.24 1.54 0.13
0.48 0.25 0.07 0.04 0.06 0.02
1.13 0.34 0.1 0.27 0.31 0.08
2.7 1.19 0.32 0.14 0.88 0.18
7.92 0.46 0.19 0.27 6.89 0.11
2.34 0.76 0.09 0.28 1.11 0.06
3.33 0.87 0.12 0.34 1.92 0.04
5.47 0.73 0.44 0.08 4.13 0.01
1.24 0.34 0.15 0.54 0.08 0.11
2.84 0.62 0.01 0.16 1.92 0.06
4.94 0.82 0.28 0.45 3.14 0.08
7.93 0.8 0.19 0.38 6.37 0.19
8.22 1.13 0.3 0.67 5.9 0.12
2.91 0.19 0.98 0.55 0.98 0.05
2.32 0.62 0.13 0.08 1.41 0
3.57 0.45 0.74 0.12 2.13 0.09
1.65 0.5 0.01 0.19 0.79 0.05
2.07 NA NA NA NA NA
1.03 0.34 0.14 0.04 0.42 0.04
0.99 0.19 0.18 0.33 0.24 0.01
1.37 0.2 0.32 0.29 0.53 0.01

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	17 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 time17 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=316144&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]

17 seconds[/C][/ROW] [ROW]

R Server[/C]

Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=316144&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=316144&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	17 seconds
R Server	Big Analytics Cloud Computing Center

Multiple Linear Regression - Estimated Regression Equation
Total_Ecological_Footprint[t] = + 0.0227639 + 1.04269Cropland_Footprint[t] + 1.00556Grazing_Footprint[t] + 1.04617Forest_Footprint[t] + 1.00188Carbon_Footprint[t] + 0.981319Fish_Footprint[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Total_Ecological_Footprint[t] =  +  0.0227639 +  1.04269Cropland_Footprint[t] +  1.00556Grazing_Footprint[t] +  1.04617Forest_Footprint[t] +  1.00188Carbon_Footprint[t] +  0.981319Fish_Footprint[t]  + e[t] \tabularnewline
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=316144&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Total_Ecological_Footprint[t] =  +  0.0227639 +  1.04269Cropland_Footprint[t] +  1.00556Grazing_Footprint[t] +  1.04617Forest_Footprint[t] +  1.00188Carbon_Footprint[t] +  0.981319Fish_Footprint[t]  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=316144&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=316144&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
Total_Ecological_Footprint[t] = + 0.0227639 + 1.04269Cropland_Footprint[t] + 1.00556Grazing_Footprint[t] + 1.04617Forest_Footprint[t] + 1.00188Carbon_Footprint[t] + 0.981319Fish_Footprint[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)	+0.02276	0.007914	+2.8760e+00	0.004547	0.002273
Cropland_Footprint	+1.043	0.01232	+8.4610e+01	4.747e-139	2.373e-139
Grazing_Footprint	+1.006	0.01051	+9.5680e+01	8.711e-148	4.355e-148
Forest_Footprint	+1.046	0.01137	+9.2010e+01	5.286e-145	2.643e-145
Carbon_Footprint	+1.002	0.002147	+4.6660e+02	4.373e-262	2.186e-262
Fish_Footprint	+0.9813	0.02354	+4.1690e+01	3.414e-90	1.707e-90

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Ordinary Least Squares \tabularnewline
Variable & Parameter & S.D. & T-STATH0: parameter = 0 & 2-tail p-value & 1-tail p-value \tabularnewline
(Intercept) & +0.02276 &  0.007914 & +2.8760e+00 &  0.004547 &  0.002273 \tabularnewline
Cropland_Footprint & +1.043 &  0.01232 & +8.4610e+01 &  4.747e-139 &  2.373e-139 \tabularnewline
Grazing_Footprint & +1.006 &  0.01051 & +9.5680e+01 &  8.711e-148 &  4.355e-148 \tabularnewline
Forest_Footprint & +1.046 &  0.01137 & +9.2010e+01 &  5.286e-145 &  2.643e-145 \tabularnewline
Carbon_Footprint & +1.002 &  0.002147 & +4.6660e+02 &  4.373e-262 &  2.186e-262 \tabularnewline
Fish_Footprint & +0.9813 &  0.02354 & +4.1690e+01 &  3.414e-90 &  1.707e-90 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=316144&T=2

[TABLE]
[ROW][C]Multiple Linear Regression - Ordinary Least Squares[/C][/ROW]
[ROW][C]Variable[/C][C]Parameter[/C][C]S.D.[/C][C]T-STATH0: parameter = 0[/C][C]2-tail p-value[/C][C]1-tail p-value[/C][/ROW]
[ROW][C](Intercept)[/C][C]+0.02276[/C][C] 0.007914[/C][C]+2.8760e+00[/C][C] 0.004547[/C][C] 0.002273[/C][/ROW]
[ROW][C]Cropland_Footprint[/C][C]+1.043[/C][C] 0.01232[/C][C]+8.4610e+01[/C][C] 4.747e-139[/C][C] 2.373e-139[/C][/ROW]
[ROW][C]Grazing_Footprint[/C][C]+1.006[/C][C] 0.01051[/C][C]+9.5680e+01[/C][C] 8.711e-148[/C][C] 4.355e-148[/C][/ROW]
[ROW][C]Forest_Footprint[/C][C]+1.046[/C][C] 0.01137[/C][C]+9.2010e+01[/C][C] 5.286e-145[/C][C] 2.643e-145[/C][/ROW]
[ROW][C]Carbon_Footprint[/C][C]+1.002[/C][C] 0.002147[/C][C]+4.6660e+02[/C][C] 4.373e-262[/C][C] 2.186e-262[/C][/ROW]
[ROW][C]Fish_Footprint[/C][C]+0.9813[/C][C] 0.02354[/C][C]+4.1690e+01[/C][C] 3.414e-90[/C][C] 1.707e-90[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=316144&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=316144&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)	+0.02276	0.007914	+2.8760e+00	0.004547	0.002273
Cropland_Footprint	+1.043	0.01232	+8.4610e+01	4.747e-139	2.373e-139
Grazing_Footprint	+1.006	0.01051	+9.5680e+01	8.711e-148	4.355e-148
Forest_Footprint	+1.046	0.01137	+9.2010e+01	5.286e-145	2.643e-145
Carbon_Footprint	+1.002	0.002147	+4.6660e+02	4.373e-262	2.186e-262
Fish_Footprint	+0.9813	0.02354	+4.1690e+01	3.414e-90	1.707e-90

Multiple Linear Regression - Regression Statistics
Multiple R	0.9998
R-squared	0.9996
Adjusted R-squared	0.9996
F-TEST (value)	7.952e+04
F-TEST (DF numerator)	5
F-TEST (DF denominator)	167
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.04797
Sum Squared Residuals	0.3843

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.9998 \tabularnewline
R-squared &  0.9996 \tabularnewline
Adjusted R-squared &  0.9996 \tabularnewline
F-TEST (value) &  7.952e+04 \tabularnewline
F-TEST (DF numerator) & 5 \tabularnewline
F-TEST (DF denominator) & 167 \tabularnewline
p-value &  0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  0.04797 \tabularnewline
Sum Squared Residuals &  0.3843 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=316144&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.9998[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.9996[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.9996[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 7.952e+04[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]5[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]167[/C][/ROW]
[ROW][C]p-value[/C][C] 0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C] 0.04797[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 0.3843[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=316144&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=316144&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	0.9998
R-squared	0.9996
Adjusted R-squared	0.9996
F-TEST (value)	7.952e+04
F-TEST (DF numerator)	5
F-TEST (DF denominator)	167
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.04797
Sum Squared Residuals	0.3843

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=316144&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=316144&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=316144&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	0.79	0.8007	-0.01072
2	2.21	2.21	-9.386e-05
3	2.12	2.139	-0.01908
4	0.93	0.9319	-0.001922
5	3.14	3.114	0.02599
6	2.23	2.233	-0.002543
7	9.31	9.349	-0.03885
8	6.06	6.015	0.04496
9	2.31	2.309	0.0005895
10	6.84	6.895	-0.05457
11	7.49	7.455	0.03479
12	0.72	0.689	0.03105
13	4.48	4.497	-0.01654
14	5.09	5.125	-0.03514
15	7.44	7.296	0.1437
16	1.41	1.435	-0.02538
17	4.84	4.787	0.05285
18	2.96	2.967	-0.006881
19	3.12	3.174	-0.05438
20	3.83	3.856	-0.02586
21	3.11	3.103	0.007373
22	4.06	4.083	-0.02315
23	3.32	3.259	0.06138
24	1.21	1.21	-5.998e-05
25	0.8	0.823	-0.02297
26	1.17	1.175	-0.004932
27	8.17	8.26	-0.08951
28	5.65	5.753	-0.1032
29	1.24	1.26	-0.02041
30	1.46	1.455	0.0054
31	4.36	4.316	0.04448
32	3.38	3.329	0.05138
33	1.87	1.839	0.03098
34	1.03	1.081	-0.05113
35	1.29	1.309	-0.01922
36	0.82	0.8027	0.01729
37	2.84	2.813	0.02651
38	1.27	1.243	0.0271
39	3.92	3.951	-0.03136
40	1.95	1.98	-0.02997
41	4.21	4.23	-0.01962
42	5.19	5.141	0.04938
43	5.51	5.372	0.1383
44	2.57	2.627	-0.05698
45	1.53	1.535	-0.004787
46	2.17	2.147	0.02301
47	2.15	2.051	0.09874
48	2.07	2.09	-0.02015
49	3.97	3.988	-0.01827
50	0.42	0.4107	0.009326
51	1.02	1.018	0.001965
52	2.9	2.907	-0.007162
53	5.14	5.033	0.1072
54	2.34	2.387	-0.04713
55	4.73	4.783	-0.05312
56	2.02	2.071	-0.05134
57	1.03	1.051	-0.02081
58	1.58	1.586	-0.006333
59	5.3	5.148	0.1521
60	1.97	1.974	-0.00433
61	4.38	4.405	-0.02484
62	3.23	3.254	-0.02414
63	1.89	1.887	0.002641
64	1.41	1.431	-0.02134
65	1.53	1.551	-0.02062
66	3.07	3.091	-0.02066
67	0.61	0.6092	0.0007745
68	1.68	1.69	-0.01032
69	2.92	2.841	0.07903
70	1.16	1.165	-0.004574
71	1.58	1.568	0.01177
72	2.79	2.747	0.04305
73	1.88	1.884	-0.004126
74	5.57	5.526	0.04446
75	6.22	6.216	0.004055
76	4.61	4.626	-0.01576
77	1.89	1.89	0.0001756
78	5.02	4.979	0.0408
79	2.1	2.066	0.03384
80	5.55	5.572	-0.02221
81	1.03	1.047	-0.01662
82	1.17	1.162	0.007911
83	5.69	5.684	0.006363
84	8.13	8.049	0.08142
85	1.91	1.875	0.03529
86	1.22	1.174	0.04585
87	6.29	6.375	-0.08493
88	3.84	3.858	-0.01839
89	1.66	1.695	-0.03523
90	1.21	1.247	-0.03708
91	3.69	3.747	-0.05726
92	5.83	5.855	-0.02477
93	15.82	15.81	0.007084
94	3.26	3.298	-0.03803
95	0.99	0.987	0.00298
96	0.81	0.8106	-0.0005776
97	3.71	3.697	0.01319
98	1.53	1.536	-0.00611
99	2.08	2.076	0.003522
100	2.54	2.554	-0.01402
101	3.46	3.508	-0.04797
102	2.89	2.901	-0.01128
103	1.78	1.799	-0.01931
104	6.08	6.086	-0.006227
105	3.78	3.854	-0.07363
106	1.68	1.696	-0.01593
107	0.87	0.8793	-0.009314
108	1.43	1.39	0.03995
109	2.48	2.504	-0.02445
110	0.98	0.9215	0.05849
111	5.28	5.189	0.09092
112	3.58	3.641	-0.06093
113	5.6	5.573	0.02692
114	1.39	1.418	-0.02762
115	1.56	1.596	-0.03585
116	1.16	1.164	-0.003619
117	7.52	7.38	0.1403
118	0.79	0.7984	-0.008382
119	2.79	2.805	-0.01493
120	1.91	1.829	0.08051
121	4.16	4.136	0.02377
122	2.28	2.251	0.02918
123	1.1	1.089	0.0112
124	4.44	4.449	-0.008899
125	3.88	3.901	-0.02054
126	10.8	10.82	-0.02001
127	3.65	3.7	-0.0502
128	2.71	2.661	0.04879
129	5.69	5.741	-0.05115
130	0.87	0.8629	0.007114
131	4.94	4.971	-0.03086
132	2.45	2.486	-0.03603
133	2.77	2.851	-0.08149
134	1.49	1.533	-0.04263
135	5.61	5.645	-0.03476
136	1.21	1.226	-0.01647
137	2.7	2.718	-0.01779
138	1.24	1.238	0.001686
139	7.97	8.052	-0.08174
140	4.06	4.034	0.02556
141	5.81	5.868	-0.05815
142	1.29	1.089	0.2014
143	1.24	1.246	-0.005938
144	3.31	3.318	-0.008464
145	3.67	3.703	-0.03295
146	1.32	1.309	0.01062
147	4.25	4.229	0.02148
148	2.01	2.025	-0.01507
149	7.25	7.163	0.08733
150	5.79	5.76	0.02973
151	1.51	1.519	-0.00865
152	0.91	0.878	0.03196
153	1.32	1.323	-0.002696
154	2.66	2.663	-0.003032
155	0.48	0.4754	0.004588
156	1.13	1.149	-0.01939
157	2.7	2.79	-0.0901
158	7.92	7.987	-0.06685
159	2.34	2.37	-0.02961
160	3.33	3.369	-0.03914
161	5.47	5.458	0.01233
162	1.24	1.281	-0.04114
163	2.84	2.829	0.01083
164	4.94	4.855	0.08547
165	7.93	8.014	-0.08397
166	8.22	8.232	-0.01248
167	2.91	2.813	0.09737
168	2.32	2.296	0.02369
169	3.57	3.584	-0.01396
170	1.65	1.593	0.05651
171	1.03	1.02	0.01005
172	0.99	0.9974	-0.007377
173	1.37	1.397	-0.02728

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 &  0.79 &  0.8007 & -0.01072 \tabularnewline
2 &  2.21 &  2.21 & -9.386e-05 \tabularnewline
3 &  2.12 &  2.139 & -0.01908 \tabularnewline
4 &  0.93 &  0.9319 & -0.001922 \tabularnewline
5 &  3.14 &  3.114 &  0.02599 \tabularnewline
6 &  2.23 &  2.233 & -0.002543 \tabularnewline
7 &  9.31 &  9.349 & -0.03885 \tabularnewline
8 &  6.06 &  6.015 &  0.04496 \tabularnewline
9 &  2.31 &  2.309 &  0.0005895 \tabularnewline
10 &  6.84 &  6.895 & -0.05457 \tabularnewline
11 &  7.49 &  7.455 &  0.03479 \tabularnewline
12 &  0.72 &  0.689 &  0.03105 \tabularnewline
13 &  4.48 &  4.497 & -0.01654 \tabularnewline
14 &  5.09 &  5.125 & -0.03514 \tabularnewline
15 &  7.44 &  7.296 &  0.1437 \tabularnewline
16 &  1.41 &  1.435 & -0.02538 \tabularnewline
17 &  4.84 &  4.787 &  0.05285 \tabularnewline
18 &  2.96 &  2.967 & -0.006881 \tabularnewline
19 &  3.12 &  3.174 & -0.05438 \tabularnewline
20 &  3.83 &  3.856 & -0.02586 \tabularnewline
21 &  3.11 &  3.103 &  0.007373 \tabularnewline
22 &  4.06 &  4.083 & -0.02315 \tabularnewline
23 &  3.32 &  3.259 &  0.06138 \tabularnewline
24 &  1.21 &  1.21 & -5.998e-05 \tabularnewline
25 &  0.8 &  0.823 & -0.02297 \tabularnewline
26 &  1.17 &  1.175 & -0.004932 \tabularnewline
27 &  8.17 &  8.26 & -0.08951 \tabularnewline
28 &  5.65 &  5.753 & -0.1032 \tabularnewline
29 &  1.24 &  1.26 & -0.02041 \tabularnewline
30 &  1.46 &  1.455 &  0.0054 \tabularnewline
31 &  4.36 &  4.316 &  0.04448 \tabularnewline
32 &  3.38 &  3.329 &  0.05138 \tabularnewline
33 &  1.87 &  1.839 &  0.03098 \tabularnewline
34 &  1.03 &  1.081 & -0.05113 \tabularnewline
35 &  1.29 &  1.309 & -0.01922 \tabularnewline
36 &  0.82 &  0.8027 &  0.01729 \tabularnewline
37 &  2.84 &  2.813 &  0.02651 \tabularnewline
38 &  1.27 &  1.243 &  0.0271 \tabularnewline
39 &  3.92 &  3.951 & -0.03136 \tabularnewline
40 &  1.95 &  1.98 & -0.02997 \tabularnewline
41 &  4.21 &  4.23 & -0.01962 \tabularnewline
42 &  5.19 &  5.141 &  0.04938 \tabularnewline
43 &  5.51 &  5.372 &  0.1383 \tabularnewline
44 &  2.57 &  2.627 & -0.05698 \tabularnewline
45 &  1.53 &  1.535 & -0.004787 \tabularnewline
46 &  2.17 &  2.147 &  0.02301 \tabularnewline
47 &  2.15 &  2.051 &  0.09874 \tabularnewline
48 &  2.07 &  2.09 & -0.02015 \tabularnewline
49 &  3.97 &  3.988 & -0.01827 \tabularnewline
50 &  0.42 &  0.4107 &  0.009326 \tabularnewline
51 &  1.02 &  1.018 &  0.001965 \tabularnewline
52 &  2.9 &  2.907 & -0.007162 \tabularnewline
53 &  5.14 &  5.033 &  0.1072 \tabularnewline
54 &  2.34 &  2.387 & -0.04713 \tabularnewline
55 &  4.73 &  4.783 & -0.05312 \tabularnewline
56 &  2.02 &  2.071 & -0.05134 \tabularnewline
57 &  1.03 &  1.051 & -0.02081 \tabularnewline
58 &  1.58 &  1.586 & -0.006333 \tabularnewline
59 &  5.3 &  5.148 &  0.1521 \tabularnewline
60 &  1.97 &  1.974 & -0.00433 \tabularnewline
61 &  4.38 &  4.405 & -0.02484 \tabularnewline
62 &  3.23 &  3.254 & -0.02414 \tabularnewline
63 &  1.89 &  1.887 &  0.002641 \tabularnewline
64 &  1.41 &  1.431 & -0.02134 \tabularnewline
65 &  1.53 &  1.551 & -0.02062 \tabularnewline
66 &  3.07 &  3.091 & -0.02066 \tabularnewline
67 &  0.61 &  0.6092 &  0.0007745 \tabularnewline
68 &  1.68 &  1.69 & -0.01032 \tabularnewline
69 &  2.92 &  2.841 &  0.07903 \tabularnewline
70 &  1.16 &  1.165 & -0.004574 \tabularnewline
71 &  1.58 &  1.568 &  0.01177 \tabularnewline
72 &  2.79 &  2.747 &  0.04305 \tabularnewline
73 &  1.88 &  1.884 & -0.004126 \tabularnewline
74 &  5.57 &  5.526 &  0.04446 \tabularnewline
75 &  6.22 &  6.216 &  0.004055 \tabularnewline
76 &  4.61 &  4.626 & -0.01576 \tabularnewline
77 &  1.89 &  1.89 &  0.0001756 \tabularnewline
78 &  5.02 &  4.979 &  0.0408 \tabularnewline
79 &  2.1 &  2.066 &  0.03384 \tabularnewline
80 &  5.55 &  5.572 & -0.02221 \tabularnewline
81 &  1.03 &  1.047 & -0.01662 \tabularnewline
82 &  1.17 &  1.162 &  0.007911 \tabularnewline
83 &  5.69 &  5.684 &  0.006363 \tabularnewline
84 &  8.13 &  8.049 &  0.08142 \tabularnewline
85 &  1.91 &  1.875 &  0.03529 \tabularnewline
86 &  1.22 &  1.174 &  0.04585 \tabularnewline
87 &  6.29 &  6.375 & -0.08493 \tabularnewline
88 &  3.84 &  3.858 & -0.01839 \tabularnewline
89 &  1.66 &  1.695 & -0.03523 \tabularnewline
90 &  1.21 &  1.247 & -0.03708 \tabularnewline
91 &  3.69 &  3.747 & -0.05726 \tabularnewline
92 &  5.83 &  5.855 & -0.02477 \tabularnewline
93 &  15.82 &  15.81 &  0.007084 \tabularnewline
94 &  3.26 &  3.298 & -0.03803 \tabularnewline
95 &  0.99 &  0.987 &  0.00298 \tabularnewline
96 &  0.81 &  0.8106 & -0.0005776 \tabularnewline
97 &  3.71 &  3.697 &  0.01319 \tabularnewline
98 &  1.53 &  1.536 & -0.00611 \tabularnewline
99 &  2.08 &  2.076 &  0.003522 \tabularnewline
100 &  2.54 &  2.554 & -0.01402 \tabularnewline
101 &  3.46 &  3.508 & -0.04797 \tabularnewline
102 &  2.89 &  2.901 & -0.01128 \tabularnewline
103 &  1.78 &  1.799 & -0.01931 \tabularnewline
104 &  6.08 &  6.086 & -0.006227 \tabularnewline
105 &  3.78 &  3.854 & -0.07363 \tabularnewline
106 &  1.68 &  1.696 & -0.01593 \tabularnewline
107 &  0.87 &  0.8793 & -0.009314 \tabularnewline
108 &  1.43 &  1.39 &  0.03995 \tabularnewline
109 &  2.48 &  2.504 & -0.02445 \tabularnewline
110 &  0.98 &  0.9215 &  0.05849 \tabularnewline
111 &  5.28 &  5.189 &  0.09092 \tabularnewline
112 &  3.58 &  3.641 & -0.06093 \tabularnewline
113 &  5.6 &  5.573 &  0.02692 \tabularnewline
114 &  1.39 &  1.418 & -0.02762 \tabularnewline
115 &  1.56 &  1.596 & -0.03585 \tabularnewline
116 &  1.16 &  1.164 & -0.003619 \tabularnewline
117 &  7.52 &  7.38 &  0.1403 \tabularnewline
118 &  0.79 &  0.7984 & -0.008382 \tabularnewline
119 &  2.79 &  2.805 & -0.01493 \tabularnewline
120 &  1.91 &  1.829 &  0.08051 \tabularnewline
121 &  4.16 &  4.136 &  0.02377 \tabularnewline
122 &  2.28 &  2.251 &  0.02918 \tabularnewline
123 &  1.1 &  1.089 &  0.0112 \tabularnewline
124 &  4.44 &  4.449 & -0.008899 \tabularnewline
125 &  3.88 &  3.901 & -0.02054 \tabularnewline
126 &  10.8 &  10.82 & -0.02001 \tabularnewline
127 &  3.65 &  3.7 & -0.0502 \tabularnewline
128 &  2.71 &  2.661 &  0.04879 \tabularnewline
129 &  5.69 &  5.741 & -0.05115 \tabularnewline
130 &  0.87 &  0.8629 &  0.007114 \tabularnewline
131 &  4.94 &  4.971 & -0.03086 \tabularnewline
132 &  2.45 &  2.486 & -0.03603 \tabularnewline
133 &  2.77 &  2.851 & -0.08149 \tabularnewline
134 &  1.49 &  1.533 & -0.04263 \tabularnewline
135 &  5.61 &  5.645 & -0.03476 \tabularnewline
136 &  1.21 &  1.226 & -0.01647 \tabularnewline
137 &  2.7 &  2.718 & -0.01779 \tabularnewline
138 &  1.24 &  1.238 &  0.001686 \tabularnewline
139 &  7.97 &  8.052 & -0.08174 \tabularnewline
140 &  4.06 &  4.034 &  0.02556 \tabularnewline
141 &  5.81 &  5.868 & -0.05815 \tabularnewline
142 &  1.29 &  1.089 &  0.2014 \tabularnewline
143 &  1.24 &  1.246 & -0.005938 \tabularnewline
144 &  3.31 &  3.318 & -0.008464 \tabularnewline
145 &  3.67 &  3.703 & -0.03295 \tabularnewline
146 &  1.32 &  1.309 &  0.01062 \tabularnewline
147 &  4.25 &  4.229 &  0.02148 \tabularnewline
148 &  2.01 &  2.025 & -0.01507 \tabularnewline
149 &  7.25 &  7.163 &  0.08733 \tabularnewline
150 &  5.79 &  5.76 &  0.02973 \tabularnewline
151 &  1.51 &  1.519 & -0.00865 \tabularnewline
152 &  0.91 &  0.878 &  0.03196 \tabularnewline
153 &  1.32 &  1.323 & -0.002696 \tabularnewline
154 &  2.66 &  2.663 & -0.003032 \tabularnewline
155 &  0.48 &  0.4754 &  0.004588 \tabularnewline
156 &  1.13 &  1.149 & -0.01939 \tabularnewline
157 &  2.7 &  2.79 & -0.0901 \tabularnewline
158 &  7.92 &  7.987 & -0.06685 \tabularnewline
159 &  2.34 &  2.37 & -0.02961 \tabularnewline
160 &  3.33 &  3.369 & -0.03914 \tabularnewline
161 &  5.47 &  5.458 &  0.01233 \tabularnewline
162 &  1.24 &  1.281 & -0.04114 \tabularnewline
163 &  2.84 &  2.829 &  0.01083 \tabularnewline
164 &  4.94 &  4.855 &  0.08547 \tabularnewline
165 &  7.93 &  8.014 & -0.08397 \tabularnewline
166 &  8.22 &  8.232 & -0.01248 \tabularnewline
167 &  2.91 &  2.813 &  0.09737 \tabularnewline
168 &  2.32 &  2.296 &  0.02369 \tabularnewline
169 &  3.57 &  3.584 & -0.01396 \tabularnewline
170 &  1.65 &  1.593 &  0.05651 \tabularnewline
171 &  1.03 &  1.02 &  0.01005 \tabularnewline
172 &  0.99 &  0.9974 & -0.007377 \tabularnewline
173 &  1.37 &  1.397 & -0.02728 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=316144&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] 0.79[/C][C] 0.8007[/C][C]-0.01072[/C][/ROW]
[ROW][C]2[/C][C] 2.21[/C][C] 2.21[/C][C]-9.386e-05[/C][/ROW]
[ROW][C]3[/C][C] 2.12[/C][C] 2.139[/C][C]-0.01908[/C][/ROW]
[ROW][C]4[/C][C] 0.93[/C][C] 0.9319[/C][C]-0.001922[/C][/ROW]
[ROW][C]5[/C][C] 3.14[/C][C] 3.114[/C][C] 0.02599[/C][/ROW]
[ROW][C]6[/C][C] 2.23[/C][C] 2.233[/C][C]-0.002543[/C][/ROW]
[ROW][C]7[/C][C] 9.31[/C][C] 9.349[/C][C]-0.03885[/C][/ROW]
[ROW][C]8[/C][C] 6.06[/C][C] 6.015[/C][C] 0.04496[/C][/ROW]
[ROW][C]9[/C][C] 2.31[/C][C] 2.309[/C][C] 0.0005895[/C][/ROW]
[ROW][C]10[/C][C] 6.84[/C][C] 6.895[/C][C]-0.05457[/C][/ROW]
[ROW][C]11[/C][C] 7.49[/C][C] 7.455[/C][C] 0.03479[/C][/ROW]
[ROW][C]12[/C][C] 0.72[/C][C] 0.689[/C][C] 0.03105[/C][/ROW]
[ROW][C]13[/C][C] 4.48[/C][C] 4.497[/C][C]-0.01654[/C][/ROW]
[ROW][C]14[/C][C] 5.09[/C][C] 5.125[/C][C]-0.03514[/C][/ROW]
[ROW][C]15[/C][C] 7.44[/C][C] 7.296[/C][C] 0.1437[/C][/ROW]
[ROW][C]16[/C][C] 1.41[/C][C] 1.435[/C][C]-0.02538[/C][/ROW]
[ROW][C]17[/C][C] 4.84[/C][C] 4.787[/C][C] 0.05285[/C][/ROW]
[ROW][C]18[/C][C] 2.96[/C][C] 2.967[/C][C]-0.006881[/C][/ROW]
[ROW][C]19[/C][C] 3.12[/C][C] 3.174[/C][C]-0.05438[/C][/ROW]
[ROW][C]20[/C][C] 3.83[/C][C] 3.856[/C][C]-0.02586[/C][/ROW]
[ROW][C]21[/C][C] 3.11[/C][C] 3.103[/C][C] 0.007373[/C][/ROW]
[ROW][C]22[/C][C] 4.06[/C][C] 4.083[/C][C]-0.02315[/C][/ROW]
[ROW][C]23[/C][C] 3.32[/C][C] 3.259[/C][C] 0.06138[/C][/ROW]
[ROW][C]24[/C][C] 1.21[/C][C] 1.21[/C][C]-5.998e-05[/C][/ROW]
[ROW][C]25[/C][C] 0.8[/C][C] 0.823[/C][C]-0.02297[/C][/ROW]
[ROW][C]26[/C][C] 1.17[/C][C] 1.175[/C][C]-0.004932[/C][/ROW]
[ROW][C]27[/C][C] 8.17[/C][C] 8.26[/C][C]-0.08951[/C][/ROW]
[ROW][C]28[/C][C] 5.65[/C][C] 5.753[/C][C]-0.1032[/C][/ROW]
[ROW][C]29[/C][C] 1.24[/C][C] 1.26[/C][C]-0.02041[/C][/ROW]
[ROW][C]30[/C][C] 1.46[/C][C] 1.455[/C][C] 0.0054[/C][/ROW]
[ROW][C]31[/C][C] 4.36[/C][C] 4.316[/C][C] 0.04448[/C][/ROW]
[ROW][C]32[/C][C] 3.38[/C][C] 3.329[/C][C] 0.05138[/C][/ROW]
[ROW][C]33[/C][C] 1.87[/C][C] 1.839[/C][C] 0.03098[/C][/ROW]
[ROW][C]34[/C][C] 1.03[/C][C] 1.081[/C][C]-0.05113[/C][/ROW]
[ROW][C]35[/C][C] 1.29[/C][C] 1.309[/C][C]-0.01922[/C][/ROW]
[ROW][C]36[/C][C] 0.82[/C][C] 0.8027[/C][C] 0.01729[/C][/ROW]
[ROW][C]37[/C][C] 2.84[/C][C] 2.813[/C][C] 0.02651[/C][/ROW]
[ROW][C]38[/C][C] 1.27[/C][C] 1.243[/C][C] 0.0271[/C][/ROW]
[ROW][C]39[/C][C] 3.92[/C][C] 3.951[/C][C]-0.03136[/C][/ROW]
[ROW][C]40[/C][C] 1.95[/C][C] 1.98[/C][C]-0.02997[/C][/ROW]
[ROW][C]41[/C][C] 4.21[/C][C] 4.23[/C][C]-0.01962[/C][/ROW]
[ROW][C]42[/C][C] 5.19[/C][C] 5.141[/C][C] 0.04938[/C][/ROW]
[ROW][C]43[/C][C] 5.51[/C][C] 5.372[/C][C] 0.1383[/C][/ROW]
[ROW][C]44[/C][C] 2.57[/C][C] 2.627[/C][C]-0.05698[/C][/ROW]
[ROW][C]45[/C][C] 1.53[/C][C] 1.535[/C][C]-0.004787[/C][/ROW]
[ROW][C]46[/C][C] 2.17[/C][C] 2.147[/C][C] 0.02301[/C][/ROW]
[ROW][C]47[/C][C] 2.15[/C][C] 2.051[/C][C] 0.09874[/C][/ROW]
[ROW][C]48[/C][C] 2.07[/C][C] 2.09[/C][C]-0.02015[/C][/ROW]
[ROW][C]49[/C][C] 3.97[/C][C] 3.988[/C][C]-0.01827[/C][/ROW]
[ROW][C]50[/C][C] 0.42[/C][C] 0.4107[/C][C] 0.009326[/C][/ROW]
[ROW][C]51[/C][C] 1.02[/C][C] 1.018[/C][C] 0.001965[/C][/ROW]
[ROW][C]52[/C][C] 2.9[/C][C] 2.907[/C][C]-0.007162[/C][/ROW]
[ROW][C]53[/C][C] 5.14[/C][C] 5.033[/C][C] 0.1072[/C][/ROW]
[ROW][C]54[/C][C] 2.34[/C][C] 2.387[/C][C]-0.04713[/C][/ROW]
[ROW][C]55[/C][C] 4.73[/C][C] 4.783[/C][C]-0.05312[/C][/ROW]
[ROW][C]56[/C][C] 2.02[/C][C] 2.071[/C][C]-0.05134[/C][/ROW]
[ROW][C]57[/C][C] 1.03[/C][C] 1.051[/C][C]-0.02081[/C][/ROW]
[ROW][C]58[/C][C] 1.58[/C][C] 1.586[/C][C]-0.006333[/C][/ROW]
[ROW][C]59[/C][C] 5.3[/C][C] 5.148[/C][C] 0.1521[/C][/ROW]
[ROW][C]60[/C][C] 1.97[/C][C] 1.974[/C][C]-0.00433[/C][/ROW]
[ROW][C]61[/C][C] 4.38[/C][C] 4.405[/C][C]-0.02484[/C][/ROW]
[ROW][C]62[/C][C] 3.23[/C][C] 3.254[/C][C]-0.02414[/C][/ROW]
[ROW][C]63[/C][C] 1.89[/C][C] 1.887[/C][C] 0.002641[/C][/ROW]
[ROW][C]64[/C][C] 1.41[/C][C] 1.431[/C][C]-0.02134[/C][/ROW]
[ROW][C]65[/C][C] 1.53[/C][C] 1.551[/C][C]-0.02062[/C][/ROW]
[ROW][C]66[/C][C] 3.07[/C][C] 3.091[/C][C]-0.02066[/C][/ROW]
[ROW][C]67[/C][C] 0.61[/C][C] 0.6092[/C][C] 0.0007745[/C][/ROW]
[ROW][C]68[/C][C] 1.68[/C][C] 1.69[/C][C]-0.01032[/C][/ROW]
[ROW][C]69[/C][C] 2.92[/C][C] 2.841[/C][C] 0.07903[/C][/ROW]
[ROW][C]70[/C][C] 1.16[/C][C] 1.165[/C][C]-0.004574[/C][/ROW]
[ROW][C]71[/C][C] 1.58[/C][C] 1.568[/C][C] 0.01177[/C][/ROW]
[ROW][C]72[/C][C] 2.79[/C][C] 2.747[/C][C] 0.04305[/C][/ROW]
[ROW][C]73[/C][C] 1.88[/C][C] 1.884[/C][C]-0.004126[/C][/ROW]
[ROW][C]74[/C][C] 5.57[/C][C] 5.526[/C][C] 0.04446[/C][/ROW]
[ROW][C]75[/C][C] 6.22[/C][C] 6.216[/C][C] 0.004055[/C][/ROW]
[ROW][C]76[/C][C] 4.61[/C][C] 4.626[/C][C]-0.01576[/C][/ROW]
[ROW][C]77[/C][C] 1.89[/C][C] 1.89[/C][C] 0.0001756[/C][/ROW]
[ROW][C]78[/C][C] 5.02[/C][C] 4.979[/C][C] 0.0408[/C][/ROW]
[ROW][C]79[/C][C] 2.1[/C][C] 2.066[/C][C] 0.03384[/C][/ROW]
[ROW][C]80[/C][C] 5.55[/C][C] 5.572[/C][C]-0.02221[/C][/ROW]
[ROW][C]81[/C][C] 1.03[/C][C] 1.047[/C][C]-0.01662[/C][/ROW]
[ROW][C]82[/C][C] 1.17[/C][C] 1.162[/C][C] 0.007911[/C][/ROW]
[ROW][C]83[/C][C] 5.69[/C][C] 5.684[/C][C] 0.006363[/C][/ROW]
[ROW][C]84[/C][C] 8.13[/C][C] 8.049[/C][C] 0.08142[/C][/ROW]
[ROW][C]85[/C][C] 1.91[/C][C] 1.875[/C][C] 0.03529[/C][/ROW]
[ROW][C]86[/C][C] 1.22[/C][C] 1.174[/C][C] 0.04585[/C][/ROW]
[ROW][C]87[/C][C] 6.29[/C][C] 6.375[/C][C]-0.08493[/C][/ROW]
[ROW][C]88[/C][C] 3.84[/C][C] 3.858[/C][C]-0.01839[/C][/ROW]
[ROW][C]89[/C][C] 1.66[/C][C] 1.695[/C][C]-0.03523[/C][/ROW]
[ROW][C]90[/C][C] 1.21[/C][C] 1.247[/C][C]-0.03708[/C][/ROW]
[ROW][C]91[/C][C] 3.69[/C][C] 3.747[/C][C]-0.05726[/C][/ROW]
[ROW][C]92[/C][C] 5.83[/C][C] 5.855[/C][C]-0.02477[/C][/ROW]
[ROW][C]93[/C][C] 15.82[/C][C] 15.81[/C][C] 0.007084[/C][/ROW]
[ROW][C]94[/C][C] 3.26[/C][C] 3.298[/C][C]-0.03803[/C][/ROW]
[ROW][C]95[/C][C] 0.99[/C][C] 0.987[/C][C] 0.00298[/C][/ROW]
[ROW][C]96[/C][C] 0.81[/C][C] 0.8106[/C][C]-0.0005776[/C][/ROW]
[ROW][C]97[/C][C] 3.71[/C][C] 3.697[/C][C] 0.01319[/C][/ROW]
[ROW][C]98[/C][C] 1.53[/C][C] 1.536[/C][C]-0.00611[/C][/ROW]
[ROW][C]99[/C][C] 2.08[/C][C] 2.076[/C][C] 0.003522[/C][/ROW]
[ROW][C]100[/C][C] 2.54[/C][C] 2.554[/C][C]-0.01402[/C][/ROW]
[ROW][C]101[/C][C] 3.46[/C][C] 3.508[/C][C]-0.04797[/C][/ROW]
[ROW][C]102[/C][C] 2.89[/C][C] 2.901[/C][C]-0.01128[/C][/ROW]
[ROW][C]103[/C][C] 1.78[/C][C] 1.799[/C][C]-0.01931[/C][/ROW]
[ROW][C]104[/C][C] 6.08[/C][C] 6.086[/C][C]-0.006227[/C][/ROW]
[ROW][C]105[/C][C] 3.78[/C][C] 3.854[/C][C]-0.07363[/C][/ROW]
[ROW][C]106[/C][C] 1.68[/C][C] 1.696[/C][C]-0.01593[/C][/ROW]
[ROW][C]107[/C][C] 0.87[/C][C] 0.8793[/C][C]-0.009314[/C][/ROW]
[ROW][C]108[/C][C] 1.43[/C][C] 1.39[/C][C] 0.03995[/C][/ROW]
[ROW][C]109[/C][C] 2.48[/C][C] 2.504[/C][C]-0.02445[/C][/ROW]
[ROW][C]110[/C][C] 0.98[/C][C] 0.9215[/C][C] 0.05849[/C][/ROW]
[ROW][C]111[/C][C] 5.28[/C][C] 5.189[/C][C] 0.09092[/C][/ROW]
[ROW][C]112[/C][C] 3.58[/C][C] 3.641[/C][C]-0.06093[/C][/ROW]
[ROW][C]113[/C][C] 5.6[/C][C] 5.573[/C][C] 0.02692[/C][/ROW]
[ROW][C]114[/C][C] 1.39[/C][C] 1.418[/C][C]-0.02762[/C][/ROW]
[ROW][C]115[/C][C] 1.56[/C][C] 1.596[/C][C]-0.03585[/C][/ROW]
[ROW][C]116[/C][C] 1.16[/C][C] 1.164[/C][C]-0.003619[/C][/ROW]
[ROW][C]117[/C][C] 7.52[/C][C] 7.38[/C][C] 0.1403[/C][/ROW]
[ROW][C]118[/C][C] 0.79[/C][C] 0.7984[/C][C]-0.008382[/C][/ROW]
[ROW][C]119[/C][C] 2.79[/C][C] 2.805[/C][C]-0.01493[/C][/ROW]
[ROW][C]120[/C][C] 1.91[/C][C] 1.829[/C][C] 0.08051[/C][/ROW]
[ROW][C]121[/C][C] 4.16[/C][C] 4.136[/C][C] 0.02377[/C][/ROW]
[ROW][C]122[/C][C] 2.28[/C][C] 2.251[/C][C] 0.02918[/C][/ROW]
[ROW][C]123[/C][C] 1.1[/C][C] 1.089[/C][C] 0.0112[/C][/ROW]
[ROW][C]124[/C][C] 4.44[/C][C] 4.449[/C][C]-0.008899[/C][/ROW]
[ROW][C]125[/C][C] 3.88[/C][C] 3.901[/C][C]-0.02054[/C][/ROW]
[ROW][C]126[/C][C] 10.8[/C][C] 10.82[/C][C]-0.02001[/C][/ROW]
[ROW][C]127[/C][C] 3.65[/C][C] 3.7[/C][C]-0.0502[/C][/ROW]
[ROW][C]128[/C][C] 2.71[/C][C] 2.661[/C][C] 0.04879[/C][/ROW]
[ROW][C]129[/C][C] 5.69[/C][C] 5.741[/C][C]-0.05115[/C][/ROW]
[ROW][C]130[/C][C] 0.87[/C][C] 0.8629[/C][C] 0.007114[/C][/ROW]
[ROW][C]131[/C][C] 4.94[/C][C] 4.971[/C][C]-0.03086[/C][/ROW]
[ROW][C]132[/C][C] 2.45[/C][C] 2.486[/C][C]-0.03603[/C][/ROW]
[ROW][C]133[/C][C] 2.77[/C][C] 2.851[/C][C]-0.08149[/C][/ROW]
[ROW][C]134[/C][C] 1.49[/C][C] 1.533[/C][C]-0.04263[/C][/ROW]
[ROW][C]135[/C][C] 5.61[/C][C] 5.645[/C][C]-0.03476[/C][/ROW]
[ROW][C]136[/C][C] 1.21[/C][C] 1.226[/C][C]-0.01647[/C][/ROW]
[ROW][C]137[/C][C] 2.7[/C][C] 2.718[/C][C]-0.01779[/C][/ROW]
[ROW][C]138[/C][C] 1.24[/C][C] 1.238[/C][C] 0.001686[/C][/ROW]
[ROW][C]139[/C][C] 7.97[/C][C] 8.052[/C][C]-0.08174[/C][/ROW]
[ROW][C]140[/C][C] 4.06[/C][C] 4.034[/C][C] 0.02556[/C][/ROW]
[ROW][C]141[/C][C] 5.81[/C][C] 5.868[/C][C]-0.05815[/C][/ROW]
[ROW][C]142[/C][C] 1.29[/C][C] 1.089[/C][C] 0.2014[/C][/ROW]
[ROW][C]143[/C][C] 1.24[/C][C] 1.246[/C][C]-0.005938[/C][/ROW]
[ROW][C]144[/C][C] 3.31[/C][C] 3.318[/C][C]-0.008464[/C][/ROW]
[ROW][C]145[/C][C] 3.67[/C][C] 3.703[/C][C]-0.03295[/C][/ROW]
[ROW][C]146[/C][C] 1.32[/C][C] 1.309[/C][C] 0.01062[/C][/ROW]
[ROW][C]147[/C][C] 4.25[/C][C] 4.229[/C][C] 0.02148[/C][/ROW]
[ROW][C]148[/C][C] 2.01[/C][C] 2.025[/C][C]-0.01507[/C][/ROW]
[ROW][C]149[/C][C] 7.25[/C][C] 7.163[/C][C] 0.08733[/C][/ROW]
[ROW][C]150[/C][C] 5.79[/C][C] 5.76[/C][C] 0.02973[/C][/ROW]
[ROW][C]151[/C][C] 1.51[/C][C] 1.519[/C][C]-0.00865[/C][/ROW]
[ROW][C]152[/C][C] 0.91[/C][C] 0.878[/C][C] 0.03196[/C][/ROW]
[ROW][C]153[/C][C] 1.32[/C][C] 1.323[/C][C]-0.002696[/C][/ROW]
[ROW][C]154[/C][C] 2.66[/C][C] 2.663[/C][C]-0.003032[/C][/ROW]
[ROW][C]155[/C][C] 0.48[/C][C] 0.4754[/C][C] 0.004588[/C][/ROW]
[ROW][C]156[/C][C] 1.13[/C][C] 1.149[/C][C]-0.01939[/C][/ROW]
[ROW][C]157[/C][C] 2.7[/C][C] 2.79[/C][C]-0.0901[/C][/ROW]
[ROW][C]158[/C][C] 7.92[/C][C] 7.987[/C][C]-0.06685[/C][/ROW]
[ROW][C]159[/C][C] 2.34[/C][C] 2.37[/C][C]-0.02961[/C][/ROW]
[ROW][C]160[/C][C] 3.33[/C][C] 3.369[/C][C]-0.03914[/C][/ROW]
[ROW][C]161[/C][C] 5.47[/C][C] 5.458[/C][C] 0.01233[/C][/ROW]
[ROW][C]162[/C][C] 1.24[/C][C] 1.281[/C][C]-0.04114[/C][/ROW]
[ROW][C]163[/C][C] 2.84[/C][C] 2.829[/C][C] 0.01083[/C][/ROW]
[ROW][C]164[/C][C] 4.94[/C][C] 4.855[/C][C] 0.08547[/C][/ROW]
[ROW][C]165[/C][C] 7.93[/C][C] 8.014[/C][C]-0.08397[/C][/ROW]
[ROW][C]166[/C][C] 8.22[/C][C] 8.232[/C][C]-0.01248[/C][/ROW]
[ROW][C]167[/C][C] 2.91[/C][C] 2.813[/C][C] 0.09737[/C][/ROW]
[ROW][C]168[/C][C] 2.32[/C][C] 2.296[/C][C] 0.02369[/C][/ROW]
[ROW][C]169[/C][C] 3.57[/C][C] 3.584[/C][C]-0.01396[/C][/ROW]
[ROW][C]170[/C][C] 1.65[/C][C] 1.593[/C][C] 0.05651[/C][/ROW]
[ROW][C]171[/C][C] 1.03[/C][C] 1.02[/C][C] 0.01005[/C][/ROW]
[ROW][C]172[/C][C] 0.99[/C][C] 0.9974[/C][C]-0.007377[/C][/ROW]
[ROW][C]173[/C][C] 1.37[/C][C] 1.397[/C][C]-0.02728[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=316144&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=316144&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	0.79	0.8007	-0.01072
2	2.21	2.21	-9.386e-05
3	2.12	2.139	-0.01908
4	0.93	0.9319	-0.001922
5	3.14	3.114	0.02599
6	2.23	2.233	-0.002543
7	9.31	9.349	-0.03885
8	6.06	6.015	0.04496
9	2.31	2.309	0.0005895
10	6.84	6.895	-0.05457
11	7.49	7.455	0.03479
12	0.72	0.689	0.03105
13	4.48	4.497	-0.01654
14	5.09	5.125	-0.03514
15	7.44	7.296	0.1437
16	1.41	1.435	-0.02538
17	4.84	4.787	0.05285
18	2.96	2.967	-0.006881
19	3.12	3.174	-0.05438
20	3.83	3.856	-0.02586
21	3.11	3.103	0.007373
22	4.06	4.083	-0.02315
23	3.32	3.259	0.06138
24	1.21	1.21	-5.998e-05
25	0.8	0.823	-0.02297
26	1.17	1.175	-0.004932
27	8.17	8.26	-0.08951
28	5.65	5.753	-0.1032
29	1.24	1.26	-0.02041
30	1.46	1.455	0.0054
31	4.36	4.316	0.04448
32	3.38	3.329	0.05138
33	1.87	1.839	0.03098
34	1.03	1.081	-0.05113
35	1.29	1.309	-0.01922
36	0.82	0.8027	0.01729
37	2.84	2.813	0.02651
38	1.27	1.243	0.0271
39	3.92	3.951	-0.03136
40	1.95	1.98	-0.02997
41	4.21	4.23	-0.01962
42	5.19	5.141	0.04938
43	5.51	5.372	0.1383
44	2.57	2.627	-0.05698
45	1.53	1.535	-0.004787
46	2.17	2.147	0.02301
47	2.15	2.051	0.09874
48	2.07	2.09	-0.02015
49	3.97	3.988	-0.01827
50	0.42	0.4107	0.009326
51	1.02	1.018	0.001965
52	2.9	2.907	-0.007162
53	5.14	5.033	0.1072
54	2.34	2.387	-0.04713
55	4.73	4.783	-0.05312
56	2.02	2.071	-0.05134
57	1.03	1.051	-0.02081
58	1.58	1.586	-0.006333
59	5.3	5.148	0.1521
60	1.97	1.974	-0.00433
61	4.38	4.405	-0.02484
62	3.23	3.254	-0.02414
63	1.89	1.887	0.002641
64	1.41	1.431	-0.02134
65	1.53	1.551	-0.02062
66	3.07	3.091	-0.02066
67	0.61	0.6092	0.0007745
68	1.68	1.69	-0.01032
69	2.92	2.841	0.07903
70	1.16	1.165	-0.004574
71	1.58	1.568	0.01177
72	2.79	2.747	0.04305
73	1.88	1.884	-0.004126
74	5.57	5.526	0.04446
75	6.22	6.216	0.004055
76	4.61	4.626	-0.01576
77	1.89	1.89	0.0001756
78	5.02	4.979	0.0408
79	2.1	2.066	0.03384
80	5.55	5.572	-0.02221
81	1.03	1.047	-0.01662
82	1.17	1.162	0.007911
83	5.69	5.684	0.006363
84	8.13	8.049	0.08142
85	1.91	1.875	0.03529
86	1.22	1.174	0.04585
87	6.29	6.375	-0.08493
88	3.84	3.858	-0.01839
89	1.66	1.695	-0.03523
90	1.21	1.247	-0.03708
91	3.69	3.747	-0.05726
92	5.83	5.855	-0.02477
93	15.82	15.81	0.007084
94	3.26	3.298	-0.03803
95	0.99	0.987	0.00298
96	0.81	0.8106	-0.0005776
97	3.71	3.697	0.01319
98	1.53	1.536	-0.00611
99	2.08	2.076	0.003522
100	2.54	2.554	-0.01402
101	3.46	3.508	-0.04797
102	2.89	2.901	-0.01128
103	1.78	1.799	-0.01931
104	6.08	6.086	-0.006227
105	3.78	3.854	-0.07363
106	1.68	1.696	-0.01593
107	0.87	0.8793	-0.009314
108	1.43	1.39	0.03995
109	2.48	2.504	-0.02445
110	0.98	0.9215	0.05849
111	5.28	5.189	0.09092
112	3.58	3.641	-0.06093
113	5.6	5.573	0.02692
114	1.39	1.418	-0.02762
115	1.56	1.596	-0.03585
116	1.16	1.164	-0.003619
117	7.52	7.38	0.1403
118	0.79	0.7984	-0.008382
119	2.79	2.805	-0.01493
120	1.91	1.829	0.08051
121	4.16	4.136	0.02377
122	2.28	2.251	0.02918
123	1.1	1.089	0.0112
124	4.44	4.449	-0.008899
125	3.88	3.901	-0.02054
126	10.8	10.82	-0.02001
127	3.65	3.7	-0.0502
128	2.71	2.661	0.04879
129	5.69	5.741	-0.05115
130	0.87	0.8629	0.007114
131	4.94	4.971	-0.03086
132	2.45	2.486	-0.03603
133	2.77	2.851	-0.08149
134	1.49	1.533	-0.04263
135	5.61	5.645	-0.03476
136	1.21	1.226	-0.01647
137	2.7	2.718	-0.01779
138	1.24	1.238	0.001686
139	7.97	8.052	-0.08174
140	4.06	4.034	0.02556
141	5.81	5.868	-0.05815
142	1.29	1.089	0.2014
143	1.24	1.246	-0.005938
144	3.31	3.318	-0.008464
145	3.67	3.703	-0.03295
146	1.32	1.309	0.01062
147	4.25	4.229	0.02148
148	2.01	2.025	-0.01507
149	7.25	7.163	0.08733
150	5.79	5.76	0.02973
151	1.51	1.519	-0.00865
152	0.91	0.878	0.03196
153	1.32	1.323	-0.002696
154	2.66	2.663	-0.003032
155	0.48	0.4754	0.004588
156	1.13	1.149	-0.01939
157	2.7	2.79	-0.0901
158	7.92	7.987	-0.06685
159	2.34	2.37	-0.02961
160	3.33	3.369	-0.03914
161	5.47	5.458	0.01233
162	1.24	1.281	-0.04114
163	2.84	2.829	0.01083
164	4.94	4.855	0.08547
165	7.93	8.014	-0.08397
166	8.22	8.232	-0.01248
167	2.91	2.813	0.09737
168	2.32	2.296	0.02369
169	3.57	3.584	-0.01396
170	1.65	1.593	0.05651
171	1.03	1.02	0.01005
172	0.99	0.9974	-0.007377
173	1.37	1.397	-0.02728

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
9	0.02908	0.05816	0.9709
10	0.04588	0.09176	0.9541
11	0.0734	0.1468	0.9266
12	0.04998	0.09996	0.95
13	0.02451	0.04902	0.9755
14	0.05542	0.1108	0.9446
15	0.3014	0.6029	0.6986
16	0.2442	0.4884	0.7558
17	0.509	0.9821	0.491
18	0.4211	0.8422	0.5789
19	0.4861	0.9722	0.5139
20	0.4484	0.8969	0.5516
21	0.3752	0.7504	0.6248
22	0.3557	0.7113	0.6443
23	0.3883	0.7765	0.6117
24	0.3196	0.6392	0.6804
25	0.2787	0.5574	0.7213
26	0.2233	0.4466	0.7767
27	0.4553	0.9107	0.5447
28	0.7394	0.5212	0.2606
29	0.6919	0.6161	0.3081
30	0.6367	0.7266	0.3633
31	0.6094	0.7812	0.3906
32	0.6167	0.7666	0.3833
33	0.5842	0.8316	0.4158
34	0.6046	0.7908	0.3954
35	0.5559	0.8882	0.4441
36	0.5036	0.9928	0.4964
37	0.4618	0.9236	0.5382
38	0.4265	0.853	0.5735
39	0.3951	0.7902	0.6049
40	0.3597	0.7195	0.6403
41	0.3148	0.6295	0.6852
42	0.3159	0.6318	0.6841
43	0.6561	0.6879	0.3439
44	0.6829	0.6342	0.3171
45	0.6354	0.7292	0.3646
46	0.5919	0.8162	0.4081
47	0.7412	0.5177	0.2588
48	0.7099	0.5801	0.2901
49	0.6743	0.6513	0.3257
50	0.6302	0.7395	0.3698
51	0.5825	0.835	0.4175
52	0.5398	0.9204	0.4602
53	0.699	0.6019	0.301
54	0.6945	0.611	0.3055
55	0.6936	0.6128	0.3064
56	0.7016	0.5968	0.2984
57	0.6667	0.6666	0.3333
58	0.6229	0.7542	0.3771
59	0.8951	0.2098	0.1049
60	0.8724	0.2552	0.1276
61	0.8563	0.2874	0.1437
62	0.8339	0.3323	0.1661
63	0.8035	0.393	0.1965
64	0.776	0.4479	0.224
65	0.7464	0.5072	0.2536
66	0.7173	0.5655	0.2827
67	0.6772	0.6456	0.3228
68	0.6368	0.7265	0.3632
69	0.7002	0.5997	0.2998
70	0.6599	0.6802	0.3401
71	0.621	0.7581	0.379
72	0.6095	0.7811	0.3905
73	0.5662	0.8676	0.4338
74	0.5589	0.8821	0.4411
75	0.5168	0.9664	0.4832
76	0.4789	0.9578	0.5211
77	0.4341	0.8683	0.5659
78	0.4221	0.8441	0.5779
79	0.3992	0.7983	0.6008
80	0.3695	0.739	0.6305
81	0.3323	0.6646	0.6677
82	0.2934	0.5867	0.7066
83	0.2567	0.5134	0.7433
84	0.3158	0.6317	0.6842
85	0.2986	0.5973	0.7014
86	0.2962	0.5925	0.7038
87	0.3838	0.7677	0.6162
88	0.35	0.6999	0.65
89	0.331	0.662	0.669
90	0.3193	0.6387	0.6807
91	0.3323	0.6647	0.6677
92	0.303	0.606	0.697
93	0.2849	0.5698	0.7151
94	0.2708	0.5415	0.7292
95	0.2351	0.4702	0.7649
96	0.202	0.4039	0.798
97	0.1744	0.3488	0.8256
98	0.1473	0.2946	0.8527
99	0.1235	0.2469	0.8765
100	0.1037	0.2074	0.8963
101	0.1012	0.2024	0.8988
102	0.08351	0.167	0.9165
103	0.06972	0.1394	0.9303
104	0.05952	0.119	0.9405
105	0.08147	0.1629	0.9185
106	0.06697	0.1339	0.933
107	0.05385	0.1077	0.9461
108	0.05079	0.1016	0.9492
109	0.04454	0.08908	0.9555
110	0.05035	0.1007	0.9496
111	0.08504	0.1701	0.915
112	0.1008	0.2015	0.8992
113	0.08827	0.1765	0.9117
114	0.07644	0.1529	0.9236
115	0.07097	0.1419	0.929
116	0.05635	0.1127	0.9437
117	0.2623	0.5246	0.7377
118	0.2255	0.451	0.7745
119	0.1965	0.3929	0.8035
120	0.2483	0.4966	0.7517
121	0.2193	0.4385	0.7807
122	0.1932	0.3863	0.8068
123	0.1634	0.3267	0.8366
124	0.1357	0.2713	0.8643
125	0.113	0.226	0.887
126	0.1166	0.2333	0.8834
127	0.1064	0.2129	0.8936
128	0.1066	0.2131	0.8934
129	0.1023	0.2045	0.8977
130	0.08124	0.1625	0.9188
131	0.06608	0.1322	0.9339
132	0.05627	0.1125	0.9437
133	0.101	0.202	0.899
134	0.1331	0.2662	0.8669
135	0.1114	0.2229	0.8886
136	0.09579	0.1916	0.9042
137	0.0757	0.1514	0.9243
138	0.06176	0.1235	0.9382
139	0.1071	0.2142	0.8929
140	0.08763	0.1753	0.9124
141	0.08912	0.1782	0.9109
142	0.8041	0.3917	0.1959
143	0.7933	0.4133	0.2067
144	0.7438	0.5125	0.2562
145	0.7034	0.5932	0.2966
146	0.8017	0.3966	0.1983
147	0.8144	0.3711	0.1856
148	0.8629	0.2741	0.1371
149	0.8358	0.3284	0.1642
150	0.8121	0.3758	0.1879
151	0.7625	0.475	0.2375
152	0.7032	0.5937	0.2968
153	0.6304	0.7392	0.3696
154	0.6335	0.733	0.3665
155	0.5528	0.8944	0.4472
156	0.4656	0.9311	0.5344
157	0.4716	0.9432	0.5284
158	0.3889	0.7778	0.6111
159	0.3547	0.7093	0.6453
160	0.5864	0.8273	0.4136
161	0.4743	0.9485	0.5257
162	0.8322	0.3356	0.1678
163	0.713	0.574	0.287
164	0.6339	0.7322	0.3661

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
9 &  0.02908 &  0.05816 &  0.9709 \tabularnewline
10 &  0.04588 &  0.09176 &  0.9541 \tabularnewline
11 &  0.0734 &  0.1468 &  0.9266 \tabularnewline
12 &  0.04998 &  0.09996 &  0.95 \tabularnewline
13 &  0.02451 &  0.04902 &  0.9755 \tabularnewline
14 &  0.05542 &  0.1108 &  0.9446 \tabularnewline
15 &  0.3014 &  0.6029 &  0.6986 \tabularnewline
16 &  0.2442 &  0.4884 &  0.7558 \tabularnewline
17 &  0.509 &  0.9821 &  0.491 \tabularnewline
18 &  0.4211 &  0.8422 &  0.5789 \tabularnewline
19 &  0.4861 &  0.9722 &  0.5139 \tabularnewline
20 &  0.4484 &  0.8969 &  0.5516 \tabularnewline
21 &  0.3752 &  0.7504 &  0.6248 \tabularnewline
22 &  0.3557 &  0.7113 &  0.6443 \tabularnewline
23 &  0.3883 &  0.7765 &  0.6117 \tabularnewline
24 &  0.3196 &  0.6392 &  0.6804 \tabularnewline
25 &  0.2787 &  0.5574 &  0.7213 \tabularnewline
26 &  0.2233 &  0.4466 &  0.7767 \tabularnewline
27 &  0.4553 &  0.9107 &  0.5447 \tabularnewline
28 &  0.7394 &  0.5212 &  0.2606 \tabularnewline
29 &  0.6919 &  0.6161 &  0.3081 \tabularnewline
30 &  0.6367 &  0.7266 &  0.3633 \tabularnewline
31 &  0.6094 &  0.7812 &  0.3906 \tabularnewline
32 &  0.6167 &  0.7666 &  0.3833 \tabularnewline
33 &  0.5842 &  0.8316 &  0.4158 \tabularnewline
34 &  0.6046 &  0.7908 &  0.3954 \tabularnewline
35 &  0.5559 &  0.8882 &  0.4441 \tabularnewline
36 &  0.5036 &  0.9928 &  0.4964 \tabularnewline
37 &  0.4618 &  0.9236 &  0.5382 \tabularnewline
38 &  0.4265 &  0.853 &  0.5735 \tabularnewline
39 &  0.3951 &  0.7902 &  0.6049 \tabularnewline
40 &  0.3597 &  0.7195 &  0.6403 \tabularnewline
41 &  0.3148 &  0.6295 &  0.6852 \tabularnewline
42 &  0.3159 &  0.6318 &  0.6841 \tabularnewline
43 &  0.6561 &  0.6879 &  0.3439 \tabularnewline
44 &  0.6829 &  0.6342 &  0.3171 \tabularnewline
45 &  0.6354 &  0.7292 &  0.3646 \tabularnewline
46 &  0.5919 &  0.8162 &  0.4081 \tabularnewline
47 &  0.7412 &  0.5177 &  0.2588 \tabularnewline
48 &  0.7099 &  0.5801 &  0.2901 \tabularnewline
49 &  0.6743 &  0.6513 &  0.3257 \tabularnewline
50 &  0.6302 &  0.7395 &  0.3698 \tabularnewline
51 &  0.5825 &  0.835 &  0.4175 \tabularnewline
52 &  0.5398 &  0.9204 &  0.4602 \tabularnewline
53 &  0.699 &  0.6019 &  0.301 \tabularnewline
54 &  0.6945 &  0.611 &  0.3055 \tabularnewline
55 &  0.6936 &  0.6128 &  0.3064 \tabularnewline
56 &  0.7016 &  0.5968 &  0.2984 \tabularnewline
57 &  0.6667 &  0.6666 &  0.3333 \tabularnewline
58 &  0.6229 &  0.7542 &  0.3771 \tabularnewline
59 &  0.8951 &  0.2098 &  0.1049 \tabularnewline
60 &  0.8724 &  0.2552 &  0.1276 \tabularnewline
61 &  0.8563 &  0.2874 &  0.1437 \tabularnewline
62 &  0.8339 &  0.3323 &  0.1661 \tabularnewline
63 &  0.8035 &  0.393 &  0.1965 \tabularnewline
64 &  0.776 &  0.4479 &  0.224 \tabularnewline
65 &  0.7464 &  0.5072 &  0.2536 \tabularnewline
66 &  0.7173 &  0.5655 &  0.2827 \tabularnewline
67 &  0.6772 &  0.6456 &  0.3228 \tabularnewline
68 &  0.6368 &  0.7265 &  0.3632 \tabularnewline
69 &  0.7002 &  0.5997 &  0.2998 \tabularnewline
70 &  0.6599 &  0.6802 &  0.3401 \tabularnewline
71 &  0.621 &  0.7581 &  0.379 \tabularnewline
72 &  0.6095 &  0.7811 &  0.3905 \tabularnewline
73 &  0.5662 &  0.8676 &  0.4338 \tabularnewline
74 &  0.5589 &  0.8821 &  0.4411 \tabularnewline
75 &  0.5168 &  0.9664 &  0.4832 \tabularnewline
76 &  0.4789 &  0.9578 &  0.5211 \tabularnewline
77 &  0.4341 &  0.8683 &  0.5659 \tabularnewline
78 &  0.4221 &  0.8441 &  0.5779 \tabularnewline
79 &  0.3992 &  0.7983 &  0.6008 \tabularnewline
80 &  0.3695 &  0.739 &  0.6305 \tabularnewline
81 &  0.3323 &  0.6646 &  0.6677 \tabularnewline
82 &  0.2934 &  0.5867 &  0.7066 \tabularnewline
83 &  0.2567 &  0.5134 &  0.7433 \tabularnewline
84 &  0.3158 &  0.6317 &  0.6842 \tabularnewline
85 &  0.2986 &  0.5973 &  0.7014 \tabularnewline
86 &  0.2962 &  0.5925 &  0.7038 \tabularnewline
87 &  0.3838 &  0.7677 &  0.6162 \tabularnewline
88 &  0.35 &  0.6999 &  0.65 \tabularnewline
89 &  0.331 &  0.662 &  0.669 \tabularnewline
90 &  0.3193 &  0.6387 &  0.6807 \tabularnewline
91 &  0.3323 &  0.6647 &  0.6677 \tabularnewline
92 &  0.303 &  0.606 &  0.697 \tabularnewline
93 &  0.2849 &  0.5698 &  0.7151 \tabularnewline
94 &  0.2708 &  0.5415 &  0.7292 \tabularnewline
95 &  0.2351 &  0.4702 &  0.7649 \tabularnewline
96 &  0.202 &  0.4039 &  0.798 \tabularnewline
97 &  0.1744 &  0.3488 &  0.8256 \tabularnewline
98 &  0.1473 &  0.2946 &  0.8527 \tabularnewline
99 &  0.1235 &  0.2469 &  0.8765 \tabularnewline
100 &  0.1037 &  0.2074 &  0.8963 \tabularnewline
101 &  0.1012 &  0.2024 &  0.8988 \tabularnewline
102 &  0.08351 &  0.167 &  0.9165 \tabularnewline
103 &  0.06972 &  0.1394 &  0.9303 \tabularnewline
104 &  0.05952 &  0.119 &  0.9405 \tabularnewline
105 &  0.08147 &  0.1629 &  0.9185 \tabularnewline
106 &  0.06697 &  0.1339 &  0.933 \tabularnewline
107 &  0.05385 &  0.1077 &  0.9461 \tabularnewline
108 &  0.05079 &  0.1016 &  0.9492 \tabularnewline
109 &  0.04454 &  0.08908 &  0.9555 \tabularnewline
110 &  0.05035 &  0.1007 &  0.9496 \tabularnewline
111 &  0.08504 &  0.1701 &  0.915 \tabularnewline
112 &  0.1008 &  0.2015 &  0.8992 \tabularnewline
113 &  0.08827 &  0.1765 &  0.9117 \tabularnewline
114 &  0.07644 &  0.1529 &  0.9236 \tabularnewline
115 &  0.07097 &  0.1419 &  0.929 \tabularnewline
116 &  0.05635 &  0.1127 &  0.9437 \tabularnewline
117 &  0.2623 &  0.5246 &  0.7377 \tabularnewline
118 &  0.2255 &  0.451 &  0.7745 \tabularnewline
119 &  0.1965 &  0.3929 &  0.8035 \tabularnewline
120 &  0.2483 &  0.4966 &  0.7517 \tabularnewline
121 &  0.2193 &  0.4385 &  0.7807 \tabularnewline
122 &  0.1932 &  0.3863 &  0.8068 \tabularnewline
123 &  0.1634 &  0.3267 &  0.8366 \tabularnewline
124 &  0.1357 &  0.2713 &  0.8643 \tabularnewline
125 &  0.113 &  0.226 &  0.887 \tabularnewline
126 &  0.1166 &  0.2333 &  0.8834 \tabularnewline
127 &  0.1064 &  0.2129 &  0.8936 \tabularnewline
128 &  0.1066 &  0.2131 &  0.8934 \tabularnewline
129 &  0.1023 &  0.2045 &  0.8977 \tabularnewline
130 &  0.08124 &  0.1625 &  0.9188 \tabularnewline
131 &  0.06608 &  0.1322 &  0.9339 \tabularnewline
132 &  0.05627 &  0.1125 &  0.9437 \tabularnewline
133 &  0.101 &  0.202 &  0.899 \tabularnewline
134 &  0.1331 &  0.2662 &  0.8669 \tabularnewline
135 &  0.1114 &  0.2229 &  0.8886 \tabularnewline
136 &  0.09579 &  0.1916 &  0.9042 \tabularnewline
137 &  0.0757 &  0.1514 &  0.9243 \tabularnewline
138 &  0.06176 &  0.1235 &  0.9382 \tabularnewline
139 &  0.1071 &  0.2142 &  0.8929 \tabularnewline
140 &  0.08763 &  0.1753 &  0.9124 \tabularnewline
141 &  0.08912 &  0.1782 &  0.9109 \tabularnewline
142 &  0.8041 &  0.3917 &  0.1959 \tabularnewline
143 &  0.7933 &  0.4133 &  0.2067 \tabularnewline
144 &  0.7438 &  0.5125 &  0.2562 \tabularnewline
145 &  0.7034 &  0.5932 &  0.2966 \tabularnewline
146 &  0.8017 &  0.3966 &  0.1983 \tabularnewline
147 &  0.8144 &  0.3711 &  0.1856 \tabularnewline
148 &  0.8629 &  0.2741 &  0.1371 \tabularnewline
149 &  0.8358 &  0.3284 &  0.1642 \tabularnewline
150 &  0.8121 &  0.3758 &  0.1879 \tabularnewline
151 &  0.7625 &  0.475 &  0.2375 \tabularnewline
152 &  0.7032 &  0.5937 &  0.2968 \tabularnewline
153 &  0.6304 &  0.7392 &  0.3696 \tabularnewline
154 &  0.6335 &  0.733 &  0.3665 \tabularnewline
155 &  0.5528 &  0.8944 &  0.4472 \tabularnewline
156 &  0.4656 &  0.9311 &  0.5344 \tabularnewline
157 &  0.4716 &  0.9432 &  0.5284 \tabularnewline
158 &  0.3889 &  0.7778 &  0.6111 \tabularnewline
159 &  0.3547 &  0.7093 &  0.6453 \tabularnewline
160 &  0.5864 &  0.8273 &  0.4136 \tabularnewline
161 &  0.4743 &  0.9485 &  0.5257 \tabularnewline
162 &  0.8322 &  0.3356 &  0.1678 \tabularnewline
163 &  0.713 &  0.574 &  0.287 \tabularnewline
164 &  0.6339 &  0.7322 &  0.3661 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=316144&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]9[/C][C] 0.02908[/C][C] 0.05816[/C][C] 0.9709[/C][/ROW]
[ROW][C]10[/C][C] 0.04588[/C][C] 0.09176[/C][C] 0.9541[/C][/ROW]
[ROW][C]11[/C][C] 0.0734[/C][C] 0.1468[/C][C] 0.9266[/C][/ROW]
[ROW][C]12[/C][C] 0.04998[/C][C] 0.09996[/C][C] 0.95[/C][/ROW]
[ROW][C]13[/C][C] 0.02451[/C][C] 0.04902[/C][C] 0.9755[/C][/ROW]
[ROW][C]14[/C][C] 0.05542[/C][C] 0.1108[/C][C] 0.9446[/C][/ROW]
[ROW][C]15[/C][C] 0.3014[/C][C] 0.6029[/C][C] 0.6986[/C][/ROW]
[ROW][C]16[/C][C] 0.2442[/C][C] 0.4884[/C][C] 0.7558[/C][/ROW]
[ROW][C]17[/C][C] 0.509[/C][C] 0.9821[/C][C] 0.491[/C][/ROW]
[ROW][C]18[/C][C] 0.4211[/C][C] 0.8422[/C][C] 0.5789[/C][/ROW]
[ROW][C]19[/C][C] 0.4861[/C][C] 0.9722[/C][C] 0.5139[/C][/ROW]
[ROW][C]20[/C][C] 0.4484[/C][C] 0.8969[/C][C] 0.5516[/C][/ROW]
[ROW][C]21[/C][C] 0.3752[/C][C] 0.7504[/C][C] 0.6248[/C][/ROW]
[ROW][C]22[/C][C] 0.3557[/C][C] 0.7113[/C][C] 0.6443[/C][/ROW]
[ROW][C]23[/C][C] 0.3883[/C][C] 0.7765[/C][C] 0.6117[/C][/ROW]
[ROW][C]24[/C][C] 0.3196[/C][C] 0.6392[/C][C] 0.6804[/C][/ROW]
[ROW][C]25[/C][C] 0.2787[/C][C] 0.5574[/C][C] 0.7213[/C][/ROW]
[ROW][C]26[/C][C] 0.2233[/C][C] 0.4466[/C][C] 0.7767[/C][/ROW]
[ROW][C]27[/C][C] 0.4553[/C][C] 0.9107[/C][C] 0.5447[/C][/ROW]
[ROW][C]28[/C][C] 0.7394[/C][C] 0.5212[/C][C] 0.2606[/C][/ROW]
[ROW][C]29[/C][C] 0.6919[/C][C] 0.6161[/C][C] 0.3081[/C][/ROW]
[ROW][C]30[/C][C] 0.6367[/C][C] 0.7266[/C][C] 0.3633[/C][/ROW]
[ROW][C]31[/C][C] 0.6094[/C][C] 0.7812[/C][C] 0.3906[/C][/ROW]
[ROW][C]32[/C][C] 0.6167[/C][C] 0.7666[/C][C] 0.3833[/C][/ROW]
[ROW][C]33[/C][C] 0.5842[/C][C] 0.8316[/C][C] 0.4158[/C][/ROW]
[ROW][C]34[/C][C] 0.6046[/C][C] 0.7908[/C][C] 0.3954[/C][/ROW]
[ROW][C]35[/C][C] 0.5559[/C][C] 0.8882[/C][C] 0.4441[/C][/ROW]
[ROW][C]36[/C][C] 0.5036[/C][C] 0.9928[/C][C] 0.4964[/C][/ROW]
[ROW][C]37[/C][C] 0.4618[/C][C] 0.9236[/C][C] 0.5382[/C][/ROW]
[ROW][C]38[/C][C] 0.4265[/C][C] 0.853[/C][C] 0.5735[/C][/ROW]
[ROW][C]39[/C][C] 0.3951[/C][C] 0.7902[/C][C] 0.6049[/C][/ROW]
[ROW][C]40[/C][C] 0.3597[/C][C] 0.7195[/C][C] 0.6403[/C][/ROW]
[ROW][C]41[/C][C] 0.3148[/C][C] 0.6295[/C][C] 0.6852[/C][/ROW]
[ROW][C]42[/C][C] 0.3159[/C][C] 0.6318[/C][C] 0.6841[/C][/ROW]
[ROW][C]43[/C][C] 0.6561[/C][C] 0.6879[/C][C] 0.3439[/C][/ROW]
[ROW][C]44[/C][C] 0.6829[/C][C] 0.6342[/C][C] 0.3171[/C][/ROW]
[ROW][C]45[/C][C] 0.6354[/C][C] 0.7292[/C][C] 0.3646[/C][/ROW]
[ROW][C]46[/C][C] 0.5919[/C][C] 0.8162[/C][C] 0.4081[/C][/ROW]
[ROW][C]47[/C][C] 0.7412[/C][C] 0.5177[/C][C] 0.2588[/C][/ROW]
[ROW][C]48[/C][C] 0.7099[/C][C] 0.5801[/C][C] 0.2901[/C][/ROW]
[ROW][C]49[/C][C] 0.6743[/C][C] 0.6513[/C][C] 0.3257[/C][/ROW]
[ROW][C]50[/C][C] 0.6302[/C][C] 0.7395[/C][C] 0.3698[/C][/ROW]
[ROW][C]51[/C][C] 0.5825[/C][C] 0.835[/C][C] 0.4175[/C][/ROW]
[ROW][C]52[/C][C] 0.5398[/C][C] 0.9204[/C][C] 0.4602[/C][/ROW]
[ROW][C]53[/C][C] 0.699[/C][C] 0.6019[/C][C] 0.301[/C][/ROW]
[ROW][C]54[/C][C] 0.6945[/C][C] 0.611[/C][C] 0.3055[/C][/ROW]
[ROW][C]55[/C][C] 0.6936[/C][C] 0.6128[/C][C] 0.3064[/C][/ROW]
[ROW][C]56[/C][C] 0.7016[/C][C] 0.5968[/C][C] 0.2984[/C][/ROW]
[ROW][C]57[/C][C] 0.6667[/C][C] 0.6666[/C][C] 0.3333[/C][/ROW]
[ROW][C]58[/C][C] 0.6229[/C][C] 0.7542[/C][C] 0.3771[/C][/ROW]
[ROW][C]59[/C][C] 0.8951[/C][C] 0.2098[/C][C] 0.1049[/C][/ROW]
[ROW][C]60[/C][C] 0.8724[/C][C] 0.2552[/C][C] 0.1276[/C][/ROW]
[ROW][C]61[/C][C] 0.8563[/C][C] 0.2874[/C][C] 0.1437[/C][/ROW]
[ROW][C]62[/C][C] 0.8339[/C][C] 0.3323[/C][C] 0.1661[/C][/ROW]
[ROW][C]63[/C][C] 0.8035[/C][C] 0.393[/C][C] 0.1965[/C][/ROW]
[ROW][C]64[/C][C] 0.776[/C][C] 0.4479[/C][C] 0.224[/C][/ROW]
[ROW][C]65[/C][C] 0.7464[/C][C] 0.5072[/C][C] 0.2536[/C][/ROW]
[ROW][C]66[/C][C] 0.7173[/C][C] 0.5655[/C][C] 0.2827[/C][/ROW]
[ROW][C]67[/C][C] 0.6772[/C][C] 0.6456[/C][C] 0.3228[/C][/ROW]
[ROW][C]68[/C][C] 0.6368[/C][C] 0.7265[/C][C] 0.3632[/C][/ROW]
[ROW][C]69[/C][C] 0.7002[/C][C] 0.5997[/C][C] 0.2998[/C][/ROW]
[ROW][C]70[/C][C] 0.6599[/C][C] 0.6802[/C][C] 0.3401[/C][/ROW]
[ROW][C]71[/C][C] 0.621[/C][C] 0.7581[/C][C] 0.379[/C][/ROW]
[ROW][C]72[/C][C] 0.6095[/C][C] 0.7811[/C][C] 0.3905[/C][/ROW]
[ROW][C]73[/C][C] 0.5662[/C][C] 0.8676[/C][C] 0.4338[/C][/ROW]
[ROW][C]74[/C][C] 0.5589[/C][C] 0.8821[/C][C] 0.4411[/C][/ROW]
[ROW][C]75[/C][C] 0.5168[/C][C] 0.9664[/C][C] 0.4832[/C][/ROW]
[ROW][C]76[/C][C] 0.4789[/C][C] 0.9578[/C][C] 0.5211[/C][/ROW]
[ROW][C]77[/C][C] 0.4341[/C][C] 0.8683[/C][C] 0.5659[/C][/ROW]
[ROW][C]78[/C][C] 0.4221[/C][C] 0.8441[/C][C] 0.5779[/C][/ROW]
[ROW][C]79[/C][C] 0.3992[/C][C] 0.7983[/C][C] 0.6008[/C][/ROW]
[ROW][C]80[/C][C] 0.3695[/C][C] 0.739[/C][C] 0.6305[/C][/ROW]
[ROW][C]81[/C][C] 0.3323[/C][C] 0.6646[/C][C] 0.6677[/C][/ROW]
[ROW][C]82[/C][C] 0.2934[/C][C] 0.5867[/C][C] 0.7066[/C][/ROW]
[ROW][C]83[/C][C] 0.2567[/C][C] 0.5134[/C][C] 0.7433[/C][/ROW]
[ROW][C]84[/C][C] 0.3158[/C][C] 0.6317[/C][C] 0.6842[/C][/ROW]
[ROW][C]85[/C][C] 0.2986[/C][C] 0.5973[/C][C] 0.7014[/C][/ROW]
[ROW][C]86[/C][C] 0.2962[/C][C] 0.5925[/C][C] 0.7038[/C][/ROW]
[ROW][C]87[/C][C] 0.3838[/C][C] 0.7677[/C][C] 0.6162[/C][/ROW]
[ROW][C]88[/C][C] 0.35[/C][C] 0.6999[/C][C] 0.65[/C][/ROW]
[ROW][C]89[/C][C] 0.331[/C][C] 0.662[/C][C] 0.669[/C][/ROW]
[ROW][C]90[/C][C] 0.3193[/C][C] 0.6387[/C][C] 0.6807[/C][/ROW]
[ROW][C]91[/C][C] 0.3323[/C][C] 0.6647[/C][C] 0.6677[/C][/ROW]
[ROW][C]92[/C][C] 0.303[/C][C] 0.606[/C][C] 0.697[/C][/ROW]
[ROW][C]93[/C][C] 0.2849[/C][C] 0.5698[/C][C] 0.7151[/C][/ROW]
[ROW][C]94[/C][C] 0.2708[/C][C] 0.5415[/C][C] 0.7292[/C][/ROW]
[ROW][C]95[/C][C] 0.2351[/C][C] 0.4702[/C][C] 0.7649[/C][/ROW]
[ROW][C]96[/C][C] 0.202[/C][C] 0.4039[/C][C] 0.798[/C][/ROW]
[ROW][C]97[/C][C] 0.1744[/C][C] 0.3488[/C][C] 0.8256[/C][/ROW]
[ROW][C]98[/C][C] 0.1473[/C][C] 0.2946[/C][C] 0.8527[/C][/ROW]
[ROW][C]99[/C][C] 0.1235[/C][C] 0.2469[/C][C] 0.8765[/C][/ROW]
[ROW][C]100[/C][C] 0.1037[/C][C] 0.2074[/C][C] 0.8963[/C][/ROW]
[ROW][C]101[/C][C] 0.1012[/C][C] 0.2024[/C][C] 0.8988[/C][/ROW]
[ROW][C]102[/C][C] 0.08351[/C][C] 0.167[/C][C] 0.9165[/C][/ROW]
[ROW][C]103[/C][C] 0.06972[/C][C] 0.1394[/C][C] 0.9303[/C][/ROW]
[ROW][C]104[/C][C] 0.05952[/C][C] 0.119[/C][C] 0.9405[/C][/ROW]
[ROW][C]105[/C][C] 0.08147[/C][C] 0.1629[/C][C] 0.9185[/C][/ROW]
[ROW][C]106[/C][C] 0.06697[/C][C] 0.1339[/C][C] 0.933[/C][/ROW]
[ROW][C]107[/C][C] 0.05385[/C][C] 0.1077[/C][C] 0.9461[/C][/ROW]
[ROW][C]108[/C][C] 0.05079[/C][C] 0.1016[/C][C] 0.9492[/C][/ROW]
[ROW][C]109[/C][C] 0.04454[/C][C] 0.08908[/C][C] 0.9555[/C][/ROW]
[ROW][C]110[/C][C] 0.05035[/C][C] 0.1007[/C][C] 0.9496[/C][/ROW]
[ROW][C]111[/C][C] 0.08504[/C][C] 0.1701[/C][C] 0.915[/C][/ROW]
[ROW][C]112[/C][C] 0.1008[/C][C] 0.2015[/C][C] 0.8992[/C][/ROW]
[ROW][C]113[/C][C] 0.08827[/C][C] 0.1765[/C][C] 0.9117[/C][/ROW]
[ROW][C]114[/C][C] 0.07644[/C][C] 0.1529[/C][C] 0.9236[/C][/ROW]
[ROW][C]115[/C][C] 0.07097[/C][C] 0.1419[/C][C] 0.929[/C][/ROW]
[ROW][C]116[/C][C] 0.05635[/C][C] 0.1127[/C][C] 0.9437[/C][/ROW]
[ROW][C]117[/C][C] 0.2623[/C][C] 0.5246[/C][C] 0.7377[/C][/ROW]
[ROW][C]118[/C][C] 0.2255[/C][C] 0.451[/C][C] 0.7745[/C][/ROW]
[ROW][C]119[/C][C] 0.1965[/C][C] 0.3929[/C][C] 0.8035[/C][/ROW]
[ROW][C]120[/C][C] 0.2483[/C][C] 0.4966[/C][C] 0.7517[/C][/ROW]
[ROW][C]121[/C][C] 0.2193[/C][C] 0.4385[/C][C] 0.7807[/C][/ROW]
[ROW][C]122[/C][C] 0.1932[/C][C] 0.3863[/C][C] 0.8068[/C][/ROW]
[ROW][C]123[/C][C] 0.1634[/C][C] 0.3267[/C][C] 0.8366[/C][/ROW]
[ROW][C]124[/C][C] 0.1357[/C][C] 0.2713[/C][C] 0.8643[/C][/ROW]
[ROW][C]125[/C][C] 0.113[/C][C] 0.226[/C][C] 0.887[/C][/ROW]
[ROW][C]126[/C][C] 0.1166[/C][C] 0.2333[/C][C] 0.8834[/C][/ROW]
[ROW][C]127[/C][C] 0.1064[/C][C] 0.2129[/C][C] 0.8936[/C][/ROW]
[ROW][C]128[/C][C] 0.1066[/C][C] 0.2131[/C][C] 0.8934[/C][/ROW]
[ROW][C]129[/C][C] 0.1023[/C][C] 0.2045[/C][C] 0.8977[/C][/ROW]
[ROW][C]130[/C][C] 0.08124[/C][C] 0.1625[/C][C] 0.9188[/C][/ROW]
[ROW][C]131[/C][C] 0.06608[/C][C] 0.1322[/C][C] 0.9339[/C][/ROW]
[ROW][C]132[/C][C] 0.05627[/C][C] 0.1125[/C][C] 0.9437[/C][/ROW]
[ROW][C]133[/C][C] 0.101[/C][C] 0.202[/C][C] 0.899[/C][/ROW]
[ROW][C]134[/C][C] 0.1331[/C][C] 0.2662[/C][C] 0.8669[/C][/ROW]
[ROW][C]135[/C][C] 0.1114[/C][C] 0.2229[/C][C] 0.8886[/C][/ROW]
[ROW][C]136[/C][C] 0.09579[/C][C] 0.1916[/C][C] 0.9042[/C][/ROW]
[ROW][C]137[/C][C] 0.0757[/C][C] 0.1514[/C][C] 0.9243[/C][/ROW]
[ROW][C]138[/C][C] 0.06176[/C][C] 0.1235[/C][C] 0.9382[/C][/ROW]
[ROW][C]139[/C][C] 0.1071[/C][C] 0.2142[/C][C] 0.8929[/C][/ROW]
[ROW][C]140[/C][C] 0.08763[/C][C] 0.1753[/C][C] 0.9124[/C][/ROW]
[ROW][C]141[/C][C] 0.08912[/C][C] 0.1782[/C][C] 0.9109[/C][/ROW]
[ROW][C]142[/C][C] 0.8041[/C][C] 0.3917[/C][C] 0.1959[/C][/ROW]
[ROW][C]143[/C][C] 0.7933[/C][C] 0.4133[/C][C] 0.2067[/C][/ROW]
[ROW][C]144[/C][C] 0.7438[/C][C] 0.5125[/C][C] 0.2562[/C][/ROW]
[ROW][C]145[/C][C] 0.7034[/C][C] 0.5932[/C][C] 0.2966[/C][/ROW]
[ROW][C]146[/C][C] 0.8017[/C][C] 0.3966[/C][C] 0.1983[/C][/ROW]
[ROW][C]147[/C][C] 0.8144[/C][C] 0.3711[/C][C] 0.1856[/C][/ROW]
[ROW][C]148[/C][C] 0.8629[/C][C] 0.2741[/C][C] 0.1371[/C][/ROW]
[ROW][C]149[/C][C] 0.8358[/C][C] 0.3284[/C][C] 0.1642[/C][/ROW]
[ROW][C]150[/C][C] 0.8121[/C][C] 0.3758[/C][C] 0.1879[/C][/ROW]
[ROW][C]151[/C][C] 0.7625[/C][C] 0.475[/C][C] 0.2375[/C][/ROW]
[ROW][C]152[/C][C] 0.7032[/C][C] 0.5937[/C][C] 0.2968[/C][/ROW]
[ROW][C]153[/C][C] 0.6304[/C][C] 0.7392[/C][C] 0.3696[/C][/ROW]
[ROW][C]154[/C][C] 0.6335[/C][C] 0.733[/C][C] 0.3665[/C][/ROW]
[ROW][C]155[/C][C] 0.5528[/C][C] 0.8944[/C][C] 0.4472[/C][/ROW]
[ROW][C]156[/C][C] 0.4656[/C][C] 0.9311[/C][C] 0.5344[/C][/ROW]
[ROW][C]157[/C][C] 0.4716[/C][C] 0.9432[/C][C] 0.5284[/C][/ROW]
[ROW][C]158[/C][C] 0.3889[/C][C] 0.7778[/C][C] 0.6111[/C][/ROW]
[ROW][C]159[/C][C] 0.3547[/C][C] 0.7093[/C][C] 0.6453[/C][/ROW]
[ROW][C]160[/C][C] 0.5864[/C][C] 0.8273[/C][C] 0.4136[/C][/ROW]
[ROW][C]161[/C][C] 0.4743[/C][C] 0.9485[/C][C] 0.5257[/C][/ROW]
[ROW][C]162[/C][C] 0.8322[/C][C] 0.3356[/C][C] 0.1678[/C][/ROW]
[ROW][C]163[/C][C] 0.713[/C][C] 0.574[/C][C] 0.287[/C][/ROW]
[ROW][C]164[/C][C] 0.6339[/C][C] 0.7322[/C][C] 0.3661[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=316144&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=316144&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
9	0.02908	0.05816	0.9709
10	0.04588	0.09176	0.9541
11	0.0734	0.1468	0.9266
12	0.04998	0.09996	0.95
13	0.02451	0.04902	0.9755
14	0.05542	0.1108	0.9446
15	0.3014	0.6029	0.6986
16	0.2442	0.4884	0.7558
17	0.509	0.9821	0.491
18	0.4211	0.8422	0.5789
19	0.4861	0.9722	0.5139
20	0.4484	0.8969	0.5516
21	0.3752	0.7504	0.6248
22	0.3557	0.7113	0.6443
23	0.3883	0.7765	0.6117
24	0.3196	0.6392	0.6804
25	0.2787	0.5574	0.7213
26	0.2233	0.4466	0.7767
27	0.4553	0.9107	0.5447
28	0.7394	0.5212	0.2606
29	0.6919	0.6161	0.3081
30	0.6367	0.7266	0.3633
31	0.6094	0.7812	0.3906
32	0.6167	0.7666	0.3833
33	0.5842	0.8316	0.4158
34	0.6046	0.7908	0.3954
35	0.5559	0.8882	0.4441
36	0.5036	0.9928	0.4964
37	0.4618	0.9236	0.5382
38	0.4265	0.853	0.5735
39	0.3951	0.7902	0.6049
40	0.3597	0.7195	0.6403
41	0.3148	0.6295	0.6852
42	0.3159	0.6318	0.6841
43	0.6561	0.6879	0.3439
44	0.6829	0.6342	0.3171
45	0.6354	0.7292	0.3646
46	0.5919	0.8162	0.4081
47	0.7412	0.5177	0.2588
48	0.7099	0.5801	0.2901
49	0.6743	0.6513	0.3257
50	0.6302	0.7395	0.3698
51	0.5825	0.835	0.4175
52	0.5398	0.9204	0.4602
53	0.699	0.6019	0.301
54	0.6945	0.611	0.3055
55	0.6936	0.6128	0.3064
56	0.7016	0.5968	0.2984
57	0.6667	0.6666	0.3333
58	0.6229	0.7542	0.3771
59	0.8951	0.2098	0.1049
60	0.8724	0.2552	0.1276
61	0.8563	0.2874	0.1437
62	0.8339	0.3323	0.1661
63	0.8035	0.393	0.1965
64	0.776	0.4479	0.224
65	0.7464	0.5072	0.2536
66	0.7173	0.5655	0.2827
67	0.6772	0.6456	0.3228
68	0.6368	0.7265	0.3632
69	0.7002	0.5997	0.2998
70	0.6599	0.6802	0.3401
71	0.621	0.7581	0.379
72	0.6095	0.7811	0.3905
73	0.5662	0.8676	0.4338
74	0.5589	0.8821	0.4411
75	0.5168	0.9664	0.4832
76	0.4789	0.9578	0.5211
77	0.4341	0.8683	0.5659
78	0.4221	0.8441	0.5779
79	0.3992	0.7983	0.6008
80	0.3695	0.739	0.6305
81	0.3323	0.6646	0.6677
82	0.2934	0.5867	0.7066
83	0.2567	0.5134	0.7433
84	0.3158	0.6317	0.6842
85	0.2986	0.5973	0.7014
86	0.2962	0.5925	0.7038
87	0.3838	0.7677	0.6162
88	0.35	0.6999	0.65
89	0.331	0.662	0.669
90	0.3193	0.6387	0.6807
91	0.3323	0.6647	0.6677
92	0.303	0.606	0.697
93	0.2849	0.5698	0.7151
94	0.2708	0.5415	0.7292
95	0.2351	0.4702	0.7649
96	0.202	0.4039	0.798
97	0.1744	0.3488	0.8256
98	0.1473	0.2946	0.8527
99	0.1235	0.2469	0.8765
100	0.1037	0.2074	0.8963
101	0.1012	0.2024	0.8988
102	0.08351	0.167	0.9165
103	0.06972	0.1394	0.9303
104	0.05952	0.119	0.9405
105	0.08147	0.1629	0.9185
106	0.06697	0.1339	0.933
107	0.05385	0.1077	0.9461
108	0.05079	0.1016	0.9492
109	0.04454	0.08908	0.9555
110	0.05035	0.1007	0.9496
111	0.08504	0.1701	0.915
112	0.1008	0.2015	0.8992
113	0.08827	0.1765	0.9117
114	0.07644	0.1529	0.9236
115	0.07097	0.1419	0.929
116	0.05635	0.1127	0.9437
117	0.2623	0.5246	0.7377
118	0.2255	0.451	0.7745
119	0.1965	0.3929	0.8035
120	0.2483	0.4966	0.7517
121	0.2193	0.4385	0.7807
122	0.1932	0.3863	0.8068
123	0.1634	0.3267	0.8366
124	0.1357	0.2713	0.8643
125	0.113	0.226	0.887
126	0.1166	0.2333	0.8834
127	0.1064	0.2129	0.8936
128	0.1066	0.2131	0.8934
129	0.1023	0.2045	0.8977
130	0.08124	0.1625	0.9188
131	0.06608	0.1322	0.9339
132	0.05627	0.1125	0.9437
133	0.101	0.202	0.899
134	0.1331	0.2662	0.8669
135	0.1114	0.2229	0.8886
136	0.09579	0.1916	0.9042
137	0.0757	0.1514	0.9243
138	0.06176	0.1235	0.9382
139	0.1071	0.2142	0.8929
140	0.08763	0.1753	0.9124
141	0.08912	0.1782	0.9109
142	0.8041	0.3917	0.1959
143	0.7933	0.4133	0.2067
144	0.7438	0.5125	0.2562
145	0.7034	0.5932	0.2966
146	0.8017	0.3966	0.1983
147	0.8144	0.3711	0.1856
148	0.8629	0.2741	0.1371
149	0.8358	0.3284	0.1642
150	0.8121	0.3758	0.1879
151	0.7625	0.475	0.2375
152	0.7032	0.5937	0.2968
153	0.6304	0.7392	0.3696
154	0.6335	0.733	0.3665
155	0.5528	0.8944	0.4472
156	0.4656	0.9311	0.5344
157	0.4716	0.9432	0.5284
158	0.3889	0.7778	0.6111
159	0.3547	0.7093	0.6453
160	0.5864	0.8273	0.4136
161	0.4743	0.9485	0.5257
162	0.8322	0.3356	0.1678
163	0.713	0.574	0.287
164	0.6339	0.7322	0.3661

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	0	0	OK
5% type I error level	1	0.00641026	OK
10% type I error level	5	0.0320513	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 & 0 &  0 & OK \tabularnewline
5% type I error level & 1 & 0.00641026 & OK \tabularnewline
10% type I error level & 5 & 0.0320513 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=316144&T=7

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C] 0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]1[/C][C]0.00641026[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]5[/C][C]0.0320513[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=316144&T=7

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=316144&T=7

As an alternative you can also use a QR Code:

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	0	0	OK
5% type I error level	1	0.00641026	OK
10% type I error level	5	0.0320513	OK

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted RESET test data: mylm RESET = 0.016963, df1 = 2, df2 = 165, p-value = 0.9832
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 1.1527, df1 = 10, df2 = 157, p-value = 0.327
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 0.0045825, df1 = 2, df2 = 165, p-value = 0.9954

\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 = 0.016963, df1 = 2, df2 = 165, p-value = 0.9832
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of regressors \tabularnewline
> reset_test_regressors
	RESET test
data:  mylm
RESET = 1.1527, df1 = 10, df2 = 157, p-value = 0.327
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of principal components \tabularnewline
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 0.0045825, df1 = 2, df2 = 165, p-value = 0.9954
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=316144&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 = 0.016963, df1 = 2, df2 = 165, p-value = 0.9832
[/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 = 1.1527, df1 = 10, df2 = 157, p-value = 0.327
[/C][/ROW]
[ROW][C]Ramsey RESET F-Test for powers (2 and 3) of principal components[/C][/ROW]
[ROW][C]> reset_test_principal_components
	RESET test
data:  mylm
RESET = 0.0045825, df1 = 2, df2 = 165, p-value = 0.9954
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=316144&T=8

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=316144&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 = 0.016963, df1 = 2, df2 = 165, p-value = 0.9832
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 1.1527, df1 = 10, df2 = 157, p-value = 0.327
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 0.0045825, df1 = 2, df2 = 165, p-value = 0.9954

Variance Inflation Factors (Multicollinearity)
> vif Cropland_Footprint Grazing_Footprint Forest_Footprint Carbon_Footprint 1.436020 1.023277 1.247736 1.241528 Fish_Footprint 1.039309

\begin{tabular}{lllllllll}
\hline
Variance Inflation Factors (Multicollinearity) \tabularnewline
> vif
Cropland_Footprint  Grazing_Footprint   Forest_Footprint   Carbon_Footprint 
          1.436020           1.023277           1.247736           1.241528 
    Fish_Footprint 
          1.039309 
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=316144&T=9

[TABLE]
[ROW][C]Variance Inflation Factors (Multicollinearity)[/C][/ROW]
[ROW][C]> vif
Cropland_Footprint  Grazing_Footprint   Forest_Footprint   Carbon_Footprint 
          1.436020           1.023277           1.247736           1.241528 
    Fish_Footprint 
          1.039309 
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=316144&T=9

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=316144&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 Cropland_Footprint Grazing_Footprint Forest_Footprint Carbon_Footprint 1.436020 1.023277 1.247736 1.241528 Fish_Footprint 1.039309

Figure 1

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Figure 8

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Figure 9

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Figure 10

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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):

library(lattice)
library(lmtest)
library(car)
library(MASS)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
mywarning <- ''
par6 <- as.numeric(par6)
if(is.na(par6)) {
par6 <- 12
mywarning = 'Warning: you did not specify the seasonality. The seasonal period was set to s = 12.'
}
par1 <- as.numeric(par1)
if(is.na(par1)) {
par1 <- 1
mywarning = 'Warning: you did not specify the column number of the endogenous series! The first column was selected by default.'
}
if (par4=='') par4 <- 0
par4 <- as.numeric(par4)
if (!is.numeric(par4)) par4 <- 0
if (par5=='') par5 <- 0
par5 <- as.numeric(par5)
if (!is.numeric(par5)) par5 <- 0
x <- na.omit(t(y))
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
(n <- n -1)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par3 == 'Seasonal Differences (s)'){
(n <- n - par6)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-Bs)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+par6,j] - x[i,j]
}
}
x <- x2
}
if (par3 == 'First and Seasonal Differences (s)'){
(n <- n -1)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
(n <- n - par6)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-Bs)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+par6,j] - x[i,j]
}
}
x <- x2
}
if(par4 > 0) {
x2 <- array(0, dim=c(n-par4,par4), dimnames=list(1:(n-par4), paste(colnames(x)[par1],'(t-',1:par4,')',sep='')))
for (i in 1:(n-par4)) {
for (j in 1:par4) {
x2[i,j] <- x[i+par4-j,par1]
}
}
x <- cbind(x[(par4+1):n,], x2)
n <- n - par4
}
if(par5 > 0) {
x2 <- array(0, dim=c(n-par5*par6,par5), dimnames=list(1:(n-par5*par6), paste(colnames(x)[par1],'(t-',1:par5,'s)',sep='')))
for (i in 1:(n-par5*par6)) {
for (j in 1:par5) {
x2[i,j] <- x[i+par5*par6-j*par6,par1]
}
}
x <- cbind(x[(par5*par6+1):n,], x2)
n <- n - par5*par6
}
if (par2 == 'Include Seasonal Dummies'){
x2 <- array(0, dim=c(n,par6-1), dimnames=list(1:n, paste('M', seq(1:(par6-1)), sep ='')))
for (i in 1:(par6-1)){
x2[seq(i,n,par6),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
(k <- length(x[n,]))
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
print(x)
(k <- length(x[n,]))
head(x)
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
sresid <- studres(mylm)
hist(sresid, freq=FALSE, main='Distribution of Studentized Residuals')
xfit<-seq(min(sresid),max(sresid),length=40)
yfit<-dnorm(xfit)
lines(xfit, yfit)
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqPlot(mylm, main='QQ Plot')
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
print(z)
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, signif(mysum$coefficients[i,1],6), sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, mywarning)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Multiple Linear Regression - Ordinary Least Squares', 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,formatC(signif(mysum$coefficients[i,1],5),format='g',flag='+'))
a<-table.element(a,formatC(signif(mysum$coefficients[i,2],5),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$coefficients[i,3],4),format='e',flag='+'))
a<-table.element(a,formatC(signif(mysum$coefficients[i,4],4),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$coefficients[i,4]/2,4),format='g',flag=' '))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a,formatC(signif(sqrt(mysum$r.squared),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a,formatC(signif(mysum$r.squared,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a,formatC(signif(mysum$adj.r.squared,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a,formatC(signif(mysum$fstatistic[1],6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[2],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[3],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a,formatC(signif(1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a,formatC(signif(mysum$sigma,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a,formatC(signif(sum(myerror*myerror),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
myr <- as.numeric(mysum$resid)
myr
a <-table.start()
a <- table.row.start(a)
a <- table.element(a,'Menu of Residual Diagnostics',2,TRUE)
a <- table.row.end(a)
a <- table.row.start(a)
a <- table.element(a,'Description',1,TRUE)
a <- table.element(a,'Link',1,TRUE)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Histogram',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_histogram.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Central Tendency',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_centraltendency.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'QQ Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_fitdistrnorm.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Kernel Density Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_density.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Skewness/Kurtosis Test',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_skewness_kurtosis.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Skewness-Kurtosis Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_skewness_kurtosis_plot.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Harrell-Davis Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_harrell_davis.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Bootstrap Plot -- Central Tendency',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_bootstrapplot1.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Blocked Bootstrap Plot -- Central Tendency',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_bootstrapplot.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'(Partial) Autocorrelation Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_autocorrelation.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Spectral Analysis',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_spectrum.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Tukey lambda PPCC Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_tukeylambda.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Box-Cox Normality Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_boxcoxnorm.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <- table.element(a,'Summary Statistics',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_summary1.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable7.tab')
if(n < 200) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,formatC(signif(x[i],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(x[i]-mysum$resid[i],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$resid[i],6),format='g',flag=' '))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
if (n > n25) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-values',header=TRUE)
a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'breakpoint index',header=TRUE)
a<-table.element(a,'greater',header=TRUE)
a<-table.element(a,'2-sided',header=TRUE)
a<-table.element(a,'less',header=TRUE)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
a<-table.element(a,mypoint,header=TRUE)
a<-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,1],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,2],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,3],6),format='g',flag=' '))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,signif(numsignificant1,6))
a<-table.element(a,formatC(signif(numsignificant1/numgqtests,6),format='g',flag=' '))
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,signif(numsignificant5,6))
a<-table.element(a,signif(numsignificant5/numgqtests,6))
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,signif(numsignificant10,6))
a<-table.element(a,signif(numsignificant10/numgqtests,6))
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}
}
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Ramsey RESET F-Test for powers (2 and 3) of fitted values',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
reset_test_fitted <- resettest(mylm,power=2:3,type='fitted')
a<-table.element(a,paste('',RC.texteval('reset_test_fitted'),'
',sep=''))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Ramsey RESET F-Test for powers (2 and 3) of regressors',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
reset_test_regressors <- resettest(mylm,power=2:3,type='regressor')
a<-table.element(a,paste('',RC.texteval('reset_test_regressors'),'
',sep=''))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Ramsey RESET F-Test for powers (2 and 3) of principal components',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
reset_test_principal_components <- resettest(mylm,power=2:3,type='princomp')
a<-table.element(a,paste('',RC.texteval('reset_test_principal_components'),'
',sep=''))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable8.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Variance Inflation Factors (Multicollinearity)',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
vif <- vif(mylm)
a<-table.element(a,paste('',RC.texteval('vif'),'
',sep=''))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable9.tab')

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code