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rwasp_multipleregression.wasp

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Multiple Regression

Date of computation

Wed, 25 Jan 2017 10:45:31 +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/2017/Jan/25/t1485337574yz0to1npwodid27.htm/, Retrieved Tue, 14 May 2024 09:23:51 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=306337, Retrieved Tue, 14 May 2024 09:23:51 +0000

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-       [Multiple Regression] [] [2017-01-25 09:45:31] [0fd2044a1b35b563c533995bdc13f5a1] [Current]

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22 4 2 4 3 5 4
24 5 3 3 4 5 4
26 4 4 5 4 5 4
21 3 4 3 3 4 4
26 4 4 5 4 5 4
25 3 4 4 4 5 5
21 3 4 4 3 3 4
24 3 4 5 4 4 4
27 4 5 4 4 5 5
28 4 5 5 4 5 5
23 4 4 2 4 5 4
25 4 4 5 3 5 4
24 4 4 4 3 4 5
24 3 3 5 4 4 5
24 4 4 5 4 2 5
25 3 4 5 4 4 5
25 3 4 5 4 4 5
NA NA NA 5 NA 5 5
25 5 5 4 3 4 4
25 4 4 4 4 5 4
24 3 4 5 3 4 5
26 4 4 4 4 5 5
26 4 4 5 4 4 5
25 4 4 5 4 4 4
26 4 4 5 4 4 5
23 3 4 4 4 4 4
24 3 4 4 3 5 5
24 4 4 4 4 4 4
25 2 4 5 4 5 5
25 5 4 4 4 4 4
24 4 3 5 4 4 4
28 4 5 5 4 5 5
27 5 4 5 4 4 5
NA 4 3 5 4 NA 5
23 2 3 5 4 5 4
23 4 5 2 4 4 4
24 3 4 5 4 4 4
24 4 3 5 3 4 5
22 4 3 3 4 4 4
25 4 4 5 4 4 4
25 5 4 4 4 4 4
28 4 5 5 4 5 5
22 3 3 4 4 4 4
28 5 5 5 3 5 5
25 5 4 5 3 4 4
24 4 4 4 3 4 5
24 4 4 4 4 4 4
23 3 5 5 3 3 4
25 4 4 4 4 5 4
NA 2 3 4 2 NA 4
26 4 5 5 4 4 4
25 5 5 2 4 5 4
27 5 5 5 4 4 4
26 4 3 5 4 5 5
23 4 3 4 3 4 5
25 4 4 5 4 4 4
21 3 4 4 3 3 4
22 3 4 4 4 4 3
24 4 4 4 3 5 4
25 4 4 4 4 5 4
27 5 5 3 4 5 5
24 2 4 4 4 5 5
26 4 4 4 4 5 5
21 3 4 4 4 2 4
27 4 4 5 4 5 5
22 4 2 4 4 4 4
23 4 4 4 3 5 3
24 4 4 4 3 5 4
25 5 4 5 3 3 5
24 3 4 4 3 5 5
23 3 4 4 3 4 5
28 4 5 5 5 5 4
NA 4 4 3 4 NA 4
24 4 4 4 4 4 4
26 4 4 4 5 5 4
22 3 4 3 4 4 4
25 4 4 4 4 5 4
25 3 4 5 3 5 5
24 3 3 5 4 4 5
24 4 3 5 4 4 4
26 4 4 5 4 4 5
21 3 3 3 4 4 4
25 4 4 4 4 5 4
25 4 4 3 4 5 5
26 4 4 4 4 5 5
25 5 4 4 4 4 4
26 5 4 3 5 4 5
27 4 4 5 4 5 5
25 3 4 5 4 4 5
NA 3 NA 4 4 4 4
20 4 2 3 3 4 4
24 4 4 5 4 4 3
26 4 4 5 4 4 5
25 4 4 4 4 5 4
25 4 5 4 4 5 3
24 3 4 4 3 5 5
26 4 4 5 4 4 5
25 5 4 3 4 4 5
28 5 4 5 5 4 5
27 4 5 4 4 5 5
25 3 4 5 4 4 5
26 5 3 4 4 5 5
26 4 4 5 4 4 5
26 5 4 4 4 4 5
NA 3 4 4 3 NA 4
28 5 4 4 5 5 5
NA 4 4 5 3 NA 5
21 4 4 3 3 4 3
25 4 4 5 4 4 4
25 4 4 5 4 4 4
24 3 4 5 4 5 3
24 4 4 4 4 4 4
24 4 4 4 3 4 5
23 3 3 4 3 5 5
23 4 4 4 3 4 4
24 3 4 5 4 4 4
24 4 4 5 4 3 4
25 5 4 5 1 5 5
28 5 4 5 4 5 5
23 4 4 4 4 4 3
24 4 4 5 3 4 4
23 3 4 4 3 4 5
24 4 4 4 4 4 4
25 4 4 4 4 5 4
24 4 5 3 4 4 4
23 3 4 4 4 4 4
23 4 4 4 3 4 4
25 4 4 4 4 4 5
21 3 4 3 3 4 4
22 4 4 4 3 4 3
19 3 2 4 2 4 4
24 4 4 4 3 5 4
25 5 4 4 3 5 4
21 2 4 4 3 3 5
22 3 3 4 4 4 4
23 4 4 4 3 4 4
27 5 5 4 4 5 4
NA NA NA 2 NA NA NA
26 4 5 5 4 4 4
29 5 5 5 5 5 4
28 4 5 5 4 5 5
24 4 4 4 3 4 5
25 3 4 5 4 5 4
25 4 4 5 4 4 4
22 4 4 2 4 4 4
25 4 4 3 4 5 5
26 4 4 4 4 5 5
26 5 4 5 3 5 4
24 4 3 5 4 4 4
25 4 4 5 4 4 4
19 3 3 2 3 4 4
25 4 5 5 4 4 3
23 4 4 4 3 4 4
25 4 4 4 4 4 5
25 3 4 5 3 5 5
26 4 4 5 4 4 5
27 5 4 5 4 5 4
24 4 4 5 4 3 4
22 2 3 5 4 4 4
25 4 4 4 4 4 5
24 4 3 4 3 5 5
23 4 4 4 4 4 3
27 4 5 5 5 4 4
24 5 4 3 4 4 4
24 5 4 4 3 4 4
21 3 3 1 4 5 5
25 4 4 4 4 4 5
25 4 4 4 4 5 4
23 2 3 4 5 5 4

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	11 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 time11 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=306337&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]

11 seconds[/C][/ROW] [ROW]

R Server[/C]

Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=306337&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=306337&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	11 seconds
R Server	Big Analytics Cloud Computing Center

Multiple Linear Regression - Estimated Regression Equation
SKEOUSUM[t] = -9.83748e-14 + 1SKEOU1[t] + 1SKEOU2[t] + 1SKEOU3[t] + 1SKEOU4[t] + 1SKEOU5[t] + 1SKEOU6[t] + 4.28241e-17`SKEOUSUM(t-1)`[t] + 1.23891e-15`SKEOUSUM(t-2)`[t] -2.04006e-15`SKEOUSUM(t-3)`[t] -1.94759e-15`SKEOUSUM(t-4)`[t] + 2.5094e-16`SKEOUSUM(t-5)`[t] + 2.76799e-15`SKEOUSUM(t-6)`[t] -7.38911e-16`SKEOUSUM(t-7)`[t] -1.03368e-15`SKEOUSUM(t-8)`[t] + 2.46778e-15`SKEOUSUM(t-9)`[t] -1.49639e-15`SKEOUSUM(t-10)`[t] + 4.44534e-16`SKEOUSUM(t-11)`[t] + 2.5392e-15`SKEOUSUM(t-12)`[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
SKEOUSUM[t] =  -9.83748e-14 +  1SKEOU1[t] +  1SKEOU2[t] +  1SKEOU3[t] +  1SKEOU4[t] +  1SKEOU5[t] +  1SKEOU6[t] +  4.28241e-17`SKEOUSUM(t-1)`[t] +  1.23891e-15`SKEOUSUM(t-2)`[t] -2.04006e-15`SKEOUSUM(t-3)`[t] -1.94759e-15`SKEOUSUM(t-4)`[t] +  2.5094e-16`SKEOUSUM(t-5)`[t] +  2.76799e-15`SKEOUSUM(t-6)`[t] -7.38911e-16`SKEOUSUM(t-7)`[t] -1.03368e-15`SKEOUSUM(t-8)`[t] +  2.46778e-15`SKEOUSUM(t-9)`[t] -1.49639e-15`SKEOUSUM(t-10)`[t] +  4.44534e-16`SKEOUSUM(t-11)`[t] +  2.5392e-15`SKEOUSUM(t-12)`[t]  + e[t] \tabularnewline
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=306337&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]SKEOUSUM[t] =  -9.83748e-14 +  1SKEOU1[t] +  1SKEOU2[t] +  1SKEOU3[t] +  1SKEOU4[t] +  1SKEOU5[t] +  1SKEOU6[t] +  4.28241e-17`SKEOUSUM(t-1)`[t] +  1.23891e-15`SKEOUSUM(t-2)`[t] -2.04006e-15`SKEOUSUM(t-3)`[t] -1.94759e-15`SKEOUSUM(t-4)`[t] +  2.5094e-16`SKEOUSUM(t-5)`[t] +  2.76799e-15`SKEOUSUM(t-6)`[t] -7.38911e-16`SKEOUSUM(t-7)`[t] -1.03368e-15`SKEOUSUM(t-8)`[t] +  2.46778e-15`SKEOUSUM(t-9)`[t] -1.49639e-15`SKEOUSUM(t-10)`[t] +  4.44534e-16`SKEOUSUM(t-11)`[t] +  2.5392e-15`SKEOUSUM(t-12)`[t]  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=306337&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=306337&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
SKEOUSUM[t] = -9.83748e-14 + 1SKEOU1[t] + 1SKEOU2[t] + 1SKEOU3[t] + 1SKEOU4[t] + 1SKEOU5[t] + 1SKEOU6[t] + 4.28241e-17`SKEOUSUM(t-1)`[t] + 1.23891e-15`SKEOUSUM(t-2)`[t] -2.04006e-15`SKEOUSUM(t-3)`[t] -1.94759e-15`SKEOUSUM(t-4)`[t] + 2.5094e-16`SKEOUSUM(t-5)`[t] + 2.76799e-15`SKEOUSUM(t-6)`[t] -7.38911e-16`SKEOUSUM(t-7)`[t] -1.03368e-15`SKEOUSUM(t-8)`[t] + 2.46778e-15`SKEOUSUM(t-9)`[t] -1.49639e-15`SKEOUSUM(t-10)`[t] + 4.44534e-16`SKEOUSUM(t-11)`[t] + 2.5392e-15`SKEOUSUM(t-12)`[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)	-9.837e-14	1.183e-13	-8.3170e-01	0.4071	0.2036
SKEOU1	+1	3.669e-15	+2.7260e+14	0	0
SKEOU2	+1	4.86e-15	+2.0580e+14	0	0
SKEOU3	+1	3.334e-15	+2.9990e+14	0	0
SKEOU4	+1	4.668e-15	+2.1420e+14	0	0
SKEOU5	+1	4.734e-15	+2.1120e+14	0	0
SKEOU6	+1	4.483e-15	+2.2300e+14	0	0
`SKEOUSUM(t-1)`	+4.282e-17	1.448e-15	+2.9570e-02	0.9765	0.4882
`SKEOUSUM(t-2)`	+1.239e-15	1.438e-15	+8.6180e-01	0.3904	0.1952
`SKEOUSUM(t-3)`	-2.04e-15	1.417e-15	-1.4400e+00	0.1524	0.0762
`SKEOUSUM(t-4)`	-1.948e-15	1.443e-15	-1.3490e+00	0.1796	0.08978
`SKEOUSUM(t-5)`	+2.509e-16	1.461e-15	+1.7170e-01	0.8639	0.432
`SKEOUSUM(t-6)`	+2.768e-15	1.444e-15	+1.9170e+00	0.05742	0.02871
`SKEOUSUM(t-7)`	-7.389e-16	1.429e-15	-5.1700e-01	0.6061	0.303
`SKEOUSUM(t-8)`	-1.034e-15	1.451e-15	-7.1240e-01	0.4775	0.2387
`SKEOUSUM(t-9)`	+2.468e-15	1.427e-15	+1.7290e+00	0.08611	0.04305
`SKEOUSUM(t-10)`	-1.496e-15	1.406e-15	-1.0640e+00	0.2892	0.1446
`SKEOUSUM(t-11)`	+4.445e-16	1.379e-15	+3.2240e-01	0.7477	0.3739
`SKEOUSUM(t-12)`	+2.539e-15	1.389e-15	+1.8270e+00	0.06992	0.03496

\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) & -9.837e-14 &  1.183e-13 & -8.3170e-01 &  0.4071 &  0.2036 \tabularnewline
SKEOU1 & +1 &  3.669e-15 & +2.7260e+14 &  0 &  0 \tabularnewline
SKEOU2 & +1 &  4.86e-15 & +2.0580e+14 &  0 &  0 \tabularnewline
SKEOU3 & +1 &  3.334e-15 & +2.9990e+14 &  0 &  0 \tabularnewline
SKEOU4 & +1 &  4.668e-15 & +2.1420e+14 &  0 &  0 \tabularnewline
SKEOU5 & +1 &  4.734e-15 & +2.1120e+14 &  0 &  0 \tabularnewline
SKEOU6 & +1 &  4.483e-15 & +2.2300e+14 &  0 &  0 \tabularnewline
`SKEOUSUM(t-1)` & +4.282e-17 &  1.448e-15 & +2.9570e-02 &  0.9765 &  0.4882 \tabularnewline
`SKEOUSUM(t-2)` & +1.239e-15 &  1.438e-15 & +8.6180e-01 &  0.3904 &  0.1952 \tabularnewline
`SKEOUSUM(t-3)` & -2.04e-15 &  1.417e-15 & -1.4400e+00 &  0.1524 &  0.0762 \tabularnewline
`SKEOUSUM(t-4)` & -1.948e-15 &  1.443e-15 & -1.3490e+00 &  0.1796 &  0.08978 \tabularnewline
`SKEOUSUM(t-5)` & +2.509e-16 &  1.461e-15 & +1.7170e-01 &  0.8639 &  0.432 \tabularnewline
`SKEOUSUM(t-6)` & +2.768e-15 &  1.444e-15 & +1.9170e+00 &  0.05742 &  0.02871 \tabularnewline
`SKEOUSUM(t-7)` & -7.389e-16 &  1.429e-15 & -5.1700e-01 &  0.6061 &  0.303 \tabularnewline
`SKEOUSUM(t-8)` & -1.034e-15 &  1.451e-15 & -7.1240e-01 &  0.4775 &  0.2387 \tabularnewline
`SKEOUSUM(t-9)` & +2.468e-15 &  1.427e-15 & +1.7290e+00 &  0.08611 &  0.04305 \tabularnewline
`SKEOUSUM(t-10)` & -1.496e-15 &  1.406e-15 & -1.0640e+00 &  0.2892 &  0.1446 \tabularnewline
`SKEOUSUM(t-11)` & +4.445e-16 &  1.379e-15 & +3.2240e-01 &  0.7477 &  0.3739 \tabularnewline
`SKEOUSUM(t-12)` & +2.539e-15 &  1.389e-15 & +1.8270e+00 &  0.06992 &  0.03496 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=306337&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]-9.837e-14[/C][C] 1.183e-13[/C][C]-8.3170e-01[/C][C] 0.4071[/C][C] 0.2036[/C][/ROW]
[ROW][C]SKEOU1[/C][C]+1[/C][C] 3.669e-15[/C][C]+2.7260e+14[/C][C] 0[/C][C] 0[/C][/ROW]
[ROW][C]SKEOU2[/C][C]+1[/C][C] 4.86e-15[/C][C]+2.0580e+14[/C][C] 0[/C][C] 0[/C][/ROW]
[ROW][C]SKEOU3[/C][C]+1[/C][C] 3.334e-15[/C][C]+2.9990e+14[/C][C] 0[/C][C] 0[/C][/ROW]
[ROW][C]SKEOU4[/C][C]+1[/C][C] 4.668e-15[/C][C]+2.1420e+14[/C][C] 0[/C][C] 0[/C][/ROW]
[ROW][C]SKEOU5[/C][C]+1[/C][C] 4.734e-15[/C][C]+2.1120e+14[/C][C] 0[/C][C] 0[/C][/ROW]
[ROW][C]SKEOU6[/C][C]+1[/C][C] 4.483e-15[/C][C]+2.2300e+14[/C][C] 0[/C][C] 0[/C][/ROW]
[ROW][C]`SKEOUSUM(t-1)`[/C][C]+4.282e-17[/C][C] 1.448e-15[/C][C]+2.9570e-02[/C][C] 0.9765[/C][C] 0.4882[/C][/ROW]
[ROW][C]`SKEOUSUM(t-2)`[/C][C]+1.239e-15[/C][C] 1.438e-15[/C][C]+8.6180e-01[/C][C] 0.3904[/C][C] 0.1952[/C][/ROW]
[ROW][C]`SKEOUSUM(t-3)`[/C][C]-2.04e-15[/C][C] 1.417e-15[/C][C]-1.4400e+00[/C][C] 0.1524[/C][C] 0.0762[/C][/ROW]
[ROW][C]`SKEOUSUM(t-4)`[/C][C]-1.948e-15[/C][C] 1.443e-15[/C][C]-1.3490e+00[/C][C] 0.1796[/C][C] 0.08978[/C][/ROW]
[ROW][C]`SKEOUSUM(t-5)`[/C][C]+2.509e-16[/C][C] 1.461e-15[/C][C]+1.7170e-01[/C][C] 0.8639[/C][C] 0.432[/C][/ROW]
[ROW][C]`SKEOUSUM(t-6)`[/C][C]+2.768e-15[/C][C] 1.444e-15[/C][C]+1.9170e+00[/C][C] 0.05742[/C][C] 0.02871[/C][/ROW]
[ROW][C]`SKEOUSUM(t-7)`[/C][C]-7.389e-16[/C][C] 1.429e-15[/C][C]-5.1700e-01[/C][C] 0.6061[/C][C] 0.303[/C][/ROW]
[ROW][C]`SKEOUSUM(t-8)`[/C][C]-1.034e-15[/C][C] 1.451e-15[/C][C]-7.1240e-01[/C][C] 0.4775[/C][C] 0.2387[/C][/ROW]
[ROW][C]`SKEOUSUM(t-9)`[/C][C]+2.468e-15[/C][C] 1.427e-15[/C][C]+1.7290e+00[/C][C] 0.08611[/C][C] 0.04305[/C][/ROW]
[ROW][C]`SKEOUSUM(t-10)`[/C][C]-1.496e-15[/C][C] 1.406e-15[/C][C]-1.0640e+00[/C][C] 0.2892[/C][C] 0.1446[/C][/ROW]
[ROW][C]`SKEOUSUM(t-11)`[/C][C]+4.445e-16[/C][C] 1.379e-15[/C][C]+3.2240e-01[/C][C] 0.7477[/C][C] 0.3739[/C][/ROW]
[ROW][C]`SKEOUSUM(t-12)`[/C][C]+2.539e-15[/C][C] 1.389e-15[/C][C]+1.8270e+00[/C][C] 0.06992[/C][C] 0.03496[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=306337&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=306337&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)	-9.837e-14	1.183e-13	-8.3170e-01	0.4071	0.2036
SKEOU1	+1	3.669e-15	+2.7260e+14	0	0
SKEOU2	+1	4.86e-15	+2.0580e+14	0	0
SKEOU3	+1	3.334e-15	+2.9990e+14	0	0
SKEOU4	+1	4.668e-15	+2.1420e+14	0	0
SKEOU5	+1	4.734e-15	+2.1120e+14	0	0
SKEOU6	+1	4.483e-15	+2.2300e+14	0	0
`SKEOUSUM(t-1)`	+4.282e-17	1.448e-15	+2.9570e-02	0.9765	0.4882
`SKEOUSUM(t-2)`	+1.239e-15	1.438e-15	+8.6180e-01	0.3904	0.1952
`SKEOUSUM(t-3)`	-2.04e-15	1.417e-15	-1.4400e+00	0.1524	0.0762
`SKEOUSUM(t-4)`	-1.948e-15	1.443e-15	-1.3490e+00	0.1796	0.08978
`SKEOUSUM(t-5)`	+2.509e-16	1.461e-15	+1.7170e-01	0.8639	0.432
`SKEOUSUM(t-6)`	+2.768e-15	1.444e-15	+1.9170e+00	0.05742	0.02871
`SKEOUSUM(t-7)`	-7.389e-16	1.429e-15	-5.1700e-01	0.6061	0.303
`SKEOUSUM(t-8)`	-1.034e-15	1.451e-15	-7.1240e-01	0.4775	0.2387
`SKEOUSUM(t-9)`	+2.468e-15	1.427e-15	+1.7290e+00	0.08611	0.04305
`SKEOUSUM(t-10)`	-1.496e-15	1.406e-15	-1.0640e+00	0.2892	0.1446
`SKEOUSUM(t-11)`	+4.445e-16	1.379e-15	+3.2240e-01	0.7477	0.3739
`SKEOUSUM(t-12)`	+2.539e-15	1.389e-15	+1.8270e+00	0.06992	0.03496

Multiple Linear Regression - Regression Statistics
Multiple R	1
R-squared	1
Adjusted R-squared	1
F-TEST (value)	2.977e+28
F-TEST (DF numerator)	18
F-TEST (DF denominator)	130
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	3.046e-14
Sum Squared Residuals	1.206e-25

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  1 \tabularnewline
R-squared &  1 \tabularnewline
Adjusted R-squared &  1 \tabularnewline
F-TEST (value) &  2.977e+28 \tabularnewline
F-TEST (DF numerator) & 18 \tabularnewline
F-TEST (DF denominator) & 130 \tabularnewline
p-value &  0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  3.046e-14 \tabularnewline
Sum Squared Residuals &  1.206e-25 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=306337&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 1[/C][/ROW]
[ROW][C]R-squared[/C][C] 1[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 1[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 2.977e+28[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]18[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]130[/C][/ROW]
[ROW][C]p-value[/C][C] 0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C] 3.046e-14[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 1.206e-25[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=306337&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=306337&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	1
R-squared	1
Adjusted R-squared	1
F-TEST (value)	2.977e+28
F-TEST (DF numerator)	18
F-TEST (DF denominator)	130
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	3.046e-14
Sum Squared Residuals	1.206e-25

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	24	24	-3.224e-13
2	24	24	3.348e-15
3	24	24	-1.624e-14
4	25	25	8.504e-15
5	25	25	5.649e-15
6	25	25	-5.635e-15
7	25	25	5.611e-15
8	24	24	1.871e-14
9	26	26	-5.246e-15
10	26	26	-2.872e-15
11	25	25	-6.929e-16
12	26	26	2.235e-15
13	23	23	5.722e-15
14	24	24	1.281e-14
15	24	24	3.243e-15
16	25	25	-1.973e-15
17	25	25	-1.499e-15
18	24	24	-1.17e-14
19	28	28	5.328e-15
20	27	27	8.494e-15
21	23	23	-1.348e-14
22	23	23	7.975e-15
23	24	24	8.707e-15
24	24	24	8.434e-15
25	22	22	-1.076e-14
26	25	25	-7.822e-15
27	25	25	1.456e-14
28	28	28	-9.357e-15
29	22	22	-6.751e-15
30	28	28	1.039e-14
31	25	25	9.85e-15
32	24	24	-4.84e-15
33	24	24	3.306e-15
34	23	23	1.299e-14
35	25	25	6.537e-15
36	26	26	-7.486e-15
37	25	25	-4.054e-15
38	27	27	1.02e-14
39	26	26	-7.866e-15
40	23	23	8.224e-15
41	25	25	6.529e-15
42	21	21	1.413e-15
43	22	22	-1.877e-15
44	24	24	-1.274e-15
45	25	25	-5.239e-15
46	27	27	6.641e-15
47	24	24	-8.838e-15
48	26	26	6.481e-15
49	21	21	1.803e-14
50	27	27	-1.02e-14
51	22	22	8.328e-15
52	23	23	-9.994e-15
53	24	24	6.143e-15
54	25	25	1.485e-14
55	24	24	1.123e-14
56	23	23	2.714e-15
57	28	28	-5.533e-15
58	24	24	1.258e-14
59	26	26	-2.499e-14
60	22	22	1.269e-14
61	25	25	1.363e-14
62	25	25	8.251e-15
63	24	24	-5.97e-15
64	24	24	2.346e-15
65	26	26	9.573e-15
66	21	21	-8.055e-16
67	25	25	3.137e-15
68	25	25	6.832e-15
69	26	26	1.653e-15
70	25	25	-9.241e-15
71	26	26	-3.719e-15
72	27	27	2.397e-14
73	25	25	1.609e-15
74	20	20	-3.703e-15
75	24	24	1.186e-14
76	26	26	8.695e-15
77	25	25	-1.301e-14
78	25	25	-1.509e-14
79	24	24	1.187e-14
80	26	26	1.72e-14
81	25	25	-4.992e-16
82	28	28	-1.161e-14
83	27	27	1.177e-14
84	25	25	-8.726e-15
85	26	26	6.777e-15
86	26	26	2.298e-14
87	26	26	6.944e-15
88	28	28	-7.689e-15
89	21	21	-3.406e-15
90	25	25	4.324e-15
91	25	25	4.376e-15
92	24	24	-1.449e-14
93	24	24	-7.936e-15
94	24	24	-8.102e-15
95	23	23	1.379e-14
96	23	23	-8.345e-16
97	24	24	-1.623e-14
98	24	24	9.468e-15
99	25	25	-4.483e-15
100	28	28	-8.844e-15
101	23	23	8.766e-15
102	24	24	-2.989e-16
103	23	23	1.684e-14
104	24	24	9.373e-15
105	25	25	-2.024e-15
106	24	24	-8.804e-15
107	23	23	9.089e-15
108	23	23	1.14e-14
109	25	25	2.783e-15
110	21	21	1.453e-14
111	22	22	-1.292e-14
112	19	19	6.323e-15
113	24	24	8.409e-15
114	25	25	2.392e-16
115	21	21	-2.645e-15
116	22	22	1.93e-15
117	23	23	1.548e-14
118	27	27	5.006e-15
119	26	26	-2.876e-15
120	29	29	-1.845e-14
121	28	28	2.29e-14
122	24	24	1.88e-14
123	25	25	1.119e-14
124	25	25	2.795e-14
125	22	22	7.711e-15
126	25	25	-3.76e-15
127	26	26	6.335e-15
128	26	26	1.142e-14
129	24	24	-1.146e-14
130	25	25	-8.765e-15
131	19	19	2.38e-14
132	25	25	-2.152e-14
133	23	23	-4.708e-15
134	25	25	-2.917e-16
135	25	25	-4.489e-15
136	26	26	-1.448e-15
137	27	27	1.44e-14
138	24	24	-3.157e-15
139	22	22	-7e-15
140	25	25	2.233e-14
141	24	24	2.787e-15
142	23	23	-7.7e-15
143	27	27	-2.228e-15
144	24	24	8.603e-15
145	24	24	6.187e-15
146	21	21	4.916e-15
147	25	25	1.215e-14
148	25	25	6.732e-15
149	23	23	-3.031e-14

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 &  24 &  24 & -3.224e-13 \tabularnewline
2 &  24 &  24 &  3.348e-15 \tabularnewline
3 &  24 &  24 & -1.624e-14 \tabularnewline
4 &  25 &  25 &  8.504e-15 \tabularnewline
5 &  25 &  25 &  5.649e-15 \tabularnewline
6 &  25 &  25 & -5.635e-15 \tabularnewline
7 &  25 &  25 &  5.611e-15 \tabularnewline
8 &  24 &  24 &  1.871e-14 \tabularnewline
9 &  26 &  26 & -5.246e-15 \tabularnewline
10 &  26 &  26 & -2.872e-15 \tabularnewline
11 &  25 &  25 & -6.929e-16 \tabularnewline
12 &  26 &  26 &  2.235e-15 \tabularnewline
13 &  23 &  23 &  5.722e-15 \tabularnewline
14 &  24 &  24 &  1.281e-14 \tabularnewline
15 &  24 &  24 &  3.243e-15 \tabularnewline
16 &  25 &  25 & -1.973e-15 \tabularnewline
17 &  25 &  25 & -1.499e-15 \tabularnewline
18 &  24 &  24 & -1.17e-14 \tabularnewline
19 &  28 &  28 &  5.328e-15 \tabularnewline
20 &  27 &  27 &  8.494e-15 \tabularnewline
21 &  23 &  23 & -1.348e-14 \tabularnewline
22 &  23 &  23 &  7.975e-15 \tabularnewline
23 &  24 &  24 &  8.707e-15 \tabularnewline
24 &  24 &  24 &  8.434e-15 \tabularnewline
25 &  22 &  22 & -1.076e-14 \tabularnewline
26 &  25 &  25 & -7.822e-15 \tabularnewline
27 &  25 &  25 &  1.456e-14 \tabularnewline
28 &  28 &  28 & -9.357e-15 \tabularnewline
29 &  22 &  22 & -6.751e-15 \tabularnewline
30 &  28 &  28 &  1.039e-14 \tabularnewline
31 &  25 &  25 &  9.85e-15 \tabularnewline
32 &  24 &  24 & -4.84e-15 \tabularnewline
33 &  24 &  24 &  3.306e-15 \tabularnewline
34 &  23 &  23 &  1.299e-14 \tabularnewline
35 &  25 &  25 &  6.537e-15 \tabularnewline
36 &  26 &  26 & -7.486e-15 \tabularnewline
37 &  25 &  25 & -4.054e-15 \tabularnewline
38 &  27 &  27 &  1.02e-14 \tabularnewline
39 &  26 &  26 & -7.866e-15 \tabularnewline
40 &  23 &  23 &  8.224e-15 \tabularnewline
41 &  25 &  25 &  6.529e-15 \tabularnewline
42 &  21 &  21 &  1.413e-15 \tabularnewline
43 &  22 &  22 & -1.877e-15 \tabularnewline
44 &  24 &  24 & -1.274e-15 \tabularnewline
45 &  25 &  25 & -5.239e-15 \tabularnewline
46 &  27 &  27 &  6.641e-15 \tabularnewline
47 &  24 &  24 & -8.838e-15 \tabularnewline
48 &  26 &  26 &  6.481e-15 \tabularnewline
49 &  21 &  21 &  1.803e-14 \tabularnewline
50 &  27 &  27 & -1.02e-14 \tabularnewline
51 &  22 &  22 &  8.328e-15 \tabularnewline
52 &  23 &  23 & -9.994e-15 \tabularnewline
53 &  24 &  24 &  6.143e-15 \tabularnewline
54 &  25 &  25 &  1.485e-14 \tabularnewline
55 &  24 &  24 &  1.123e-14 \tabularnewline
56 &  23 &  23 &  2.714e-15 \tabularnewline
57 &  28 &  28 & -5.533e-15 \tabularnewline
58 &  24 &  24 &  1.258e-14 \tabularnewline
59 &  26 &  26 & -2.499e-14 \tabularnewline
60 &  22 &  22 &  1.269e-14 \tabularnewline
61 &  25 &  25 &  1.363e-14 \tabularnewline
62 &  25 &  25 &  8.251e-15 \tabularnewline
63 &  24 &  24 & -5.97e-15 \tabularnewline
64 &  24 &  24 &  2.346e-15 \tabularnewline
65 &  26 &  26 &  9.573e-15 \tabularnewline
66 &  21 &  21 & -8.055e-16 \tabularnewline
67 &  25 &  25 &  3.137e-15 \tabularnewline
68 &  25 &  25 &  6.832e-15 \tabularnewline
69 &  26 &  26 &  1.653e-15 \tabularnewline
70 &  25 &  25 & -9.241e-15 \tabularnewline
71 &  26 &  26 & -3.719e-15 \tabularnewline
72 &  27 &  27 &  2.397e-14 \tabularnewline
73 &  25 &  25 &  1.609e-15 \tabularnewline
74 &  20 &  20 & -3.703e-15 \tabularnewline
75 &  24 &  24 &  1.186e-14 \tabularnewline
76 &  26 &  26 &  8.695e-15 \tabularnewline
77 &  25 &  25 & -1.301e-14 \tabularnewline
78 &  25 &  25 & -1.509e-14 \tabularnewline
79 &  24 &  24 &  1.187e-14 \tabularnewline
80 &  26 &  26 &  1.72e-14 \tabularnewline
81 &  25 &  25 & -4.992e-16 \tabularnewline
82 &  28 &  28 & -1.161e-14 \tabularnewline
83 &  27 &  27 &  1.177e-14 \tabularnewline
84 &  25 &  25 & -8.726e-15 \tabularnewline
85 &  26 &  26 &  6.777e-15 \tabularnewline
86 &  26 &  26 &  2.298e-14 \tabularnewline
87 &  26 &  26 &  6.944e-15 \tabularnewline
88 &  28 &  28 & -7.689e-15 \tabularnewline
89 &  21 &  21 & -3.406e-15 \tabularnewline
90 &  25 &  25 &  4.324e-15 \tabularnewline
91 &  25 &  25 &  4.376e-15 \tabularnewline
92 &  24 &  24 & -1.449e-14 \tabularnewline
93 &  24 &  24 & -7.936e-15 \tabularnewline
94 &  24 &  24 & -8.102e-15 \tabularnewline
95 &  23 &  23 &  1.379e-14 \tabularnewline
96 &  23 &  23 & -8.345e-16 \tabularnewline
97 &  24 &  24 & -1.623e-14 \tabularnewline
98 &  24 &  24 &  9.468e-15 \tabularnewline
99 &  25 &  25 & -4.483e-15 \tabularnewline
100 &  28 &  28 & -8.844e-15 \tabularnewline
101 &  23 &  23 &  8.766e-15 \tabularnewline
102 &  24 &  24 & -2.989e-16 \tabularnewline
103 &  23 &  23 &  1.684e-14 \tabularnewline
104 &  24 &  24 &  9.373e-15 \tabularnewline
105 &  25 &  25 & -2.024e-15 \tabularnewline
106 &  24 &  24 & -8.804e-15 \tabularnewline
107 &  23 &  23 &  9.089e-15 \tabularnewline
108 &  23 &  23 &  1.14e-14 \tabularnewline
109 &  25 &  25 &  2.783e-15 \tabularnewline
110 &  21 &  21 &  1.453e-14 \tabularnewline
111 &  22 &  22 & -1.292e-14 \tabularnewline
112 &  19 &  19 &  6.323e-15 \tabularnewline
113 &  24 &  24 &  8.409e-15 \tabularnewline
114 &  25 &  25 &  2.392e-16 \tabularnewline
115 &  21 &  21 & -2.645e-15 \tabularnewline
116 &  22 &  22 &  1.93e-15 \tabularnewline
117 &  23 &  23 &  1.548e-14 \tabularnewline
118 &  27 &  27 &  5.006e-15 \tabularnewline
119 &  26 &  26 & -2.876e-15 \tabularnewline
120 &  29 &  29 & -1.845e-14 \tabularnewline
121 &  28 &  28 &  2.29e-14 \tabularnewline
122 &  24 &  24 &  1.88e-14 \tabularnewline
123 &  25 &  25 &  1.119e-14 \tabularnewline
124 &  25 &  25 &  2.795e-14 \tabularnewline
125 &  22 &  22 &  7.711e-15 \tabularnewline
126 &  25 &  25 & -3.76e-15 \tabularnewline
127 &  26 &  26 &  6.335e-15 \tabularnewline
128 &  26 &  26 &  1.142e-14 \tabularnewline
129 &  24 &  24 & -1.146e-14 \tabularnewline
130 &  25 &  25 & -8.765e-15 \tabularnewline
131 &  19 &  19 &  2.38e-14 \tabularnewline
132 &  25 &  25 & -2.152e-14 \tabularnewline
133 &  23 &  23 & -4.708e-15 \tabularnewline
134 &  25 &  25 & -2.917e-16 \tabularnewline
135 &  25 &  25 & -4.489e-15 \tabularnewline
136 &  26 &  26 & -1.448e-15 \tabularnewline
137 &  27 &  27 &  1.44e-14 \tabularnewline
138 &  24 &  24 & -3.157e-15 \tabularnewline
139 &  22 &  22 & -7e-15 \tabularnewline
140 &  25 &  25 &  2.233e-14 \tabularnewline
141 &  24 &  24 &  2.787e-15 \tabularnewline
142 &  23 &  23 & -7.7e-15 \tabularnewline
143 &  27 &  27 & -2.228e-15 \tabularnewline
144 &  24 &  24 &  8.603e-15 \tabularnewline
145 &  24 &  24 &  6.187e-15 \tabularnewline
146 &  21 &  21 &  4.916e-15 \tabularnewline
147 &  25 &  25 &  1.215e-14 \tabularnewline
148 &  25 &  25 &  6.732e-15 \tabularnewline
149 &  23 &  23 & -3.031e-14 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=306337&T=4

[TABLE]
[ROW][C]Multiple Linear Regression - Actuals, Interpolation, and Residuals[/C][/ROW]
[ROW][C]Time or Index[/C][C]Actuals[/C][C]InterpolationForecast[/C][C]ResidualsPrediction Error[/C][/ROW]
[ROW][C]1[/C][C] 24[/C][C] 24[/C][C]-3.224e-13[/C][/ROW]
[ROW][C]2[/C][C] 24[/C][C] 24[/C][C] 3.348e-15[/C][/ROW]
[ROW][C]3[/C][C] 24[/C][C] 24[/C][C]-1.624e-14[/C][/ROW]
[ROW][C]4[/C][C] 25[/C][C] 25[/C][C] 8.504e-15[/C][/ROW]
[ROW][C]5[/C][C] 25[/C][C] 25[/C][C] 5.649e-15[/C][/ROW]
[ROW][C]6[/C][C] 25[/C][C] 25[/C][C]-5.635e-15[/C][/ROW]
[ROW][C]7[/C][C] 25[/C][C] 25[/C][C] 5.611e-15[/C][/ROW]
[ROW][C]8[/C][C] 24[/C][C] 24[/C][C] 1.871e-14[/C][/ROW]
[ROW][C]9[/C][C] 26[/C][C] 26[/C][C]-5.246e-15[/C][/ROW]
[ROW][C]10[/C][C] 26[/C][C] 26[/C][C]-2.872e-15[/C][/ROW]
[ROW][C]11[/C][C] 25[/C][C] 25[/C][C]-6.929e-16[/C][/ROW]
[ROW][C]12[/C][C] 26[/C][C] 26[/C][C] 2.235e-15[/C][/ROW]
[ROW][C]13[/C][C] 23[/C][C] 23[/C][C] 5.722e-15[/C][/ROW]
[ROW][C]14[/C][C] 24[/C][C] 24[/C][C] 1.281e-14[/C][/ROW]
[ROW][C]15[/C][C] 24[/C][C] 24[/C][C] 3.243e-15[/C][/ROW]
[ROW][C]16[/C][C] 25[/C][C] 25[/C][C]-1.973e-15[/C][/ROW]
[ROW][C]17[/C][C] 25[/C][C] 25[/C][C]-1.499e-15[/C][/ROW]
[ROW][C]18[/C][C] 24[/C][C] 24[/C][C]-1.17e-14[/C][/ROW]
[ROW][C]19[/C][C] 28[/C][C] 28[/C][C] 5.328e-15[/C][/ROW]
[ROW][C]20[/C][C] 27[/C][C] 27[/C][C] 8.494e-15[/C][/ROW]
[ROW][C]21[/C][C] 23[/C][C] 23[/C][C]-1.348e-14[/C][/ROW]
[ROW][C]22[/C][C] 23[/C][C] 23[/C][C] 7.975e-15[/C][/ROW]
[ROW][C]23[/C][C] 24[/C][C] 24[/C][C] 8.707e-15[/C][/ROW]
[ROW][C]24[/C][C] 24[/C][C] 24[/C][C] 8.434e-15[/C][/ROW]
[ROW][C]25[/C][C] 22[/C][C] 22[/C][C]-1.076e-14[/C][/ROW]
[ROW][C]26[/C][C] 25[/C][C] 25[/C][C]-7.822e-15[/C][/ROW]
[ROW][C]27[/C][C] 25[/C][C] 25[/C][C] 1.456e-14[/C][/ROW]
[ROW][C]28[/C][C] 28[/C][C] 28[/C][C]-9.357e-15[/C][/ROW]
[ROW][C]29[/C][C] 22[/C][C] 22[/C][C]-6.751e-15[/C][/ROW]
[ROW][C]30[/C][C] 28[/C][C] 28[/C][C] 1.039e-14[/C][/ROW]
[ROW][C]31[/C][C] 25[/C][C] 25[/C][C] 9.85e-15[/C][/ROW]
[ROW][C]32[/C][C] 24[/C][C] 24[/C][C]-4.84e-15[/C][/ROW]
[ROW][C]33[/C][C] 24[/C][C] 24[/C][C] 3.306e-15[/C][/ROW]
[ROW][C]34[/C][C] 23[/C][C] 23[/C][C] 1.299e-14[/C][/ROW]
[ROW][C]35[/C][C] 25[/C][C] 25[/C][C] 6.537e-15[/C][/ROW]
[ROW][C]36[/C][C] 26[/C][C] 26[/C][C]-7.486e-15[/C][/ROW]
[ROW][C]37[/C][C] 25[/C][C] 25[/C][C]-4.054e-15[/C][/ROW]
[ROW][C]38[/C][C] 27[/C][C] 27[/C][C] 1.02e-14[/C][/ROW]
[ROW][C]39[/C][C] 26[/C][C] 26[/C][C]-7.866e-15[/C][/ROW]
[ROW][C]40[/C][C] 23[/C][C] 23[/C][C] 8.224e-15[/C][/ROW]
[ROW][C]41[/C][C] 25[/C][C] 25[/C][C] 6.529e-15[/C][/ROW]
[ROW][C]42[/C][C] 21[/C][C] 21[/C][C] 1.413e-15[/C][/ROW]
[ROW][C]43[/C][C] 22[/C][C] 22[/C][C]-1.877e-15[/C][/ROW]
[ROW][C]44[/C][C] 24[/C][C] 24[/C][C]-1.274e-15[/C][/ROW]
[ROW][C]45[/C][C] 25[/C][C] 25[/C][C]-5.239e-15[/C][/ROW]
[ROW][C]46[/C][C] 27[/C][C] 27[/C][C] 6.641e-15[/C][/ROW]
[ROW][C]47[/C][C] 24[/C][C] 24[/C][C]-8.838e-15[/C][/ROW]
[ROW][C]48[/C][C] 26[/C][C] 26[/C][C] 6.481e-15[/C][/ROW]
[ROW][C]49[/C][C] 21[/C][C] 21[/C][C] 1.803e-14[/C][/ROW]
[ROW][C]50[/C][C] 27[/C][C] 27[/C][C]-1.02e-14[/C][/ROW]
[ROW][C]51[/C][C] 22[/C][C] 22[/C][C] 8.328e-15[/C][/ROW]
[ROW][C]52[/C][C] 23[/C][C] 23[/C][C]-9.994e-15[/C][/ROW]
[ROW][C]53[/C][C] 24[/C][C] 24[/C][C] 6.143e-15[/C][/ROW]
[ROW][C]54[/C][C] 25[/C][C] 25[/C][C] 1.485e-14[/C][/ROW]
[ROW][C]55[/C][C] 24[/C][C] 24[/C][C] 1.123e-14[/C][/ROW]
[ROW][C]56[/C][C] 23[/C][C] 23[/C][C] 2.714e-15[/C][/ROW]
[ROW][C]57[/C][C] 28[/C][C] 28[/C][C]-5.533e-15[/C][/ROW]
[ROW][C]58[/C][C] 24[/C][C] 24[/C][C] 1.258e-14[/C][/ROW]
[ROW][C]59[/C][C] 26[/C][C] 26[/C][C]-2.499e-14[/C][/ROW]
[ROW][C]60[/C][C] 22[/C][C] 22[/C][C] 1.269e-14[/C][/ROW]
[ROW][C]61[/C][C] 25[/C][C] 25[/C][C] 1.363e-14[/C][/ROW]
[ROW][C]62[/C][C] 25[/C][C] 25[/C][C] 8.251e-15[/C][/ROW]
[ROW][C]63[/C][C] 24[/C][C] 24[/C][C]-5.97e-15[/C][/ROW]
[ROW][C]64[/C][C] 24[/C][C] 24[/C][C] 2.346e-15[/C][/ROW]
[ROW][C]65[/C][C] 26[/C][C] 26[/C][C] 9.573e-15[/C][/ROW]
[ROW][C]66[/C][C] 21[/C][C] 21[/C][C]-8.055e-16[/C][/ROW]
[ROW][C]67[/C][C] 25[/C][C] 25[/C][C] 3.137e-15[/C][/ROW]
[ROW][C]68[/C][C] 25[/C][C] 25[/C][C] 6.832e-15[/C][/ROW]
[ROW][C]69[/C][C] 26[/C][C] 26[/C][C] 1.653e-15[/C][/ROW]
[ROW][C]70[/C][C] 25[/C][C] 25[/C][C]-9.241e-15[/C][/ROW]
[ROW][C]71[/C][C] 26[/C][C] 26[/C][C]-3.719e-15[/C][/ROW]
[ROW][C]72[/C][C] 27[/C][C] 27[/C][C] 2.397e-14[/C][/ROW]
[ROW][C]73[/C][C] 25[/C][C] 25[/C][C] 1.609e-15[/C][/ROW]
[ROW][C]74[/C][C] 20[/C][C] 20[/C][C]-3.703e-15[/C][/ROW]
[ROW][C]75[/C][C] 24[/C][C] 24[/C][C] 1.186e-14[/C][/ROW]
[ROW][C]76[/C][C] 26[/C][C] 26[/C][C] 8.695e-15[/C][/ROW]
[ROW][C]77[/C][C] 25[/C][C] 25[/C][C]-1.301e-14[/C][/ROW]
[ROW][C]78[/C][C] 25[/C][C] 25[/C][C]-1.509e-14[/C][/ROW]
[ROW][C]79[/C][C] 24[/C][C] 24[/C][C] 1.187e-14[/C][/ROW]
[ROW][C]80[/C][C] 26[/C][C] 26[/C][C] 1.72e-14[/C][/ROW]
[ROW][C]81[/C][C] 25[/C][C] 25[/C][C]-4.992e-16[/C][/ROW]
[ROW][C]82[/C][C] 28[/C][C] 28[/C][C]-1.161e-14[/C][/ROW]
[ROW][C]83[/C][C] 27[/C][C] 27[/C][C] 1.177e-14[/C][/ROW]
[ROW][C]84[/C][C] 25[/C][C] 25[/C][C]-8.726e-15[/C][/ROW]
[ROW][C]85[/C][C] 26[/C][C] 26[/C][C] 6.777e-15[/C][/ROW]
[ROW][C]86[/C][C] 26[/C][C] 26[/C][C] 2.298e-14[/C][/ROW]
[ROW][C]87[/C][C] 26[/C][C] 26[/C][C] 6.944e-15[/C][/ROW]
[ROW][C]88[/C][C] 28[/C][C] 28[/C][C]-7.689e-15[/C][/ROW]
[ROW][C]89[/C][C] 21[/C][C] 21[/C][C]-3.406e-15[/C][/ROW]
[ROW][C]90[/C][C] 25[/C][C] 25[/C][C] 4.324e-15[/C][/ROW]
[ROW][C]91[/C][C] 25[/C][C] 25[/C][C] 4.376e-15[/C][/ROW]
[ROW][C]92[/C][C] 24[/C][C] 24[/C][C]-1.449e-14[/C][/ROW]
[ROW][C]93[/C][C] 24[/C][C] 24[/C][C]-7.936e-15[/C][/ROW]
[ROW][C]94[/C][C] 24[/C][C] 24[/C][C]-8.102e-15[/C][/ROW]
[ROW][C]95[/C][C] 23[/C][C] 23[/C][C] 1.379e-14[/C][/ROW]
[ROW][C]96[/C][C] 23[/C][C] 23[/C][C]-8.345e-16[/C][/ROW]
[ROW][C]97[/C][C] 24[/C][C] 24[/C][C]-1.623e-14[/C][/ROW]
[ROW][C]98[/C][C] 24[/C][C] 24[/C][C] 9.468e-15[/C][/ROW]
[ROW][C]99[/C][C] 25[/C][C] 25[/C][C]-4.483e-15[/C][/ROW]
[ROW][C]100[/C][C] 28[/C][C] 28[/C][C]-8.844e-15[/C][/ROW]
[ROW][C]101[/C][C] 23[/C][C] 23[/C][C] 8.766e-15[/C][/ROW]
[ROW][C]102[/C][C] 24[/C][C] 24[/C][C]-2.989e-16[/C][/ROW]
[ROW][C]103[/C][C] 23[/C][C] 23[/C][C] 1.684e-14[/C][/ROW]
[ROW][C]104[/C][C] 24[/C][C] 24[/C][C] 9.373e-15[/C][/ROW]
[ROW][C]105[/C][C] 25[/C][C] 25[/C][C]-2.024e-15[/C][/ROW]
[ROW][C]106[/C][C] 24[/C][C] 24[/C][C]-8.804e-15[/C][/ROW]
[ROW][C]107[/C][C] 23[/C][C] 23[/C][C] 9.089e-15[/C][/ROW]
[ROW][C]108[/C][C] 23[/C][C] 23[/C][C] 1.14e-14[/C][/ROW]
[ROW][C]109[/C][C] 25[/C][C] 25[/C][C] 2.783e-15[/C][/ROW]
[ROW][C]110[/C][C] 21[/C][C] 21[/C][C] 1.453e-14[/C][/ROW]
[ROW][C]111[/C][C] 22[/C][C] 22[/C][C]-1.292e-14[/C][/ROW]
[ROW][C]112[/C][C] 19[/C][C] 19[/C][C] 6.323e-15[/C][/ROW]
[ROW][C]113[/C][C] 24[/C][C] 24[/C][C] 8.409e-15[/C][/ROW]
[ROW][C]114[/C][C] 25[/C][C] 25[/C][C] 2.392e-16[/C][/ROW]
[ROW][C]115[/C][C] 21[/C][C] 21[/C][C]-2.645e-15[/C][/ROW]
[ROW][C]116[/C][C] 22[/C][C] 22[/C][C] 1.93e-15[/C][/ROW]
[ROW][C]117[/C][C] 23[/C][C] 23[/C][C] 1.548e-14[/C][/ROW]
[ROW][C]118[/C][C] 27[/C][C] 27[/C][C] 5.006e-15[/C][/ROW]
[ROW][C]119[/C][C] 26[/C][C] 26[/C][C]-2.876e-15[/C][/ROW]
[ROW][C]120[/C][C] 29[/C][C] 29[/C][C]-1.845e-14[/C][/ROW]
[ROW][C]121[/C][C] 28[/C][C] 28[/C][C] 2.29e-14[/C][/ROW]
[ROW][C]122[/C][C] 24[/C][C] 24[/C][C] 1.88e-14[/C][/ROW]
[ROW][C]123[/C][C] 25[/C][C] 25[/C][C] 1.119e-14[/C][/ROW]
[ROW][C]124[/C][C] 25[/C][C] 25[/C][C] 2.795e-14[/C][/ROW]
[ROW][C]125[/C][C] 22[/C][C] 22[/C][C] 7.711e-15[/C][/ROW]
[ROW][C]126[/C][C] 25[/C][C] 25[/C][C]-3.76e-15[/C][/ROW]
[ROW][C]127[/C][C] 26[/C][C] 26[/C][C] 6.335e-15[/C][/ROW]
[ROW][C]128[/C][C] 26[/C][C] 26[/C][C] 1.142e-14[/C][/ROW]
[ROW][C]129[/C][C] 24[/C][C] 24[/C][C]-1.146e-14[/C][/ROW]
[ROW][C]130[/C][C] 25[/C][C] 25[/C][C]-8.765e-15[/C][/ROW]
[ROW][C]131[/C][C] 19[/C][C] 19[/C][C] 2.38e-14[/C][/ROW]
[ROW][C]132[/C][C] 25[/C][C] 25[/C][C]-2.152e-14[/C][/ROW]
[ROW][C]133[/C][C] 23[/C][C] 23[/C][C]-4.708e-15[/C][/ROW]
[ROW][C]134[/C][C] 25[/C][C] 25[/C][C]-2.917e-16[/C][/ROW]
[ROW][C]135[/C][C] 25[/C][C] 25[/C][C]-4.489e-15[/C][/ROW]
[ROW][C]136[/C][C] 26[/C][C] 26[/C][C]-1.448e-15[/C][/ROW]
[ROW][C]137[/C][C] 27[/C][C] 27[/C][C] 1.44e-14[/C][/ROW]
[ROW][C]138[/C][C] 24[/C][C] 24[/C][C]-3.157e-15[/C][/ROW]
[ROW][C]139[/C][C] 22[/C][C] 22[/C][C]-7e-15[/C][/ROW]
[ROW][C]140[/C][C] 25[/C][C] 25[/C][C] 2.233e-14[/C][/ROW]
[ROW][C]141[/C][C] 24[/C][C] 24[/C][C] 2.787e-15[/C][/ROW]
[ROW][C]142[/C][C] 23[/C][C] 23[/C][C]-7.7e-15[/C][/ROW]
[ROW][C]143[/C][C] 27[/C][C] 27[/C][C]-2.228e-15[/C][/ROW]
[ROW][C]144[/C][C] 24[/C][C] 24[/C][C] 8.603e-15[/C][/ROW]
[ROW][C]145[/C][C] 24[/C][C] 24[/C][C] 6.187e-15[/C][/ROW]
[ROW][C]146[/C][C] 21[/C][C] 21[/C][C] 4.916e-15[/C][/ROW]
[ROW][C]147[/C][C] 25[/C][C] 25[/C][C] 1.215e-14[/C][/ROW]
[ROW][C]148[/C][C] 25[/C][C] 25[/C][C] 6.732e-15[/C][/ROW]
[ROW][C]149[/C][C] 23[/C][C] 23[/C][C]-3.031e-14[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=306337&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=306337&T=4

As an alternative you can also use a QR Code:

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	24	24	-3.224e-13
2	24	24	3.348e-15
3	24	24	-1.624e-14
4	25	25	8.504e-15
5	25	25	5.649e-15
6	25	25	-5.635e-15
7	25	25	5.611e-15
8	24	24	1.871e-14
9	26	26	-5.246e-15
10	26	26	-2.872e-15
11	25	25	-6.929e-16
12	26	26	2.235e-15
13	23	23	5.722e-15
14	24	24	1.281e-14
15	24	24	3.243e-15
16	25	25	-1.973e-15
17	25	25	-1.499e-15
18	24	24	-1.17e-14
19	28	28	5.328e-15
20	27	27	8.494e-15
21	23	23	-1.348e-14
22	23	23	7.975e-15
23	24	24	8.707e-15
24	24	24	8.434e-15
25	22	22	-1.076e-14
26	25	25	-7.822e-15
27	25	25	1.456e-14
28	28	28	-9.357e-15
29	22	22	-6.751e-15
30	28	28	1.039e-14
31	25	25	9.85e-15
32	24	24	-4.84e-15
33	24	24	3.306e-15
34	23	23	1.299e-14
35	25	25	6.537e-15
36	26	26	-7.486e-15
37	25	25	-4.054e-15
38	27	27	1.02e-14
39	26	26	-7.866e-15
40	23	23	8.224e-15
41	25	25	6.529e-15
42	21	21	1.413e-15
43	22	22	-1.877e-15
44	24	24	-1.274e-15
45	25	25	-5.239e-15
46	27	27	6.641e-15
47	24	24	-8.838e-15
48	26	26	6.481e-15
49	21	21	1.803e-14
50	27	27	-1.02e-14
51	22	22	8.328e-15
52	23	23	-9.994e-15
53	24	24	6.143e-15
54	25	25	1.485e-14
55	24	24	1.123e-14
56	23	23	2.714e-15
57	28	28	-5.533e-15
58	24	24	1.258e-14
59	26	26	-2.499e-14
60	22	22	1.269e-14
61	25	25	1.363e-14
62	25	25	8.251e-15
63	24	24	-5.97e-15
64	24	24	2.346e-15
65	26	26	9.573e-15
66	21	21	-8.055e-16
67	25	25	3.137e-15
68	25	25	6.832e-15
69	26	26	1.653e-15
70	25	25	-9.241e-15
71	26	26	-3.719e-15
72	27	27	2.397e-14
73	25	25	1.609e-15
74	20	20	-3.703e-15
75	24	24	1.186e-14
76	26	26	8.695e-15
77	25	25	-1.301e-14
78	25	25	-1.509e-14
79	24	24	1.187e-14
80	26	26	1.72e-14
81	25	25	-4.992e-16
82	28	28	-1.161e-14
83	27	27	1.177e-14
84	25	25	-8.726e-15
85	26	26	6.777e-15
86	26	26	2.298e-14
87	26	26	6.944e-15
88	28	28	-7.689e-15
89	21	21	-3.406e-15
90	25	25	4.324e-15
91	25	25	4.376e-15
92	24	24	-1.449e-14
93	24	24	-7.936e-15
94	24	24	-8.102e-15
95	23	23	1.379e-14
96	23	23	-8.345e-16
97	24	24	-1.623e-14
98	24	24	9.468e-15
99	25	25	-4.483e-15
100	28	28	-8.844e-15
101	23	23	8.766e-15
102	24	24	-2.989e-16
103	23	23	1.684e-14
104	24	24	9.373e-15
105	25	25	-2.024e-15
106	24	24	-8.804e-15
107	23	23	9.089e-15
108	23	23	1.14e-14
109	25	25	2.783e-15
110	21	21	1.453e-14
111	22	22	-1.292e-14
112	19	19	6.323e-15
113	24	24	8.409e-15
114	25	25	2.392e-16
115	21	21	-2.645e-15
116	22	22	1.93e-15
117	23	23	1.548e-14
118	27	27	5.006e-15
119	26	26	-2.876e-15
120	29	29	-1.845e-14
121	28	28	2.29e-14
122	24	24	1.88e-14
123	25	25	1.119e-14
124	25	25	2.795e-14
125	22	22	7.711e-15
126	25	25	-3.76e-15
127	26	26	6.335e-15
128	26	26	1.142e-14
129	24	24	-1.146e-14
130	25	25	-8.765e-15
131	19	19	2.38e-14
132	25	25	-2.152e-14
133	23	23	-4.708e-15
134	25	25	-2.917e-16
135	25	25	-4.489e-15
136	26	26	-1.448e-15
137	27	27	1.44e-14
138	24	24	-3.157e-15
139	22	22	-7e-15
140	25	25	2.233e-14
141	24	24	2.787e-15
142	23	23	-7.7e-15
143	27	27	-2.228e-15
144	24	24	8.603e-15
145	24	24	6.187e-15
146	21	21	4.916e-15
147	25	25	1.215e-14
148	25	25	6.732e-15
149	23	23	-3.031e-14

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
22	0.1542	0.3084	0.8458
23	0.07019	0.1404	0.9298
24	0.0001512	0.0003023	0.9998
25	0.0005818	0.001164	0.9994
26	0.3141	0.6283	0.6859
27	0.003832	0.007665	0.9962
28	0.01551	0.03102	0.9845
29	1.07e-09	2.14e-09	1
30	6.947e-06	1.389e-05	1
31	0.9967	0.00669	0.003345
32	2.407e-05	4.815e-05	1
33	1.878e-09	3.757e-09	1
34	4.029e-15	8.059e-15	1
35	0.9921	0.01582	0.007911
36	1.705e-11	3.411e-11	1
37	1.661e-21	3.321e-21	1
38	1.847e-07	3.694e-07	1
39	0.001957	0.003914	0.998
40	0.02437	0.04874	0.9756
41	1	4.777e-36	2.389e-36
42	1.997e-06	3.994e-06	1
43	0.03777	0.07553	0.9622
44	4.237e-05	8.475e-05	1
45	2.793e-10	5.586e-10	1
46	5.211e-24	1.042e-23	1
47	0.1075	0.2149	0.8925
48	1	3.652e-51	1.826e-51
49	8.721e-17	1.744e-16	1
50	0.897	0.206	0.103
51	4.819e-05	9.639e-05	1
52	0.9929	0.01412	0.007058
53	6.072e-33	1.214e-32	1
54	2.263e-37	4.525e-37	1
55	0.001727	0.003453	0.9983
56	1.001e-41	2.001e-41	1
57	1.189e-18	2.378e-18	1
58	0.002024	0.004048	0.998
59	3.801e-36	7.601e-36	1
60	3.369e-26	6.738e-26	1
61	0.2533	0.5067	0.7467
62	0.0425	0.08501	0.9575
63	1.858e-09	3.716e-09	1
64	4.463e-45	8.926e-45	1
65	1	8.372e-12	4.186e-12
66	1	2.494e-78	1.247e-78
67	1.602e-06	3.203e-06	1
68	1.322e-13	2.643e-13	1
69	3.556e-35	7.111e-35	1
70	0.1803	0.3606	0.8197
71	1	9.035e-20	4.518e-20
72	1	2.371e-09	1.186e-09
73	2.577e-28	5.153e-28	1
74	0.543	0.914	0.457
75	0.6695	0.661	0.3305
76	0.7869	0.4263	0.2131
77	9.57e-29	1.914e-28	1
78	8.576e-15	1.715e-14	1
79	1	2.906e-18	1.453e-18
80	4.46e-05	8.92e-05	1
81	3.173e-22	6.346e-22	1
82	1	1.122e-09	5.608e-10
83	6.292e-39	1.259e-38	1
84	0.0007022	0.001404	0.9993
85	1	1.056e-36	5.278e-37
86	0.2593	0.5186	0.7407
87	0.8743	0.2514	0.1257
88	0.1422	0.2843	0.8578
89	1.597e-05	3.195e-05	1
90	1	1.198e-30	5.99e-31
91	1	1.073e-18	5.363e-19
92	1	1.401e-14	7.005e-15
93	1	3.935e-39	1.967e-39
94	0.2839	0.5677	0.7161
95	1	8.64e-31	4.32e-31
96	1	7.623e-22	3.812e-22
97	6.292e-27	1.258e-26	1
98	0.1621	0.3241	0.8379
99	5.101e-09	1.02e-08	1
100	0.9978	0.004453	0.002226
101	0.9983	0.003385	0.001693
102	2.675e-09	5.351e-09	1
103	1	9.967e-12	4.983e-12
104	0.5164	0.9673	0.4836
105	1	1.133e-07	5.667e-08
106	0.8178	0.3644	0.1822
107	0.001631	0.003263	0.9984
108	4.167e-41	8.334e-41	1
109	0.9993	0.001465	0.0007324
110	0.3707	0.7414	0.6293
111	1	1.279e-10	6.396e-11
112	1	2.038e-13	1.019e-13
113	1	4.213e-12	2.107e-12
114	1.625e-34	3.249e-34	1
115	1	3.112e-09	1.556e-09
116	0.01088	0.02176	0.9891
117	1	6.504e-05	3.252e-05
118	0.9759	0.04815	0.02407
119	1	2.816e-05	1.408e-05
120	1	7.152e-07	3.576e-07
121	0.7316	0.5368	0.2684
122	0.999	0.00205	0.001025
123	1.995e-15	3.989e-15	1
124	0.9997	0.0005987	0.0002994
125	0.9998	0.0003978	0.0001989
126	0.9997	0.0006956	0.0003478
127	0.8492	0.3016	0.1508

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
22 &  0.1542 &  0.3084 &  0.8458 \tabularnewline
23 &  0.07019 &  0.1404 &  0.9298 \tabularnewline
24 &  0.0001512 &  0.0003023 &  0.9998 \tabularnewline
25 &  0.0005818 &  0.001164 &  0.9994 \tabularnewline
26 &  0.3141 &  0.6283 &  0.6859 \tabularnewline
27 &  0.003832 &  0.007665 &  0.9962 \tabularnewline
28 &  0.01551 &  0.03102 &  0.9845 \tabularnewline
29 &  1.07e-09 &  2.14e-09 &  1 \tabularnewline
30 &  6.947e-06 &  1.389e-05 &  1 \tabularnewline
31 &  0.9967 &  0.00669 &  0.003345 \tabularnewline
32 &  2.407e-05 &  4.815e-05 &  1 \tabularnewline
33 &  1.878e-09 &  3.757e-09 &  1 \tabularnewline
34 &  4.029e-15 &  8.059e-15 &  1 \tabularnewline
35 &  0.9921 &  0.01582 &  0.007911 \tabularnewline
36 &  1.705e-11 &  3.411e-11 &  1 \tabularnewline
37 &  1.661e-21 &  3.321e-21 &  1 \tabularnewline
38 &  1.847e-07 &  3.694e-07 &  1 \tabularnewline
39 &  0.001957 &  0.003914 &  0.998 \tabularnewline
40 &  0.02437 &  0.04874 &  0.9756 \tabularnewline
41 &  1 &  4.777e-36 &  2.389e-36 \tabularnewline
42 &  1.997e-06 &  3.994e-06 &  1 \tabularnewline
43 &  0.03777 &  0.07553 &  0.9622 \tabularnewline
44 &  4.237e-05 &  8.475e-05 &  1 \tabularnewline
45 &  2.793e-10 &  5.586e-10 &  1 \tabularnewline
46 &  5.211e-24 &  1.042e-23 &  1 \tabularnewline
47 &  0.1075 &  0.2149 &  0.8925 \tabularnewline
48 &  1 &  3.652e-51 &  1.826e-51 \tabularnewline
49 &  8.721e-17 &  1.744e-16 &  1 \tabularnewline
50 &  0.897 &  0.206 &  0.103 \tabularnewline
51 &  4.819e-05 &  9.639e-05 &  1 \tabularnewline
52 &  0.9929 &  0.01412 &  0.007058 \tabularnewline
53 &  6.072e-33 &  1.214e-32 &  1 \tabularnewline
54 &  2.263e-37 &  4.525e-37 &  1 \tabularnewline
55 &  0.001727 &  0.003453 &  0.9983 \tabularnewline
56 &  1.001e-41 &  2.001e-41 &  1 \tabularnewline
57 &  1.189e-18 &  2.378e-18 &  1 \tabularnewline
58 &  0.002024 &  0.004048 &  0.998 \tabularnewline
59 &  3.801e-36 &  7.601e-36 &  1 \tabularnewline
60 &  3.369e-26 &  6.738e-26 &  1 \tabularnewline
61 &  0.2533 &  0.5067 &  0.7467 \tabularnewline
62 &  0.0425 &  0.08501 &  0.9575 \tabularnewline
63 &  1.858e-09 &  3.716e-09 &  1 \tabularnewline
64 &  4.463e-45 &  8.926e-45 &  1 \tabularnewline
65 &  1 &  8.372e-12 &  4.186e-12 \tabularnewline
66 &  1 &  2.494e-78 &  1.247e-78 \tabularnewline
67 &  1.602e-06 &  3.203e-06 &  1 \tabularnewline
68 &  1.322e-13 &  2.643e-13 &  1 \tabularnewline
69 &  3.556e-35 &  7.111e-35 &  1 \tabularnewline
70 &  0.1803 &  0.3606 &  0.8197 \tabularnewline
71 &  1 &  9.035e-20 &  4.518e-20 \tabularnewline
72 &  1 &  2.371e-09 &  1.186e-09 \tabularnewline
73 &  2.577e-28 &  5.153e-28 &  1 \tabularnewline
74 &  0.543 &  0.914 &  0.457 \tabularnewline
75 &  0.6695 &  0.661 &  0.3305 \tabularnewline
76 &  0.7869 &  0.4263 &  0.2131 \tabularnewline
77 &  9.57e-29 &  1.914e-28 &  1 \tabularnewline
78 &  8.576e-15 &  1.715e-14 &  1 \tabularnewline
79 &  1 &  2.906e-18 &  1.453e-18 \tabularnewline
80 &  4.46e-05 &  8.92e-05 &  1 \tabularnewline
81 &  3.173e-22 &  6.346e-22 &  1 \tabularnewline
82 &  1 &  1.122e-09 &  5.608e-10 \tabularnewline
83 &  6.292e-39 &  1.259e-38 &  1 \tabularnewline
84 &  0.0007022 &  0.001404 &  0.9993 \tabularnewline
85 &  1 &  1.056e-36 &  5.278e-37 \tabularnewline
86 &  0.2593 &  0.5186 &  0.7407 \tabularnewline
87 &  0.8743 &  0.2514 &  0.1257 \tabularnewline
88 &  0.1422 &  0.2843 &  0.8578 \tabularnewline
89 &  1.597e-05 &  3.195e-05 &  1 \tabularnewline
90 &  1 &  1.198e-30 &  5.99e-31 \tabularnewline
91 &  1 &  1.073e-18 &  5.363e-19 \tabularnewline
92 &  1 &  1.401e-14 &  7.005e-15 \tabularnewline
93 &  1 &  3.935e-39 &  1.967e-39 \tabularnewline
94 &  0.2839 &  0.5677 &  0.7161 \tabularnewline
95 &  1 &  8.64e-31 &  4.32e-31 \tabularnewline
96 &  1 &  7.623e-22 &  3.812e-22 \tabularnewline
97 &  6.292e-27 &  1.258e-26 &  1 \tabularnewline
98 &  0.1621 &  0.3241 &  0.8379 \tabularnewline
99 &  5.101e-09 &  1.02e-08 &  1 \tabularnewline
100 &  0.9978 &  0.004453 &  0.002226 \tabularnewline
101 &  0.9983 &  0.003385 &  0.001693 \tabularnewline
102 &  2.675e-09 &  5.351e-09 &  1 \tabularnewline
103 &  1 &  9.967e-12 &  4.983e-12 \tabularnewline
104 &  0.5164 &  0.9673 &  0.4836 \tabularnewline
105 &  1 &  1.133e-07 &  5.667e-08 \tabularnewline
106 &  0.8178 &  0.3644 &  0.1822 \tabularnewline
107 &  0.001631 &  0.003263 &  0.9984 \tabularnewline
108 &  4.167e-41 &  8.334e-41 &  1 \tabularnewline
109 &  0.9993 &  0.001465 &  0.0007324 \tabularnewline
110 &  0.3707 &  0.7414 &  0.6293 \tabularnewline
111 &  1 &  1.279e-10 &  6.396e-11 \tabularnewline
112 &  1 &  2.038e-13 &  1.019e-13 \tabularnewline
113 &  1 &  4.213e-12 &  2.107e-12 \tabularnewline
114 &  1.625e-34 &  3.249e-34 &  1 \tabularnewline
115 &  1 &  3.112e-09 &  1.556e-09 \tabularnewline
116 &  0.01088 &  0.02176 &  0.9891 \tabularnewline
117 &  1 &  6.504e-05 &  3.252e-05 \tabularnewline
118 &  0.9759 &  0.04815 &  0.02407 \tabularnewline
119 &  1 &  2.816e-05 &  1.408e-05 \tabularnewline
120 &  1 &  7.152e-07 &  3.576e-07 \tabularnewline
121 &  0.7316 &  0.5368 &  0.2684 \tabularnewline
122 &  0.999 &  0.00205 &  0.001025 \tabularnewline
123 &  1.995e-15 &  3.989e-15 &  1 \tabularnewline
124 &  0.9997 &  0.0005987 &  0.0002994 \tabularnewline
125 &  0.9998 &  0.0003978 &  0.0001989 \tabularnewline
126 &  0.9997 &  0.0006956 &  0.0003478 \tabularnewline
127 &  0.8492 &  0.3016 &  0.1508 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=306337&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]22[/C][C] 0.1542[/C][C] 0.3084[/C][C] 0.8458[/C][/ROW]
[ROW][C]23[/C][C] 0.07019[/C][C] 0.1404[/C][C] 0.9298[/C][/ROW]
[ROW][C]24[/C][C] 0.0001512[/C][C] 0.0003023[/C][C] 0.9998[/C][/ROW]
[ROW][C]25[/C][C] 0.0005818[/C][C] 0.001164[/C][C] 0.9994[/C][/ROW]
[ROW][C]26[/C][C] 0.3141[/C][C] 0.6283[/C][C] 0.6859[/C][/ROW]
[ROW][C]27[/C][C] 0.003832[/C][C] 0.007665[/C][C] 0.9962[/C][/ROW]
[ROW][C]28[/C][C] 0.01551[/C][C] 0.03102[/C][C] 0.9845[/C][/ROW]
[ROW][C]29[/C][C] 1.07e-09[/C][C] 2.14e-09[/C][C] 1[/C][/ROW]
[ROW][C]30[/C][C] 6.947e-06[/C][C] 1.389e-05[/C][C] 1[/C][/ROW]
[ROW][C]31[/C][C] 0.9967[/C][C] 0.00669[/C][C] 0.003345[/C][/ROW]
[ROW][C]32[/C][C] 2.407e-05[/C][C] 4.815e-05[/C][C] 1[/C][/ROW]
[ROW][C]33[/C][C] 1.878e-09[/C][C] 3.757e-09[/C][C] 1[/C][/ROW]
[ROW][C]34[/C][C] 4.029e-15[/C][C] 8.059e-15[/C][C] 1[/C][/ROW]
[ROW][C]35[/C][C] 0.9921[/C][C] 0.01582[/C][C] 0.007911[/C][/ROW]
[ROW][C]36[/C][C] 1.705e-11[/C][C] 3.411e-11[/C][C] 1[/C][/ROW]
[ROW][C]37[/C][C] 1.661e-21[/C][C] 3.321e-21[/C][C] 1[/C][/ROW]
[ROW][C]38[/C][C] 1.847e-07[/C][C] 3.694e-07[/C][C] 1[/C][/ROW]
[ROW][C]39[/C][C] 0.001957[/C][C] 0.003914[/C][C] 0.998[/C][/ROW]
[ROW][C]40[/C][C] 0.02437[/C][C] 0.04874[/C][C] 0.9756[/C][/ROW]
[ROW][C]41[/C][C] 1[/C][C] 4.777e-36[/C][C] 2.389e-36[/C][/ROW]
[ROW][C]42[/C][C] 1.997e-06[/C][C] 3.994e-06[/C][C] 1[/C][/ROW]
[ROW][C]43[/C][C] 0.03777[/C][C] 0.07553[/C][C] 0.9622[/C][/ROW]
[ROW][C]44[/C][C] 4.237e-05[/C][C] 8.475e-05[/C][C] 1[/C][/ROW]
[ROW][C]45[/C][C] 2.793e-10[/C][C] 5.586e-10[/C][C] 1[/C][/ROW]
[ROW][C]46[/C][C] 5.211e-24[/C][C] 1.042e-23[/C][C] 1[/C][/ROW]
[ROW][C]47[/C][C] 0.1075[/C][C] 0.2149[/C][C] 0.8925[/C][/ROW]
[ROW][C]48[/C][C] 1[/C][C] 3.652e-51[/C][C] 1.826e-51[/C][/ROW]
[ROW][C]49[/C][C] 8.721e-17[/C][C] 1.744e-16[/C][C] 1[/C][/ROW]
[ROW][C]50[/C][C] 0.897[/C][C] 0.206[/C][C] 0.103[/C][/ROW]
[ROW][C]51[/C][C] 4.819e-05[/C][C] 9.639e-05[/C][C] 1[/C][/ROW]
[ROW][C]52[/C][C] 0.9929[/C][C] 0.01412[/C][C] 0.007058[/C][/ROW]
[ROW][C]53[/C][C] 6.072e-33[/C][C] 1.214e-32[/C][C] 1[/C][/ROW]
[ROW][C]54[/C][C] 2.263e-37[/C][C] 4.525e-37[/C][C] 1[/C][/ROW]
[ROW][C]55[/C][C] 0.001727[/C][C] 0.003453[/C][C] 0.9983[/C][/ROW]
[ROW][C]56[/C][C] 1.001e-41[/C][C] 2.001e-41[/C][C] 1[/C][/ROW]
[ROW][C]57[/C][C] 1.189e-18[/C][C] 2.378e-18[/C][C] 1[/C][/ROW]
[ROW][C]58[/C][C] 0.002024[/C][C] 0.004048[/C][C] 0.998[/C][/ROW]
[ROW][C]59[/C][C] 3.801e-36[/C][C] 7.601e-36[/C][C] 1[/C][/ROW]
[ROW][C]60[/C][C] 3.369e-26[/C][C] 6.738e-26[/C][C] 1[/C][/ROW]
[ROW][C]61[/C][C] 0.2533[/C][C] 0.5067[/C][C] 0.7467[/C][/ROW]
[ROW][C]62[/C][C] 0.0425[/C][C] 0.08501[/C][C] 0.9575[/C][/ROW]
[ROW][C]63[/C][C] 1.858e-09[/C][C] 3.716e-09[/C][C] 1[/C][/ROW]
[ROW][C]64[/C][C] 4.463e-45[/C][C] 8.926e-45[/C][C] 1[/C][/ROW]
[ROW][C]65[/C][C] 1[/C][C] 8.372e-12[/C][C] 4.186e-12[/C][/ROW]
[ROW][C]66[/C][C] 1[/C][C] 2.494e-78[/C][C] 1.247e-78[/C][/ROW]
[ROW][C]67[/C][C] 1.602e-06[/C][C] 3.203e-06[/C][C] 1[/C][/ROW]
[ROW][C]68[/C][C] 1.322e-13[/C][C] 2.643e-13[/C][C] 1[/C][/ROW]
[ROW][C]69[/C][C] 3.556e-35[/C][C] 7.111e-35[/C][C] 1[/C][/ROW]
[ROW][C]70[/C][C] 0.1803[/C][C] 0.3606[/C][C] 0.8197[/C][/ROW]
[ROW][C]71[/C][C] 1[/C][C] 9.035e-20[/C][C] 4.518e-20[/C][/ROW]
[ROW][C]72[/C][C] 1[/C][C] 2.371e-09[/C][C] 1.186e-09[/C][/ROW]
[ROW][C]73[/C][C] 2.577e-28[/C][C] 5.153e-28[/C][C] 1[/C][/ROW]
[ROW][C]74[/C][C] 0.543[/C][C] 0.914[/C][C] 0.457[/C][/ROW]
[ROW][C]75[/C][C] 0.6695[/C][C] 0.661[/C][C] 0.3305[/C][/ROW]
[ROW][C]76[/C][C] 0.7869[/C][C] 0.4263[/C][C] 0.2131[/C][/ROW]
[ROW][C]77[/C][C] 9.57e-29[/C][C] 1.914e-28[/C][C] 1[/C][/ROW]
[ROW][C]78[/C][C] 8.576e-15[/C][C] 1.715e-14[/C][C] 1[/C][/ROW]
[ROW][C]79[/C][C] 1[/C][C] 2.906e-18[/C][C] 1.453e-18[/C][/ROW]
[ROW][C]80[/C][C] 4.46e-05[/C][C] 8.92e-05[/C][C] 1[/C][/ROW]
[ROW][C]81[/C][C] 3.173e-22[/C][C] 6.346e-22[/C][C] 1[/C][/ROW]
[ROW][C]82[/C][C] 1[/C][C] 1.122e-09[/C][C] 5.608e-10[/C][/ROW]
[ROW][C]83[/C][C] 6.292e-39[/C][C] 1.259e-38[/C][C] 1[/C][/ROW]
[ROW][C]84[/C][C] 0.0007022[/C][C] 0.001404[/C][C] 0.9993[/C][/ROW]
[ROW][C]85[/C][C] 1[/C][C] 1.056e-36[/C][C] 5.278e-37[/C][/ROW]
[ROW][C]86[/C][C] 0.2593[/C][C] 0.5186[/C][C] 0.7407[/C][/ROW]
[ROW][C]87[/C][C] 0.8743[/C][C] 0.2514[/C][C] 0.1257[/C][/ROW]
[ROW][C]88[/C][C] 0.1422[/C][C] 0.2843[/C][C] 0.8578[/C][/ROW]
[ROW][C]89[/C][C] 1.597e-05[/C][C] 3.195e-05[/C][C] 1[/C][/ROW]
[ROW][C]90[/C][C] 1[/C][C] 1.198e-30[/C][C] 5.99e-31[/C][/ROW]
[ROW][C]91[/C][C] 1[/C][C] 1.073e-18[/C][C] 5.363e-19[/C][/ROW]
[ROW][C]92[/C][C] 1[/C][C] 1.401e-14[/C][C] 7.005e-15[/C][/ROW]
[ROW][C]93[/C][C] 1[/C][C] 3.935e-39[/C][C] 1.967e-39[/C][/ROW]
[ROW][C]94[/C][C] 0.2839[/C][C] 0.5677[/C][C] 0.7161[/C][/ROW]
[ROW][C]95[/C][C] 1[/C][C] 8.64e-31[/C][C] 4.32e-31[/C][/ROW]
[ROW][C]96[/C][C] 1[/C][C] 7.623e-22[/C][C] 3.812e-22[/C][/ROW]
[ROW][C]97[/C][C] 6.292e-27[/C][C] 1.258e-26[/C][C] 1[/C][/ROW]
[ROW][C]98[/C][C] 0.1621[/C][C] 0.3241[/C][C] 0.8379[/C][/ROW]
[ROW][C]99[/C][C] 5.101e-09[/C][C] 1.02e-08[/C][C] 1[/C][/ROW]
[ROW][C]100[/C][C] 0.9978[/C][C] 0.004453[/C][C] 0.002226[/C][/ROW]
[ROW][C]101[/C][C] 0.9983[/C][C] 0.003385[/C][C] 0.001693[/C][/ROW]
[ROW][C]102[/C][C] 2.675e-09[/C][C] 5.351e-09[/C][C] 1[/C][/ROW]
[ROW][C]103[/C][C] 1[/C][C] 9.967e-12[/C][C] 4.983e-12[/C][/ROW]
[ROW][C]104[/C][C] 0.5164[/C][C] 0.9673[/C][C] 0.4836[/C][/ROW]
[ROW][C]105[/C][C] 1[/C][C] 1.133e-07[/C][C] 5.667e-08[/C][/ROW]
[ROW][C]106[/C][C] 0.8178[/C][C] 0.3644[/C][C] 0.1822[/C][/ROW]
[ROW][C]107[/C][C] 0.001631[/C][C] 0.003263[/C][C] 0.9984[/C][/ROW]
[ROW][C]108[/C][C] 4.167e-41[/C][C] 8.334e-41[/C][C] 1[/C][/ROW]
[ROW][C]109[/C][C] 0.9993[/C][C] 0.001465[/C][C] 0.0007324[/C][/ROW]
[ROW][C]110[/C][C] 0.3707[/C][C] 0.7414[/C][C] 0.6293[/C][/ROW]
[ROW][C]111[/C][C] 1[/C][C] 1.279e-10[/C][C] 6.396e-11[/C][/ROW]
[ROW][C]112[/C][C] 1[/C][C] 2.038e-13[/C][C] 1.019e-13[/C][/ROW]
[ROW][C]113[/C][C] 1[/C][C] 4.213e-12[/C][C] 2.107e-12[/C][/ROW]
[ROW][C]114[/C][C] 1.625e-34[/C][C] 3.249e-34[/C][C] 1[/C][/ROW]
[ROW][C]115[/C][C] 1[/C][C] 3.112e-09[/C][C] 1.556e-09[/C][/ROW]
[ROW][C]116[/C][C] 0.01088[/C][C] 0.02176[/C][C] 0.9891[/C][/ROW]
[ROW][C]117[/C][C] 1[/C][C] 6.504e-05[/C][C] 3.252e-05[/C][/ROW]
[ROW][C]118[/C][C] 0.9759[/C][C] 0.04815[/C][C] 0.02407[/C][/ROW]
[ROW][C]119[/C][C] 1[/C][C] 2.816e-05[/C][C] 1.408e-05[/C][/ROW]
[ROW][C]120[/C][C] 1[/C][C] 7.152e-07[/C][C] 3.576e-07[/C][/ROW]
[ROW][C]121[/C][C] 0.7316[/C][C] 0.5368[/C][C] 0.2684[/C][/ROW]
[ROW][C]122[/C][C] 0.999[/C][C] 0.00205[/C][C] 0.001025[/C][/ROW]
[ROW][C]123[/C][C] 1.995e-15[/C][C] 3.989e-15[/C][C] 1[/C][/ROW]
[ROW][C]124[/C][C] 0.9997[/C][C] 0.0005987[/C][C] 0.0002994[/C][/ROW]
[ROW][C]125[/C][C] 0.9998[/C][C] 0.0003978[/C][C] 0.0001989[/C][/ROW]
[ROW][C]126[/C][C] 0.9997[/C][C] 0.0006956[/C][C] 0.0003478[/C][/ROW]
[ROW][C]127[/C][C] 0.8492[/C][C] 0.3016[/C][C] 0.1508[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=306337&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=306337&T=5

As an alternative you can also use a QR Code:

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

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
22	0.1542	0.3084	0.8458
23	0.07019	0.1404	0.9298
24	0.0001512	0.0003023	0.9998
25	0.0005818	0.001164	0.9994
26	0.3141	0.6283	0.6859
27	0.003832	0.007665	0.9962
28	0.01551	0.03102	0.9845
29	1.07e-09	2.14e-09	1
30	6.947e-06	1.389e-05	1
31	0.9967	0.00669	0.003345
32	2.407e-05	4.815e-05	1
33	1.878e-09	3.757e-09	1
34	4.029e-15	8.059e-15	1
35	0.9921	0.01582	0.007911
36	1.705e-11	3.411e-11	1
37	1.661e-21	3.321e-21	1
38	1.847e-07	3.694e-07	1
39	0.001957	0.003914	0.998
40	0.02437	0.04874	0.9756
41	1	4.777e-36	2.389e-36
42	1.997e-06	3.994e-06	1
43	0.03777	0.07553	0.9622
44	4.237e-05	8.475e-05	1
45	2.793e-10	5.586e-10	1
46	5.211e-24	1.042e-23	1
47	0.1075	0.2149	0.8925
48	1	3.652e-51	1.826e-51
49	8.721e-17	1.744e-16	1
50	0.897	0.206	0.103
51	4.819e-05	9.639e-05	1
52	0.9929	0.01412	0.007058
53	6.072e-33	1.214e-32	1
54	2.263e-37	4.525e-37	1
55	0.001727	0.003453	0.9983
56	1.001e-41	2.001e-41	1
57	1.189e-18	2.378e-18	1
58	0.002024	0.004048	0.998
59	3.801e-36	7.601e-36	1
60	3.369e-26	6.738e-26	1
61	0.2533	0.5067	0.7467
62	0.0425	0.08501	0.9575
63	1.858e-09	3.716e-09	1
64	4.463e-45	8.926e-45	1
65	1	8.372e-12	4.186e-12
66	1	2.494e-78	1.247e-78
67	1.602e-06	3.203e-06	1
68	1.322e-13	2.643e-13	1
69	3.556e-35	7.111e-35	1
70	0.1803	0.3606	0.8197
71	1	9.035e-20	4.518e-20
72	1	2.371e-09	1.186e-09
73	2.577e-28	5.153e-28	1
74	0.543	0.914	0.457
75	0.6695	0.661	0.3305
76	0.7869	0.4263	0.2131
77	9.57e-29	1.914e-28	1
78	8.576e-15	1.715e-14	1
79	1	2.906e-18	1.453e-18
80	4.46e-05	8.92e-05	1
81	3.173e-22	6.346e-22	1
82	1	1.122e-09	5.608e-10
83	6.292e-39	1.259e-38	1
84	0.0007022	0.001404	0.9993
85	1	1.056e-36	5.278e-37
86	0.2593	0.5186	0.7407
87	0.8743	0.2514	0.1257
88	0.1422	0.2843	0.8578
89	1.597e-05	3.195e-05	1
90	1	1.198e-30	5.99e-31
91	1	1.073e-18	5.363e-19
92	1	1.401e-14	7.005e-15
93	1	3.935e-39	1.967e-39
94	0.2839	0.5677	0.7161
95	1	8.64e-31	4.32e-31
96	1	7.623e-22	3.812e-22
97	6.292e-27	1.258e-26	1
98	0.1621	0.3241	0.8379
99	5.101e-09	1.02e-08	1
100	0.9978	0.004453	0.002226
101	0.9983	0.003385	0.001693
102	2.675e-09	5.351e-09	1
103	1	9.967e-12	4.983e-12
104	0.5164	0.9673	0.4836
105	1	1.133e-07	5.667e-08
106	0.8178	0.3644	0.1822
107	0.001631	0.003263	0.9984
108	4.167e-41	8.334e-41	1
109	0.9993	0.001465	0.0007324
110	0.3707	0.7414	0.6293
111	1	1.279e-10	6.396e-11
112	1	2.038e-13	1.019e-13
113	1	4.213e-12	2.107e-12
114	1.625e-34	3.249e-34	1
115	1	3.112e-09	1.556e-09
116	0.01088	0.02176	0.9891
117	1	6.504e-05	3.252e-05
118	0.9759	0.04815	0.02407
119	1	2.816e-05	1.408e-05
120	1	7.152e-07	3.576e-07
121	0.7316	0.5368	0.2684
122	0.999	0.00205	0.001025
123	1.995e-15	3.989e-15	1
124	0.9997	0.0005987	0.0002994
125	0.9998	0.0003978	0.0001989
126	0.9997	0.0006956	0.0003478
127	0.8492	0.3016	0.1508

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	78	0.7358	NOK
5% type I error level	84	0.792453	NOK
10% type I error level	86	0.811321	NOK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 78 &  0.7358 & NOK \tabularnewline
5% type I error level & 84 & 0.792453 & NOK \tabularnewline
10% type I error level & 86 & 0.811321 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=306337&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]78[/C][C] 0.7358[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]84[/C][C]0.792453[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]86[/C][C]0.811321[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=306337&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=306337&T=6

As an alternative you can also use a QR Code:

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	78	0.7358	NOK
5% type I error level	84	0.792453	NOK
10% type I error level	86	0.811321	NOK

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted RESET test data: mylm RESET = 0.76546, df1 = 2, df2 = 128, p-value = 0.4672
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 0.77535, df1 = 36, df2 = 94, p-value = 0.8037
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 0.35439, df1 = 2, df2 = 128, p-value = 0.7023

\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 = 0.76546, df1 = 2, df2 = 128, p-value = 0.4672
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of regressors \tabularnewline
> reset_test_regressors
	RESET test
data:  mylm
RESET = 0.77535, df1 = 36, df2 = 94, p-value = 0.8037
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of principal components \tabularnewline
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 0.35439, df1 = 2, df2 = 128, p-value = 0.7023
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=306337&T=7

[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 = 0.76546, df1 = 2, df2 = 128, p-value = 0.4672
[/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 = 0.77535, df1 = 36, df2 = 94, p-value = 0.8037
[/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 = 0.35439, df1 = 2, df2 = 128, p-value = 0.7023
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=306337&T=7

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=306337&T=7

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 = 0.76546, df1 = 2, df2 = 128, p-value = 0.4672
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 0.77535, df1 = 36, df2 = 94, p-value = 0.8037
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 0.35439, df1 = 2, df2 = 128, p-value = 0.7023

Variance Inflation Factors (Multicollinearity)

> vif
          SKEOU1           SKEOU2           SKEOU3           SKEOU4 
        1.169024         1.317599         1.081972         1.216680 
          SKEOU5           SKEOU6  `SKEOUSUM(t-1)`  `SKEOUSUM(t-2)` 
        1.328470         1.173026         1.119807         1.107610 
 `SKEOUSUM(t-3)`  `SKEOUSUM(t-4)`  `SKEOUSUM(t-5)`  `SKEOUSUM(t-6)` 
        1.102269         1.128965         1.157593         1.157368 
 `SKEOUSUM(t-7)`  `SKEOUSUM(t-8)`  `SKEOUSUM(t-9)` `SKEOUSUM(t-10)` 
        1.121182         1.154889         1.143747         1.114586 
`SKEOUSUM(t-11)` `SKEOUSUM(t-12)` 
        1.059718         1.088517

\begin{tabular}{lllllllll}
\hline
Variance Inflation Factors (Multicollinearity) \tabularnewline
> vif
          SKEOU1           SKEOU2           SKEOU3           SKEOU4 
        1.169024         1.317599         1.081972         1.216680 
          SKEOU5           SKEOU6  `SKEOUSUM(t-1)`  `SKEOUSUM(t-2)` 
        1.328470         1.173026         1.119807         1.107610 
 `SKEOUSUM(t-3)`  `SKEOUSUM(t-4)`  `SKEOUSUM(t-5)`  `SKEOUSUM(t-6)` 
        1.102269         1.128965         1.157593         1.157368 
 `SKEOUSUM(t-7)`  `SKEOUSUM(t-8)`  `SKEOUSUM(t-9)` `SKEOUSUM(t-10)` 
        1.121182         1.154889         1.143747         1.114586 
`SKEOUSUM(t-11)` `SKEOUSUM(t-12)` 
        1.059718         1.088517 
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=306337&T=8

[TABLE]
[ROW][C]Variance Inflation Factors (Multicollinearity)[/C][/ROW]
[ROW][C]> vif
          SKEOU1           SKEOU2           SKEOU3           SKEOU4 
        1.169024         1.317599         1.081972         1.216680 
          SKEOU5           SKEOU6  `SKEOUSUM(t-1)`  `SKEOUSUM(t-2)` 
        1.328470         1.173026         1.119807         1.107610 
 `SKEOUSUM(t-3)`  `SKEOUSUM(t-4)`  `SKEOUSUM(t-5)`  `SKEOUSUM(t-6)` 
        1.102269         1.128965         1.157593         1.157368 
 `SKEOUSUM(t-7)`  `SKEOUSUM(t-8)`  `SKEOUSUM(t-9)` `SKEOUSUM(t-10)` 
        1.121182         1.154889         1.143747         1.114586 
`SKEOUSUM(t-11)` `SKEOUSUM(t-12)` 
        1.059718         1.088517 
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=306337&T=8

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=306337&T=8

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
          SKEOU1           SKEOU2           SKEOU3           SKEOU4 
        1.169024         1.317599         1.081972         1.216680 
          SKEOU5           SKEOU6  `SKEOUSUM(t-1)`  `SKEOUSUM(t-2)` 
        1.328470         1.173026         1.119807         1.107610 
 `SKEOUSUM(t-3)`  `SKEOUSUM(t-4)`  `SKEOUSUM(t-5)`  `SKEOUSUM(t-6)` 
        1.102269         1.128965         1.157593         1.157368 
 `SKEOUSUM(t-7)`  `SKEOUSUM(t-8)`  `SKEOUSUM(t-9)` `SKEOUSUM(t-10)` 
        1.121182         1.154889         1.143747         1.114586 
`SKEOUSUM(t-11)` `SKEOUSUM(t-12)` 
        1.059718         1.088517

Figure 1

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Figure 9

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Figure 10

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Parameters (Session):

par1 = 12 ; par2 = Single ; par3 = additive ; par4 = 12 ;

Parameters (R input):

par1 = 1 ; par2 = Single ; par3 = additive ; par4 = 12 ; par5 = ;

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 <- ''
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 (par5=='') par5 <- 0
par5 <- as.numeric(par5)
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=12)'){
(n <- n - 12)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B12)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+12,j] - x[i,j]
}
}
x <- x2
}
if (par3 == 'First and Seasonal Differences (s=12)'){
(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 - 12)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B12)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+12,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*12,par5), dimnames=list(1:(n-par5*12), paste(colnames(x)[par1],'(t-',1:par5,'s)',sep='')))
for (i in 1:(n-par5*12)) {
for (j in 1:par5) {
x2[i,j] <- x[i+par5*12-j*12,par1]
}
}
x <- cbind(x[(par5*12+1):n,], x2)
n <- n - par5*12
}
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$resid)
myr
if(n < 200) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,formatC(signif(x[i],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(x[i]-mysum$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