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rwasp_multipleregression.wasp

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Multiple Regression

Date of computation

Thu, 01 Feb 2018 11:32:17 +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/Feb/01/t1517481217238aubxi0u0v0wt.htm/, Retrieved Mon, 29 Apr 2024 01:12:11 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=314762, Retrieved Mon, 29 Apr 2024 01:12:11 +0000

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-       [Multiple Regression] [] [2018-02-01 10:32:17] [814e681488f8450cd741da3dc59dcc6f] [Current]

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10 10 10 10 21 36 1 0
8 8 9 15 22 32 1 1
8 6 12 14 17 33 1 1
9 10 14 14 21 39 1 1
5 8 6 8 19 34 1 0
10 10 13 19 23 39 1 1
8 7 12 17 21 36 1 1
9 10 13 18 22 33 1 1
8 6 6 10 11 30 1 0
7 7 12 15 20 39 1 0
10 9 10 16 18 37 1 0
10 6 9 12 16 37 1 0
9 7 12 13 18 35 1 1
4 6 7 10 13 32 1 0
4 4 10 14 17 36 1 1
8 6 11 15 20 36 1 1
9 8 15 20 20 41 1 1
10 9 10 9 15 36 1 1
8 8 12 12 18 37 1 0
5 6 10 13 15 29 1 0
10 6 12 16 19 39 1 1
8 10 11 12 19 37 1 0
7 8 11 14 19 32 1 1
8 8 12 15 20 36 1 1
8 7 15 19 20 43 1 1
9 4 12 16 16 30 1 0
8 9 11 16 18 33 1 0
6 8 9 14 17 28 1 1
8 10 11 14 18 30 1 1
8 8 11 14 13 28 1 0
5 6 9 13 20 39 0 1
9 7 15 18 21 34 1 1
8 8 12 15 17 34 1 0
8 5 9 15 19 29 1 0
8 10 12 15 20 32 1 0
6 2 12 13 15 33 1 0
6 6 9 14 15 27 1 0
9 7 9 15 19 35 1 1
8 5 11 14 18 38 1 1
9 8 12 19 22 40 1 1
10 7 12 16 20 34 1 1
8 7 12 16 18 34 0 0
8 10 12 12 14 26 1 0
7 7 6 10 15 39 1 0
7 6 11 11 17 34 1 1
10 10 12 13 16 39 1 1
8 6 9 14 17 26 1 1
7 5 11 11 15 30 1 1
10 8 9 11 17 34 1 1
7 8 10 16 18 34 1 1
7 5 10 9 16 29 1 0
9 8 9 16 18 41 1 0
9 10 12 19 22 43 1 0
8 7 11 13 16 31 1 0
6 7 9 15 16 33 1 0
8 7 9 14 20 34 1 0
9 7 12 15 18 30 1 1
2 2 6 11 16 23 0 0
6 4 10 14 16 29 1 0
8 6 12 15 20 35 1 1
8 7 11 17 21 40 0 1
7 9 14 16 18 27 0 0
8 9 8 13 15 30 1 0
6 4 9 15 18 27 1 0
10 9 10 14 18 29 1 0
10 9 10 15 20 33 1 0
10 8 10 14 18 32 1 0
8 7 11 12 16 33 1 0
8 9 10 12 19 36 1 1
7 7 12 15 20 34 1 1
10 6 14 17 22 45 1 1
5 7 10 13 18 30 0 0
3 2 8 5 8 22 0 1
2 3 8 7 13 24 0 1
3 4 7 10 13 25 0 1
4 5 11 15 18 26 0 1
2 2 6 9 12 27 0 0
6 6 9 9 16 27 0 0
8 8 12 15 21 35 1 0
8 5 12 14 20 36 1 0
5 4 12 11 18 32 0 0
10 10 9 18 22 35 1 1
9 10 15 20 23 35 1 1
8 10 15 20 23 36 1 1
9 9 13 16 21 37 1 1
8 5 9 15 16 33 1 1
5 5 12 14 14 25 1 0
7 7 9 13 18 35 1 1
9 10 15 18 22 37 1 1
8 9 11 14 20 36 1 0
4 8 11 12 18 35 1 1
7 8 6 9 12 29 1 1
8 8 14 19 17 35 1 1
7 8 11 13 15 31 1 0
7 8 8 12 18 30 1 1
9 7 10 14 18 37 1 0
6 6 10 6 15 36 1 1
7 8 9 14 16 35 1 0
4 2 8 11 15 32 1 0
6 5 9 11 16 34 1 1
10 4 10 14 19 37 1 0
9 9 11 12 19 36 1 1
10 10 14 19 23 39 1 1
8 6 12 13 20 37 1 0
4 4 9 14 18 31 0 0
8 10 13 17 21 40 1 1
5 6 8 12 19 38 1 0
8 7 12 16 18 35 0 1
9 7 14 15 19 38 0 1
8 8 9 15 17 32 1 0
4 6 10 15 21 41 1 1
8 5 12 16 19 28 1 0
10 6 12 15 24 40 1 1
6 7 9 12 12 25 1 0
7 6 9 13 15 28 1 0
10 9 12 14 18 37 1 1
9 9 15 17 19 37 1 1
8 7 12 14 22 40 1 1
3 6 11 14 19 26 0 0
8 7 8 14 16 30 1 0
7 7 11 15 19 32 1 0
7 8 11 11 18 31 1 0
8 7 10 11 18 28 1 0
8 8 12 16 19 34 1 1
7 7 9 12 21 39 1 0
7 4 11 12 19 33 0 1
9 10 15 19 22 43 1 0
9 8 14 18 23 37 0 1
9 8 6 16 17 31 1 0
4 2 9 16 18 31 0 1
6 6 9 13 19 34 1 0
6 4 8 11 15 32 1 1
6 4 7 10 14 27 0 0
8 9 10 14 18 34 1 0
3 2 6 14 17 28 0 0
8 6 9 14 19 32 0 0
8 7 9 16 16 39 0 1
6 4 7 10 14 28 0 1
10 10 11 16 20 39 1 0
2 3 9 7 16 32 0 0
9 7 12 16 18 36 0 1
6 4 9 15 16 31 0 1
6 8 10 17 21 39 0 0
5 4 11 11 16 23 0 0
4 5 7 11 14 25 0 0
7 6 12 10 16 32 1 0
5 5 8 13 19 32 0 1
8 9 13 14 19 36 0 1
6 6 11 13 19 39 0 0
9 8 11 13 18 31 0 1
6 4 12 12 16 32 1 0
4 4 11 10 14 28 0 1
7 8 12 15 19 34 0 0
2 4 3 6 11 28 0 1
8 10 10 15 18 38 1 1
9 8 13 15 18 35 1 1
6 5 10 11 16 32 1 0
5 3 6 14 20 26 0 1
7 7 11 14 18 32 0 1
8 6 12 16 20 28 1 1
4 5 9 12 16 31 1 0
9 5 10 15 18 33 0 1
9 9 15 20 19 38 1 0
9 2 9 12 19 38 0 1
7 7 6 9 15 36 0 0
5 7 9 13 17 31 1 1
7 5 15 15 21 36 0 0
9 9 15 19 24 43 1 1
8 4 9 11 16 37 1 1
6 5 11 11 13 28 0 1
9 9 9 17 21 35 0 1
8 7 11 15 16 34 1 1
7 6 10 14 17 40 1 1
7 8 9 15 17 31 1 0
7 7 6 11 18 41 0 0
8 6 12 12 18 35 1 0
10 8 13 15 23 38 1 1
6 6 12 16 20 37 0 0
6 7 12 16 20 31 0 0

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=314762&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=314762&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=314762&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
Intention_to_Use[t] = -3.25538 + 0.338869Relative_Advantage[t] + 0.0619599Perceived_Usefulness[t] + 0.0966016Perceived_Ease_of_Use[t] + 0.0321793Information_Quality[t] + 0.0967844System_Quality[t] + 0.767678groupB[t] + 0.414423genderB[t] + 0.0226345`Intention_to_Use(t-1)`[t] + 0.0104794`Intention_to_Use(t-1s)`[t] -0.0123603`Intention_to_Use(t-2s)`[t] -0.0600236`Intention_to_Use(t-3s)`[t] + 0.11702`Intention_to_Use(t-4s)`[t] -0.0234479`Intention_to_Use(t-5s)`[t] -0.0717622`Intention_to_Use(t-6s)`[t] + 0.0995035`Intention_to_Use(t-7s)`[t] -0.040039`Intention_to_Use(t-8s)`[t] -0.0716597`Intention_to_Use(t-9s)`[t] + 0.135671`Intention_to_Use(t-10s)`[t] + 0.105795`Intention_to_Use(t-11s)`[t] + 0.0177579`Intention_to_Use(t-12s)`[t] + e[t]
Warning: you did not specify the column number of the endogenous series! The first column was selected by default.

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Intention_to_Use[t] =  -3.25538 +  0.338869Relative_Advantage[t] +  0.0619599Perceived_Usefulness[t] +  0.0966016Perceived_Ease_of_Use[t] +  0.0321793Information_Quality[t] +  0.0967844System_Quality[t] +  0.767678groupB[t] +  0.414423genderB[t] +  0.0226345`Intention_to_Use(t-1)`[t] +  0.0104794`Intention_to_Use(t-1s)`[t] -0.0123603`Intention_to_Use(t-2s)`[t] -0.0600236`Intention_to_Use(t-3s)`[t] +  0.11702`Intention_to_Use(t-4s)`[t] -0.0234479`Intention_to_Use(t-5s)`[t] -0.0717622`Intention_to_Use(t-6s)`[t] +  0.0995035`Intention_to_Use(t-7s)`[t] -0.040039`Intention_to_Use(t-8s)`[t] -0.0716597`Intention_to_Use(t-9s)`[t] +  0.135671`Intention_to_Use(t-10s)`[t] +  0.105795`Intention_to_Use(t-11s)`[t] +  0.0177579`Intention_to_Use(t-12s)`[t]  + e[t] \tabularnewline
Warning: you did not specify the column number of the endogenous series! The first column was selected by default. \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=314762&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Intention_to_Use[t] =  -3.25538 +  0.338869Relative_Advantage[t] +  0.0619599Perceived_Usefulness[t] +  0.0966016Perceived_Ease_of_Use[t] +  0.0321793Information_Quality[t] +  0.0967844System_Quality[t] +  0.767678groupB[t] +  0.414423genderB[t] +  0.0226345`Intention_to_Use(t-1)`[t] +  0.0104794`Intention_to_Use(t-1s)`[t] -0.0123603`Intention_to_Use(t-2s)`[t] -0.0600236`Intention_to_Use(t-3s)`[t] +  0.11702`Intention_to_Use(t-4s)`[t] -0.0234479`Intention_to_Use(t-5s)`[t] -0.0717622`Intention_to_Use(t-6s)`[t] +  0.0995035`Intention_to_Use(t-7s)`[t] -0.040039`Intention_to_Use(t-8s)`[t] -0.0716597`Intention_to_Use(t-9s)`[t] +  0.135671`Intention_to_Use(t-10s)`[t] +  0.105795`Intention_to_Use(t-11s)`[t] +  0.0177579`Intention_to_Use(t-12s)`[t]  + e[t][/C][/ROW]
[ROW][C]Warning: you did not specify the column number of the endogenous series! The first column was selected by default.[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=314762&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=314762&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
Intention_to_Use[t] = -3.25538 + 0.338869Relative_Advantage[t] + 0.0619599Perceived_Usefulness[t] + 0.0966016Perceived_Ease_of_Use[t] + 0.0321793Information_Quality[t] + 0.0967844System_Quality[t] + 0.767678groupB[t] + 0.414423genderB[t] + 0.0226345`Intention_to_Use(t-1)`[t] + 0.0104794`Intention_to_Use(t-1s)`[t] -0.0123603`Intention_to_Use(t-2s)`[t] -0.0600236`Intention_to_Use(t-3s)`[t] + 0.11702`Intention_to_Use(t-4s)`[t] -0.0234479`Intention_to_Use(t-5s)`[t] -0.0717622`Intention_to_Use(t-6s)`[t] + 0.0995035`Intention_to_Use(t-7s)`[t] -0.040039`Intention_to_Use(t-8s)`[t] -0.0716597`Intention_to_Use(t-9s)`[t] + 0.135671`Intention_to_Use(t-10s)`[t] + 0.105795`Intention_to_Use(t-11s)`[t] + 0.0177579`Intention_to_Use(t-12s)`[t] + e[t]
Warning: you did not specify the column number of the endogenous series! The first column was selected by default.

Multiple Linear Regression - Ordinary Least Squares
Variable	Parameter	S.D.	T-STATH0: parameter = 0	2-tail p-value	1-tail p-value
(Intercept)	-3.255	1.784	-1.8250e+00	0.07046	0.03523
Relative_Advantage	+0.3389	0.07218	+4.6950e+00	7.104e-06	3.552e-06
Perceived_Usefulness	+0.06196	0.06856	+9.0380e-01	0.3679	0.184
Perceived_Ease_of_Use	+0.0966	0.06265	+1.5420e+00	0.1257	0.06286
Information_Quality	+0.03218	0.07237	+4.4460e-01	0.6574	0.3287
System_Quality	+0.09678	0.03389	+2.8560e+00	0.005057	0.002528
groupB	+0.7677	0.2745	+2.7960e+00	0.006011	0.003006
genderB	+0.4144	0.2342	+1.7700e+00	0.07928	0.03964
`Intention_to_Use(t-1)`	+0.02263	0.06029	+3.7540e-01	0.708	0.354
`Intention_to_Use(t-1s)`	+0.01048	0.05747	+1.8230e-01	0.8556	0.4278
`Intention_to_Use(t-2s)`	-0.01236	0.05932	-2.0840e-01	0.8353	0.4176
`Intention_to_Use(t-3s)`	-0.06002	0.05714	-1.0500e+00	0.2956	0.1478
`Intention_to_Use(t-4s)`	+0.117	0.05905	+1.9820e+00	0.04979	0.02489
`Intention_to_Use(t-5s)`	-0.02345	0.06093	-3.8480e-01	0.701	0.3505
`Intention_to_Use(t-6s)`	-0.07176	0.0588	-1.2200e+00	0.2247	0.1123
`Intention_to_Use(t-7s)`	+0.0995	0.05709	+1.7430e+00	0.08392	0.04196
`Intention_to_Use(t-8s)`	-0.04004	0.05925	-6.7580e-01	0.5005	0.2502
`Intention_to_Use(t-9s)`	-0.07166	0.05597	-1.2800e+00	0.2029	0.1014
`Intention_to_Use(t-10s)`	+0.1357	0.05827	+2.3280e+00	0.02156	0.01078
`Intention_to_Use(t-11s)`	+0.1058	0.05621	+1.8820e+00	0.06222	0.03111
`Intention_to_Use(t-12s)`	+0.01776	0.05636	+3.1510e-01	0.7533	0.3766

\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) & -3.255 &  1.784 & -1.8250e+00 &  0.07046 &  0.03523 \tabularnewline
Relative_Advantage & +0.3389 &  0.07218 & +4.6950e+00 &  7.104e-06 &  3.552e-06 \tabularnewline
Perceived_Usefulness & +0.06196 &  0.06856 & +9.0380e-01 &  0.3679 &  0.184 \tabularnewline
Perceived_Ease_of_Use & +0.0966 &  0.06265 & +1.5420e+00 &  0.1257 &  0.06286 \tabularnewline
Information_Quality & +0.03218 &  0.07237 & +4.4460e-01 &  0.6574 &  0.3287 \tabularnewline
System_Quality & +0.09678 &  0.03389 & +2.8560e+00 &  0.005057 &  0.002528 \tabularnewline
groupB & +0.7677 &  0.2745 & +2.7960e+00 &  0.006011 &  0.003006 \tabularnewline
genderB & +0.4144 &  0.2342 & +1.7700e+00 &  0.07928 &  0.03964 \tabularnewline
`Intention_to_Use(t-1)` & +0.02263 &  0.06029 & +3.7540e-01 &  0.708 &  0.354 \tabularnewline
`Intention_to_Use(t-1s)` & +0.01048 &  0.05747 & +1.8230e-01 &  0.8556 &  0.4278 \tabularnewline
`Intention_to_Use(t-2s)` & -0.01236 &  0.05932 & -2.0840e-01 &  0.8353 &  0.4176 \tabularnewline
`Intention_to_Use(t-3s)` & -0.06002 &  0.05714 & -1.0500e+00 &  0.2956 &  0.1478 \tabularnewline
`Intention_to_Use(t-4s)` & +0.117 &  0.05905 & +1.9820e+00 &  0.04979 &  0.02489 \tabularnewline
`Intention_to_Use(t-5s)` & -0.02345 &  0.06093 & -3.8480e-01 &  0.701 &  0.3505 \tabularnewline
`Intention_to_Use(t-6s)` & -0.07176 &  0.0588 & -1.2200e+00 &  0.2247 &  0.1123 \tabularnewline
`Intention_to_Use(t-7s)` & +0.0995 &  0.05709 & +1.7430e+00 &  0.08392 &  0.04196 \tabularnewline
`Intention_to_Use(t-8s)` & -0.04004 &  0.05925 & -6.7580e-01 &  0.5005 &  0.2502 \tabularnewline
`Intention_to_Use(t-9s)` & -0.07166 &  0.05597 & -1.2800e+00 &  0.2029 &  0.1014 \tabularnewline
`Intention_to_Use(t-10s)` & +0.1357 &  0.05827 & +2.3280e+00 &  0.02156 &  0.01078 \tabularnewline
`Intention_to_Use(t-11s)` & +0.1058 &  0.05621 & +1.8820e+00 &  0.06222 &  0.03111 \tabularnewline
`Intention_to_Use(t-12s)` & +0.01776 &  0.05636 & +3.1510e-01 &  0.7533 &  0.3766 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=314762&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]-3.255[/C][C] 1.784[/C][C]-1.8250e+00[/C][C] 0.07046[/C][C] 0.03523[/C][/ROW]
[ROW][C]Relative_Advantage[/C][C]+0.3389[/C][C] 0.07218[/C][C]+4.6950e+00[/C][C] 7.104e-06[/C][C] 3.552e-06[/C][/ROW]
[ROW][C]Perceived_Usefulness[/C][C]+0.06196[/C][C] 0.06856[/C][C]+9.0380e-01[/C][C] 0.3679[/C][C] 0.184[/C][/ROW]
[ROW][C]Perceived_Ease_of_Use[/C][C]+0.0966[/C][C] 0.06265[/C][C]+1.5420e+00[/C][C] 0.1257[/C][C] 0.06286[/C][/ROW]
[ROW][C]Information_Quality[/C][C]+0.03218[/C][C] 0.07237[/C][C]+4.4460e-01[/C][C] 0.6574[/C][C] 0.3287[/C][/ROW]
[ROW][C]System_Quality[/C][C]+0.09678[/C][C] 0.03389[/C][C]+2.8560e+00[/C][C] 0.005057[/C][C] 0.002528[/C][/ROW]
[ROW][C]groupB[/C][C]+0.7677[/C][C] 0.2745[/C][C]+2.7960e+00[/C][C] 0.006011[/C][C] 0.003006[/C][/ROW]
[ROW][C]genderB[/C][C]+0.4144[/C][C] 0.2342[/C][C]+1.7700e+00[/C][C] 0.07928[/C][C] 0.03964[/C][/ROW]
[ROW][C]`Intention_to_Use(t-1)`[/C][C]+0.02263[/C][C] 0.06029[/C][C]+3.7540e-01[/C][C] 0.708[/C][C] 0.354[/C][/ROW]
[ROW][C]`Intention_to_Use(t-1s)`[/C][C]+0.01048[/C][C] 0.05747[/C][C]+1.8230e-01[/C][C] 0.8556[/C][C] 0.4278[/C][/ROW]
[ROW][C]`Intention_to_Use(t-2s)`[/C][C]-0.01236[/C][C] 0.05932[/C][C]-2.0840e-01[/C][C] 0.8353[/C][C] 0.4176[/C][/ROW]
[ROW][C]`Intention_to_Use(t-3s)`[/C][C]-0.06002[/C][C] 0.05714[/C][C]-1.0500e+00[/C][C] 0.2956[/C][C] 0.1478[/C][/ROW]
[ROW][C]`Intention_to_Use(t-4s)`[/C][C]+0.117[/C][C] 0.05905[/C][C]+1.9820e+00[/C][C] 0.04979[/C][C] 0.02489[/C][/ROW]
[ROW][C]`Intention_to_Use(t-5s)`[/C][C]-0.02345[/C][C] 0.06093[/C][C]-3.8480e-01[/C][C] 0.701[/C][C] 0.3505[/C][/ROW]
[ROW][C]`Intention_to_Use(t-6s)`[/C][C]-0.07176[/C][C] 0.0588[/C][C]-1.2200e+00[/C][C] 0.2247[/C][C] 0.1123[/C][/ROW]
[ROW][C]`Intention_to_Use(t-7s)`[/C][C]+0.0995[/C][C] 0.05709[/C][C]+1.7430e+00[/C][C] 0.08392[/C][C] 0.04196[/C][/ROW]
[ROW][C]`Intention_to_Use(t-8s)`[/C][C]-0.04004[/C][C] 0.05925[/C][C]-6.7580e-01[/C][C] 0.5005[/C][C] 0.2502[/C][/ROW]
[ROW][C]`Intention_to_Use(t-9s)`[/C][C]-0.07166[/C][C] 0.05597[/C][C]-1.2800e+00[/C][C] 0.2029[/C][C] 0.1014[/C][/ROW]
[ROW][C]`Intention_to_Use(t-10s)`[/C][C]+0.1357[/C][C] 0.05827[/C][C]+2.3280e+00[/C][C] 0.02156[/C][C] 0.01078[/C][/ROW]
[ROW][C]`Intention_to_Use(t-11s)`[/C][C]+0.1058[/C][C] 0.05621[/C][C]+1.8820e+00[/C][C] 0.06222[/C][C] 0.03111[/C][/ROW]
[ROW][C]`Intention_to_Use(t-12s)`[/C][C]+0.01776[/C][C] 0.05636[/C][C]+3.1510e-01[/C][C] 0.7533[/C][C] 0.3766[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=314762&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=314762&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)	-3.255	1.784	-1.8250e+00	0.07046	0.03523
Relative_Advantage	+0.3389	0.07218	+4.6950e+00	7.104e-06	3.552e-06
Perceived_Usefulness	+0.06196	0.06856	+9.0380e-01	0.3679	0.184
Perceived_Ease_of_Use	+0.0966	0.06265	+1.5420e+00	0.1257	0.06286
Information_Quality	+0.03218	0.07237	+4.4460e-01	0.6574	0.3287
System_Quality	+0.09678	0.03389	+2.8560e+00	0.005057	0.002528
groupB	+0.7677	0.2745	+2.7960e+00	0.006011	0.003006
genderB	+0.4144	0.2342	+1.7700e+00	0.07928	0.03964
`Intention_to_Use(t-1)`	+0.02263	0.06029	+3.7540e-01	0.708	0.354
`Intention_to_Use(t-1s)`	+0.01048	0.05747	+1.8230e-01	0.8556	0.4278
`Intention_to_Use(t-2s)`	-0.01236	0.05932	-2.0840e-01	0.8353	0.4176
`Intention_to_Use(t-3s)`	-0.06002	0.05714	-1.0500e+00	0.2956	0.1478
`Intention_to_Use(t-4s)`	+0.117	0.05905	+1.9820e+00	0.04979	0.02489
`Intention_to_Use(t-5s)`	-0.02345	0.06093	-3.8480e-01	0.701	0.3505
`Intention_to_Use(t-6s)`	-0.07176	0.0588	-1.2200e+00	0.2247	0.1123
`Intention_to_Use(t-7s)`	+0.0995	0.05709	+1.7430e+00	0.08392	0.04196
`Intention_to_Use(t-8s)`	-0.04004	0.05925	-6.7580e-01	0.5005	0.2502
`Intention_to_Use(t-9s)`	-0.07166	0.05597	-1.2800e+00	0.2029	0.1014
`Intention_to_Use(t-10s)`	+0.1357	0.05827	+2.3280e+00	0.02156	0.01078
`Intention_to_Use(t-11s)`	+0.1058	0.05621	+1.8820e+00	0.06222	0.03111
`Intention_to_Use(t-12s)`	+0.01776	0.05636	+3.1510e-01	0.7533	0.3766

Multiple Linear Regression - Regression Statistics
Multiple R	0.8095
R-squared	0.6552
Adjusted R-squared	0.5983
F-TEST (value)	11.5
F-TEST (DF numerator)	20
F-TEST (DF denominator)	121
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.276
Sum Squared Residuals	196.9

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.8095 \tabularnewline
R-squared &  0.6552 \tabularnewline
Adjusted R-squared &  0.5983 \tabularnewline
F-TEST (value) &  11.5 \tabularnewline
F-TEST (DF numerator) & 20 \tabularnewline
F-TEST (DF denominator) & 121 \tabularnewline
p-value &  0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  1.276 \tabularnewline
Sum Squared Residuals &  196.9 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=314762&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.8095[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.6552[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.5983[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 11.5[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]20[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]121[/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] 1.276[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 196.9[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=314762&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=314762&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	0.8095
R-squared	0.6552
Adjusted R-squared	0.5983
F-TEST (value)	11.5
F-TEST (DF numerator)	20
F-TEST (DF denominator)	121
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.276
Sum Squared Residuals	196.9

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=314762&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=314762&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=314762&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	9	8.374	0.6264
2	8	8.034	-0.03421
3	9	9.368	-0.368
4	10	8.652	1.348
5	8	7.895	0.1053
6	8	7.368	0.6319
7	7	7.005	-0.005456
8	7	6.921	0.0787
9	10	9.828	0.1723
10	8	6.799	1.201
11	7	6.084	0.9162
12	10	7.67	2.33
13	7	8.152	-1.152
14	7	6.292	0.7081
15	9	8.63	0.3705
16	9	10.06	-1.061
17	8	7.649	0.3513
18	6	7.686	-1.686
19	8	7.244	0.7563
20	9	7.603	1.397
21	2	3.21	-1.21
22	6	5.906	0.09438
23	8	8.032	-0.0318
24	8	7.889	0.1115
25	7	7.423	-0.4226
26	8	7.623	0.3771
27	6	5.775	0.2254
28	10	7.721	2.279
29	10	8.014	1.986
30	10	7.494	2.506
31	8	7.568	0.4324
32	8	8.626	-0.6262
33	7	7.624	-0.6239
34	10	9.426	0.5745
35	5	6.285	-1.285
36	3	3.286	-0.2862
37	2	3.876	-1.876
38	3	4.206	-1.206
39	4	5.746	-1.746
40	2	3.684	-1.684
41	6	5.182	0.8179
42	8	8.525	-0.5255
43	8	6.759	1.241
44	5	5.294	-0.2939
45	10	10.57	-0.5714
46	9	10.72	-1.722
47	8	9.864	-1.864
48	9	9.078	-0.07778
49	8	7.288	0.7123
50	5	5.969	-0.9685
51	7	6.825	0.1745
52	9	9.107	-0.1072
53	8	8.856	-0.8559
54	4	7.885	-3.885
55	7	7.048	-0.0479
56	8	8.757	-0.7566
57	7	6.933	0.067
58	7	7.567	-0.5669
59	9	8.118	0.8816
60	6	7.088	-1.088
61	7	7.439	-0.439
62	4	5.444	-1.444
63	6	7.299	-1.299
64	10	7.908	2.092
65	9	8.579	0.4208
66	10	9.677	0.3234
67	8	7.531	0.4688
68	4	4.909	-0.9091
69	8	9.201	-1.201
70	5	5.743	-0.7428
71	8	6.921	1.079
72	9	7.35	1.65
73	8	6.846	1.154
74	4	7.42	-3.42
75	8	6.109	1.891
76	10	8.889	1.111
77	6	6.488	-0.4881
78	7	7.235	-0.2346
79	10	9.247	0.7528
80	9	9.086	-0.08563
81	8	8.993	-0.9927
82	3	5.443	-2.443
83	8	6.416	1.584
84	7	6.868	0.1321
85	7	7.515	-0.5149
86	8	6.731	1.269
87	8	8.537	-0.5373
88	7	8.037	-1.037
89	7	5.758	1.242
90	9	9.788	-0.7881
91	9	8.445	0.5553
92	9	7.639	1.361
93	4	4.928	-0.9282
94	6	6.582	-0.5823
95	6	5.973	0.02665
96	6	4.239	1.761
97	8	8.687	-0.6865
98	3	3.648	-0.6483
99	8	6.117	1.883
100	8	7.738	0.2622
101	6	4.767	1.233
102	10	10	-0.002448
103	2	3.783	-1.783
104	9	7.375	1.625
105	6	5.623	0.377
106	6	7.886	-1.886
107	5	3.76	1.24
108	4	4.239	-0.2385
109	7	7.423	-0.4231
110	5	5.347	-0.3471
111	8	8.493	-0.4931
112	6	7.236	-1.236
113	9	7.36	1.64
114	6	6.197	-0.1972
115	4	3.727	0.2733
116	7	7.897	-0.8969
117	2	3.88	-1.88
118	8	8.969	-0.9694
119	9	7.997	1.003
120	6	6.242	-0.2418
121	5	5.536	-0.5365
122	7	6.795	0.2052
123	8	7.733	0.2675
124	4	5.636	-1.637
125	9	7.236	1.764
126	9	9.054	-0.05396
127	9	5.195	3.805
128	7	5.154	1.846
129	5	6.205	-1.205
130	7	7.598	-0.5984
131	9	9.509	-0.5093
132	8	7.403	0.5968
133	6	4.886	1.114
134	9	8.427	0.5728
135	8	8.577	-0.5765
136	7	6.568	0.4318
137	7	7.457	-0.4567
138	7	6.117	0.8827
139	8	7.579	0.4211
140	10	8.356	1.644
141	6	6.514	-0.5143
142	6	6.695	-0.6955

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 &  9 &  8.374 &  0.6264 \tabularnewline
2 &  8 &  8.034 & -0.03421 \tabularnewline
3 &  9 &  9.368 & -0.368 \tabularnewline
4 &  10 &  8.652 &  1.348 \tabularnewline
5 &  8 &  7.895 &  0.1053 \tabularnewline
6 &  8 &  7.368 &  0.6319 \tabularnewline
7 &  7 &  7.005 & -0.005456 \tabularnewline
8 &  7 &  6.921 &  0.0787 \tabularnewline
9 &  10 &  9.828 &  0.1723 \tabularnewline
10 &  8 &  6.799 &  1.201 \tabularnewline
11 &  7 &  6.084 &  0.9162 \tabularnewline
12 &  10 &  7.67 &  2.33 \tabularnewline
13 &  7 &  8.152 & -1.152 \tabularnewline
14 &  7 &  6.292 &  0.7081 \tabularnewline
15 &  9 &  8.63 &  0.3705 \tabularnewline
16 &  9 &  10.06 & -1.061 \tabularnewline
17 &  8 &  7.649 &  0.3513 \tabularnewline
18 &  6 &  7.686 & -1.686 \tabularnewline
19 &  8 &  7.244 &  0.7563 \tabularnewline
20 &  9 &  7.603 &  1.397 \tabularnewline
21 &  2 &  3.21 & -1.21 \tabularnewline
22 &  6 &  5.906 &  0.09438 \tabularnewline
23 &  8 &  8.032 & -0.0318 \tabularnewline
24 &  8 &  7.889 &  0.1115 \tabularnewline
25 &  7 &  7.423 & -0.4226 \tabularnewline
26 &  8 &  7.623 &  0.3771 \tabularnewline
27 &  6 &  5.775 &  0.2254 \tabularnewline
28 &  10 &  7.721 &  2.279 \tabularnewline
29 &  10 &  8.014 &  1.986 \tabularnewline
30 &  10 &  7.494 &  2.506 \tabularnewline
31 &  8 &  7.568 &  0.4324 \tabularnewline
32 &  8 &  8.626 & -0.6262 \tabularnewline
33 &  7 &  7.624 & -0.6239 \tabularnewline
34 &  10 &  9.426 &  0.5745 \tabularnewline
35 &  5 &  6.285 & -1.285 \tabularnewline
36 &  3 &  3.286 & -0.2862 \tabularnewline
37 &  2 &  3.876 & -1.876 \tabularnewline
38 &  3 &  4.206 & -1.206 \tabularnewline
39 &  4 &  5.746 & -1.746 \tabularnewline
40 &  2 &  3.684 & -1.684 \tabularnewline
41 &  6 &  5.182 &  0.8179 \tabularnewline
42 &  8 &  8.525 & -0.5255 \tabularnewline
43 &  8 &  6.759 &  1.241 \tabularnewline
44 &  5 &  5.294 & -0.2939 \tabularnewline
45 &  10 &  10.57 & -0.5714 \tabularnewline
46 &  9 &  10.72 & -1.722 \tabularnewline
47 &  8 &  9.864 & -1.864 \tabularnewline
48 &  9 &  9.078 & -0.07778 \tabularnewline
49 &  8 &  7.288 &  0.7123 \tabularnewline
50 &  5 &  5.969 & -0.9685 \tabularnewline
51 &  7 &  6.825 &  0.1745 \tabularnewline
52 &  9 &  9.107 & -0.1072 \tabularnewline
53 &  8 &  8.856 & -0.8559 \tabularnewline
54 &  4 &  7.885 & -3.885 \tabularnewline
55 &  7 &  7.048 & -0.0479 \tabularnewline
56 &  8 &  8.757 & -0.7566 \tabularnewline
57 &  7 &  6.933 &  0.067 \tabularnewline
58 &  7 &  7.567 & -0.5669 \tabularnewline
59 &  9 &  8.118 &  0.8816 \tabularnewline
60 &  6 &  7.088 & -1.088 \tabularnewline
61 &  7 &  7.439 & -0.439 \tabularnewline
62 &  4 &  5.444 & -1.444 \tabularnewline
63 &  6 &  7.299 & -1.299 \tabularnewline
64 &  10 &  7.908 &  2.092 \tabularnewline
65 &  9 &  8.579 &  0.4208 \tabularnewline
66 &  10 &  9.677 &  0.3234 \tabularnewline
67 &  8 &  7.531 &  0.4688 \tabularnewline
68 &  4 &  4.909 & -0.9091 \tabularnewline
69 &  8 &  9.201 & -1.201 \tabularnewline
70 &  5 &  5.743 & -0.7428 \tabularnewline
71 &  8 &  6.921 &  1.079 \tabularnewline
72 &  9 &  7.35 &  1.65 \tabularnewline
73 &  8 &  6.846 &  1.154 \tabularnewline
74 &  4 &  7.42 & -3.42 \tabularnewline
75 &  8 &  6.109 &  1.891 \tabularnewline
76 &  10 &  8.889 &  1.111 \tabularnewline
77 &  6 &  6.488 & -0.4881 \tabularnewline
78 &  7 &  7.235 & -0.2346 \tabularnewline
79 &  10 &  9.247 &  0.7528 \tabularnewline
80 &  9 &  9.086 & -0.08563 \tabularnewline
81 &  8 &  8.993 & -0.9927 \tabularnewline
82 &  3 &  5.443 & -2.443 \tabularnewline
83 &  8 &  6.416 &  1.584 \tabularnewline
84 &  7 &  6.868 &  0.1321 \tabularnewline
85 &  7 &  7.515 & -0.5149 \tabularnewline
86 &  8 &  6.731 &  1.269 \tabularnewline
87 &  8 &  8.537 & -0.5373 \tabularnewline
88 &  7 &  8.037 & -1.037 \tabularnewline
89 &  7 &  5.758 &  1.242 \tabularnewline
90 &  9 &  9.788 & -0.7881 \tabularnewline
91 &  9 &  8.445 &  0.5553 \tabularnewline
92 &  9 &  7.639 &  1.361 \tabularnewline
93 &  4 &  4.928 & -0.9282 \tabularnewline
94 &  6 &  6.582 & -0.5823 \tabularnewline
95 &  6 &  5.973 &  0.02665 \tabularnewline
96 &  6 &  4.239 &  1.761 \tabularnewline
97 &  8 &  8.687 & -0.6865 \tabularnewline
98 &  3 &  3.648 & -0.6483 \tabularnewline
99 &  8 &  6.117 &  1.883 \tabularnewline
100 &  8 &  7.738 &  0.2622 \tabularnewline
101 &  6 &  4.767 &  1.233 \tabularnewline
102 &  10 &  10 & -0.002448 \tabularnewline
103 &  2 &  3.783 & -1.783 \tabularnewline
104 &  9 &  7.375 &  1.625 \tabularnewline
105 &  6 &  5.623 &  0.377 \tabularnewline
106 &  6 &  7.886 & -1.886 \tabularnewline
107 &  5 &  3.76 &  1.24 \tabularnewline
108 &  4 &  4.239 & -0.2385 \tabularnewline
109 &  7 &  7.423 & -0.4231 \tabularnewline
110 &  5 &  5.347 & -0.3471 \tabularnewline
111 &  8 &  8.493 & -0.4931 \tabularnewline
112 &  6 &  7.236 & -1.236 \tabularnewline
113 &  9 &  7.36 &  1.64 \tabularnewline
114 &  6 &  6.197 & -0.1972 \tabularnewline
115 &  4 &  3.727 &  0.2733 \tabularnewline
116 &  7 &  7.897 & -0.8969 \tabularnewline
117 &  2 &  3.88 & -1.88 \tabularnewline
118 &  8 &  8.969 & -0.9694 \tabularnewline
119 &  9 &  7.997 &  1.003 \tabularnewline
120 &  6 &  6.242 & -0.2418 \tabularnewline
121 &  5 &  5.536 & -0.5365 \tabularnewline
122 &  7 &  6.795 &  0.2052 \tabularnewline
123 &  8 &  7.733 &  0.2675 \tabularnewline
124 &  4 &  5.636 & -1.637 \tabularnewline
125 &  9 &  7.236 &  1.764 \tabularnewline
126 &  9 &  9.054 & -0.05396 \tabularnewline
127 &  9 &  5.195 &  3.805 \tabularnewline
128 &  7 &  5.154 &  1.846 \tabularnewline
129 &  5 &  6.205 & -1.205 \tabularnewline
130 &  7 &  7.598 & -0.5984 \tabularnewline
131 &  9 &  9.509 & -0.5093 \tabularnewline
132 &  8 &  7.403 &  0.5968 \tabularnewline
133 &  6 &  4.886 &  1.114 \tabularnewline
134 &  9 &  8.427 &  0.5728 \tabularnewline
135 &  8 &  8.577 & -0.5765 \tabularnewline
136 &  7 &  6.568 &  0.4318 \tabularnewline
137 &  7 &  7.457 & -0.4567 \tabularnewline
138 &  7 &  6.117 &  0.8827 \tabularnewline
139 &  8 &  7.579 &  0.4211 \tabularnewline
140 &  10 &  8.356 &  1.644 \tabularnewline
141 &  6 &  6.514 & -0.5143 \tabularnewline
142 &  6 &  6.695 & -0.6955 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=314762&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] 9[/C][C] 8.374[/C][C] 0.6264[/C][/ROW]
[ROW][C]2[/C][C] 8[/C][C] 8.034[/C][C]-0.03421[/C][/ROW]
[ROW][C]3[/C][C] 9[/C][C] 9.368[/C][C]-0.368[/C][/ROW]
[ROW][C]4[/C][C] 10[/C][C] 8.652[/C][C] 1.348[/C][/ROW]
[ROW][C]5[/C][C] 8[/C][C] 7.895[/C][C] 0.1053[/C][/ROW]
[ROW][C]6[/C][C] 8[/C][C] 7.368[/C][C] 0.6319[/C][/ROW]
[ROW][C]7[/C][C] 7[/C][C] 7.005[/C][C]-0.005456[/C][/ROW]
[ROW][C]8[/C][C] 7[/C][C] 6.921[/C][C] 0.0787[/C][/ROW]
[ROW][C]9[/C][C] 10[/C][C] 9.828[/C][C] 0.1723[/C][/ROW]
[ROW][C]10[/C][C] 8[/C][C] 6.799[/C][C] 1.201[/C][/ROW]
[ROW][C]11[/C][C] 7[/C][C] 6.084[/C][C] 0.9162[/C][/ROW]
[ROW][C]12[/C][C] 10[/C][C] 7.67[/C][C] 2.33[/C][/ROW]
[ROW][C]13[/C][C] 7[/C][C] 8.152[/C][C]-1.152[/C][/ROW]
[ROW][C]14[/C][C] 7[/C][C] 6.292[/C][C] 0.7081[/C][/ROW]
[ROW][C]15[/C][C] 9[/C][C] 8.63[/C][C] 0.3705[/C][/ROW]
[ROW][C]16[/C][C] 9[/C][C] 10.06[/C][C]-1.061[/C][/ROW]
[ROW][C]17[/C][C] 8[/C][C] 7.649[/C][C] 0.3513[/C][/ROW]
[ROW][C]18[/C][C] 6[/C][C] 7.686[/C][C]-1.686[/C][/ROW]
[ROW][C]19[/C][C] 8[/C][C] 7.244[/C][C] 0.7563[/C][/ROW]
[ROW][C]20[/C][C] 9[/C][C] 7.603[/C][C] 1.397[/C][/ROW]
[ROW][C]21[/C][C] 2[/C][C] 3.21[/C][C]-1.21[/C][/ROW]
[ROW][C]22[/C][C] 6[/C][C] 5.906[/C][C] 0.09438[/C][/ROW]
[ROW][C]23[/C][C] 8[/C][C] 8.032[/C][C]-0.0318[/C][/ROW]
[ROW][C]24[/C][C] 8[/C][C] 7.889[/C][C] 0.1115[/C][/ROW]
[ROW][C]25[/C][C] 7[/C][C] 7.423[/C][C]-0.4226[/C][/ROW]
[ROW][C]26[/C][C] 8[/C][C] 7.623[/C][C] 0.3771[/C][/ROW]
[ROW][C]27[/C][C] 6[/C][C] 5.775[/C][C] 0.2254[/C][/ROW]
[ROW][C]28[/C][C] 10[/C][C] 7.721[/C][C] 2.279[/C][/ROW]
[ROW][C]29[/C][C] 10[/C][C] 8.014[/C][C] 1.986[/C][/ROW]
[ROW][C]30[/C][C] 10[/C][C] 7.494[/C][C] 2.506[/C][/ROW]
[ROW][C]31[/C][C] 8[/C][C] 7.568[/C][C] 0.4324[/C][/ROW]
[ROW][C]32[/C][C] 8[/C][C] 8.626[/C][C]-0.6262[/C][/ROW]
[ROW][C]33[/C][C] 7[/C][C] 7.624[/C][C]-0.6239[/C][/ROW]
[ROW][C]34[/C][C] 10[/C][C] 9.426[/C][C] 0.5745[/C][/ROW]
[ROW][C]35[/C][C] 5[/C][C] 6.285[/C][C]-1.285[/C][/ROW]
[ROW][C]36[/C][C] 3[/C][C] 3.286[/C][C]-0.2862[/C][/ROW]
[ROW][C]37[/C][C] 2[/C][C] 3.876[/C][C]-1.876[/C][/ROW]
[ROW][C]38[/C][C] 3[/C][C] 4.206[/C][C]-1.206[/C][/ROW]
[ROW][C]39[/C][C] 4[/C][C] 5.746[/C][C]-1.746[/C][/ROW]
[ROW][C]40[/C][C] 2[/C][C] 3.684[/C][C]-1.684[/C][/ROW]
[ROW][C]41[/C][C] 6[/C][C] 5.182[/C][C] 0.8179[/C][/ROW]
[ROW][C]42[/C][C] 8[/C][C] 8.525[/C][C]-0.5255[/C][/ROW]
[ROW][C]43[/C][C] 8[/C][C] 6.759[/C][C] 1.241[/C][/ROW]
[ROW][C]44[/C][C] 5[/C][C] 5.294[/C][C]-0.2939[/C][/ROW]
[ROW][C]45[/C][C] 10[/C][C] 10.57[/C][C]-0.5714[/C][/ROW]
[ROW][C]46[/C][C] 9[/C][C] 10.72[/C][C]-1.722[/C][/ROW]
[ROW][C]47[/C][C] 8[/C][C] 9.864[/C][C]-1.864[/C][/ROW]
[ROW][C]48[/C][C] 9[/C][C] 9.078[/C][C]-0.07778[/C][/ROW]
[ROW][C]49[/C][C] 8[/C][C] 7.288[/C][C] 0.7123[/C][/ROW]
[ROW][C]50[/C][C] 5[/C][C] 5.969[/C][C]-0.9685[/C][/ROW]
[ROW][C]51[/C][C] 7[/C][C] 6.825[/C][C] 0.1745[/C][/ROW]
[ROW][C]52[/C][C] 9[/C][C] 9.107[/C][C]-0.1072[/C][/ROW]
[ROW][C]53[/C][C] 8[/C][C] 8.856[/C][C]-0.8559[/C][/ROW]
[ROW][C]54[/C][C] 4[/C][C] 7.885[/C][C]-3.885[/C][/ROW]
[ROW][C]55[/C][C] 7[/C][C] 7.048[/C][C]-0.0479[/C][/ROW]
[ROW][C]56[/C][C] 8[/C][C] 8.757[/C][C]-0.7566[/C][/ROW]
[ROW][C]57[/C][C] 7[/C][C] 6.933[/C][C] 0.067[/C][/ROW]
[ROW][C]58[/C][C] 7[/C][C] 7.567[/C][C]-0.5669[/C][/ROW]
[ROW][C]59[/C][C] 9[/C][C] 8.118[/C][C] 0.8816[/C][/ROW]
[ROW][C]60[/C][C] 6[/C][C] 7.088[/C][C]-1.088[/C][/ROW]
[ROW][C]61[/C][C] 7[/C][C] 7.439[/C][C]-0.439[/C][/ROW]
[ROW][C]62[/C][C] 4[/C][C] 5.444[/C][C]-1.444[/C][/ROW]
[ROW][C]63[/C][C] 6[/C][C] 7.299[/C][C]-1.299[/C][/ROW]
[ROW][C]64[/C][C] 10[/C][C] 7.908[/C][C] 2.092[/C][/ROW]
[ROW][C]65[/C][C] 9[/C][C] 8.579[/C][C] 0.4208[/C][/ROW]
[ROW][C]66[/C][C] 10[/C][C] 9.677[/C][C] 0.3234[/C][/ROW]
[ROW][C]67[/C][C] 8[/C][C] 7.531[/C][C] 0.4688[/C][/ROW]
[ROW][C]68[/C][C] 4[/C][C] 4.909[/C][C]-0.9091[/C][/ROW]
[ROW][C]69[/C][C] 8[/C][C] 9.201[/C][C]-1.201[/C][/ROW]
[ROW][C]70[/C][C] 5[/C][C] 5.743[/C][C]-0.7428[/C][/ROW]
[ROW][C]71[/C][C] 8[/C][C] 6.921[/C][C] 1.079[/C][/ROW]
[ROW][C]72[/C][C] 9[/C][C] 7.35[/C][C] 1.65[/C][/ROW]
[ROW][C]73[/C][C] 8[/C][C] 6.846[/C][C] 1.154[/C][/ROW]
[ROW][C]74[/C][C] 4[/C][C] 7.42[/C][C]-3.42[/C][/ROW]
[ROW][C]75[/C][C] 8[/C][C] 6.109[/C][C] 1.891[/C][/ROW]
[ROW][C]76[/C][C] 10[/C][C] 8.889[/C][C] 1.111[/C][/ROW]
[ROW][C]77[/C][C] 6[/C][C] 6.488[/C][C]-0.4881[/C][/ROW]
[ROW][C]78[/C][C] 7[/C][C] 7.235[/C][C]-0.2346[/C][/ROW]
[ROW][C]79[/C][C] 10[/C][C] 9.247[/C][C] 0.7528[/C][/ROW]
[ROW][C]80[/C][C] 9[/C][C] 9.086[/C][C]-0.08563[/C][/ROW]
[ROW][C]81[/C][C] 8[/C][C] 8.993[/C][C]-0.9927[/C][/ROW]
[ROW][C]82[/C][C] 3[/C][C] 5.443[/C][C]-2.443[/C][/ROW]
[ROW][C]83[/C][C] 8[/C][C] 6.416[/C][C] 1.584[/C][/ROW]
[ROW][C]84[/C][C] 7[/C][C] 6.868[/C][C] 0.1321[/C][/ROW]
[ROW][C]85[/C][C] 7[/C][C] 7.515[/C][C]-0.5149[/C][/ROW]
[ROW][C]86[/C][C] 8[/C][C] 6.731[/C][C] 1.269[/C][/ROW]
[ROW][C]87[/C][C] 8[/C][C] 8.537[/C][C]-0.5373[/C][/ROW]
[ROW][C]88[/C][C] 7[/C][C] 8.037[/C][C]-1.037[/C][/ROW]
[ROW][C]89[/C][C] 7[/C][C] 5.758[/C][C] 1.242[/C][/ROW]
[ROW][C]90[/C][C] 9[/C][C] 9.788[/C][C]-0.7881[/C][/ROW]
[ROW][C]91[/C][C] 9[/C][C] 8.445[/C][C] 0.5553[/C][/ROW]
[ROW][C]92[/C][C] 9[/C][C] 7.639[/C][C] 1.361[/C][/ROW]
[ROW][C]93[/C][C] 4[/C][C] 4.928[/C][C]-0.9282[/C][/ROW]
[ROW][C]94[/C][C] 6[/C][C] 6.582[/C][C]-0.5823[/C][/ROW]
[ROW][C]95[/C][C] 6[/C][C] 5.973[/C][C] 0.02665[/C][/ROW]
[ROW][C]96[/C][C] 6[/C][C] 4.239[/C][C] 1.761[/C][/ROW]
[ROW][C]97[/C][C] 8[/C][C] 8.687[/C][C]-0.6865[/C][/ROW]
[ROW][C]98[/C][C] 3[/C][C] 3.648[/C][C]-0.6483[/C][/ROW]
[ROW][C]99[/C][C] 8[/C][C] 6.117[/C][C] 1.883[/C][/ROW]
[ROW][C]100[/C][C] 8[/C][C] 7.738[/C][C] 0.2622[/C][/ROW]
[ROW][C]101[/C][C] 6[/C][C] 4.767[/C][C] 1.233[/C][/ROW]
[ROW][C]102[/C][C] 10[/C][C] 10[/C][C]-0.002448[/C][/ROW]
[ROW][C]103[/C][C] 2[/C][C] 3.783[/C][C]-1.783[/C][/ROW]
[ROW][C]104[/C][C] 9[/C][C] 7.375[/C][C] 1.625[/C][/ROW]
[ROW][C]105[/C][C] 6[/C][C] 5.623[/C][C] 0.377[/C][/ROW]
[ROW][C]106[/C][C] 6[/C][C] 7.886[/C][C]-1.886[/C][/ROW]
[ROW][C]107[/C][C] 5[/C][C] 3.76[/C][C] 1.24[/C][/ROW]
[ROW][C]108[/C][C] 4[/C][C] 4.239[/C][C]-0.2385[/C][/ROW]
[ROW][C]109[/C][C] 7[/C][C] 7.423[/C][C]-0.4231[/C][/ROW]
[ROW][C]110[/C][C] 5[/C][C] 5.347[/C][C]-0.3471[/C][/ROW]
[ROW][C]111[/C][C] 8[/C][C] 8.493[/C][C]-0.4931[/C][/ROW]
[ROW][C]112[/C][C] 6[/C][C] 7.236[/C][C]-1.236[/C][/ROW]
[ROW][C]113[/C][C] 9[/C][C] 7.36[/C][C] 1.64[/C][/ROW]
[ROW][C]114[/C][C] 6[/C][C] 6.197[/C][C]-0.1972[/C][/ROW]
[ROW][C]115[/C][C] 4[/C][C] 3.727[/C][C] 0.2733[/C][/ROW]
[ROW][C]116[/C][C] 7[/C][C] 7.897[/C][C]-0.8969[/C][/ROW]
[ROW][C]117[/C][C] 2[/C][C] 3.88[/C][C]-1.88[/C][/ROW]
[ROW][C]118[/C][C] 8[/C][C] 8.969[/C][C]-0.9694[/C][/ROW]
[ROW][C]119[/C][C] 9[/C][C] 7.997[/C][C] 1.003[/C][/ROW]
[ROW][C]120[/C][C] 6[/C][C] 6.242[/C][C]-0.2418[/C][/ROW]
[ROW][C]121[/C][C] 5[/C][C] 5.536[/C][C]-0.5365[/C][/ROW]
[ROW][C]122[/C][C] 7[/C][C] 6.795[/C][C] 0.2052[/C][/ROW]
[ROW][C]123[/C][C] 8[/C][C] 7.733[/C][C] 0.2675[/C][/ROW]
[ROW][C]124[/C][C] 4[/C][C] 5.636[/C][C]-1.637[/C][/ROW]
[ROW][C]125[/C][C] 9[/C][C] 7.236[/C][C] 1.764[/C][/ROW]
[ROW][C]126[/C][C] 9[/C][C] 9.054[/C][C]-0.05396[/C][/ROW]
[ROW][C]127[/C][C] 9[/C][C] 5.195[/C][C] 3.805[/C][/ROW]
[ROW][C]128[/C][C] 7[/C][C] 5.154[/C][C] 1.846[/C][/ROW]
[ROW][C]129[/C][C] 5[/C][C] 6.205[/C][C]-1.205[/C][/ROW]
[ROW][C]130[/C][C] 7[/C][C] 7.598[/C][C]-0.5984[/C][/ROW]
[ROW][C]131[/C][C] 9[/C][C] 9.509[/C][C]-0.5093[/C][/ROW]
[ROW][C]132[/C][C] 8[/C][C] 7.403[/C][C] 0.5968[/C][/ROW]
[ROW][C]133[/C][C] 6[/C][C] 4.886[/C][C] 1.114[/C][/ROW]
[ROW][C]134[/C][C] 9[/C][C] 8.427[/C][C] 0.5728[/C][/ROW]
[ROW][C]135[/C][C] 8[/C][C] 8.577[/C][C]-0.5765[/C][/ROW]
[ROW][C]136[/C][C] 7[/C][C] 6.568[/C][C] 0.4318[/C][/ROW]
[ROW][C]137[/C][C] 7[/C][C] 7.457[/C][C]-0.4567[/C][/ROW]
[ROW][C]138[/C][C] 7[/C][C] 6.117[/C][C] 0.8827[/C][/ROW]
[ROW][C]139[/C][C] 8[/C][C] 7.579[/C][C] 0.4211[/C][/ROW]
[ROW][C]140[/C][C] 10[/C][C] 8.356[/C][C] 1.644[/C][/ROW]
[ROW][C]141[/C][C] 6[/C][C] 6.514[/C][C]-0.5143[/C][/ROW]
[ROW][C]142[/C][C] 6[/C][C] 6.695[/C][C]-0.6955[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=314762&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=314762&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	9	8.374	0.6264
2	8	8.034	-0.03421
3	9	9.368	-0.368
4	10	8.652	1.348
5	8	7.895	0.1053
6	8	7.368	0.6319
7	7	7.005	-0.005456
8	7	6.921	0.0787
9	10	9.828	0.1723
10	8	6.799	1.201
11	7	6.084	0.9162
12	10	7.67	2.33
13	7	8.152	-1.152
14	7	6.292	0.7081
15	9	8.63	0.3705
16	9	10.06	-1.061
17	8	7.649	0.3513
18	6	7.686	-1.686
19	8	7.244	0.7563
20	9	7.603	1.397
21	2	3.21	-1.21
22	6	5.906	0.09438
23	8	8.032	-0.0318
24	8	7.889	0.1115
25	7	7.423	-0.4226
26	8	7.623	0.3771
27	6	5.775	0.2254
28	10	7.721	2.279
29	10	8.014	1.986
30	10	7.494	2.506
31	8	7.568	0.4324
32	8	8.626	-0.6262
33	7	7.624	-0.6239
34	10	9.426	0.5745
35	5	6.285	-1.285
36	3	3.286	-0.2862
37	2	3.876	-1.876
38	3	4.206	-1.206
39	4	5.746	-1.746
40	2	3.684	-1.684
41	6	5.182	0.8179
42	8	8.525	-0.5255
43	8	6.759	1.241
44	5	5.294	-0.2939
45	10	10.57	-0.5714
46	9	10.72	-1.722
47	8	9.864	-1.864
48	9	9.078	-0.07778
49	8	7.288	0.7123
50	5	5.969	-0.9685
51	7	6.825	0.1745
52	9	9.107	-0.1072
53	8	8.856	-0.8559
54	4	7.885	-3.885
55	7	7.048	-0.0479
56	8	8.757	-0.7566
57	7	6.933	0.067
58	7	7.567	-0.5669
59	9	8.118	0.8816
60	6	7.088	-1.088
61	7	7.439	-0.439
62	4	5.444	-1.444
63	6	7.299	-1.299
64	10	7.908	2.092
65	9	8.579	0.4208
66	10	9.677	0.3234
67	8	7.531	0.4688
68	4	4.909	-0.9091
69	8	9.201	-1.201
70	5	5.743	-0.7428
71	8	6.921	1.079
72	9	7.35	1.65
73	8	6.846	1.154
74	4	7.42	-3.42
75	8	6.109	1.891
76	10	8.889	1.111
77	6	6.488	-0.4881
78	7	7.235	-0.2346
79	10	9.247	0.7528
80	9	9.086	-0.08563
81	8	8.993	-0.9927
82	3	5.443	-2.443
83	8	6.416	1.584
84	7	6.868	0.1321
85	7	7.515	-0.5149
86	8	6.731	1.269
87	8	8.537	-0.5373
88	7	8.037	-1.037
89	7	5.758	1.242
90	9	9.788	-0.7881
91	9	8.445	0.5553
92	9	7.639	1.361
93	4	4.928	-0.9282
94	6	6.582	-0.5823
95	6	5.973	0.02665
96	6	4.239	1.761
97	8	8.687	-0.6865
98	3	3.648	-0.6483
99	8	6.117	1.883
100	8	7.738	0.2622
101	6	4.767	1.233
102	10	10	-0.002448
103	2	3.783	-1.783
104	9	7.375	1.625
105	6	5.623	0.377
106	6	7.886	-1.886
107	5	3.76	1.24
108	4	4.239	-0.2385
109	7	7.423	-0.4231
110	5	5.347	-0.3471
111	8	8.493	-0.4931
112	6	7.236	-1.236
113	9	7.36	1.64
114	6	6.197	-0.1972
115	4	3.727	0.2733
116	7	7.897	-0.8969
117	2	3.88	-1.88
118	8	8.969	-0.9694
119	9	7.997	1.003
120	6	6.242	-0.2418
121	5	5.536	-0.5365
122	7	6.795	0.2052
123	8	7.733	0.2675
124	4	5.636	-1.637
125	9	7.236	1.764
126	9	9.054	-0.05396
127	9	5.195	3.805
128	7	5.154	1.846
129	5	6.205	-1.205
130	7	7.598	-0.5984
131	9	9.509	-0.5093
132	8	7.403	0.5968
133	6	4.886	1.114
134	9	8.427	0.5728
135	8	8.577	-0.5765
136	7	6.568	0.4318
137	7	7.457	-0.4567
138	7	6.117	0.8827
139	8	7.579	0.4211
140	10	8.356	1.644
141	6	6.514	-0.5143
142	6	6.695	-0.6955

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
24	0.1392	0.2783	0.8608
25	0.06309	0.1262	0.9369
26	0.02835	0.05669	0.9717
27	0.01065	0.02131	0.9893
28	0.00584	0.01168	0.9942
29	0.06853	0.1371	0.9315
30	0.2994	0.5988	0.7006
31	0.2276	0.4551	0.7724
32	0.357	0.714	0.643
33	0.3037	0.6073	0.6963
34	0.2396	0.4791	0.7604
35	0.2274	0.4548	0.7726
36	0.1744	0.3487	0.8256
37	0.1448	0.2896	0.8552
38	0.1299	0.2597	0.8701
39	0.1889	0.3778	0.8111
40	0.2107	0.4214	0.7893
41	0.1686	0.3372	0.8314
42	0.2645	0.529	0.7355
43	0.2565	0.513	0.7435
44	0.2036	0.4072	0.7964
45	0.175	0.3501	0.825
46	0.1926	0.3852	0.8074
47	0.2672	0.5344	0.7328
48	0.2171	0.4341	0.7829
49	0.193	0.386	0.807
50	0.1571	0.3143	0.8429
51	0.1294	0.2589	0.8706
52	0.09902	0.198	0.901
53	0.1144	0.2288	0.8856
54	0.4413	0.8826	0.5587
55	0.4006	0.8013	0.5994
56	0.3708	0.7416	0.6292
57	0.3217	0.6434	0.6783
58	0.2898	0.5797	0.7102
59	0.3048	0.6096	0.6952
60	0.2984	0.5968	0.7016
61	0.2596	0.5192	0.7404
62	0.2512	0.5023	0.7488
63	0.2319	0.4638	0.7681
64	0.3693	0.7386	0.6307
65	0.3236	0.6473	0.6764
66	0.2875	0.575	0.7125
67	0.2737	0.5473	0.7263
68	0.2637	0.5273	0.7363
69	0.2469	0.4937	0.7531
70	0.2251	0.4501	0.7749
71	0.3045	0.609	0.6955
72	0.3726	0.7452	0.6274
73	0.3549	0.7097	0.6451
74	0.6188	0.7625	0.3812
75	0.6536	0.6928	0.3464
76	0.6701	0.6599	0.3299
77	0.6232	0.7536	0.3768
78	0.5722	0.8557	0.4278
79	0.5503	0.8994	0.4497
80	0.4954	0.9908	0.5046
81	0.4688	0.9377	0.5312
82	0.5847	0.8307	0.4153
83	0.6589	0.6821	0.3411
84	0.6037	0.7926	0.3963
85	0.5739	0.8522	0.4261
86	0.5714	0.8571	0.4286
87	0.5282	0.9435	0.4718
88	0.5286	0.9429	0.4714
89	0.5522	0.8956	0.4478
90	0.5149	0.9702	0.4851
91	0.5144	0.9711	0.4856
92	0.5198	0.9604	0.4802
93	0.5259	0.9482	0.4741
94	0.4985	0.997	0.5015
95	0.4472	0.8945	0.5528
96	0.558	0.884	0.442
97	0.5007	0.9985	0.4993
98	0.4742	0.9484	0.5258
99	0.5444	0.9111	0.4556
100	0.5401	0.9197	0.4599
101	0.5227	0.9545	0.4773
102	0.5314	0.9373	0.4686
103	0.5503	0.8993	0.4497
104	0.5466	0.9068	0.4534
105	0.4707	0.9414	0.5293
106	0.4518	0.9036	0.5482
107	0.4815	0.9629	0.5185
108	0.4075	0.8151	0.5925
109	0.3616	0.7233	0.6384
110	0.2851	0.5701	0.7149
111	0.3249	0.6497	0.6751
112	0.5911	0.8179	0.4089
113	0.5478	0.9044	0.4522
114	0.4502	0.9004	0.5498
115	0.369	0.738	0.631
116	0.2603	0.5206	0.7397
117	0.5607	0.8786	0.4393
118	0.5742	0.8517	0.4258

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
24 &  0.1392 &  0.2783 &  0.8608 \tabularnewline
25 &  0.06309 &  0.1262 &  0.9369 \tabularnewline
26 &  0.02835 &  0.05669 &  0.9717 \tabularnewline
27 &  0.01065 &  0.02131 &  0.9893 \tabularnewline
28 &  0.00584 &  0.01168 &  0.9942 \tabularnewline
29 &  0.06853 &  0.1371 &  0.9315 \tabularnewline
30 &  0.2994 &  0.5988 &  0.7006 \tabularnewline
31 &  0.2276 &  0.4551 &  0.7724 \tabularnewline
32 &  0.357 &  0.714 &  0.643 \tabularnewline
33 &  0.3037 &  0.6073 &  0.6963 \tabularnewline
34 &  0.2396 &  0.4791 &  0.7604 \tabularnewline
35 &  0.2274 &  0.4548 &  0.7726 \tabularnewline
36 &  0.1744 &  0.3487 &  0.8256 \tabularnewline
37 &  0.1448 &  0.2896 &  0.8552 \tabularnewline
38 &  0.1299 &  0.2597 &  0.8701 \tabularnewline
39 &  0.1889 &  0.3778 &  0.8111 \tabularnewline
40 &  0.2107 &  0.4214 &  0.7893 \tabularnewline
41 &  0.1686 &  0.3372 &  0.8314 \tabularnewline
42 &  0.2645 &  0.529 &  0.7355 \tabularnewline
43 &  0.2565 &  0.513 &  0.7435 \tabularnewline
44 &  0.2036 &  0.4072 &  0.7964 \tabularnewline
45 &  0.175 &  0.3501 &  0.825 \tabularnewline
46 &  0.1926 &  0.3852 &  0.8074 \tabularnewline
47 &  0.2672 &  0.5344 &  0.7328 \tabularnewline
48 &  0.2171 &  0.4341 &  0.7829 \tabularnewline
49 &  0.193 &  0.386 &  0.807 \tabularnewline
50 &  0.1571 &  0.3143 &  0.8429 \tabularnewline
51 &  0.1294 &  0.2589 &  0.8706 \tabularnewline
52 &  0.09902 &  0.198 &  0.901 \tabularnewline
53 &  0.1144 &  0.2288 &  0.8856 \tabularnewline
54 &  0.4413 &  0.8826 &  0.5587 \tabularnewline
55 &  0.4006 &  0.8013 &  0.5994 \tabularnewline
56 &  0.3708 &  0.7416 &  0.6292 \tabularnewline
57 &  0.3217 &  0.6434 &  0.6783 \tabularnewline
58 &  0.2898 &  0.5797 &  0.7102 \tabularnewline
59 &  0.3048 &  0.6096 &  0.6952 \tabularnewline
60 &  0.2984 &  0.5968 &  0.7016 \tabularnewline
61 &  0.2596 &  0.5192 &  0.7404 \tabularnewline
62 &  0.2512 &  0.5023 &  0.7488 \tabularnewline
63 &  0.2319 &  0.4638 &  0.7681 \tabularnewline
64 &  0.3693 &  0.7386 &  0.6307 \tabularnewline
65 &  0.3236 &  0.6473 &  0.6764 \tabularnewline
66 &  0.2875 &  0.575 &  0.7125 \tabularnewline
67 &  0.2737 &  0.5473 &  0.7263 \tabularnewline
68 &  0.2637 &  0.5273 &  0.7363 \tabularnewline
69 &  0.2469 &  0.4937 &  0.7531 \tabularnewline
70 &  0.2251 &  0.4501 &  0.7749 \tabularnewline
71 &  0.3045 &  0.609 &  0.6955 \tabularnewline
72 &  0.3726 &  0.7452 &  0.6274 \tabularnewline
73 &  0.3549 &  0.7097 &  0.6451 \tabularnewline
74 &  0.6188 &  0.7625 &  0.3812 \tabularnewline
75 &  0.6536 &  0.6928 &  0.3464 \tabularnewline
76 &  0.6701 &  0.6599 &  0.3299 \tabularnewline
77 &  0.6232 &  0.7536 &  0.3768 \tabularnewline
78 &  0.5722 &  0.8557 &  0.4278 \tabularnewline
79 &  0.5503 &  0.8994 &  0.4497 \tabularnewline
80 &  0.4954 &  0.9908 &  0.5046 \tabularnewline
81 &  0.4688 &  0.9377 &  0.5312 \tabularnewline
82 &  0.5847 &  0.8307 &  0.4153 \tabularnewline
83 &  0.6589 &  0.6821 &  0.3411 \tabularnewline
84 &  0.6037 &  0.7926 &  0.3963 \tabularnewline
85 &  0.5739 &  0.8522 &  0.4261 \tabularnewline
86 &  0.5714 &  0.8571 &  0.4286 \tabularnewline
87 &  0.5282 &  0.9435 &  0.4718 \tabularnewline
88 &  0.5286 &  0.9429 &  0.4714 \tabularnewline
89 &  0.5522 &  0.8956 &  0.4478 \tabularnewline
90 &  0.5149 &  0.9702 &  0.4851 \tabularnewline
91 &  0.5144 &  0.9711 &  0.4856 \tabularnewline
92 &  0.5198 &  0.9604 &  0.4802 \tabularnewline
93 &  0.5259 &  0.9482 &  0.4741 \tabularnewline
94 &  0.4985 &  0.997 &  0.5015 \tabularnewline
95 &  0.4472 &  0.8945 &  0.5528 \tabularnewline
96 &  0.558 &  0.884 &  0.442 \tabularnewline
97 &  0.5007 &  0.9985 &  0.4993 \tabularnewline
98 &  0.4742 &  0.9484 &  0.5258 \tabularnewline
99 &  0.5444 &  0.9111 &  0.4556 \tabularnewline
100 &  0.5401 &  0.9197 &  0.4599 \tabularnewline
101 &  0.5227 &  0.9545 &  0.4773 \tabularnewline
102 &  0.5314 &  0.9373 &  0.4686 \tabularnewline
103 &  0.5503 &  0.8993 &  0.4497 \tabularnewline
104 &  0.5466 &  0.9068 &  0.4534 \tabularnewline
105 &  0.4707 &  0.9414 &  0.5293 \tabularnewline
106 &  0.4518 &  0.9036 &  0.5482 \tabularnewline
107 &  0.4815 &  0.9629 &  0.5185 \tabularnewline
108 &  0.4075 &  0.8151 &  0.5925 \tabularnewline
109 &  0.3616 &  0.7233 &  0.6384 \tabularnewline
110 &  0.2851 &  0.5701 &  0.7149 \tabularnewline
111 &  0.3249 &  0.6497 &  0.6751 \tabularnewline
112 &  0.5911 &  0.8179 &  0.4089 \tabularnewline
113 &  0.5478 &  0.9044 &  0.4522 \tabularnewline
114 &  0.4502 &  0.9004 &  0.5498 \tabularnewline
115 &  0.369 &  0.738 &  0.631 \tabularnewline
116 &  0.2603 &  0.5206 &  0.7397 \tabularnewline
117 &  0.5607 &  0.8786 &  0.4393 \tabularnewline
118 &  0.5742 &  0.8517 &  0.4258 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=314762&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]24[/C][C] 0.1392[/C][C] 0.2783[/C][C] 0.8608[/C][/ROW]
[ROW][C]25[/C][C] 0.06309[/C][C] 0.1262[/C][C] 0.9369[/C][/ROW]
[ROW][C]26[/C][C] 0.02835[/C][C] 0.05669[/C][C] 0.9717[/C][/ROW]
[ROW][C]27[/C][C] 0.01065[/C][C] 0.02131[/C][C] 0.9893[/C][/ROW]
[ROW][C]28[/C][C] 0.00584[/C][C] 0.01168[/C][C] 0.9942[/C][/ROW]
[ROW][C]29[/C][C] 0.06853[/C][C] 0.1371[/C][C] 0.9315[/C][/ROW]
[ROW][C]30[/C][C] 0.2994[/C][C] 0.5988[/C][C] 0.7006[/C][/ROW]
[ROW][C]31[/C][C] 0.2276[/C][C] 0.4551[/C][C] 0.7724[/C][/ROW]
[ROW][C]32[/C][C] 0.357[/C][C] 0.714[/C][C] 0.643[/C][/ROW]
[ROW][C]33[/C][C] 0.3037[/C][C] 0.6073[/C][C] 0.6963[/C][/ROW]
[ROW][C]34[/C][C] 0.2396[/C][C] 0.4791[/C][C] 0.7604[/C][/ROW]
[ROW][C]35[/C][C] 0.2274[/C][C] 0.4548[/C][C] 0.7726[/C][/ROW]
[ROW][C]36[/C][C] 0.1744[/C][C] 0.3487[/C][C] 0.8256[/C][/ROW]
[ROW][C]37[/C][C] 0.1448[/C][C] 0.2896[/C][C] 0.8552[/C][/ROW]
[ROW][C]38[/C][C] 0.1299[/C][C] 0.2597[/C][C] 0.8701[/C][/ROW]
[ROW][C]39[/C][C] 0.1889[/C][C] 0.3778[/C][C] 0.8111[/C][/ROW]
[ROW][C]40[/C][C] 0.2107[/C][C] 0.4214[/C][C] 0.7893[/C][/ROW]
[ROW][C]41[/C][C] 0.1686[/C][C] 0.3372[/C][C] 0.8314[/C][/ROW]
[ROW][C]42[/C][C] 0.2645[/C][C] 0.529[/C][C] 0.7355[/C][/ROW]
[ROW][C]43[/C][C] 0.2565[/C][C] 0.513[/C][C] 0.7435[/C][/ROW]
[ROW][C]44[/C][C] 0.2036[/C][C] 0.4072[/C][C] 0.7964[/C][/ROW]
[ROW][C]45[/C][C] 0.175[/C][C] 0.3501[/C][C] 0.825[/C][/ROW]
[ROW][C]46[/C][C] 0.1926[/C][C] 0.3852[/C][C] 0.8074[/C][/ROW]
[ROW][C]47[/C][C] 0.2672[/C][C] 0.5344[/C][C] 0.7328[/C][/ROW]
[ROW][C]48[/C][C] 0.2171[/C][C] 0.4341[/C][C] 0.7829[/C][/ROW]
[ROW][C]49[/C][C] 0.193[/C][C] 0.386[/C][C] 0.807[/C][/ROW]
[ROW][C]50[/C][C] 0.1571[/C][C] 0.3143[/C][C] 0.8429[/C][/ROW]
[ROW][C]51[/C][C] 0.1294[/C][C] 0.2589[/C][C] 0.8706[/C][/ROW]
[ROW][C]52[/C][C] 0.09902[/C][C] 0.198[/C][C] 0.901[/C][/ROW]
[ROW][C]53[/C][C] 0.1144[/C][C] 0.2288[/C][C] 0.8856[/C][/ROW]
[ROW][C]54[/C][C] 0.4413[/C][C] 0.8826[/C][C] 0.5587[/C][/ROW]
[ROW][C]55[/C][C] 0.4006[/C][C] 0.8013[/C][C] 0.5994[/C][/ROW]
[ROW][C]56[/C][C] 0.3708[/C][C] 0.7416[/C][C] 0.6292[/C][/ROW]
[ROW][C]57[/C][C] 0.3217[/C][C] 0.6434[/C][C] 0.6783[/C][/ROW]
[ROW][C]58[/C][C] 0.2898[/C][C] 0.5797[/C][C] 0.7102[/C][/ROW]
[ROW][C]59[/C][C] 0.3048[/C][C] 0.6096[/C][C] 0.6952[/C][/ROW]
[ROW][C]60[/C][C] 0.2984[/C][C] 0.5968[/C][C] 0.7016[/C][/ROW]
[ROW][C]61[/C][C] 0.2596[/C][C] 0.5192[/C][C] 0.7404[/C][/ROW]
[ROW][C]62[/C][C] 0.2512[/C][C] 0.5023[/C][C] 0.7488[/C][/ROW]
[ROW][C]63[/C][C] 0.2319[/C][C] 0.4638[/C][C] 0.7681[/C][/ROW]
[ROW][C]64[/C][C] 0.3693[/C][C] 0.7386[/C][C] 0.6307[/C][/ROW]
[ROW][C]65[/C][C] 0.3236[/C][C] 0.6473[/C][C] 0.6764[/C][/ROW]
[ROW][C]66[/C][C] 0.2875[/C][C] 0.575[/C][C] 0.7125[/C][/ROW]
[ROW][C]67[/C][C] 0.2737[/C][C] 0.5473[/C][C] 0.7263[/C][/ROW]
[ROW][C]68[/C][C] 0.2637[/C][C] 0.5273[/C][C] 0.7363[/C][/ROW]
[ROW][C]69[/C][C] 0.2469[/C][C] 0.4937[/C][C] 0.7531[/C][/ROW]
[ROW][C]70[/C][C] 0.2251[/C][C] 0.4501[/C][C] 0.7749[/C][/ROW]
[ROW][C]71[/C][C] 0.3045[/C][C] 0.609[/C][C] 0.6955[/C][/ROW]
[ROW][C]72[/C][C] 0.3726[/C][C] 0.7452[/C][C] 0.6274[/C][/ROW]
[ROW][C]73[/C][C] 0.3549[/C][C] 0.7097[/C][C] 0.6451[/C][/ROW]
[ROW][C]74[/C][C] 0.6188[/C][C] 0.7625[/C][C] 0.3812[/C][/ROW]
[ROW][C]75[/C][C] 0.6536[/C][C] 0.6928[/C][C] 0.3464[/C][/ROW]
[ROW][C]76[/C][C] 0.6701[/C][C] 0.6599[/C][C] 0.3299[/C][/ROW]
[ROW][C]77[/C][C] 0.6232[/C][C] 0.7536[/C][C] 0.3768[/C][/ROW]
[ROW][C]78[/C][C] 0.5722[/C][C] 0.8557[/C][C] 0.4278[/C][/ROW]
[ROW][C]79[/C][C] 0.5503[/C][C] 0.8994[/C][C] 0.4497[/C][/ROW]
[ROW][C]80[/C][C] 0.4954[/C][C] 0.9908[/C][C] 0.5046[/C][/ROW]
[ROW][C]81[/C][C] 0.4688[/C][C] 0.9377[/C][C] 0.5312[/C][/ROW]
[ROW][C]82[/C][C] 0.5847[/C][C] 0.8307[/C][C] 0.4153[/C][/ROW]
[ROW][C]83[/C][C] 0.6589[/C][C] 0.6821[/C][C] 0.3411[/C][/ROW]
[ROW][C]84[/C][C] 0.6037[/C][C] 0.7926[/C][C] 0.3963[/C][/ROW]
[ROW][C]85[/C][C] 0.5739[/C][C] 0.8522[/C][C] 0.4261[/C][/ROW]
[ROW][C]86[/C][C] 0.5714[/C][C] 0.8571[/C][C] 0.4286[/C][/ROW]
[ROW][C]87[/C][C] 0.5282[/C][C] 0.9435[/C][C] 0.4718[/C][/ROW]
[ROW][C]88[/C][C] 0.5286[/C][C] 0.9429[/C][C] 0.4714[/C][/ROW]
[ROW][C]89[/C][C] 0.5522[/C][C] 0.8956[/C][C] 0.4478[/C][/ROW]
[ROW][C]90[/C][C] 0.5149[/C][C] 0.9702[/C][C] 0.4851[/C][/ROW]
[ROW][C]91[/C][C] 0.5144[/C][C] 0.9711[/C][C] 0.4856[/C][/ROW]
[ROW][C]92[/C][C] 0.5198[/C][C] 0.9604[/C][C] 0.4802[/C][/ROW]
[ROW][C]93[/C][C] 0.5259[/C][C] 0.9482[/C][C] 0.4741[/C][/ROW]
[ROW][C]94[/C][C] 0.4985[/C][C] 0.997[/C][C] 0.5015[/C][/ROW]
[ROW][C]95[/C][C] 0.4472[/C][C] 0.8945[/C][C] 0.5528[/C][/ROW]
[ROW][C]96[/C][C] 0.558[/C][C] 0.884[/C][C] 0.442[/C][/ROW]
[ROW][C]97[/C][C] 0.5007[/C][C] 0.9985[/C][C] 0.4993[/C][/ROW]
[ROW][C]98[/C][C] 0.4742[/C][C] 0.9484[/C][C] 0.5258[/C][/ROW]
[ROW][C]99[/C][C] 0.5444[/C][C] 0.9111[/C][C] 0.4556[/C][/ROW]
[ROW][C]100[/C][C] 0.5401[/C][C] 0.9197[/C][C] 0.4599[/C][/ROW]
[ROW][C]101[/C][C] 0.5227[/C][C] 0.9545[/C][C] 0.4773[/C][/ROW]
[ROW][C]102[/C][C] 0.5314[/C][C] 0.9373[/C][C] 0.4686[/C][/ROW]
[ROW][C]103[/C][C] 0.5503[/C][C] 0.8993[/C][C] 0.4497[/C][/ROW]
[ROW][C]104[/C][C] 0.5466[/C][C] 0.9068[/C][C] 0.4534[/C][/ROW]
[ROW][C]105[/C][C] 0.4707[/C][C] 0.9414[/C][C] 0.5293[/C][/ROW]
[ROW][C]106[/C][C] 0.4518[/C][C] 0.9036[/C][C] 0.5482[/C][/ROW]
[ROW][C]107[/C][C] 0.4815[/C][C] 0.9629[/C][C] 0.5185[/C][/ROW]
[ROW][C]108[/C][C] 0.4075[/C][C] 0.8151[/C][C] 0.5925[/C][/ROW]
[ROW][C]109[/C][C] 0.3616[/C][C] 0.7233[/C][C] 0.6384[/C][/ROW]
[ROW][C]110[/C][C] 0.2851[/C][C] 0.5701[/C][C] 0.7149[/C][/ROW]
[ROW][C]111[/C][C] 0.3249[/C][C] 0.6497[/C][C] 0.6751[/C][/ROW]
[ROW][C]112[/C][C] 0.5911[/C][C] 0.8179[/C][C] 0.4089[/C][/ROW]
[ROW][C]113[/C][C] 0.5478[/C][C] 0.9044[/C][C] 0.4522[/C][/ROW]
[ROW][C]114[/C][C] 0.4502[/C][C] 0.9004[/C][C] 0.5498[/C][/ROW]
[ROW][C]115[/C][C] 0.369[/C][C] 0.738[/C][C] 0.631[/C][/ROW]
[ROW][C]116[/C][C] 0.2603[/C][C] 0.5206[/C][C] 0.7397[/C][/ROW]
[ROW][C]117[/C][C] 0.5607[/C][C] 0.8786[/C][C] 0.4393[/C][/ROW]
[ROW][C]118[/C][C] 0.5742[/C][C] 0.8517[/C][C] 0.4258[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=314762&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=314762&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
24	0.1392	0.2783	0.8608
25	0.06309	0.1262	0.9369
26	0.02835	0.05669	0.9717
27	0.01065	0.02131	0.9893
28	0.00584	0.01168	0.9942
29	0.06853	0.1371	0.9315
30	0.2994	0.5988	0.7006
31	0.2276	0.4551	0.7724
32	0.357	0.714	0.643
33	0.3037	0.6073	0.6963
34	0.2396	0.4791	0.7604
35	0.2274	0.4548	0.7726
36	0.1744	0.3487	0.8256
37	0.1448	0.2896	0.8552
38	0.1299	0.2597	0.8701
39	0.1889	0.3778	0.8111
40	0.2107	0.4214	0.7893
41	0.1686	0.3372	0.8314
42	0.2645	0.529	0.7355
43	0.2565	0.513	0.7435
44	0.2036	0.4072	0.7964
45	0.175	0.3501	0.825
46	0.1926	0.3852	0.8074
47	0.2672	0.5344	0.7328
48	0.2171	0.4341	0.7829
49	0.193	0.386	0.807
50	0.1571	0.3143	0.8429
51	0.1294	0.2589	0.8706
52	0.09902	0.198	0.901
53	0.1144	0.2288	0.8856
54	0.4413	0.8826	0.5587
55	0.4006	0.8013	0.5994
56	0.3708	0.7416	0.6292
57	0.3217	0.6434	0.6783
58	0.2898	0.5797	0.7102
59	0.3048	0.6096	0.6952
60	0.2984	0.5968	0.7016
61	0.2596	0.5192	0.7404
62	0.2512	0.5023	0.7488
63	0.2319	0.4638	0.7681
64	0.3693	0.7386	0.6307
65	0.3236	0.6473	0.6764
66	0.2875	0.575	0.7125
67	0.2737	0.5473	0.7263
68	0.2637	0.5273	0.7363
69	0.2469	0.4937	0.7531
70	0.2251	0.4501	0.7749
71	0.3045	0.609	0.6955
72	0.3726	0.7452	0.6274
73	0.3549	0.7097	0.6451
74	0.6188	0.7625	0.3812
75	0.6536	0.6928	0.3464
76	0.6701	0.6599	0.3299
77	0.6232	0.7536	0.3768
78	0.5722	0.8557	0.4278
79	0.5503	0.8994	0.4497
80	0.4954	0.9908	0.5046
81	0.4688	0.9377	0.5312
82	0.5847	0.8307	0.4153
83	0.6589	0.6821	0.3411
84	0.6037	0.7926	0.3963
85	0.5739	0.8522	0.4261
86	0.5714	0.8571	0.4286
87	0.5282	0.9435	0.4718
88	0.5286	0.9429	0.4714
89	0.5522	0.8956	0.4478
90	0.5149	0.9702	0.4851
91	0.5144	0.9711	0.4856
92	0.5198	0.9604	0.4802
93	0.5259	0.9482	0.4741
94	0.4985	0.997	0.5015
95	0.4472	0.8945	0.5528
96	0.558	0.884	0.442
97	0.5007	0.9985	0.4993
98	0.4742	0.9484	0.5258
99	0.5444	0.9111	0.4556
100	0.5401	0.9197	0.4599
101	0.5227	0.9545	0.4773
102	0.5314	0.9373	0.4686
103	0.5503	0.8993	0.4497
104	0.5466	0.9068	0.4534
105	0.4707	0.9414	0.5293
106	0.4518	0.9036	0.5482
107	0.4815	0.9629	0.5185
108	0.4075	0.8151	0.5925
109	0.3616	0.7233	0.6384
110	0.2851	0.5701	0.7149
111	0.3249	0.6497	0.6751
112	0.5911	0.8179	0.4089
113	0.5478	0.9044	0.4522
114	0.4502	0.9004	0.5498
115	0.369	0.738	0.631
116	0.2603	0.5206	0.7397
117	0.5607	0.8786	0.4393
118	0.5742	0.8517	0.4258

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	2	0.0210526	OK
10% type I error level	3	0.0315789	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 & 2 & 0.0210526 & OK \tabularnewline
10% type I error level & 3 & 0.0315789 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=314762&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]2[/C][C]0.0210526[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]3[/C][C]0.0315789[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=314762&T=7

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=314762&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	2	0.0210526	OK
10% type I error level	3	0.0315789	OK

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted RESET test data: mylm RESET = 4.9286, df1 = 2, df2 = 119, p-value = 0.008782
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 1.3764, df1 = 40, df2 = 81, p-value = 0.1124
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 3.7963, df1 = 2, df2 = 119, p-value = 0.02522

\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 = 4.9286, df1 = 2, df2 = 119, p-value = 0.008782
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of regressors \tabularnewline
> reset_test_regressors
	RESET test
data:  mylm
RESET = 1.3764, df1 = 40, df2 = 81, p-value = 0.1124
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of principal components \tabularnewline
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 3.7963, df1 = 2, df2 = 119, p-value = 0.02522
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=314762&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 = 4.9286, df1 = 2, df2 = 119, p-value = 0.008782
[/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 = 1.3764, df1 = 40, df2 = 81, p-value = 0.1124
[/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 = 3.7963, df1 = 2, df2 = 119, p-value = 0.02522
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=314762&T=8

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=314762&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 = 4.9286, df1 = 2, df2 = 119, p-value = 0.008782
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 1.3764, df1 = 40, df2 = 81, p-value = 0.1124
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 3.7963, df1 = 2, df2 = 119, p-value = 0.02522

Variance Inflation Factors (Multicollinearity)

> vif
       Relative_Advantage      Perceived_Usefulness     Perceived_Ease_of_Use 
                 1.979496                  2.165159                  2.848839 
      Information_Quality            System_Quality                    groupB 
                 3.434033                  2.244210                  1.471312 
                  genderB   `Intention_to_Use(t-1)`  `Intention_to_Use(t-1s)` 
                 1.195866                  1.275727                  1.144090 
 `Intention_to_Use(t-2s)`  `Intention_to_Use(t-3s)`  `Intention_to_Use(t-4s)` 
                 1.227888                  1.142956                  1.220771 
 `Intention_to_Use(t-5s)`  `Intention_to_Use(t-6s)`  `Intention_to_Use(t-7s)` 
                 1.292592                  1.210271                  1.144228 
 `Intention_to_Use(t-8s)`  `Intention_to_Use(t-9s)` `Intention_to_Use(t-10s)` 
                 1.256642                  1.104644                  1.173238 
`Intention_to_Use(t-11s)` `Intention_to_Use(t-12s)` 
                 1.102741                  1.083307

\begin{tabular}{lllllllll}
\hline
Variance Inflation Factors (Multicollinearity) \tabularnewline
> vif
       Relative_Advantage      Perceived_Usefulness     Perceived_Ease_of_Use 
                 1.979496                  2.165159                  2.848839 
      Information_Quality            System_Quality                    groupB 
                 3.434033                  2.244210                  1.471312 
                  genderB   `Intention_to_Use(t-1)`  `Intention_to_Use(t-1s)` 
                 1.195866                  1.275727                  1.144090 
 `Intention_to_Use(t-2s)`  `Intention_to_Use(t-3s)`  `Intention_to_Use(t-4s)` 
                 1.227888                  1.142956                  1.220771 
 `Intention_to_Use(t-5s)`  `Intention_to_Use(t-6s)`  `Intention_to_Use(t-7s)` 
                 1.292592                  1.210271                  1.144228 
 `Intention_to_Use(t-8s)`  `Intention_to_Use(t-9s)` `Intention_to_Use(t-10s)` 
                 1.256642                  1.104644                  1.173238 
`Intention_to_Use(t-11s)` `Intention_to_Use(t-12s)` 
                 1.102741                  1.083307 
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=314762&T=9

[TABLE]
[ROW][C]Variance Inflation Factors (Multicollinearity)[/C][/ROW]
[ROW][C]> vif
       Relative_Advantage      Perceived_Usefulness     Perceived_Ease_of_Use 
                 1.979496                  2.165159                  2.848839 
      Information_Quality            System_Quality                    groupB 
                 3.434033                  2.244210                  1.471312 
                  genderB   `Intention_to_Use(t-1)`  `Intention_to_Use(t-1s)` 
                 1.195866                  1.275727                  1.144090 
 `Intention_to_Use(t-2s)`  `Intention_to_Use(t-3s)`  `Intention_to_Use(t-4s)` 
                 1.227888                  1.142956                  1.220771 
 `Intention_to_Use(t-5s)`  `Intention_to_Use(t-6s)`  `Intention_to_Use(t-7s)` 
                 1.292592                  1.210271                  1.144228 
 `Intention_to_Use(t-8s)`  `Intention_to_Use(t-9s)` `Intention_to_Use(t-10s)` 
                 1.256642                  1.104644                  1.173238 
`Intention_to_Use(t-11s)` `Intention_to_Use(t-12s)` 
                 1.102741                  1.083307 
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=314762&T=9

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=314762&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
       Relative_Advantage      Perceived_Usefulness     Perceived_Ease_of_Use 
                 1.979496                  2.165159                  2.848839 
      Information_Quality            System_Quality                    groupB 
                 3.434033                  2.244210                  1.471312 
                  genderB   `Intention_to_Use(t-1)`  `Intention_to_Use(t-1s)` 
                 1.195866                  1.275727                  1.144090 
 `Intention_to_Use(t-2s)`  `Intention_to_Use(t-3s)`  `Intention_to_Use(t-4s)` 
                 1.227888                  1.142956                  1.220771 
 `Intention_to_Use(t-5s)`  `Intention_to_Use(t-6s)`  `Intention_to_Use(t-7s)` 
                 1.292592                  1.210271                  1.144228 
 `Intention_to_Use(t-8s)`  `Intention_to_Use(t-9s)` `Intention_to_Use(t-10s)` 
                 1.256642                  1.104644                  1.173238 
`Intention_to_Use(t-11s)` `Intention_to_Use(t-12s)` 
                 1.102741                  1.083307

Figure 1

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Figure 6

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Figure 7

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Figure 9

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Figure 10

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Parameters (Session):

par1 = TRUE ; par2 = 1 ; par3 = 0 ; par4 = 1 ; par5 = 12 ; par6 = 3 ; par7 = 1 ; par8 = 1 ; par9 = 1 ;

Parameters (R input):

par1 = TRUE ; par2 = 1 ; par3 = 0 ; par4 = 1 ; par5 = 12 ; par6 = 3 ;

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