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rwasp_multipleregression.wasp

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Multiple Regression

Date of computation

Wed, 24 Jan 2018 09:47:44 +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/Jan/24/t151678375876vsxotd6f9d73x.htm/, Retrieved Sun, 05 May 2024 20:50:44 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=311863, Retrieved Sun, 05 May 2024 20:50:44 +0000

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-       [Multiple Regression] [] [2018-01-24 08:47:44] [465bf1e43e1b47c1d76331d54a6eac08] [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	16 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 time16 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=311863&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]

16 seconds[/C][/ROW] [ROW]

R Server[/C]

Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=311863&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=311863&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	16 seconds
R Server	Big Analytics Cloud Computing Center

Multiple Linear Regression - Estimated Regression Equation
Intention_to_Use[t] = -1.92814 + 0.2929Relative_Advantage[t] + 0.0853193Perceived_Usefulness[t] + 0.106187Perceived_Ease_of_Use[t] + 0.036988Information_Quality[t] + 0.0709976System_Quality[t] + 1.00629groupB[t] + 0.334666genderB[t] -0.0164075`Intention_to_Use(t-1)`[t] + 0.0247869`Intention_to_Use(t-1s)`[t] -0.040615`Intention_to_Use(t-2s)`[t] -0.0337217`Intention_to_Use(t-3s)`[t] + 0.0618448`Intention_to_Use(t-4s)`[t] -0.0764146`Intention_to_Use(t-5s)`[t] + 0.0791039`Intention_to_Use(t-6s)`[t] + 0.143331`Intention_to_Use(t-7s)`[t] + 0.0545301`Intention_to_Use(t-8s)`[t] -0.0391061`Intention_to_Use(t-9s)`[t] + 0.0400292`Intention_to_Use(t-10s)`[t] -0.0460857`Intention_to_Use(t-11s)`[t] -0.0213416`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] =  -1.92814 +  0.2929Relative_Advantage[t] +  0.0853193Perceived_Usefulness[t] +  0.106187Perceived_Ease_of_Use[t] +  0.036988Information_Quality[t] +  0.0709976System_Quality[t] +  1.00629groupB[t] +  0.334666genderB[t] -0.0164075`Intention_to_Use(t-1)`[t] +  0.0247869`Intention_to_Use(t-1s)`[t] -0.040615`Intention_to_Use(t-2s)`[t] -0.0337217`Intention_to_Use(t-3s)`[t] +  0.0618448`Intention_to_Use(t-4s)`[t] -0.0764146`Intention_to_Use(t-5s)`[t] +  0.0791039`Intention_to_Use(t-6s)`[t] +  0.143331`Intention_to_Use(t-7s)`[t] +  0.0545301`Intention_to_Use(t-8s)`[t] -0.0391061`Intention_to_Use(t-9s)`[t] +  0.0400292`Intention_to_Use(t-10s)`[t] -0.0460857`Intention_to_Use(t-11s)`[t] -0.0213416`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=311863&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Intention_to_Use[t] =  -1.92814 +  0.2929Relative_Advantage[t] +  0.0853193Perceived_Usefulness[t] +  0.106187Perceived_Ease_of_Use[t] +  0.036988Information_Quality[t] +  0.0709976System_Quality[t] +  1.00629groupB[t] +  0.334666genderB[t] -0.0164075`Intention_to_Use(t-1)`[t] +  0.0247869`Intention_to_Use(t-1s)`[t] -0.040615`Intention_to_Use(t-2s)`[t] -0.0337217`Intention_to_Use(t-3s)`[t] +  0.0618448`Intention_to_Use(t-4s)`[t] -0.0764146`Intention_to_Use(t-5s)`[t] +  0.0791039`Intention_to_Use(t-6s)`[t] +  0.143331`Intention_to_Use(t-7s)`[t] +  0.0545301`Intention_to_Use(t-8s)`[t] -0.0391061`Intention_to_Use(t-9s)`[t] +  0.0400292`Intention_to_Use(t-10s)`[t] -0.0460857`Intention_to_Use(t-11s)`[t] -0.0213416`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=311863&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=311863&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] = -1.92814 + 0.2929Relative_Advantage[t] + 0.0853193Perceived_Usefulness[t] + 0.106187Perceived_Ease_of_Use[t] + 0.036988Information_Quality[t] + 0.0709976System_Quality[t] + 1.00629groupB[t] + 0.334666genderB[t] -0.0164075`Intention_to_Use(t-1)`[t] + 0.0247869`Intention_to_Use(t-1s)`[t] -0.040615`Intention_to_Use(t-2s)`[t] -0.0337217`Intention_to_Use(t-3s)`[t] + 0.0618448`Intention_to_Use(t-4s)`[t] -0.0764146`Intention_to_Use(t-5s)`[t] + 0.0791039`Intention_to_Use(t-6s)`[t] + 0.143331`Intention_to_Use(t-7s)`[t] + 0.0545301`Intention_to_Use(t-8s)`[t] -0.0391061`Intention_to_Use(t-9s)`[t] + 0.0400292`Intention_to_Use(t-10s)`[t] -0.0460857`Intention_to_Use(t-11s)`[t] -0.0213416`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)	-1.928	1.559	-1.2370e+00	0.2183	0.1091
Relative_Advantage	+0.2929	0.06792	+4.3120e+00	3.123e-05	1.561e-05
Perceived_Usefulness	+0.08532	0.06578	+1.2970e+00	0.1969	0.09843
Perceived_Ease_of_Use	+0.1062	0.06127	+1.7330e+00	0.08541	0.0427
Information_Quality	+0.03699	0.07099	+5.2100e-01	0.6032	0.3016
System_Quality	+0.071	0.03325	+2.1350e+00	0.03457	0.01728
groupB	+1.006	0.2603	+3.8660e+00	0.0001724	8.62e-05
genderB	+0.3347	0.2292	+1.4600e+00	0.1467	0.07334
`Intention_to_Use(t-1)`	-0.01641	0.05706	-2.8750e-01	0.7741	0.3871
`Intention_to_Use(t-1s)`	+0.02479	0.05722	+4.3320e-01	0.6656	0.3328
`Intention_to_Use(t-2s)`	-0.04061	0.06109	-6.6480e-01	0.5073	0.2537
`Intention_to_Use(t-3s)`	-0.03372	0.05876	-5.7390e-01	0.567	0.2835
`Intention_to_Use(t-4s)`	+0.06184	0.05618	+1.1010e+00	0.2729	0.1365
`Intention_to_Use(t-5s)`	-0.07641	0.05654	-1.3520e+00	0.1788	0.08941
`Intention_to_Use(t-6s)`	+0.0791	0.05599	+1.4130e+00	0.1601	0.08003
`Intention_to_Use(t-7s)`	+0.1433	0.0558	+2.5690e+00	0.01131	0.005656
`Intention_to_Use(t-8s)`	+0.05453	0.05583	+9.7660e-01	0.3305	0.1653
`Intention_to_Use(t-9s)`	-0.03911	0.0564	-6.9340e-01	0.4893	0.2446
`Intention_to_Use(t-10s)`	+0.04003	0.05513	+7.2610e-01	0.4691	0.2345
`Intention_to_Use(t-11s)`	-0.04609	0.05511	-8.3630e-01	0.4045	0.2022
`Intention_to_Use(t-12s)`	-0.02134	0.05862	-3.6400e-01	0.7164	0.3582

\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) & -1.928 &  1.559 & -1.2370e+00 &  0.2183 &  0.1091 \tabularnewline
Relative_Advantage & +0.2929 &  0.06792 & +4.3120e+00 &  3.123e-05 &  1.561e-05 \tabularnewline
Perceived_Usefulness & +0.08532 &  0.06578 & +1.2970e+00 &  0.1969 &  0.09843 \tabularnewline
Perceived_Ease_of_Use & +0.1062 &  0.06127 & +1.7330e+00 &  0.08541 &  0.0427 \tabularnewline
Information_Quality & +0.03699 &  0.07099 & +5.2100e-01 &  0.6032 &  0.3016 \tabularnewline
System_Quality & +0.071 &  0.03325 & +2.1350e+00 &  0.03457 &  0.01728 \tabularnewline
groupB & +1.006 &  0.2603 & +3.8660e+00 &  0.0001724 &  8.62e-05 \tabularnewline
genderB & +0.3347 &  0.2292 & +1.4600e+00 &  0.1467 &  0.07334 \tabularnewline
`Intention_to_Use(t-1)` & -0.01641 &  0.05706 & -2.8750e-01 &  0.7741 &  0.3871 \tabularnewline
`Intention_to_Use(t-1s)` & +0.02479 &  0.05722 & +4.3320e-01 &  0.6656 &  0.3328 \tabularnewline
`Intention_to_Use(t-2s)` & -0.04061 &  0.06109 & -6.6480e-01 &  0.5073 &  0.2537 \tabularnewline
`Intention_to_Use(t-3s)` & -0.03372 &  0.05876 & -5.7390e-01 &  0.567 &  0.2835 \tabularnewline
`Intention_to_Use(t-4s)` & +0.06184 &  0.05618 & +1.1010e+00 &  0.2729 &  0.1365 \tabularnewline
`Intention_to_Use(t-5s)` & -0.07641 &  0.05654 & -1.3520e+00 &  0.1788 &  0.08941 \tabularnewline
`Intention_to_Use(t-6s)` & +0.0791 &  0.05599 & +1.4130e+00 &  0.1601 &  0.08003 \tabularnewline
`Intention_to_Use(t-7s)` & +0.1433 &  0.0558 & +2.5690e+00 &  0.01131 &  0.005656 \tabularnewline
`Intention_to_Use(t-8s)` & +0.05453 &  0.05583 & +9.7660e-01 &  0.3305 &  0.1653 \tabularnewline
`Intention_to_Use(t-9s)` & -0.03911 &  0.0564 & -6.9340e-01 &  0.4893 &  0.2446 \tabularnewline
`Intention_to_Use(t-10s)` & +0.04003 &  0.05513 & +7.2610e-01 &  0.4691 &  0.2345 \tabularnewline
`Intention_to_Use(t-11s)` & -0.04609 &  0.05511 & -8.3630e-01 &  0.4045 &  0.2022 \tabularnewline
`Intention_to_Use(t-12s)` & -0.02134 &  0.05862 & -3.6400e-01 &  0.7164 &  0.3582 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=311863&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]-1.928[/C][C] 1.559[/C][C]-1.2370e+00[/C][C] 0.2183[/C][C] 0.1091[/C][/ROW]
[ROW][C]Relative_Advantage[/C][C]+0.2929[/C][C] 0.06792[/C][C]+4.3120e+00[/C][C] 3.123e-05[/C][C] 1.561e-05[/C][/ROW]
[ROW][C]Perceived_Usefulness[/C][C]+0.08532[/C][C] 0.06578[/C][C]+1.2970e+00[/C][C] 0.1969[/C][C] 0.09843[/C][/ROW]
[ROW][C]Perceived_Ease_of_Use[/C][C]+0.1062[/C][C] 0.06127[/C][C]+1.7330e+00[/C][C] 0.08541[/C][C] 0.0427[/C][/ROW]
[ROW][C]Information_Quality[/C][C]+0.03699[/C][C] 0.07099[/C][C]+5.2100e-01[/C][C] 0.6032[/C][C] 0.3016[/C][/ROW]
[ROW][C]System_Quality[/C][C]+0.071[/C][C] 0.03325[/C][C]+2.1350e+00[/C][C] 0.03457[/C][C] 0.01728[/C][/ROW]
[ROW][C]groupB[/C][C]+1.006[/C][C] 0.2603[/C][C]+3.8660e+00[/C][C] 0.0001724[/C][C] 8.62e-05[/C][/ROW]
[ROW][C]genderB[/C][C]+0.3347[/C][C] 0.2292[/C][C]+1.4600e+00[/C][C] 0.1467[/C][C] 0.07334[/C][/ROW]
[ROW][C]`Intention_to_Use(t-1)`[/C][C]-0.01641[/C][C] 0.05706[/C][C]-2.8750e-01[/C][C] 0.7741[/C][C] 0.3871[/C][/ROW]
[ROW][C]`Intention_to_Use(t-1s)`[/C][C]+0.02479[/C][C] 0.05722[/C][C]+4.3320e-01[/C][C] 0.6656[/C][C] 0.3328[/C][/ROW]
[ROW][C]`Intention_to_Use(t-2s)`[/C][C]-0.04061[/C][C] 0.06109[/C][C]-6.6480e-01[/C][C] 0.5073[/C][C] 0.2537[/C][/ROW]
[ROW][C]`Intention_to_Use(t-3s)`[/C][C]-0.03372[/C][C] 0.05876[/C][C]-5.7390e-01[/C][C] 0.567[/C][C] 0.2835[/C][/ROW]
[ROW][C]`Intention_to_Use(t-4s)`[/C][C]+0.06184[/C][C] 0.05618[/C][C]+1.1010e+00[/C][C] 0.2729[/C][C] 0.1365[/C][/ROW]
[ROW][C]`Intention_to_Use(t-5s)`[/C][C]-0.07641[/C][C] 0.05654[/C][C]-1.3520e+00[/C][C] 0.1788[/C][C] 0.08941[/C][/ROW]
[ROW][C]`Intention_to_Use(t-6s)`[/C][C]+0.0791[/C][C] 0.05599[/C][C]+1.4130e+00[/C][C] 0.1601[/C][C] 0.08003[/C][/ROW]
[ROW][C]`Intention_to_Use(t-7s)`[/C][C]+0.1433[/C][C] 0.0558[/C][C]+2.5690e+00[/C][C] 0.01131[/C][C] 0.005656[/C][/ROW]
[ROW][C]`Intention_to_Use(t-8s)`[/C][C]+0.05453[/C][C] 0.05583[/C][C]+9.7660e-01[/C][C] 0.3305[/C][C] 0.1653[/C][/ROW]
[ROW][C]`Intention_to_Use(t-9s)`[/C][C]-0.03911[/C][C] 0.0564[/C][C]-6.9340e-01[/C][C] 0.4893[/C][C] 0.2446[/C][/ROW]
[ROW][C]`Intention_to_Use(t-10s)`[/C][C]+0.04003[/C][C] 0.05513[/C][C]+7.2610e-01[/C][C] 0.4691[/C][C] 0.2345[/C][/ROW]
[ROW][C]`Intention_to_Use(t-11s)`[/C][C]-0.04609[/C][C] 0.05511[/C][C]-8.3630e-01[/C][C] 0.4045[/C][C] 0.2022[/C][/ROW]
[ROW][C]`Intention_to_Use(t-12s)`[/C][C]-0.02134[/C][C] 0.05862[/C][C]-3.6400e-01[/C][C] 0.7164[/C][C] 0.3582[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=311863&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=311863&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)	-1.928	1.559	-1.2370e+00	0.2183	0.1091
Relative_Advantage	+0.2929	0.06792	+4.3120e+00	3.123e-05	1.561e-05
Perceived_Usefulness	+0.08532	0.06578	+1.2970e+00	0.1969	0.09843
Perceived_Ease_of_Use	+0.1062	0.06127	+1.7330e+00	0.08541	0.0427
Information_Quality	+0.03699	0.07099	+5.2100e-01	0.6032	0.3016
System_Quality	+0.071	0.03325	+2.1350e+00	0.03457	0.01728
groupB	+1.006	0.2603	+3.8660e+00	0.0001724	8.62e-05
genderB	+0.3347	0.2292	+1.4600e+00	0.1467	0.07334
`Intention_to_Use(t-1)`	-0.01641	0.05706	-2.8750e-01	0.7741	0.3871
`Intention_to_Use(t-1s)`	+0.02479	0.05722	+4.3320e-01	0.6656	0.3328
`Intention_to_Use(t-2s)`	-0.04061	0.06109	-6.6480e-01	0.5073	0.2537
`Intention_to_Use(t-3s)`	-0.03372	0.05876	-5.7390e-01	0.567	0.2835
`Intention_to_Use(t-4s)`	+0.06184	0.05618	+1.1010e+00	0.2729	0.1365
`Intention_to_Use(t-5s)`	-0.07641	0.05654	-1.3520e+00	0.1788	0.08941
`Intention_to_Use(t-6s)`	+0.0791	0.05599	+1.4130e+00	0.1601	0.08003
`Intention_to_Use(t-7s)`	+0.1433	0.0558	+2.5690e+00	0.01131	0.005656
`Intention_to_Use(t-8s)`	+0.05453	0.05583	+9.7660e-01	0.3305	0.1653
`Intention_to_Use(t-9s)`	-0.03911	0.0564	-6.9340e-01	0.4893	0.2446
`Intention_to_Use(t-10s)`	+0.04003	0.05513	+7.2610e-01	0.4691	0.2345
`Intention_to_Use(t-11s)`	-0.04609	0.05511	-8.3630e-01	0.4045	0.2022
`Intention_to_Use(t-12s)`	-0.02134	0.05862	-3.6400e-01	0.7164	0.3582

Multiple Linear Regression - Regression Statistics
Multiple R	0.7973
R-squared	0.6356
Adjusted R-squared	0.5808
F-TEST (value)	11.6
F-TEST (DF numerator)	20
F-TEST (DF denominator)	133
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.272
Sum Squared Residuals	215.3

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.7973 \tabularnewline
R-squared &  0.6356 \tabularnewline
Adjusted R-squared &  0.5808 \tabularnewline
F-TEST (value) &  11.6 \tabularnewline
F-TEST (DF numerator) & 20 \tabularnewline
F-TEST (DF denominator) & 133 \tabularnewline
p-value &  0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  1.272 \tabularnewline
Sum Squared Residuals &  215.3 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=311863&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.7973[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.6356[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.5808[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 11.6[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]20[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]133[/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] 1.272[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 215.3[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=311863&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=311863&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	0.7973
R-squared	0.6356
Adjusted R-squared	0.5808
F-TEST (value)	11.6
F-TEST (DF numerator)	20
F-TEST (DF denominator)	133
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.272
Sum Squared Residuals	215.3

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=311863&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=311863&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=311863&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	6.869	2.131
2	8	8.355	-0.3545
3	6	6.854	-0.8545
4	8	8.338	-0.3385
5	8	7.227	0.7731
6	5	6.612	-1.612
7	9	9.292	-0.2917
8	8	8.423	-0.4228
9	8	6.307	1.693
10	8	8.716	-0.7164
11	6	5.665	0.3354
12	6	6.904	-0.904
13	9	8.515	0.4848
14	8	7.361	0.6394
15	9	9.431	-0.4312
16	10	8.771	1.229
17	8	6.77	1.23
18	8	7.456	0.544
19	7	6.862	0.1376
20	7	6.744	0.2561
21	10	9.58	0.4196
22	8	6.824	1.176
23	7	6.554	0.4461
24	10	7.914	2.086
25	7	8.07	-1.07
26	7	5.582	1.418
27	9	8.603	0.3966
28	9	9.958	-0.9578
29	8	7.792	0.2076
30	6	7.967	-1.967
31	8	7.222	0.7777
32	9	7.932	1.068
33	2	3.674	-1.674
34	6	6.106	-0.1065
35	8	8.263	-0.263
36	8	8.199	-0.1986
37	7	7.249	-0.2492
38	8	7.52	0.4802
39	6	6.152	-0.1518
40	10	8.095	1.905
41	10	8.124	1.876
42	10	7.453	2.547
43	8	7.888	0.1116
44	8	8.583	-0.5832
45	7	7.647	-0.6469
46	10	8.79	1.21
47	5	5.266	-0.2661
48	3	3.148	-0.1477
49	2	3.829	-1.829
50	3	4.144	-1.144
51	4	5.801	-1.801
52	2	3.797	-1.797
53	6	4.96	1.04
54	8	9.08	-1.08
55	8	8.088	-0.08757
56	5	5.829	-0.8293
57	10	9.679	0.3211
58	9	10.52	-1.52
59	8	10.01	-2.01
60	9	9.351	-0.3508
61	8	6.531	1.469
62	5	5.914	-0.9139
63	7	6.586	0.4142
64	9	8.018	0.9817
65	8	7.533	0.4671
66	4	7.663	-3.663
67	7	6.375	0.6249
68	8	8.423	-0.4232
69	7	7.669	-0.6694
70	7	7.303	-0.3025
71	9	8.316	0.6839
72	6	7.351	-1.351
73	7	8.342	-1.342
74	4	5.526	-1.526
75	6	6.851	-0.8508
76	10	6.986	3.014
77	9	8.478	0.5217
78	10	10.22	-0.2167
79	8	7.665	0.3347
80	4	5.117	-1.117
81	8	9.64	-1.64
82	5	6.366	-1.366
83	8	7.179	0.8207
84	9	8.146	0.8539
85	8	8.015	-0.01538
86	4	7.796	-3.796
87	8	6.758	1.242
88	10	7.793	2.207
89	6	6.101	-0.1009
90	7	7.248	-0.2476
91	10	9.039	0.9608
92	9	9.992	-0.992
93	8	8.849	-0.849
94	3	4.69	-1.69
95	8	7.033	0.9673
96	7	7.632	-0.6323
97	7	7.413	-0.4129
98	8	6.682	1.318
99	8	8.883	-0.8833
100	7	7.565	-0.5649
101	7	5.601	1.399
102	9	9.822	-0.8222
103	9	8.237	0.7633
104	9	8.336	0.6644
105	4	5.53	-1.53
106	6	6.773	-0.7729
107	6	6.492	-0.4917
108	6	3.765	2.235
109	8	7.986	0.014
110	3	3.947	-0.9467
111	8	6.563	1.437
112	8	7.439	0.5606
113	6	4.343	1.657
114	10	9.046	0.954
115	2	4.397	-2.397
116	9	8.124	0.8764
117	6	5.572	0.4277
118	6	7.71	-1.71
119	5	4.317	0.6832
120	4	4.459	-0.4594
121	7	6.413	0.587
122	5	5.292	-0.2924
123	8	8.066	-0.06552
124	6	5.992	0.008303
125	9	7.219	1.781
126	6	6.258	-0.258
127	4	4.504	-0.504
128	7	7.844	-0.8438
129	2	3	-0.9998
130	8	9.562	-1.562
131	9	7.974	1.026
132	6	6.311	-0.3107
133	5	4.943	0.05662
134	7	6.15	0.8504
135	8	7.063	0.9365
136	4	5.784	-1.784
137	9	6.835	2.165
138	9	9.006	-0.005997
139	9	6.071	2.929
140	7	5.374	1.626
141	5	6.367	-1.367
142	7	6.924	0.07646
143	9	9.495	-0.4953
144	8	6.582	1.418
145	6	5.457	0.5428
146	9	8.141	0.859
147	8	7.673	0.3274
148	7	7.464	-0.4645
149	7	7.252	-0.2524
150	7	6.002	0.9979
151	8	8.416	-0.4156
152	10	8.931	1.069
153	6	6.688	-0.6884
154	6	7.084	-1.084

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 &  9 &  6.869 &  2.131 \tabularnewline
2 &  8 &  8.355 & -0.3545 \tabularnewline
3 &  6 &  6.854 & -0.8545 \tabularnewline
4 &  8 &  8.338 & -0.3385 \tabularnewline
5 &  8 &  7.227 &  0.7731 \tabularnewline
6 &  5 &  6.612 & -1.612 \tabularnewline
7 &  9 &  9.292 & -0.2917 \tabularnewline
8 &  8 &  8.423 & -0.4228 \tabularnewline
9 &  8 &  6.307 &  1.693 \tabularnewline
10 &  8 &  8.716 & -0.7164 \tabularnewline
11 &  6 &  5.665 &  0.3354 \tabularnewline
12 &  6 &  6.904 & -0.904 \tabularnewline
13 &  9 &  8.515 &  0.4848 \tabularnewline
14 &  8 &  7.361 &  0.6394 \tabularnewline
15 &  9 &  9.431 & -0.4312 \tabularnewline
16 &  10 &  8.771 &  1.229 \tabularnewline
17 &  8 &  6.77 &  1.23 \tabularnewline
18 &  8 &  7.456 &  0.544 \tabularnewline
19 &  7 &  6.862 &  0.1376 \tabularnewline
20 &  7 &  6.744 &  0.2561 \tabularnewline
21 &  10 &  9.58 &  0.4196 \tabularnewline
22 &  8 &  6.824 &  1.176 \tabularnewline
23 &  7 &  6.554 &  0.4461 \tabularnewline
24 &  10 &  7.914 &  2.086 \tabularnewline
25 &  7 &  8.07 & -1.07 \tabularnewline
26 &  7 &  5.582 &  1.418 \tabularnewline
27 &  9 &  8.603 &  0.3966 \tabularnewline
28 &  9 &  9.958 & -0.9578 \tabularnewline
29 &  8 &  7.792 &  0.2076 \tabularnewline
30 &  6 &  7.967 & -1.967 \tabularnewline
31 &  8 &  7.222 &  0.7777 \tabularnewline
32 &  9 &  7.932 &  1.068 \tabularnewline
33 &  2 &  3.674 & -1.674 \tabularnewline
34 &  6 &  6.106 & -0.1065 \tabularnewline
35 &  8 &  8.263 & -0.263 \tabularnewline
36 &  8 &  8.199 & -0.1986 \tabularnewline
37 &  7 &  7.249 & -0.2492 \tabularnewline
38 &  8 &  7.52 &  0.4802 \tabularnewline
39 &  6 &  6.152 & -0.1518 \tabularnewline
40 &  10 &  8.095 &  1.905 \tabularnewline
41 &  10 &  8.124 &  1.876 \tabularnewline
42 &  10 &  7.453 &  2.547 \tabularnewline
43 &  8 &  7.888 &  0.1116 \tabularnewline
44 &  8 &  8.583 & -0.5832 \tabularnewline
45 &  7 &  7.647 & -0.6469 \tabularnewline
46 &  10 &  8.79 &  1.21 \tabularnewline
47 &  5 &  5.266 & -0.2661 \tabularnewline
48 &  3 &  3.148 & -0.1477 \tabularnewline
49 &  2 &  3.829 & -1.829 \tabularnewline
50 &  3 &  4.144 & -1.144 \tabularnewline
51 &  4 &  5.801 & -1.801 \tabularnewline
52 &  2 &  3.797 & -1.797 \tabularnewline
53 &  6 &  4.96 &  1.04 \tabularnewline
54 &  8 &  9.08 & -1.08 \tabularnewline
55 &  8 &  8.088 & -0.08757 \tabularnewline
56 &  5 &  5.829 & -0.8293 \tabularnewline
57 &  10 &  9.679 &  0.3211 \tabularnewline
58 &  9 &  10.52 & -1.52 \tabularnewline
59 &  8 &  10.01 & -2.01 \tabularnewline
60 &  9 &  9.351 & -0.3508 \tabularnewline
61 &  8 &  6.531 &  1.469 \tabularnewline
62 &  5 &  5.914 & -0.9139 \tabularnewline
63 &  7 &  6.586 &  0.4142 \tabularnewline
64 &  9 &  8.018 &  0.9817 \tabularnewline
65 &  8 &  7.533 &  0.4671 \tabularnewline
66 &  4 &  7.663 & -3.663 \tabularnewline
67 &  7 &  6.375 &  0.6249 \tabularnewline
68 &  8 &  8.423 & -0.4232 \tabularnewline
69 &  7 &  7.669 & -0.6694 \tabularnewline
70 &  7 &  7.303 & -0.3025 \tabularnewline
71 &  9 &  8.316 &  0.6839 \tabularnewline
72 &  6 &  7.351 & -1.351 \tabularnewline
73 &  7 &  8.342 & -1.342 \tabularnewline
74 &  4 &  5.526 & -1.526 \tabularnewline
75 &  6 &  6.851 & -0.8508 \tabularnewline
76 &  10 &  6.986 &  3.014 \tabularnewline
77 &  9 &  8.478 &  0.5217 \tabularnewline
78 &  10 &  10.22 & -0.2167 \tabularnewline
79 &  8 &  7.665 &  0.3347 \tabularnewline
80 &  4 &  5.117 & -1.117 \tabularnewline
81 &  8 &  9.64 & -1.64 \tabularnewline
82 &  5 &  6.366 & -1.366 \tabularnewline
83 &  8 &  7.179 &  0.8207 \tabularnewline
84 &  9 &  8.146 &  0.8539 \tabularnewline
85 &  8 &  8.015 & -0.01538 \tabularnewline
86 &  4 &  7.796 & -3.796 \tabularnewline
87 &  8 &  6.758 &  1.242 \tabularnewline
88 &  10 &  7.793 &  2.207 \tabularnewline
89 &  6 &  6.101 & -0.1009 \tabularnewline
90 &  7 &  7.248 & -0.2476 \tabularnewline
91 &  10 &  9.039 &  0.9608 \tabularnewline
92 &  9 &  9.992 & -0.992 \tabularnewline
93 &  8 &  8.849 & -0.849 \tabularnewline
94 &  3 &  4.69 & -1.69 \tabularnewline
95 &  8 &  7.033 &  0.9673 \tabularnewline
96 &  7 &  7.632 & -0.6323 \tabularnewline
97 &  7 &  7.413 & -0.4129 \tabularnewline
98 &  8 &  6.682 &  1.318 \tabularnewline
99 &  8 &  8.883 & -0.8833 \tabularnewline
100 &  7 &  7.565 & -0.5649 \tabularnewline
101 &  7 &  5.601 &  1.399 \tabularnewline
102 &  9 &  9.822 & -0.8222 \tabularnewline
103 &  9 &  8.237 &  0.7633 \tabularnewline
104 &  9 &  8.336 &  0.6644 \tabularnewline
105 &  4 &  5.53 & -1.53 \tabularnewline
106 &  6 &  6.773 & -0.7729 \tabularnewline
107 &  6 &  6.492 & -0.4917 \tabularnewline
108 &  6 &  3.765 &  2.235 \tabularnewline
109 &  8 &  7.986 &  0.014 \tabularnewline
110 &  3 &  3.947 & -0.9467 \tabularnewline
111 &  8 &  6.563 &  1.437 \tabularnewline
112 &  8 &  7.439 &  0.5606 \tabularnewline
113 &  6 &  4.343 &  1.657 \tabularnewline
114 &  10 &  9.046 &  0.954 \tabularnewline
115 &  2 &  4.397 & -2.397 \tabularnewline
116 &  9 &  8.124 &  0.8764 \tabularnewline
117 &  6 &  5.572 &  0.4277 \tabularnewline
118 &  6 &  7.71 & -1.71 \tabularnewline
119 &  5 &  4.317 &  0.6832 \tabularnewline
120 &  4 &  4.459 & -0.4594 \tabularnewline
121 &  7 &  6.413 &  0.587 \tabularnewline
122 &  5 &  5.292 & -0.2924 \tabularnewline
123 &  8 &  8.066 & -0.06552 \tabularnewline
124 &  6 &  5.992 &  0.008303 \tabularnewline
125 &  9 &  7.219 &  1.781 \tabularnewline
126 &  6 &  6.258 & -0.258 \tabularnewline
127 &  4 &  4.504 & -0.504 \tabularnewline
128 &  7 &  7.844 & -0.8438 \tabularnewline
129 &  2 &  3 & -0.9998 \tabularnewline
130 &  8 &  9.562 & -1.562 \tabularnewline
131 &  9 &  7.974 &  1.026 \tabularnewline
132 &  6 &  6.311 & -0.3107 \tabularnewline
133 &  5 &  4.943 &  0.05662 \tabularnewline
134 &  7 &  6.15 &  0.8504 \tabularnewline
135 &  8 &  7.063 &  0.9365 \tabularnewline
136 &  4 &  5.784 & -1.784 \tabularnewline
137 &  9 &  6.835 &  2.165 \tabularnewline
138 &  9 &  9.006 & -0.005997 \tabularnewline
139 &  9 &  6.071 &  2.929 \tabularnewline
140 &  7 &  5.374 &  1.626 \tabularnewline
141 &  5 &  6.367 & -1.367 \tabularnewline
142 &  7 &  6.924 &  0.07646 \tabularnewline
143 &  9 &  9.495 & -0.4953 \tabularnewline
144 &  8 &  6.582 &  1.418 \tabularnewline
145 &  6 &  5.457 &  0.5428 \tabularnewline
146 &  9 &  8.141 &  0.859 \tabularnewline
147 &  8 &  7.673 &  0.3274 \tabularnewline
148 &  7 &  7.464 & -0.4645 \tabularnewline
149 &  7 &  7.252 & -0.2524 \tabularnewline
150 &  7 &  6.002 &  0.9979 \tabularnewline
151 &  8 &  8.416 & -0.4156 \tabularnewline
152 &  10 &  8.931 &  1.069 \tabularnewline
153 &  6 &  6.688 & -0.6884 \tabularnewline
154 &  6 &  7.084 & -1.084 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=311863&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] 6.869[/C][C] 2.131[/C][/ROW]
[ROW][C]2[/C][C] 8[/C][C] 8.355[/C][C]-0.3545[/C][/ROW]
[ROW][C]3[/C][C] 6[/C][C] 6.854[/C][C]-0.8545[/C][/ROW]
[ROW][C]4[/C][C] 8[/C][C] 8.338[/C][C]-0.3385[/C][/ROW]
[ROW][C]5[/C][C] 8[/C][C] 7.227[/C][C] 0.7731[/C][/ROW]
[ROW][C]6[/C][C] 5[/C][C] 6.612[/C][C]-1.612[/C][/ROW]
[ROW][C]7[/C][C] 9[/C][C] 9.292[/C][C]-0.2917[/C][/ROW]
[ROW][C]8[/C][C] 8[/C][C] 8.423[/C][C]-0.4228[/C][/ROW]
[ROW][C]9[/C][C] 8[/C][C] 6.307[/C][C] 1.693[/C][/ROW]
[ROW][C]10[/C][C] 8[/C][C] 8.716[/C][C]-0.7164[/C][/ROW]
[ROW][C]11[/C][C] 6[/C][C] 5.665[/C][C] 0.3354[/C][/ROW]
[ROW][C]12[/C][C] 6[/C][C] 6.904[/C][C]-0.904[/C][/ROW]
[ROW][C]13[/C][C] 9[/C][C] 8.515[/C][C] 0.4848[/C][/ROW]
[ROW][C]14[/C][C] 8[/C][C] 7.361[/C][C] 0.6394[/C][/ROW]
[ROW][C]15[/C][C] 9[/C][C] 9.431[/C][C]-0.4312[/C][/ROW]
[ROW][C]16[/C][C] 10[/C][C] 8.771[/C][C] 1.229[/C][/ROW]
[ROW][C]17[/C][C] 8[/C][C] 6.77[/C][C] 1.23[/C][/ROW]
[ROW][C]18[/C][C] 8[/C][C] 7.456[/C][C] 0.544[/C][/ROW]
[ROW][C]19[/C][C] 7[/C][C] 6.862[/C][C] 0.1376[/C][/ROW]
[ROW][C]20[/C][C] 7[/C][C] 6.744[/C][C] 0.2561[/C][/ROW]
[ROW][C]21[/C][C] 10[/C][C] 9.58[/C][C] 0.4196[/C][/ROW]
[ROW][C]22[/C][C] 8[/C][C] 6.824[/C][C] 1.176[/C][/ROW]
[ROW][C]23[/C][C] 7[/C][C] 6.554[/C][C] 0.4461[/C][/ROW]
[ROW][C]24[/C][C] 10[/C][C] 7.914[/C][C] 2.086[/C][/ROW]
[ROW][C]25[/C][C] 7[/C][C] 8.07[/C][C]-1.07[/C][/ROW]
[ROW][C]26[/C][C] 7[/C][C] 5.582[/C][C] 1.418[/C][/ROW]
[ROW][C]27[/C][C] 9[/C][C] 8.603[/C][C] 0.3966[/C][/ROW]
[ROW][C]28[/C][C] 9[/C][C] 9.958[/C][C]-0.9578[/C][/ROW]
[ROW][C]29[/C][C] 8[/C][C] 7.792[/C][C] 0.2076[/C][/ROW]
[ROW][C]30[/C][C] 6[/C][C] 7.967[/C][C]-1.967[/C][/ROW]
[ROW][C]31[/C][C] 8[/C][C] 7.222[/C][C] 0.7777[/C][/ROW]
[ROW][C]32[/C][C] 9[/C][C] 7.932[/C][C] 1.068[/C][/ROW]
[ROW][C]33[/C][C] 2[/C][C] 3.674[/C][C]-1.674[/C][/ROW]
[ROW][C]34[/C][C] 6[/C][C] 6.106[/C][C]-0.1065[/C][/ROW]
[ROW][C]35[/C][C] 8[/C][C] 8.263[/C][C]-0.263[/C][/ROW]
[ROW][C]36[/C][C] 8[/C][C] 8.199[/C][C]-0.1986[/C][/ROW]
[ROW][C]37[/C][C] 7[/C][C] 7.249[/C][C]-0.2492[/C][/ROW]
[ROW][C]38[/C][C] 8[/C][C] 7.52[/C][C] 0.4802[/C][/ROW]
[ROW][C]39[/C][C] 6[/C][C] 6.152[/C][C]-0.1518[/C][/ROW]
[ROW][C]40[/C][C] 10[/C][C] 8.095[/C][C] 1.905[/C][/ROW]
[ROW][C]41[/C][C] 10[/C][C] 8.124[/C][C] 1.876[/C][/ROW]
[ROW][C]42[/C][C] 10[/C][C] 7.453[/C][C] 2.547[/C][/ROW]
[ROW][C]43[/C][C] 8[/C][C] 7.888[/C][C] 0.1116[/C][/ROW]
[ROW][C]44[/C][C] 8[/C][C] 8.583[/C][C]-0.5832[/C][/ROW]
[ROW][C]45[/C][C] 7[/C][C] 7.647[/C][C]-0.6469[/C][/ROW]
[ROW][C]46[/C][C] 10[/C][C] 8.79[/C][C] 1.21[/C][/ROW]
[ROW][C]47[/C][C] 5[/C][C] 5.266[/C][C]-0.2661[/C][/ROW]
[ROW][C]48[/C][C] 3[/C][C] 3.148[/C][C]-0.1477[/C][/ROW]
[ROW][C]49[/C][C] 2[/C][C] 3.829[/C][C]-1.829[/C][/ROW]
[ROW][C]50[/C][C] 3[/C][C] 4.144[/C][C]-1.144[/C][/ROW]
[ROW][C]51[/C][C] 4[/C][C] 5.801[/C][C]-1.801[/C][/ROW]
[ROW][C]52[/C][C] 2[/C][C] 3.797[/C][C]-1.797[/C][/ROW]
[ROW][C]53[/C][C] 6[/C][C] 4.96[/C][C] 1.04[/C][/ROW]
[ROW][C]54[/C][C] 8[/C][C] 9.08[/C][C]-1.08[/C][/ROW]
[ROW][C]55[/C][C] 8[/C][C] 8.088[/C][C]-0.08757[/C][/ROW]
[ROW][C]56[/C][C] 5[/C][C] 5.829[/C][C]-0.8293[/C][/ROW]
[ROW][C]57[/C][C] 10[/C][C] 9.679[/C][C] 0.3211[/C][/ROW]
[ROW][C]58[/C][C] 9[/C][C] 10.52[/C][C]-1.52[/C][/ROW]
[ROW][C]59[/C][C] 8[/C][C] 10.01[/C][C]-2.01[/C][/ROW]
[ROW][C]60[/C][C] 9[/C][C] 9.351[/C][C]-0.3508[/C][/ROW]
[ROW][C]61[/C][C] 8[/C][C] 6.531[/C][C] 1.469[/C][/ROW]
[ROW][C]62[/C][C] 5[/C][C] 5.914[/C][C]-0.9139[/C][/ROW]
[ROW][C]63[/C][C] 7[/C][C] 6.586[/C][C] 0.4142[/C][/ROW]
[ROW][C]64[/C][C] 9[/C][C] 8.018[/C][C] 0.9817[/C][/ROW]
[ROW][C]65[/C][C] 8[/C][C] 7.533[/C][C] 0.4671[/C][/ROW]
[ROW][C]66[/C][C] 4[/C][C] 7.663[/C][C]-3.663[/C][/ROW]
[ROW][C]67[/C][C] 7[/C][C] 6.375[/C][C] 0.6249[/C][/ROW]
[ROW][C]68[/C][C] 8[/C][C] 8.423[/C][C]-0.4232[/C][/ROW]
[ROW][C]69[/C][C] 7[/C][C] 7.669[/C][C]-0.6694[/C][/ROW]
[ROW][C]70[/C][C] 7[/C][C] 7.303[/C][C]-0.3025[/C][/ROW]
[ROW][C]71[/C][C] 9[/C][C] 8.316[/C][C] 0.6839[/C][/ROW]
[ROW][C]72[/C][C] 6[/C][C] 7.351[/C][C]-1.351[/C][/ROW]
[ROW][C]73[/C][C] 7[/C][C] 8.342[/C][C]-1.342[/C][/ROW]
[ROW][C]74[/C][C] 4[/C][C] 5.526[/C][C]-1.526[/C][/ROW]
[ROW][C]75[/C][C] 6[/C][C] 6.851[/C][C]-0.8508[/C][/ROW]
[ROW][C]76[/C][C] 10[/C][C] 6.986[/C][C] 3.014[/C][/ROW]
[ROW][C]77[/C][C] 9[/C][C] 8.478[/C][C] 0.5217[/C][/ROW]
[ROW][C]78[/C][C] 10[/C][C] 10.22[/C][C]-0.2167[/C][/ROW]
[ROW][C]79[/C][C] 8[/C][C] 7.665[/C][C] 0.3347[/C][/ROW]
[ROW][C]80[/C][C] 4[/C][C] 5.117[/C][C]-1.117[/C][/ROW]
[ROW][C]81[/C][C] 8[/C][C] 9.64[/C][C]-1.64[/C][/ROW]
[ROW][C]82[/C][C] 5[/C][C] 6.366[/C][C]-1.366[/C][/ROW]
[ROW][C]83[/C][C] 8[/C][C] 7.179[/C][C] 0.8207[/C][/ROW]
[ROW][C]84[/C][C] 9[/C][C] 8.146[/C][C] 0.8539[/C][/ROW]
[ROW][C]85[/C][C] 8[/C][C] 8.015[/C][C]-0.01538[/C][/ROW]
[ROW][C]86[/C][C] 4[/C][C] 7.796[/C][C]-3.796[/C][/ROW]
[ROW][C]87[/C][C] 8[/C][C] 6.758[/C][C] 1.242[/C][/ROW]
[ROW][C]88[/C][C] 10[/C][C] 7.793[/C][C] 2.207[/C][/ROW]
[ROW][C]89[/C][C] 6[/C][C] 6.101[/C][C]-0.1009[/C][/ROW]
[ROW][C]90[/C][C] 7[/C][C] 7.248[/C][C]-0.2476[/C][/ROW]
[ROW][C]91[/C][C] 10[/C][C] 9.039[/C][C] 0.9608[/C][/ROW]
[ROW][C]92[/C][C] 9[/C][C] 9.992[/C][C]-0.992[/C][/ROW]
[ROW][C]93[/C][C] 8[/C][C] 8.849[/C][C]-0.849[/C][/ROW]
[ROW][C]94[/C][C] 3[/C][C] 4.69[/C][C]-1.69[/C][/ROW]
[ROW][C]95[/C][C] 8[/C][C] 7.033[/C][C] 0.9673[/C][/ROW]
[ROW][C]96[/C][C] 7[/C][C] 7.632[/C][C]-0.6323[/C][/ROW]
[ROW][C]97[/C][C] 7[/C][C] 7.413[/C][C]-0.4129[/C][/ROW]
[ROW][C]98[/C][C] 8[/C][C] 6.682[/C][C] 1.318[/C][/ROW]
[ROW][C]99[/C][C] 8[/C][C] 8.883[/C][C]-0.8833[/C][/ROW]
[ROW][C]100[/C][C] 7[/C][C] 7.565[/C][C]-0.5649[/C][/ROW]
[ROW][C]101[/C][C] 7[/C][C] 5.601[/C][C] 1.399[/C][/ROW]
[ROW][C]102[/C][C] 9[/C][C] 9.822[/C][C]-0.8222[/C][/ROW]
[ROW][C]103[/C][C] 9[/C][C] 8.237[/C][C] 0.7633[/C][/ROW]
[ROW][C]104[/C][C] 9[/C][C] 8.336[/C][C] 0.6644[/C][/ROW]
[ROW][C]105[/C][C] 4[/C][C] 5.53[/C][C]-1.53[/C][/ROW]
[ROW][C]106[/C][C] 6[/C][C] 6.773[/C][C]-0.7729[/C][/ROW]
[ROW][C]107[/C][C] 6[/C][C] 6.492[/C][C]-0.4917[/C][/ROW]
[ROW][C]108[/C][C] 6[/C][C] 3.765[/C][C] 2.235[/C][/ROW]
[ROW][C]109[/C][C] 8[/C][C] 7.986[/C][C] 0.014[/C][/ROW]
[ROW][C]110[/C][C] 3[/C][C] 3.947[/C][C]-0.9467[/C][/ROW]
[ROW][C]111[/C][C] 8[/C][C] 6.563[/C][C] 1.437[/C][/ROW]
[ROW][C]112[/C][C] 8[/C][C] 7.439[/C][C] 0.5606[/C][/ROW]
[ROW][C]113[/C][C] 6[/C][C] 4.343[/C][C] 1.657[/C][/ROW]
[ROW][C]114[/C][C] 10[/C][C] 9.046[/C][C] 0.954[/C][/ROW]
[ROW][C]115[/C][C] 2[/C][C] 4.397[/C][C]-2.397[/C][/ROW]
[ROW][C]116[/C][C] 9[/C][C] 8.124[/C][C] 0.8764[/C][/ROW]
[ROW][C]117[/C][C] 6[/C][C] 5.572[/C][C] 0.4277[/C][/ROW]
[ROW][C]118[/C][C] 6[/C][C] 7.71[/C][C]-1.71[/C][/ROW]
[ROW][C]119[/C][C] 5[/C][C] 4.317[/C][C] 0.6832[/C][/ROW]
[ROW][C]120[/C][C] 4[/C][C] 4.459[/C][C]-0.4594[/C][/ROW]
[ROW][C]121[/C][C] 7[/C][C] 6.413[/C][C] 0.587[/C][/ROW]
[ROW][C]122[/C][C] 5[/C][C] 5.292[/C][C]-0.2924[/C][/ROW]
[ROW][C]123[/C][C] 8[/C][C] 8.066[/C][C]-0.06552[/C][/ROW]
[ROW][C]124[/C][C] 6[/C][C] 5.992[/C][C] 0.008303[/C][/ROW]
[ROW][C]125[/C][C] 9[/C][C] 7.219[/C][C] 1.781[/C][/ROW]
[ROW][C]126[/C][C] 6[/C][C] 6.258[/C][C]-0.258[/C][/ROW]
[ROW][C]127[/C][C] 4[/C][C] 4.504[/C][C]-0.504[/C][/ROW]
[ROW][C]128[/C][C] 7[/C][C] 7.844[/C][C]-0.8438[/C][/ROW]
[ROW][C]129[/C][C] 2[/C][C] 3[/C][C]-0.9998[/C][/ROW]
[ROW][C]130[/C][C] 8[/C][C] 9.562[/C][C]-1.562[/C][/ROW]
[ROW][C]131[/C][C] 9[/C][C] 7.974[/C][C] 1.026[/C][/ROW]
[ROW][C]132[/C][C] 6[/C][C] 6.311[/C][C]-0.3107[/C][/ROW]
[ROW][C]133[/C][C] 5[/C][C] 4.943[/C][C] 0.05662[/C][/ROW]
[ROW][C]134[/C][C] 7[/C][C] 6.15[/C][C] 0.8504[/C][/ROW]
[ROW][C]135[/C][C] 8[/C][C] 7.063[/C][C] 0.9365[/C][/ROW]
[ROW][C]136[/C][C] 4[/C][C] 5.784[/C][C]-1.784[/C][/ROW]
[ROW][C]137[/C][C] 9[/C][C] 6.835[/C][C] 2.165[/C][/ROW]
[ROW][C]138[/C][C] 9[/C][C] 9.006[/C][C]-0.005997[/C][/ROW]
[ROW][C]139[/C][C] 9[/C][C] 6.071[/C][C] 2.929[/C][/ROW]
[ROW][C]140[/C][C] 7[/C][C] 5.374[/C][C] 1.626[/C][/ROW]
[ROW][C]141[/C][C] 5[/C][C] 6.367[/C][C]-1.367[/C][/ROW]
[ROW][C]142[/C][C] 7[/C][C] 6.924[/C][C] 0.07646[/C][/ROW]
[ROW][C]143[/C][C] 9[/C][C] 9.495[/C][C]-0.4953[/C][/ROW]
[ROW][C]144[/C][C] 8[/C][C] 6.582[/C][C] 1.418[/C][/ROW]
[ROW][C]145[/C][C] 6[/C][C] 5.457[/C][C] 0.5428[/C][/ROW]
[ROW][C]146[/C][C] 9[/C][C] 8.141[/C][C] 0.859[/C][/ROW]
[ROW][C]147[/C][C] 8[/C][C] 7.673[/C][C] 0.3274[/C][/ROW]
[ROW][C]148[/C][C] 7[/C][C] 7.464[/C][C]-0.4645[/C][/ROW]
[ROW][C]149[/C][C] 7[/C][C] 7.252[/C][C]-0.2524[/C][/ROW]
[ROW][C]150[/C][C] 7[/C][C] 6.002[/C][C] 0.9979[/C][/ROW]
[ROW][C]151[/C][C] 8[/C][C] 8.416[/C][C]-0.4156[/C][/ROW]
[ROW][C]152[/C][C] 10[/C][C] 8.931[/C][C] 1.069[/C][/ROW]
[ROW][C]153[/C][C] 6[/C][C] 6.688[/C][C]-0.6884[/C][/ROW]
[ROW][C]154[/C][C] 6[/C][C] 7.084[/C][C]-1.084[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=311863&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=311863&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	6.869	2.131
2	8	8.355	-0.3545
3	6	6.854	-0.8545
4	8	8.338	-0.3385
5	8	7.227	0.7731
6	5	6.612	-1.612
7	9	9.292	-0.2917
8	8	8.423	-0.4228
9	8	6.307	1.693
10	8	8.716	-0.7164
11	6	5.665	0.3354
12	6	6.904	-0.904
13	9	8.515	0.4848
14	8	7.361	0.6394
15	9	9.431	-0.4312
16	10	8.771	1.229
17	8	6.77	1.23
18	8	7.456	0.544
19	7	6.862	0.1376
20	7	6.744	0.2561
21	10	9.58	0.4196
22	8	6.824	1.176
23	7	6.554	0.4461
24	10	7.914	2.086
25	7	8.07	-1.07
26	7	5.582	1.418
27	9	8.603	0.3966
28	9	9.958	-0.9578
29	8	7.792	0.2076
30	6	7.967	-1.967
31	8	7.222	0.7777
32	9	7.932	1.068
33	2	3.674	-1.674
34	6	6.106	-0.1065
35	8	8.263	-0.263
36	8	8.199	-0.1986
37	7	7.249	-0.2492
38	8	7.52	0.4802
39	6	6.152	-0.1518
40	10	8.095	1.905
41	10	8.124	1.876
42	10	7.453	2.547
43	8	7.888	0.1116
44	8	8.583	-0.5832
45	7	7.647	-0.6469
46	10	8.79	1.21
47	5	5.266	-0.2661
48	3	3.148	-0.1477
49	2	3.829	-1.829
50	3	4.144	-1.144
51	4	5.801	-1.801
52	2	3.797	-1.797
53	6	4.96	1.04
54	8	9.08	-1.08
55	8	8.088	-0.08757
56	5	5.829	-0.8293
57	10	9.679	0.3211
58	9	10.52	-1.52
59	8	10.01	-2.01
60	9	9.351	-0.3508
61	8	6.531	1.469
62	5	5.914	-0.9139
63	7	6.586	0.4142
64	9	8.018	0.9817
65	8	7.533	0.4671
66	4	7.663	-3.663
67	7	6.375	0.6249
68	8	8.423	-0.4232
69	7	7.669	-0.6694
70	7	7.303	-0.3025
71	9	8.316	0.6839
72	6	7.351	-1.351
73	7	8.342	-1.342
74	4	5.526	-1.526
75	6	6.851	-0.8508
76	10	6.986	3.014
77	9	8.478	0.5217
78	10	10.22	-0.2167
79	8	7.665	0.3347
80	4	5.117	-1.117
81	8	9.64	-1.64
82	5	6.366	-1.366
83	8	7.179	0.8207
84	9	8.146	0.8539
85	8	8.015	-0.01538
86	4	7.796	-3.796
87	8	6.758	1.242
88	10	7.793	2.207
89	6	6.101	-0.1009
90	7	7.248	-0.2476
91	10	9.039	0.9608
92	9	9.992	-0.992
93	8	8.849	-0.849
94	3	4.69	-1.69
95	8	7.033	0.9673
96	7	7.632	-0.6323
97	7	7.413	-0.4129
98	8	6.682	1.318
99	8	8.883	-0.8833
100	7	7.565	-0.5649
101	7	5.601	1.399
102	9	9.822	-0.8222
103	9	8.237	0.7633
104	9	8.336	0.6644
105	4	5.53	-1.53
106	6	6.773	-0.7729
107	6	6.492	-0.4917
108	6	3.765	2.235
109	8	7.986	0.014
110	3	3.947	-0.9467
111	8	6.563	1.437
112	8	7.439	0.5606
113	6	4.343	1.657
114	10	9.046	0.954
115	2	4.397	-2.397
116	9	8.124	0.8764
117	6	5.572	0.4277
118	6	7.71	-1.71
119	5	4.317	0.6832
120	4	4.459	-0.4594
121	7	6.413	0.587
122	5	5.292	-0.2924
123	8	8.066	-0.06552
124	6	5.992	0.008303
125	9	7.219	1.781
126	6	6.258	-0.258
127	4	4.504	-0.504
128	7	7.844	-0.8438
129	2	3	-0.9998
130	8	9.562	-1.562
131	9	7.974	1.026
132	6	6.311	-0.3107
133	5	4.943	0.05662
134	7	6.15	0.8504
135	8	7.063	0.9365
136	4	5.784	-1.784
137	9	6.835	2.165
138	9	9.006	-0.005997
139	9	6.071	2.929
140	7	5.374	1.626
141	5	6.367	-1.367
142	7	6.924	0.07646
143	9	9.495	-0.4953
144	8	6.582	1.418
145	6	5.457	0.5428
146	9	8.141	0.859
147	8	7.673	0.3274
148	7	7.464	-0.4645
149	7	7.252	-0.2524
150	7	6.002	0.9979
151	8	8.416	-0.4156
152	10	8.931	1.069
153	6	6.688	-0.6884
154	6	7.084	-1.084

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
24	0.2498	0.4996	0.7502
25	0.1648	0.3296	0.8352
26	0.1222	0.2444	0.8778
27	0.1028	0.2056	0.8972
28	0.06003	0.1201	0.94
29	0.03432	0.06865	0.9657
30	0.03851	0.07702	0.9615
31	0.1163	0.2326	0.8837
32	0.09017	0.1803	0.9098
33	0.08714	0.1743	0.9129
34	0.07531	0.1506	0.9247
35	0.1007	0.2014	0.8993
36	0.07065	0.1413	0.9293
37	0.04625	0.09249	0.9538
38	0.06963	0.1393	0.9304
39	0.05808	0.1162	0.9419
40	0.07684	0.1537	0.9232
41	0.149	0.298	0.851
42	0.2649	0.5298	0.7351
43	0.3352	0.6703	0.6648
44	0.2985	0.5971	0.7015
45	0.3933	0.7866	0.6067
46	0.3664	0.7329	0.6336
47	0.3189	0.6378	0.6811
48	0.2734	0.5468	0.7266
49	0.2903	0.5806	0.7097
50	0.255	0.51	0.745
51	0.2501	0.5001	0.7499
52	0.2541	0.5081	0.7459
53	0.2869	0.5738	0.7131
54	0.3092	0.6184	0.6908
55	0.2602	0.5204	0.7398
56	0.2258	0.4516	0.7742
57	0.1875	0.375	0.8125
58	0.1992	0.3983	0.8008
59	0.2517	0.5034	0.7483
60	0.2085	0.417	0.7915
61	0.2186	0.4372	0.7814
62	0.2405	0.4809	0.7595
63	0.2094	0.4187	0.7906
64	0.1957	0.3914	0.8043
65	0.1921	0.3842	0.8079
66	0.4334	0.8667	0.5666
67	0.3949	0.7898	0.6051
68	0.3523	0.7047	0.6477
69	0.3178	0.6356	0.6822
70	0.2846	0.5691	0.7154
71	0.2483	0.4967	0.7517
72	0.2756	0.5512	0.7244
73	0.2822	0.5643	0.7178
74	0.3241	0.6483	0.6759
75	0.3133	0.6265	0.6867
76	0.5317	0.9366	0.4683
77	0.4837	0.9674	0.5163
78	0.4373	0.8747	0.5627
79	0.4261	0.8522	0.5739
80	0.3819	0.7638	0.6181
81	0.3904	0.7808	0.6096
82	0.422	0.8439	0.578
83	0.4295	0.8591	0.5705
84	0.4236	0.8471	0.5764
85	0.3773	0.7547	0.6227
86	0.7774	0.4451	0.2226
87	0.8059	0.3882	0.1941
88	0.861	0.278	0.139
89	0.8293	0.3414	0.1707
90	0.7937	0.4127	0.2063
91	0.7665	0.4669	0.2335
92	0.7357	0.5285	0.2643
93	0.708	0.584	0.292
94	0.7137	0.5725	0.2863
95	0.7394	0.5212	0.2606
96	0.7028	0.5943	0.2972
97	0.6613	0.6773	0.3387
98	0.6694	0.6613	0.3306
99	0.6408	0.7184	0.3592
100	0.5908	0.8184	0.4092
101	0.6209	0.7583	0.3791
102	0.5701	0.8597	0.4299
103	0.5278	0.9444	0.4722
104	0.5	1	0.5
105	0.5108	0.9784	0.4892
106	0.4657	0.9313	0.5343
107	0.4067	0.8134	0.5933
108	0.7315	0.5371	0.2685
109	0.6844	0.6313	0.3156
110	0.6846	0.6308	0.3154
111	0.7643	0.4713	0.2357
112	0.7188	0.5624	0.2812
113	0.7868	0.4264	0.2132
114	0.7545	0.491	0.2455
115	0.8745	0.2509	0.1255
116	0.8554	0.2891	0.1446
117	0.8098	0.3805	0.1902
118	0.7633	0.4733	0.2367
119	0.7577	0.4847	0.2423
120	0.7503	0.4993	0.2497
121	0.8555	0.2891	0.1445
122	0.7948	0.4104	0.2052
123	0.8479	0.3041	0.1521
124	0.8026	0.3948	0.1974
125	0.7427	0.5146	0.2573
126	0.6406	0.7189	0.3594
127	0.5216	0.9568	0.4784
128	0.5647	0.8707	0.4353
129	0.8975	0.2049	0.1025
130	0.8724	0.2553	0.1276

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
24 &  0.2498 &  0.4996 &  0.7502 \tabularnewline
25 &  0.1648 &  0.3296 &  0.8352 \tabularnewline
26 &  0.1222 &  0.2444 &  0.8778 \tabularnewline
27 &  0.1028 &  0.2056 &  0.8972 \tabularnewline
28 &  0.06003 &  0.1201 &  0.94 \tabularnewline
29 &  0.03432 &  0.06865 &  0.9657 \tabularnewline
30 &  0.03851 &  0.07702 &  0.9615 \tabularnewline
31 &  0.1163 &  0.2326 &  0.8837 \tabularnewline
32 &  0.09017 &  0.1803 &  0.9098 \tabularnewline
33 &  0.08714 &  0.1743 &  0.9129 \tabularnewline
34 &  0.07531 &  0.1506 &  0.9247 \tabularnewline
35 &  0.1007 &  0.2014 &  0.8993 \tabularnewline
36 &  0.07065 &  0.1413 &  0.9293 \tabularnewline
37 &  0.04625 &  0.09249 &  0.9538 \tabularnewline
38 &  0.06963 &  0.1393 &  0.9304 \tabularnewline
39 &  0.05808 &  0.1162 &  0.9419 \tabularnewline
40 &  0.07684 &  0.1537 &  0.9232 \tabularnewline
41 &  0.149 &  0.298 &  0.851 \tabularnewline
42 &  0.2649 &  0.5298 &  0.7351 \tabularnewline
43 &  0.3352 &  0.6703 &  0.6648 \tabularnewline
44 &  0.2985 &  0.5971 &  0.7015 \tabularnewline
45 &  0.3933 &  0.7866 &  0.6067 \tabularnewline
46 &  0.3664 &  0.7329 &  0.6336 \tabularnewline
47 &  0.3189 &  0.6378 &  0.6811 \tabularnewline
48 &  0.2734 &  0.5468 &  0.7266 \tabularnewline
49 &  0.2903 &  0.5806 &  0.7097 \tabularnewline
50 &  0.255 &  0.51 &  0.745 \tabularnewline
51 &  0.2501 &  0.5001 &  0.7499 \tabularnewline
52 &  0.2541 &  0.5081 &  0.7459 \tabularnewline
53 &  0.2869 &  0.5738 &  0.7131 \tabularnewline
54 &  0.3092 &  0.6184 &  0.6908 \tabularnewline
55 &  0.2602 &  0.5204 &  0.7398 \tabularnewline
56 &  0.2258 &  0.4516 &  0.7742 \tabularnewline
57 &  0.1875 &  0.375 &  0.8125 \tabularnewline
58 &  0.1992 &  0.3983 &  0.8008 \tabularnewline
59 &  0.2517 &  0.5034 &  0.7483 \tabularnewline
60 &  0.2085 &  0.417 &  0.7915 \tabularnewline
61 &  0.2186 &  0.4372 &  0.7814 \tabularnewline
62 &  0.2405 &  0.4809 &  0.7595 \tabularnewline
63 &  0.2094 &  0.4187 &  0.7906 \tabularnewline
64 &  0.1957 &  0.3914 &  0.8043 \tabularnewline
65 &  0.1921 &  0.3842 &  0.8079 \tabularnewline
66 &  0.4334 &  0.8667 &  0.5666 \tabularnewline
67 &  0.3949 &  0.7898 &  0.6051 \tabularnewline
68 &  0.3523 &  0.7047 &  0.6477 \tabularnewline
69 &  0.3178 &  0.6356 &  0.6822 \tabularnewline
70 &  0.2846 &  0.5691 &  0.7154 \tabularnewline
71 &  0.2483 &  0.4967 &  0.7517 \tabularnewline
72 &  0.2756 &  0.5512 &  0.7244 \tabularnewline
73 &  0.2822 &  0.5643 &  0.7178 \tabularnewline
74 &  0.3241 &  0.6483 &  0.6759 \tabularnewline
75 &  0.3133 &  0.6265 &  0.6867 \tabularnewline
76 &  0.5317 &  0.9366 &  0.4683 \tabularnewline
77 &  0.4837 &  0.9674 &  0.5163 \tabularnewline
78 &  0.4373 &  0.8747 &  0.5627 \tabularnewline
79 &  0.4261 &  0.8522 &  0.5739 \tabularnewline
80 &  0.3819 &  0.7638 &  0.6181 \tabularnewline
81 &  0.3904 &  0.7808 &  0.6096 \tabularnewline
82 &  0.422 &  0.8439 &  0.578 \tabularnewline
83 &  0.4295 &  0.8591 &  0.5705 \tabularnewline
84 &  0.4236 &  0.8471 &  0.5764 \tabularnewline
85 &  0.3773 &  0.7547 &  0.6227 \tabularnewline
86 &  0.7774 &  0.4451 &  0.2226 \tabularnewline
87 &  0.8059 &  0.3882 &  0.1941 \tabularnewline
88 &  0.861 &  0.278 &  0.139 \tabularnewline
89 &  0.8293 &  0.3414 &  0.1707 \tabularnewline
90 &  0.7937 &  0.4127 &  0.2063 \tabularnewline
91 &  0.7665 &  0.4669 &  0.2335 \tabularnewline
92 &  0.7357 &  0.5285 &  0.2643 \tabularnewline
93 &  0.708 &  0.584 &  0.292 \tabularnewline
94 &  0.7137 &  0.5725 &  0.2863 \tabularnewline
95 &  0.7394 &  0.5212 &  0.2606 \tabularnewline
96 &  0.7028 &  0.5943 &  0.2972 \tabularnewline
97 &  0.6613 &  0.6773 &  0.3387 \tabularnewline
98 &  0.6694 &  0.6613 &  0.3306 \tabularnewline
99 &  0.6408 &  0.7184 &  0.3592 \tabularnewline
100 &  0.5908 &  0.8184 &  0.4092 \tabularnewline
101 &  0.6209 &  0.7583 &  0.3791 \tabularnewline
102 &  0.5701 &  0.8597 &  0.4299 \tabularnewline
103 &  0.5278 &  0.9444 &  0.4722 \tabularnewline
104 &  0.5 &  1 &  0.5 \tabularnewline
105 &  0.5108 &  0.9784 &  0.4892 \tabularnewline
106 &  0.4657 &  0.9313 &  0.5343 \tabularnewline
107 &  0.4067 &  0.8134 &  0.5933 \tabularnewline
108 &  0.7315 &  0.5371 &  0.2685 \tabularnewline
109 &  0.6844 &  0.6313 &  0.3156 \tabularnewline
110 &  0.6846 &  0.6308 &  0.3154 \tabularnewline
111 &  0.7643 &  0.4713 &  0.2357 \tabularnewline
112 &  0.7188 &  0.5624 &  0.2812 \tabularnewline
113 &  0.7868 &  0.4264 &  0.2132 \tabularnewline
114 &  0.7545 &  0.491 &  0.2455 \tabularnewline
115 &  0.8745 &  0.2509 &  0.1255 \tabularnewline
116 &  0.8554 &  0.2891 &  0.1446 \tabularnewline
117 &  0.8098 &  0.3805 &  0.1902 \tabularnewline
118 &  0.7633 &  0.4733 &  0.2367 \tabularnewline
119 &  0.7577 &  0.4847 &  0.2423 \tabularnewline
120 &  0.7503 &  0.4993 &  0.2497 \tabularnewline
121 &  0.8555 &  0.2891 &  0.1445 \tabularnewline
122 &  0.7948 &  0.4104 &  0.2052 \tabularnewline
123 &  0.8479 &  0.3041 &  0.1521 \tabularnewline
124 &  0.8026 &  0.3948 &  0.1974 \tabularnewline
125 &  0.7427 &  0.5146 &  0.2573 \tabularnewline
126 &  0.6406 &  0.7189 &  0.3594 \tabularnewline
127 &  0.5216 &  0.9568 &  0.4784 \tabularnewline
128 &  0.5647 &  0.8707 &  0.4353 \tabularnewline
129 &  0.8975 &  0.2049 &  0.1025 \tabularnewline
130 &  0.8724 &  0.2553 &  0.1276 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=311863&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] 0.2498[/C][C] 0.4996[/C][C] 0.7502[/C][/ROW]
[ROW][C]25[/C][C] 0.1648[/C][C] 0.3296[/C][C] 0.8352[/C][/ROW]
[ROW][C]26[/C][C] 0.1222[/C][C] 0.2444[/C][C] 0.8778[/C][/ROW]
[ROW][C]27[/C][C] 0.1028[/C][C] 0.2056[/C][C] 0.8972[/C][/ROW]
[ROW][C]28[/C][C] 0.06003[/C][C] 0.1201[/C][C] 0.94[/C][/ROW]
[ROW][C]29[/C][C] 0.03432[/C][C] 0.06865[/C][C] 0.9657[/C][/ROW]
[ROW][C]30[/C][C] 0.03851[/C][C] 0.07702[/C][C] 0.9615[/C][/ROW]
[ROW][C]31[/C][C] 0.1163[/C][C] 0.2326[/C][C] 0.8837[/C][/ROW]
[ROW][C]32[/C][C] 0.09017[/C][C] 0.1803[/C][C] 0.9098[/C][/ROW]
[ROW][C]33[/C][C] 0.08714[/C][C] 0.1743[/C][C] 0.9129[/C][/ROW]
[ROW][C]34[/C][C] 0.07531[/C][C] 0.1506[/C][C] 0.9247[/C][/ROW]
[ROW][C]35[/C][C] 0.1007[/C][C] 0.2014[/C][C] 0.8993[/C][/ROW]
[ROW][C]36[/C][C] 0.07065[/C][C] 0.1413[/C][C] 0.9293[/C][/ROW]
[ROW][C]37[/C][C] 0.04625[/C][C] 0.09249[/C][C] 0.9538[/C][/ROW]
[ROW][C]38[/C][C] 0.06963[/C][C] 0.1393[/C][C] 0.9304[/C][/ROW]
[ROW][C]39[/C][C] 0.05808[/C][C] 0.1162[/C][C] 0.9419[/C][/ROW]
[ROW][C]40[/C][C] 0.07684[/C][C] 0.1537[/C][C] 0.9232[/C][/ROW]
[ROW][C]41[/C][C] 0.149[/C][C] 0.298[/C][C] 0.851[/C][/ROW]
[ROW][C]42[/C][C] 0.2649[/C][C] 0.5298[/C][C] 0.7351[/C][/ROW]
[ROW][C]43[/C][C] 0.3352[/C][C] 0.6703[/C][C] 0.6648[/C][/ROW]
[ROW][C]44[/C][C] 0.2985[/C][C] 0.5971[/C][C] 0.7015[/C][/ROW]
[ROW][C]45[/C][C] 0.3933[/C][C] 0.7866[/C][C] 0.6067[/C][/ROW]
[ROW][C]46[/C][C] 0.3664[/C][C] 0.7329[/C][C] 0.6336[/C][/ROW]
[ROW][C]47[/C][C] 0.3189[/C][C] 0.6378[/C][C] 0.6811[/C][/ROW]
[ROW][C]48[/C][C] 0.2734[/C][C] 0.5468[/C][C] 0.7266[/C][/ROW]
[ROW][C]49[/C][C] 0.2903[/C][C] 0.5806[/C][C] 0.7097[/C][/ROW]
[ROW][C]50[/C][C] 0.255[/C][C] 0.51[/C][C] 0.745[/C][/ROW]
[ROW][C]51[/C][C] 0.2501[/C][C] 0.5001[/C][C] 0.7499[/C][/ROW]
[ROW][C]52[/C][C] 0.2541[/C][C] 0.5081[/C][C] 0.7459[/C][/ROW]
[ROW][C]53[/C][C] 0.2869[/C][C] 0.5738[/C][C] 0.7131[/C][/ROW]
[ROW][C]54[/C][C] 0.3092[/C][C] 0.6184[/C][C] 0.6908[/C][/ROW]
[ROW][C]55[/C][C] 0.2602[/C][C] 0.5204[/C][C] 0.7398[/C][/ROW]
[ROW][C]56[/C][C] 0.2258[/C][C] 0.4516[/C][C] 0.7742[/C][/ROW]
[ROW][C]57[/C][C] 0.1875[/C][C] 0.375[/C][C] 0.8125[/C][/ROW]
[ROW][C]58[/C][C] 0.1992[/C][C] 0.3983[/C][C] 0.8008[/C][/ROW]
[ROW][C]59[/C][C] 0.2517[/C][C] 0.5034[/C][C] 0.7483[/C][/ROW]
[ROW][C]60[/C][C] 0.2085[/C][C] 0.417[/C][C] 0.7915[/C][/ROW]
[ROW][C]61[/C][C] 0.2186[/C][C] 0.4372[/C][C] 0.7814[/C][/ROW]
[ROW][C]62[/C][C] 0.2405[/C][C] 0.4809[/C][C] 0.7595[/C][/ROW]
[ROW][C]63[/C][C] 0.2094[/C][C] 0.4187[/C][C] 0.7906[/C][/ROW]
[ROW][C]64[/C][C] 0.1957[/C][C] 0.3914[/C][C] 0.8043[/C][/ROW]
[ROW][C]65[/C][C] 0.1921[/C][C] 0.3842[/C][C] 0.8079[/C][/ROW]
[ROW][C]66[/C][C] 0.4334[/C][C] 0.8667[/C][C] 0.5666[/C][/ROW]
[ROW][C]67[/C][C] 0.3949[/C][C] 0.7898[/C][C] 0.6051[/C][/ROW]
[ROW][C]68[/C][C] 0.3523[/C][C] 0.7047[/C][C] 0.6477[/C][/ROW]
[ROW][C]69[/C][C] 0.3178[/C][C] 0.6356[/C][C] 0.6822[/C][/ROW]
[ROW][C]70[/C][C] 0.2846[/C][C] 0.5691[/C][C] 0.7154[/C][/ROW]
[ROW][C]71[/C][C] 0.2483[/C][C] 0.4967[/C][C] 0.7517[/C][/ROW]
[ROW][C]72[/C][C] 0.2756[/C][C] 0.5512[/C][C] 0.7244[/C][/ROW]
[ROW][C]73[/C][C] 0.2822[/C][C] 0.5643[/C][C] 0.7178[/C][/ROW]
[ROW][C]74[/C][C] 0.3241[/C][C] 0.6483[/C][C] 0.6759[/C][/ROW]
[ROW][C]75[/C][C] 0.3133[/C][C] 0.6265[/C][C] 0.6867[/C][/ROW]
[ROW][C]76[/C][C] 0.5317[/C][C] 0.9366[/C][C] 0.4683[/C][/ROW]
[ROW][C]77[/C][C] 0.4837[/C][C] 0.9674[/C][C] 0.5163[/C][/ROW]
[ROW][C]78[/C][C] 0.4373[/C][C] 0.8747[/C][C] 0.5627[/C][/ROW]
[ROW][C]79[/C][C] 0.4261[/C][C] 0.8522[/C][C] 0.5739[/C][/ROW]
[ROW][C]80[/C][C] 0.3819[/C][C] 0.7638[/C][C] 0.6181[/C][/ROW]
[ROW][C]81[/C][C] 0.3904[/C][C] 0.7808[/C][C] 0.6096[/C][/ROW]
[ROW][C]82[/C][C] 0.422[/C][C] 0.8439[/C][C] 0.578[/C][/ROW]
[ROW][C]83[/C][C] 0.4295[/C][C] 0.8591[/C][C] 0.5705[/C][/ROW]
[ROW][C]84[/C][C] 0.4236[/C][C] 0.8471[/C][C] 0.5764[/C][/ROW]
[ROW][C]85[/C][C] 0.3773[/C][C] 0.7547[/C][C] 0.6227[/C][/ROW]
[ROW][C]86[/C][C] 0.7774[/C][C] 0.4451[/C][C] 0.2226[/C][/ROW]
[ROW][C]87[/C][C] 0.8059[/C][C] 0.3882[/C][C] 0.1941[/C][/ROW]
[ROW][C]88[/C][C] 0.861[/C][C] 0.278[/C][C] 0.139[/C][/ROW]
[ROW][C]89[/C][C] 0.8293[/C][C] 0.3414[/C][C] 0.1707[/C][/ROW]
[ROW][C]90[/C][C] 0.7937[/C][C] 0.4127[/C][C] 0.2063[/C][/ROW]
[ROW][C]91[/C][C] 0.7665[/C][C] 0.4669[/C][C] 0.2335[/C][/ROW]
[ROW][C]92[/C][C] 0.7357[/C][C] 0.5285[/C][C] 0.2643[/C][/ROW]
[ROW][C]93[/C][C] 0.708[/C][C] 0.584[/C][C] 0.292[/C][/ROW]
[ROW][C]94[/C][C] 0.7137[/C][C] 0.5725[/C][C] 0.2863[/C][/ROW]
[ROW][C]95[/C][C] 0.7394[/C][C] 0.5212[/C][C] 0.2606[/C][/ROW]
[ROW][C]96[/C][C] 0.7028[/C][C] 0.5943[/C][C] 0.2972[/C][/ROW]
[ROW][C]97[/C][C] 0.6613[/C][C] 0.6773[/C][C] 0.3387[/C][/ROW]
[ROW][C]98[/C][C] 0.6694[/C][C] 0.6613[/C][C] 0.3306[/C][/ROW]
[ROW][C]99[/C][C] 0.6408[/C][C] 0.7184[/C][C] 0.3592[/C][/ROW]
[ROW][C]100[/C][C] 0.5908[/C][C] 0.8184[/C][C] 0.4092[/C][/ROW]
[ROW][C]101[/C][C] 0.6209[/C][C] 0.7583[/C][C] 0.3791[/C][/ROW]
[ROW][C]102[/C][C] 0.5701[/C][C] 0.8597[/C][C] 0.4299[/C][/ROW]
[ROW][C]103[/C][C] 0.5278[/C][C] 0.9444[/C][C] 0.4722[/C][/ROW]
[ROW][C]104[/C][C] 0.5[/C][C] 1[/C][C] 0.5[/C][/ROW]
[ROW][C]105[/C][C] 0.5108[/C][C] 0.9784[/C][C] 0.4892[/C][/ROW]
[ROW][C]106[/C][C] 0.4657[/C][C] 0.9313[/C][C] 0.5343[/C][/ROW]
[ROW][C]107[/C][C] 0.4067[/C][C] 0.8134[/C][C] 0.5933[/C][/ROW]
[ROW][C]108[/C][C] 0.7315[/C][C] 0.5371[/C][C] 0.2685[/C][/ROW]
[ROW][C]109[/C][C] 0.6844[/C][C] 0.6313[/C][C] 0.3156[/C][/ROW]
[ROW][C]110[/C][C] 0.6846[/C][C] 0.6308[/C][C] 0.3154[/C][/ROW]
[ROW][C]111[/C][C] 0.7643[/C][C] 0.4713[/C][C] 0.2357[/C][/ROW]
[ROW][C]112[/C][C] 0.7188[/C][C] 0.5624[/C][C] 0.2812[/C][/ROW]
[ROW][C]113[/C][C] 0.7868[/C][C] 0.4264[/C][C] 0.2132[/C][/ROW]
[ROW][C]114[/C][C] 0.7545[/C][C] 0.491[/C][C] 0.2455[/C][/ROW]
[ROW][C]115[/C][C] 0.8745[/C][C] 0.2509[/C][C] 0.1255[/C][/ROW]
[ROW][C]116[/C][C] 0.8554[/C][C] 0.2891[/C][C] 0.1446[/C][/ROW]
[ROW][C]117[/C][C] 0.8098[/C][C] 0.3805[/C][C] 0.1902[/C][/ROW]
[ROW][C]118[/C][C] 0.7633[/C][C] 0.4733[/C][C] 0.2367[/C][/ROW]
[ROW][C]119[/C][C] 0.7577[/C][C] 0.4847[/C][C] 0.2423[/C][/ROW]
[ROW][C]120[/C][C] 0.7503[/C][C] 0.4993[/C][C] 0.2497[/C][/ROW]
[ROW][C]121[/C][C] 0.8555[/C][C] 0.2891[/C][C] 0.1445[/C][/ROW]
[ROW][C]122[/C][C] 0.7948[/C][C] 0.4104[/C][C] 0.2052[/C][/ROW]
[ROW][C]123[/C][C] 0.8479[/C][C] 0.3041[/C][C] 0.1521[/C][/ROW]
[ROW][C]124[/C][C] 0.8026[/C][C] 0.3948[/C][C] 0.1974[/C][/ROW]
[ROW][C]125[/C][C] 0.7427[/C][C] 0.5146[/C][C] 0.2573[/C][/ROW]
[ROW][C]126[/C][C] 0.6406[/C][C] 0.7189[/C][C] 0.3594[/C][/ROW]
[ROW][C]127[/C][C] 0.5216[/C][C] 0.9568[/C][C] 0.4784[/C][/ROW]
[ROW][C]128[/C][C] 0.5647[/C][C] 0.8707[/C][C] 0.4353[/C][/ROW]
[ROW][C]129[/C][C] 0.8975[/C][C] 0.2049[/C][C] 0.1025[/C][/ROW]
[ROW][C]130[/C][C] 0.8724[/C][C] 0.2553[/C][C] 0.1276[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=311863&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=311863&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	0.2498	0.4996	0.7502
25	0.1648	0.3296	0.8352
26	0.1222	0.2444	0.8778
27	0.1028	0.2056	0.8972
28	0.06003	0.1201	0.94
29	0.03432	0.06865	0.9657
30	0.03851	0.07702	0.9615
31	0.1163	0.2326	0.8837
32	0.09017	0.1803	0.9098
33	0.08714	0.1743	0.9129
34	0.07531	0.1506	0.9247
35	0.1007	0.2014	0.8993
36	0.07065	0.1413	0.9293
37	0.04625	0.09249	0.9538
38	0.06963	0.1393	0.9304
39	0.05808	0.1162	0.9419
40	0.07684	0.1537	0.9232
41	0.149	0.298	0.851
42	0.2649	0.5298	0.7351
43	0.3352	0.6703	0.6648
44	0.2985	0.5971	0.7015
45	0.3933	0.7866	0.6067
46	0.3664	0.7329	0.6336
47	0.3189	0.6378	0.6811
48	0.2734	0.5468	0.7266
49	0.2903	0.5806	0.7097
50	0.255	0.51	0.745
51	0.2501	0.5001	0.7499
52	0.2541	0.5081	0.7459
53	0.2869	0.5738	0.7131
54	0.3092	0.6184	0.6908
55	0.2602	0.5204	0.7398
56	0.2258	0.4516	0.7742
57	0.1875	0.375	0.8125
58	0.1992	0.3983	0.8008
59	0.2517	0.5034	0.7483
60	0.2085	0.417	0.7915
61	0.2186	0.4372	0.7814
62	0.2405	0.4809	0.7595
63	0.2094	0.4187	0.7906
64	0.1957	0.3914	0.8043
65	0.1921	0.3842	0.8079
66	0.4334	0.8667	0.5666
67	0.3949	0.7898	0.6051
68	0.3523	0.7047	0.6477
69	0.3178	0.6356	0.6822
70	0.2846	0.5691	0.7154
71	0.2483	0.4967	0.7517
72	0.2756	0.5512	0.7244
73	0.2822	0.5643	0.7178
74	0.3241	0.6483	0.6759
75	0.3133	0.6265	0.6867
76	0.5317	0.9366	0.4683
77	0.4837	0.9674	0.5163
78	0.4373	0.8747	0.5627
79	0.4261	0.8522	0.5739
80	0.3819	0.7638	0.6181
81	0.3904	0.7808	0.6096
82	0.422	0.8439	0.578
83	0.4295	0.8591	0.5705
84	0.4236	0.8471	0.5764
85	0.3773	0.7547	0.6227
86	0.7774	0.4451	0.2226
87	0.8059	0.3882	0.1941
88	0.861	0.278	0.139
89	0.8293	0.3414	0.1707
90	0.7937	0.4127	0.2063
91	0.7665	0.4669	0.2335
92	0.7357	0.5285	0.2643
93	0.708	0.584	0.292
94	0.7137	0.5725	0.2863
95	0.7394	0.5212	0.2606
96	0.7028	0.5943	0.2972
97	0.6613	0.6773	0.3387
98	0.6694	0.6613	0.3306
99	0.6408	0.7184	0.3592
100	0.5908	0.8184	0.4092
101	0.6209	0.7583	0.3791
102	0.5701	0.8597	0.4299
103	0.5278	0.9444	0.4722
104	0.5	1	0.5
105	0.5108	0.9784	0.4892
106	0.4657	0.9313	0.5343
107	0.4067	0.8134	0.5933
108	0.7315	0.5371	0.2685
109	0.6844	0.6313	0.3156
110	0.6846	0.6308	0.3154
111	0.7643	0.4713	0.2357
112	0.7188	0.5624	0.2812
113	0.7868	0.4264	0.2132
114	0.7545	0.491	0.2455
115	0.8745	0.2509	0.1255
116	0.8554	0.2891	0.1446
117	0.8098	0.3805	0.1902
118	0.7633	0.4733	0.2367
119	0.7577	0.4847	0.2423
120	0.7503	0.4993	0.2497
121	0.8555	0.2891	0.1445
122	0.7948	0.4104	0.2052
123	0.8479	0.3041	0.1521
124	0.8026	0.3948	0.1974
125	0.7427	0.5146	0.2573
126	0.6406	0.7189	0.3594
127	0.5216	0.9568	0.4784
128	0.5647	0.8707	0.4353
129	0.8975	0.2049	0.1025
130	0.8724	0.2553	0.1276

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	0	0	OK
10% type I error level	3	0.0280374	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 & 0 & 0 & OK \tabularnewline
10% type I error level & 3 & 0.0280374 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=311863&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]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]3[/C][C]0.0280374[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=311863&T=7

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=311863&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	0	0	OK
10% type I error level	3	0.0280374	OK

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted RESET test data: mylm RESET = 6.4267, df1 = 2, df2 = 131, p-value = 0.002175
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 1.1542, df1 = 40, df2 = 93, p-value = 0.2827
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 2.4541, df1 = 2, df2 = 131, p-value = 0.08988

\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 = 6.4267, df1 = 2, df2 = 131, p-value = 0.002175
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of regressors \tabularnewline
> reset_test_regressors
	RESET test
data:  mylm
RESET = 1.1542, df1 = 40, df2 = 93, p-value = 0.2827
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of principal components \tabularnewline
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 2.4541, df1 = 2, df2 = 131, p-value = 0.08988
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=311863&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 = 6.4267, df1 = 2, df2 = 131, p-value = 0.002175
[/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 = 1.1542, df1 = 40, df2 = 93, p-value = 0.2827
[/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 = 2.4541, df1 = 2, df2 = 131, p-value = 0.08988
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=311863&T=8

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=311863&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 = 6.4267, df1 = 2, df2 = 131, p-value = 0.002175
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 1.1542, df1 = 40, df2 = 93, p-value = 0.2827
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 2.4541, df1 = 2, df2 = 131, p-value = 0.08988

Variance Inflation Factors (Multicollinearity)

> vif
       Relative_Advantage      Perceived_Usefulness     Perceived_Ease_of_Use 
                 1.950154                  2.111059                  2.818270 
      Information_Quality            System_Quality                    groupB 
                 3.524588                  2.286771                  1.398254 
                  genderB   `Intention_to_Use(t-1)`  `Intention_to_Use(t-1s)` 
                 1.249427                  1.187090                  1.192436 
 `Intention_to_Use(t-2s)`  `Intention_to_Use(t-3s)`  `Intention_to_Use(t-4s)` 
                 1.340900                  1.267167                  1.173786 
 `Intention_to_Use(t-5s)`  `Intention_to_Use(t-6s)`  `Intention_to_Use(t-7s)` 
                 1.187366                  1.195849                  1.200354 
 `Intention_to_Use(t-8s)`  `Intention_to_Use(t-9s)` `Intention_to_Use(t-10s)` 
                 1.210702                  1.230331                  1.170653 
`Intention_to_Use(t-11s)` `Intention_to_Use(t-12s)` 
                 1.175372                  1.322717

\begin{tabular}{lllllllll}
\hline
Variance Inflation Factors (Multicollinearity) \tabularnewline
> vif
       Relative_Advantage      Perceived_Usefulness     Perceived_Ease_of_Use 
                 1.950154                  2.111059                  2.818270 
      Information_Quality            System_Quality                    groupB 
                 3.524588                  2.286771                  1.398254 
                  genderB   `Intention_to_Use(t-1)`  `Intention_to_Use(t-1s)` 
                 1.249427                  1.187090                  1.192436 
 `Intention_to_Use(t-2s)`  `Intention_to_Use(t-3s)`  `Intention_to_Use(t-4s)` 
                 1.340900                  1.267167                  1.173786 
 `Intention_to_Use(t-5s)`  `Intention_to_Use(t-6s)`  `Intention_to_Use(t-7s)` 
                 1.187366                  1.195849                  1.200354 
 `Intention_to_Use(t-8s)`  `Intention_to_Use(t-9s)` `Intention_to_Use(t-10s)` 
                 1.210702                  1.230331                  1.170653 
`Intention_to_Use(t-11s)` `Intention_to_Use(t-12s)` 
                 1.175372                  1.322717 
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=311863&T=9

[TABLE]
[ROW][C]Variance Inflation Factors (Multicollinearity)[/C][/ROW]
[ROW][C]> vif
       Relative_Advantage      Perceived_Usefulness     Perceived_Ease_of_Use 
                 1.950154                  2.111059                  2.818270 
      Information_Quality            System_Quality                    groupB 
                 3.524588                  2.286771                  1.398254 
                  genderB   `Intention_to_Use(t-1)`  `Intention_to_Use(t-1s)` 
                 1.249427                  1.187090                  1.192436 
 `Intention_to_Use(t-2s)`  `Intention_to_Use(t-3s)`  `Intention_to_Use(t-4s)` 
                 1.340900                  1.267167                  1.173786 
 `Intention_to_Use(t-5s)`  `Intention_to_Use(t-6s)`  `Intention_to_Use(t-7s)` 
                 1.187366                  1.195849                  1.200354 
 `Intention_to_Use(t-8s)`  `Intention_to_Use(t-9s)` `Intention_to_Use(t-10s)` 
                 1.210702                  1.230331                  1.170653 
`Intention_to_Use(t-11s)` `Intention_to_Use(t-12s)` 
                 1.175372                  1.322717 
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=311863&T=9

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=311863&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 
                 1.950154                  2.111059                  2.818270 
      Information_Quality            System_Quality                    groupB 
                 3.524588                  2.286771                  1.398254 
                  genderB   `Intention_to_Use(t-1)`  `Intention_to_Use(t-1s)` 
                 1.249427                  1.187090                  1.192436 
 `Intention_to_Use(t-2s)`  `Intention_to_Use(t-3s)`  `Intention_to_Use(t-4s)` 
                 1.340900                  1.267167                  1.173786 
 `Intention_to_Use(t-5s)`  `Intention_to_Use(t-6s)`  `Intention_to_Use(t-7s)` 
                 1.187366                  1.195849                  1.200354 
 `Intention_to_Use(t-8s)`  `Intention_to_Use(t-9s)` `Intention_to_Use(t-10s)` 
                 1.210702                  1.230331                  1.170653 
`Intention_to_Use(t-11s)` `Intention_to_Use(t-12s)` 
                 1.175372                  1.322717

Figure 1

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Figure 2

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Figure 3

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Figure 4

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Figure 5

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Figure 6

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Figure 7

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Figure 8

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Figure 9

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Figure 10

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Parameters (Session):

par1 = FALSE ; par2 = 1 ; par3 = 0 ; par4 = 1 ; par5 = 12 ; par6 = 2 ; par7 = 1 ; par8 = 1 ; par9 = 1 ;

Parameters (R input):

par1 = FALSE ; par2 = 1 ; par3 = 0 ; par4 = 1 ; par5 = 12 ; par6 = 2 ;

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