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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Sun, 20 Jan 2019 11:00:18 +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/2019/Jan/20/t1547979566msnzf6e6h7g4wfm.htm/, Retrieved Fri, 03 May 2024 07:34:34 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=316406, Retrieved Fri, 03 May 2024 07:34:34 +0000

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

-       [Multiple Regression] [] [2019-01-20 10:00:18] [9172f81d29b60ad7d026eed068ac45c3] [Current]

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11 4 5 5 4 5 4 4 4 4 2 4 3 5 4
9 5 5 5 4 5 NA 4 4 5 3 3 4 5 4
12 5 5 4 4 4 3 3 2 4 4 5 4 5 4
NA 3 4 4 4 4 3 3 3 3 4 3 3 4 4
NA 5 5 5 4 5 4 4 3 4 4 5 4 5 4
12 5 5 5 4 5 3 4 3 3 4 4 4 5 5
12 5 4 5 5 5 4 2 3 3 4 4 3 3 4
NA 4 NA 4 4 5 4 2 4 3 4 5 4 4 4
NA 5 5 4 4 5 2 2 4 4 5 4 4 5 5
11 5 5 5 5 5 1 2 4 4 5 5 4 5 5
12 4 3 4 3 4 4 3 2 4 4 2 4 5 4
12 3 5 4 3 5 4 3 2 4 4 5 3 5 4
15 4 5 5 4 5 4 5 4 4 4 4 3 4 5
13 5 5 5 4 5 5 4 5 3 3 5 4 4 5
12 4 4 4 4 4 4 3 4 4 4 5 4 2 5
11 5 4 5 4 5 1 4 4 3 4 5 4 4 5
NA 4 5 5 4 3 4 4 2 3 4 5 4 4 5
NA NA NA NA NA 5 4 NA NA NA NA 5 NA 5 5
9 5 4 4 4 5 2 NA 2 5 5 4 3 4 4
NA 5 4 5 5 5 3 4 5 4 4 4 4 5 4
11 5 5 5 4 5 3 NA 4 3 4 5 3 4 5
NA 3 5 5 4 NA 2 3 1 4 4 4 4 5 5
12 4 5 5 4 3 1 3 5 4 4 5 4 4 5
NA 4 4 4 4 4 3 2 3 4 4 5 4 4 4
NA 5 5 5 5 4 2 2 4 4 4 5 4 4 5
NA 3 4 3 3 4 NA 3 4 3 4 4 4 4 4
12 5 5 4 5 5 4 3 2 3 4 4 3 5 5
12 4 4 4 3 4 4 3 4 4 4 4 4 4 4
14 4 5 4 4 5 2 4 2 2 4 5 4 5 5
NA 4 5 4 4 4 3 4 3 5 4 4 4 4 4
12 4 3 5 4 5 4 3 4 4 3 5 4 4 4
9 5 4 5 3 4 4 4 4 4 5 5 4 5 5
13 5 5 5 4 4 4 3 4 5 4 5 4 4 5
NA 4 4 5 5 4 3 4 4 4 3 5 4 NA 5
13 5 5 5 4 5 4 3 4 2 3 5 4 5 4
12 5 5 5 5 5 4 3 4 4 5 2 4 4 4
NA 4 4 4 4 5 4 3 5 3 4 5 4 4 4
12 5 4 4 4 5 4 3 4 4 3 5 3 4 5
12 4 4 4 4 2 3 2 4 4 3 3 4 4 4
12 4 5 4 3 4 3 5 3 4 4 5 4 4 4
NA 4 4 4 4 4 4 3 4 5 4 4 4 4 4
12 4 4 4 4 4 2 1 4 4 5 5 4 5 5
11 4 3 4 3 5 3 2 3 3 3 4 4 4 4
13 5 5 4 3 5 4 2 2 5 5 5 3 5 5
13 5 4 5 4 5 4 3 5 5 4 5 3 4 4
NA 4 4 4 4 4 3 2 4 4 4 4 3 4 5
NA 4 4 4 4 4 2 3 3 4 4 4 4 4 4
13 4 NA 4 1 5 3 5 4 3 5 5 3 3 4
10 4 4 4 4 5 3 4 4 4 4 4 4 5 4
NA 4 4 4 3 5 4 5 4 2 3 4 2 NA 4
13 5 5 5 4 4 3 2 3 4 5 5 4 4 4
NA 4 4 4 4 4 3 4 4 5 5 2 4 5 4
NA 4 5 4 4 5 3 3 4 5 5 5 4 4 4
5 5 5 5 4 5 3 3 4 4 3 5 4 5 5
NA 4 5 4 4 5 3 2 4 4 3 4 3 4 5
10 4 5 4 4 4 5 3 5 4 4 5 4 4 4
NA 4 4 4 3 5 4 2 4 3 4 4 3 3 4
15 5 4 3 4 5 NA 4 2 3 4 4 4 4 3
13 4 4 4 4 4 3 NA 4 4 4 4 3 5 4
NA 5 4 4 3 4 4 3 5 4 4 4 4 5 4
12 4 5 4 4 5 4 1 2 5 5 3 4 5 5
13 4 5 5 4 5 1 1 3 2 4 4 4 5 5
13 4 5 5 4 4 4 3 4 4 4 4 4 5 5
11 5 5 5 3 4 3 NA 3 3 4 4 4 2 4
NA 5 5 5 4 5 3 2 4 4 4 5 4 5 5
NA 4 4 3 3 3 4 3 4 4 2 4 4 4 4
12 4 2 4 3 3 2 4 4 4 4 4 3 5 3
12 4 5 5 4 5 4 3 5 4 4 4 3 5 4
13 4 4 4 4 4 5 4 3 5 4 5 3 3 5
14 4 4 4 3 4 4 4 4 3 4 4 3 5 5
NA 4 5 5 4 5 4 3 4 3 4 4 3 4 5
NA 4 5 5 4 5 4 4 4 4 5 5 5 5 4
NA 2 5 4 5 4 NA 4 4 4 4 3 4 NA 4
NA 5 5 5 4 5 4 3 4 4 4 4 4 4 4
NA 4 5 4 4 4 2 3 4 4 4 4 5 5 4
12 5 5 4 3 4 4 5 4 3 4 3 4 4 4
12 5 5 5 4 4 2 2 4 4 4 4 4 5 4
10 4 5 5 5 5 5 4 4 3 4 5 3 5 5
12 5 5 5 5 4 5 3 3 3 3 5 4 4 5
12 5 5 5 4 4 2 3 3 4 3 5 4 4 4
NA 4 5 5 4 4 4 3 2 4 4 5 4 4 5
NA 4 4 4 4 4 3 4 2 3 3 3 4 4 4
12 4 4 4 4 4 3 4 2 4 4 4 4 5 4
13 4 3 4 4 2 3 NA 3 4 4 3 4 5 5
NA 5 5 5 5 4 4 5 4 4 4 4 4 5 5
14 4 5 4 3 4 4 3 4 5 4 4 4 4 4
10 4 4 4 4 5 3 4 4 5 4 3 5 4 5
12 5 5 5 5 4 3 3 4 4 4 5 4 5 5
NA 5 5 5 5 5 4 5 4 3 4 5 4 4 5
13 4 5 5 4 4 4 4 4 3 NA 4 4 4 4
11 5 4 2 4 4 2 4 4 4 2 3 3 4 4
NA 4 3 4 3 3 3 4 2 4 4 5 4 4 3
12 4 4 4 4 4 3 4 3 4 4 5 4 4 5
NA 3 4 3 4 2 3 2 2 4 4 4 4 5 4
12 4 5 5 4 4 4 3 3 4 5 4 4 5 3
13 5 5 5 5 5 4 4 4 3 4 4 3 5 5
12 5 5 5 5 3 4 3 5 4 4 5 4 4 5
9 4 5 5 4 4 4 3 4 5 4 3 4 4 5
NA 5 5 5 5 5 5 5 5 5 4 5 5 4 5
12 3 4 4 3 2 4 3 3 4 5 4 4 5 5
NA 5 5 5 5 5 3 1 5 3 4 5 4 4 5
14 4 5 4 4 5 4 3 4 5 3 4 4 5 5
NA 5 5 5 5 5 4 4 5 4 4 5 4 4 5
11 3 4 4 3 4 2 2 2 5 4 4 4 4 5
NA 4 4 4 4 4 3 3 3 3 4 4 3 NA 4
NA 5 5 5 5 5 3 4 4 5 4 4 5 5 5
NA 5 5 5 4 5 3 4 5 4 4 5 3 NA 5
NA 4 5 4 5 4 4 4 4 4 4 3 3 4 3
NA 4 5 4 4 4 4 4 5 4 4 5 4 4 4
12 4 5 4 4 5 4 NA 5 4 4 5 4 4 4
NA 5 4 5 5 5 4 4 5 3 4 5 4 5 3
NA 4 4 4 3 5 3 3 4 4 4 4 4 4 4
NA 5 4 5 4 4 3 3 4 4 4 4 3 4 5
12 4 3 4 4 5 3 3 4 3 3 4 3 5 5
NA 4 4 4 4 4 2 NA 4 4 4 4 3 4 4
9 4 4 4 4 5 3 4 4 3 4 5 4 4 4
13 5 5 5 5 4 2 2 4 4 4 5 4 3 4
NA 5 5 4 4 5 4 5 5 5 4 5 1 5 5
10 5 5 5 5 5 5 2 5 5 4 5 4 5 5
14 5 5 5 3 4 3 2 5 4 4 4 4 4 3
10 4 5 4 4 4 3 2 4 4 4 5 3 4 4
12 5 4 5 5 4 3 3 4 3 4 4 3 4 5
NA 4 5 5 4 5 2 3 4 4 4 4 4 4 4
11 5 5 5 4 5 3 4 5 4 4 4 4 5 4
NA 5 4 3 5 4 3 NA 4 4 5 3 4 4 4
14 5 5 4 4 4 3 4 4 3 4 4 4 4 4
13 4 5 4 4 5 4 3 4 4 4 4 3 4 4
12 4 4 4 4 5 4 4 4 4 4 4 4 4 5
NA 5 5 5 4 4 3 4 2 3 4 3 3 4 4
NA 5 5 4 4 4 4 3 4 4 4 4 3 4 3
10 4 5 4 4 4 1 3 2 3 2 4 2 4 4
NA 5 5 4 4 4 5 5 4 4 4 4 3 5 4
12 4 4 4 4 5 4 4 3 5 4 4 3 5 4
NA 5 5 5 5 5 3 3 5 2 4 4 3 3 5
12 4 3 4 3 4 5 3 2 3 3 4 4 4 4
NA 4 5 4 4 NA 4 3 4 4 4 4 3 4 4
15 3 3 2 5 4 3 3 3 5 5 4 4 5 4
NA 2 3 4 4 4 NA NA NA NA NA 2 NA NA NA
NA 4 5 4 4 3 4 3 3 4 5 5 4 4 4
12 4 5 5 4 4 4 2 4 5 5 5 5 5 4
12 4 4 4 4 5 3 4 5 4 5 5 4 5 5
10 4 5 NA 4 4 2 4 3 4 4 4 3 4 5
12 5 5 5 4 4 4 4 2 3 4 5 4 5 4
12 5 5 4 NA 5 3 5 5 4 4 5 4 4 4
NA 3 5 5 4 3 3 2 4 4 4 2 4 4 4
12 4 5 4 3 4 4 2 4 4 4 3 4 5 5
11 4 5 4 4 1 2 3 2 4 4 4 4 5 5
13 5 5 4 3 5 3 3 5 5 4 5 3 5 4
NA 4 5 4 4 4 4 2 3 4 3 5 4 4 4
NA 5 5 5 5 5 4 4 3 4 4 5 4 4 4
NA 3 4 4 3 3 3 2 3 3 3 2 3 4 4
13 5 5 5 5 4 4 3 4 4 5 5 4 4 3
11 5 5 5 4 4 4 NA 4 4 4 4 3 4 4
10 3 5 5 3 4 3 3 4 4 4 4 4 4 5
9 5 5 5 4 4 2 3 4 3 4 5 3 5 5
NA 4 5 4 4 5 4 4 4 4 4 5 4 4 5
12 5 5 5 4 5 2 2 4 5 4 5 4 5 4
NA 5 5 5 5 5 3 5 5 4 4 5 4 3 4
NA 5 4 5 5 5 4 4 3 2 3 5 4 4 4
13 5 5 5 4 4 3 3 NA 4 4 4 4 4 5
10 4 5 4 3 5 2 5 4 4 3 4 3 5 5
13 5 4 5 4 5 4 2 4 4 4 4 4 4 3
NA 5 4 2 5 4 1 4 5 4 5 5 5 4 4
NA 4 5 4 4 3 5 4 3 5 4 3 4 4 4
NA 4 5 5 4 4 4 4 4 5 4 4 3 4 4
NA 4 4 5 3 4 3 3 2 3 3 1 4 5 5
12 4 5 4 4 5 4 5 5 4 4 4 4 4 5
NA 4 4 4 3 4 4 3 4 4 4 4 4 5 4
12 5 5 5 3 4 3 3 3 2 3 4 5 5 4

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	10 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 time10 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=316406&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]

10 seconds[/C][/ROW] [ROW]

R Server[/C]

Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=316406&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=316406&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	10 seconds
R Server	Big Analytics Cloud Computing Center

Multiple Linear Regression - Estimated Regression Equation
GWSUM[t] = + 14.7832 + 0.138622IK1[t] + 0.153261IK2[t] -0.485353IK3[t] + 0.0132018IK4[t] + 0.000686423KVDD1[t] + 0.225461KVDD2[t] -0.0870457KVDD3[t] -0.0388076KVDD4[t] -0.24919`SK/EOU1`[t] + 0.548165`SK/EOU2`[t] -0.0398974`SK/EOU3`[t] -0.15473`SK/EOU4`[t] -0.252386`SK/EOU5`[t] -0.400778`SK/EOU6`[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
GWSUM[t] =  +  14.7832 +  0.138622IK1[t] +  0.153261IK2[t] -0.485353IK3[t] +  0.0132018IK4[t] +  0.000686423KVDD1[t] +  0.225461KVDD2[t] -0.0870457KVDD3[t] -0.0388076KVDD4[t] -0.24919`SK/EOU1`[t] +  0.548165`SK/EOU2`[t] -0.0398974`SK/EOU3`[t] -0.15473`SK/EOU4`[t] -0.252386`SK/EOU5`[t] -0.400778`SK/EOU6`[t]  + e[t] \tabularnewline
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=316406&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]GWSUM[t] =  +  14.7832 +  0.138622IK1[t] +  0.153261IK2[t] -0.485353IK3[t] +  0.0132018IK4[t] +  0.000686423KVDD1[t] +  0.225461KVDD2[t] -0.0870457KVDD3[t] -0.0388076KVDD4[t] -0.24919`SK/EOU1`[t] +  0.548165`SK/EOU2`[t] -0.0398974`SK/EOU3`[t] -0.15473`SK/EOU4`[t] -0.252386`SK/EOU5`[t] -0.400778`SK/EOU6`[t]  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=316406&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=316406&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
GWSUM[t] = + 14.7832 + 0.138622IK1[t] + 0.153261IK2[t] -0.485353IK3[t] + 0.0132018IK4[t] + 0.000686423KVDD1[t] + 0.225461KVDD2[t] -0.0870457KVDD3[t] -0.0388076KVDD4[t] -0.24919`SK/EOU1`[t] + 0.548165`SK/EOU2`[t] -0.0398974`SK/EOU3`[t] -0.15473`SK/EOU4`[t] -0.252386`SK/EOU5`[t] -0.400778`SK/EOU6`[t] + e[t]

Multiple Linear Regression - Ordinary Least Squares
Variable	Parameter	S.D.	T-STATH0: parameter = 0	2-tail p-value	1-tail p-value
(Intercept)	+14.78	3.102	+4.7650e+00	9.903e-06	4.952e-06
IK1	+0.1386	0.3432	+4.0380e-01	0.6876	0.3438
IK2	+0.1533	0.2778	+5.5160e-01	0.583	0.2915
IK3	-0.4854	0.3445	-1.4090e+00	0.1633	0.08163
IK4	+0.0132	0.2966	+4.4510e-02	0.9646	0.4823
KVDD1	+0.0006864	0.2334	+2.9410e-03	0.9977	0.4988
KVDD2	+0.2255	0.1722	+1.3090e+00	0.1947	0.09734
KVDD3	-0.08705	0.2004	-4.3440e-01	0.6653	0.3327
KVDD4	-0.03881	0.2044	-1.8980e-01	0.85	0.425
`SK/EOU1`	-0.2492	0.2506	-9.9440e-01	0.3234	0.1617
`SK/EOU2`	+0.5482	0.2785	+1.9680e+00	0.05301	0.02651
`SK/EOU3`	-0.0399	0.2429	-1.6420e-01	0.87	0.435
`SK/EOU4`	-0.1547	0.3327	-4.6510e-01	0.6433	0.3217
`SK/EOU5`	-0.2524	0.2816	-8.9630e-01	0.3731	0.1866
`SK/EOU6`	-0.4008	0.2875	-1.3940e+00	0.1678	0.08389

\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) & +14.78 &  3.102 & +4.7650e+00 &  9.903e-06 &  4.952e-06 \tabularnewline
IK1 & +0.1386 &  0.3432 & +4.0380e-01 &  0.6876 &  0.3438 \tabularnewline
IK2 & +0.1533 &  0.2778 & +5.5160e-01 &  0.583 &  0.2915 \tabularnewline
IK3 & -0.4854 &  0.3445 & -1.4090e+00 &  0.1633 &  0.08163 \tabularnewline
IK4 & +0.0132 &  0.2966 & +4.4510e-02 &  0.9646 &  0.4823 \tabularnewline
KVDD1 & +0.0006864 &  0.2334 & +2.9410e-03 &  0.9977 &  0.4988 \tabularnewline
KVDD2 & +0.2255 &  0.1722 & +1.3090e+00 &  0.1947 &  0.09734 \tabularnewline
KVDD3 & -0.08705 &  0.2004 & -4.3440e-01 &  0.6653 &  0.3327 \tabularnewline
KVDD4 & -0.03881 &  0.2044 & -1.8980e-01 &  0.85 &  0.425 \tabularnewline
`SK/EOU1` & -0.2492 &  0.2506 & -9.9440e-01 &  0.3234 &  0.1617 \tabularnewline
`SK/EOU2` & +0.5482 &  0.2785 & +1.9680e+00 &  0.05301 &  0.02651 \tabularnewline
`SK/EOU3` & -0.0399 &  0.2429 & -1.6420e-01 &  0.87 &  0.435 \tabularnewline
`SK/EOU4` & -0.1547 &  0.3327 & -4.6510e-01 &  0.6433 &  0.3217 \tabularnewline
`SK/EOU5` & -0.2524 &  0.2816 & -8.9630e-01 &  0.3731 &  0.1866 \tabularnewline
`SK/EOU6` & -0.4008 &  0.2875 & -1.3940e+00 &  0.1678 &  0.08389 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=316406&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]+14.78[/C][C] 3.102[/C][C]+4.7650e+00[/C][C] 9.903e-06[/C][C] 4.952e-06[/C][/ROW]
[ROW][C]IK1[/C][C]+0.1386[/C][C] 0.3432[/C][C]+4.0380e-01[/C][C] 0.6876[/C][C] 0.3438[/C][/ROW]
[ROW][C]IK2[/C][C]+0.1533[/C][C] 0.2778[/C][C]+5.5160e-01[/C][C] 0.583[/C][C] 0.2915[/C][/ROW]
[ROW][C]IK3[/C][C]-0.4854[/C][C] 0.3445[/C][C]-1.4090e+00[/C][C] 0.1633[/C][C] 0.08163[/C][/ROW]
[ROW][C]IK4[/C][C]+0.0132[/C][C] 0.2966[/C][C]+4.4510e-02[/C][C] 0.9646[/C][C] 0.4823[/C][/ROW]
[ROW][C]KVDD1[/C][C]+0.0006864[/C][C] 0.2334[/C][C]+2.9410e-03[/C][C] 0.9977[/C][C] 0.4988[/C][/ROW]
[ROW][C]KVDD2[/C][C]+0.2255[/C][C] 0.1722[/C][C]+1.3090e+00[/C][C] 0.1947[/C][C] 0.09734[/C][/ROW]
[ROW][C]KVDD3[/C][C]-0.08705[/C][C] 0.2004[/C][C]-4.3440e-01[/C][C] 0.6653[/C][C] 0.3327[/C][/ROW]
[ROW][C]KVDD4[/C][C]-0.03881[/C][C] 0.2044[/C][C]-1.8980e-01[/C][C] 0.85[/C][C] 0.425[/C][/ROW]
[ROW][C]`SK/EOU1`[/C][C]-0.2492[/C][C] 0.2506[/C][C]-9.9440e-01[/C][C] 0.3234[/C][C] 0.1617[/C][/ROW]
[ROW][C]`SK/EOU2`[/C][C]+0.5482[/C][C] 0.2785[/C][C]+1.9680e+00[/C][C] 0.05301[/C][C] 0.02651[/C][/ROW]
[ROW][C]`SK/EOU3`[/C][C]-0.0399[/C][C] 0.2429[/C][C]-1.6420e-01[/C][C] 0.87[/C][C] 0.435[/C][/ROW]
[ROW][C]`SK/EOU4`[/C][C]-0.1547[/C][C] 0.3327[/C][C]-4.6510e-01[/C][C] 0.6433[/C][C] 0.3217[/C][/ROW]
[ROW][C]`SK/EOU5`[/C][C]-0.2524[/C][C] 0.2816[/C][C]-8.9630e-01[/C][C] 0.3731[/C][C] 0.1866[/C][/ROW]
[ROW][C]`SK/EOU6`[/C][C]-0.4008[/C][C] 0.2875[/C][C]-1.3940e+00[/C][C] 0.1678[/C][C] 0.08389[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=316406&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=316406&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)	+14.78	3.102	+4.7650e+00	9.903e-06	4.952e-06
IK1	+0.1386	0.3432	+4.0380e-01	0.6876	0.3438
IK2	+0.1533	0.2778	+5.5160e-01	0.583	0.2915
IK3	-0.4854	0.3445	-1.4090e+00	0.1633	0.08163
IK4	+0.0132	0.2966	+4.4510e-02	0.9646	0.4823
KVDD1	+0.0006864	0.2334	+2.9410e-03	0.9977	0.4988
KVDD2	+0.2255	0.1722	+1.3090e+00	0.1947	0.09734
KVDD3	-0.08705	0.2004	-4.3440e-01	0.6653	0.3327
KVDD4	-0.03881	0.2044	-1.8980e-01	0.85	0.425
`SK/EOU1`	-0.2492	0.2506	-9.9440e-01	0.3234	0.1617
`SK/EOU2`	+0.5482	0.2785	+1.9680e+00	0.05301	0.02651
`SK/EOU3`	-0.0399	0.2429	-1.6420e-01	0.87	0.435
`SK/EOU4`	-0.1547	0.3327	-4.6510e-01	0.6433	0.3217
`SK/EOU5`	-0.2524	0.2816	-8.9630e-01	0.3731	0.1866
`SK/EOU6`	-0.4008	0.2875	-1.3940e+00	0.1678	0.08389

Multiple Linear Regression - Regression Statistics
Multiple R	0.3617
R-squared	0.1308
Adjusted R-squared	-0.04304
F-TEST (value)	0.7524
F-TEST (DF numerator)	14
F-TEST (DF denominator)	70
p-value	0.7149
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.513
Sum Squared Residuals	160.2

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.3617 \tabularnewline
R-squared &  0.1308 \tabularnewline
Adjusted R-squared & -0.04304 \tabularnewline
F-TEST (value) &  0.7524 \tabularnewline
F-TEST (DF numerator) & 14 \tabularnewline
F-TEST (DF denominator) & 70 \tabularnewline
p-value &  0.7149 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  1.513 \tabularnewline
Sum Squared Residuals &  160.2 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=316406&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.3617[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.1308[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]-0.04304[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 0.7524[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]14[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]70[/C][/ROW]
[ROW][C]p-value[/C][C] 0.7149[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C] 1.513[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 160.2[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=316406&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=316406&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	0.3617
R-squared	0.1308
Adjusted R-squared	-0.04304
F-TEST (value)	0.7524
F-TEST (DF numerator)	14
F-TEST (DF denominator)	70
p-value	0.7149
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.513
Sum Squared Residuals	160.2

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=316406&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=316406&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=316406&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	11	10.74	0.2574
2	12	12.21	-0.2068
3	12	11.48	0.5154
4	12	12.8	-0.8044
5	11	11.44	-0.4412
6	12	12.09	-0.0936
7	12	12.3	-0.2972
8	15	11.6	3.397
9	13	11.52	1.478
10	12	12.42	-0.4191
11	11	11.05	-0.05408
12	12	10.87	1.134
13	12	12.49	-0.4892
14	12	12.34	-0.3418
15	14	11.85	2.146
16	12	11.13	0.871
17	9	11.76	-2.763
18	13	11.47	1.528
19	13	11.82	1.18
20	12	12.8	-0.8034
21	12	11.66	0.3398
22	12	11.71	0.293
23	12	12.09	-0.09445
24	12	11.93	0.06669
25	11	11.79	-0.7907
26	13	12.56	0.4403
27	13	11.84	1.164
28	10	11.79	-1.791
29	13	12.57	0.4298
30	5	10.7	-5.696
31	10	12.66	-2.655
32	12	12.45	-0.4464
33	13	11.41	1.595
34	13	11.37	1.63
35	12	11.8	0.2002
36	12	11.89	0.1128
37	13	12.25	0.7505
38	14	12.01	1.994
39	12	12.75	-0.7487
40	12	11.55	0.4547
41	10	11.89	-1.886
42	12	11.7	0.3006
43	12	11.16	0.8386
44	12	11.87	0.1322
45	14	12.25	1.754
46	10	11.28	-1.278
47	12	11.26	0.7438
48	11	12.02	-1.025
49	12	11.64	0.3593
50	12	12.76	-0.7583
51	13	11.84	1.161
52	12	11.69	0.3054
53	9	11.41	-2.413
54	12	12.14	-0.1356
55	14	11.06	2.942
56	11	11.27	-0.267
57	12	11.18	0.8204
58	9	12.25	-3.253
59	13	12.02	0.9766
60	10	11.51	-1.507
61	14	12.37	1.628
62	10	12.48	-2.485
63	12	11.8	0.2008
64	11	11.56	-0.5586
65	14	12.58	1.416
66	13	12.66	0.3363
67	12	11.87	0.1321
68	10	11.37	-1.372
69	12	11.96	0.03936
70	12	12.19	-0.1927
71	15	12.91	2.093
72	12	11.96	0.03805
73	12	11.86	0.1405
74	12	12.11	-0.109
75	12	11.97	0.03113
76	11	11.48	-0.4798
77	13	11.98	1.017
78	13	13.08	-0.08381
79	10	11.24	-1.245
80	9	11.42	-2.421
81	12	11.26	0.7431
82	10	10.82	-0.8242
83	13	12.5	0.5032
84	12	11.9	0.1047
85	12	11.5	0.4952

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 &  11 &  10.74 &  0.2574 \tabularnewline
2 &  12 &  12.21 & -0.2068 \tabularnewline
3 &  12 &  11.48 &  0.5154 \tabularnewline
4 &  12 &  12.8 & -0.8044 \tabularnewline
5 &  11 &  11.44 & -0.4412 \tabularnewline
6 &  12 &  12.09 & -0.0936 \tabularnewline
7 &  12 &  12.3 & -0.2972 \tabularnewline
8 &  15 &  11.6 &  3.397 \tabularnewline
9 &  13 &  11.52 &  1.478 \tabularnewline
10 &  12 &  12.42 & -0.4191 \tabularnewline
11 &  11 &  11.05 & -0.05408 \tabularnewline
12 &  12 &  10.87 &  1.134 \tabularnewline
13 &  12 &  12.49 & -0.4892 \tabularnewline
14 &  12 &  12.34 & -0.3418 \tabularnewline
15 &  14 &  11.85 &  2.146 \tabularnewline
16 &  12 &  11.13 &  0.871 \tabularnewline
17 &  9 &  11.76 & -2.763 \tabularnewline
18 &  13 &  11.47 &  1.528 \tabularnewline
19 &  13 &  11.82 &  1.18 \tabularnewline
20 &  12 &  12.8 & -0.8034 \tabularnewline
21 &  12 &  11.66 &  0.3398 \tabularnewline
22 &  12 &  11.71 &  0.293 \tabularnewline
23 &  12 &  12.09 & -0.09445 \tabularnewline
24 &  12 &  11.93 &  0.06669 \tabularnewline
25 &  11 &  11.79 & -0.7907 \tabularnewline
26 &  13 &  12.56 &  0.4403 \tabularnewline
27 &  13 &  11.84 &  1.164 \tabularnewline
28 &  10 &  11.79 & -1.791 \tabularnewline
29 &  13 &  12.57 &  0.4298 \tabularnewline
30 &  5 &  10.7 & -5.696 \tabularnewline
31 &  10 &  12.66 & -2.655 \tabularnewline
32 &  12 &  12.45 & -0.4464 \tabularnewline
33 &  13 &  11.41 &  1.595 \tabularnewline
34 &  13 &  11.37 &  1.63 \tabularnewline
35 &  12 &  11.8 &  0.2002 \tabularnewline
36 &  12 &  11.89 &  0.1128 \tabularnewline
37 &  13 &  12.25 &  0.7505 \tabularnewline
38 &  14 &  12.01 &  1.994 \tabularnewline
39 &  12 &  12.75 & -0.7487 \tabularnewline
40 &  12 &  11.55 &  0.4547 \tabularnewline
41 &  10 &  11.89 & -1.886 \tabularnewline
42 &  12 &  11.7 &  0.3006 \tabularnewline
43 &  12 &  11.16 &  0.8386 \tabularnewline
44 &  12 &  11.87 &  0.1322 \tabularnewline
45 &  14 &  12.25 &  1.754 \tabularnewline
46 &  10 &  11.28 & -1.278 \tabularnewline
47 &  12 &  11.26 &  0.7438 \tabularnewline
48 &  11 &  12.02 & -1.025 \tabularnewline
49 &  12 &  11.64 &  0.3593 \tabularnewline
50 &  12 &  12.76 & -0.7583 \tabularnewline
51 &  13 &  11.84 &  1.161 \tabularnewline
52 &  12 &  11.69 &  0.3054 \tabularnewline
53 &  9 &  11.41 & -2.413 \tabularnewline
54 &  12 &  12.14 & -0.1356 \tabularnewline
55 &  14 &  11.06 &  2.942 \tabularnewline
56 &  11 &  11.27 & -0.267 \tabularnewline
57 &  12 &  11.18 &  0.8204 \tabularnewline
58 &  9 &  12.25 & -3.253 \tabularnewline
59 &  13 &  12.02 &  0.9766 \tabularnewline
60 &  10 &  11.51 & -1.507 \tabularnewline
61 &  14 &  12.37 &  1.628 \tabularnewline
62 &  10 &  12.48 & -2.485 \tabularnewline
63 &  12 &  11.8 &  0.2008 \tabularnewline
64 &  11 &  11.56 & -0.5586 \tabularnewline
65 &  14 &  12.58 &  1.416 \tabularnewline
66 &  13 &  12.66 &  0.3363 \tabularnewline
67 &  12 &  11.87 &  0.1321 \tabularnewline
68 &  10 &  11.37 & -1.372 \tabularnewline
69 &  12 &  11.96 &  0.03936 \tabularnewline
70 &  12 &  12.19 & -0.1927 \tabularnewline
71 &  15 &  12.91 &  2.093 \tabularnewline
72 &  12 &  11.96 &  0.03805 \tabularnewline
73 &  12 &  11.86 &  0.1405 \tabularnewline
74 &  12 &  12.11 & -0.109 \tabularnewline
75 &  12 &  11.97 &  0.03113 \tabularnewline
76 &  11 &  11.48 & -0.4798 \tabularnewline
77 &  13 &  11.98 &  1.017 \tabularnewline
78 &  13 &  13.08 & -0.08381 \tabularnewline
79 &  10 &  11.24 & -1.245 \tabularnewline
80 &  9 &  11.42 & -2.421 \tabularnewline
81 &  12 &  11.26 &  0.7431 \tabularnewline
82 &  10 &  10.82 & -0.8242 \tabularnewline
83 &  13 &  12.5 &  0.5032 \tabularnewline
84 &  12 &  11.9 &  0.1047 \tabularnewline
85 &  12 &  11.5 &  0.4952 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=316406&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] 11[/C][C] 10.74[/C][C] 0.2574[/C][/ROW]
[ROW][C]2[/C][C] 12[/C][C] 12.21[/C][C]-0.2068[/C][/ROW]
[ROW][C]3[/C][C] 12[/C][C] 11.48[/C][C] 0.5154[/C][/ROW]
[ROW][C]4[/C][C] 12[/C][C] 12.8[/C][C]-0.8044[/C][/ROW]
[ROW][C]5[/C][C] 11[/C][C] 11.44[/C][C]-0.4412[/C][/ROW]
[ROW][C]6[/C][C] 12[/C][C] 12.09[/C][C]-0.0936[/C][/ROW]
[ROW][C]7[/C][C] 12[/C][C] 12.3[/C][C]-0.2972[/C][/ROW]
[ROW][C]8[/C][C] 15[/C][C] 11.6[/C][C] 3.397[/C][/ROW]
[ROW][C]9[/C][C] 13[/C][C] 11.52[/C][C] 1.478[/C][/ROW]
[ROW][C]10[/C][C] 12[/C][C] 12.42[/C][C]-0.4191[/C][/ROW]
[ROW][C]11[/C][C] 11[/C][C] 11.05[/C][C]-0.05408[/C][/ROW]
[ROW][C]12[/C][C] 12[/C][C] 10.87[/C][C] 1.134[/C][/ROW]
[ROW][C]13[/C][C] 12[/C][C] 12.49[/C][C]-0.4892[/C][/ROW]
[ROW][C]14[/C][C] 12[/C][C] 12.34[/C][C]-0.3418[/C][/ROW]
[ROW][C]15[/C][C] 14[/C][C] 11.85[/C][C] 2.146[/C][/ROW]
[ROW][C]16[/C][C] 12[/C][C] 11.13[/C][C] 0.871[/C][/ROW]
[ROW][C]17[/C][C] 9[/C][C] 11.76[/C][C]-2.763[/C][/ROW]
[ROW][C]18[/C][C] 13[/C][C] 11.47[/C][C] 1.528[/C][/ROW]
[ROW][C]19[/C][C] 13[/C][C] 11.82[/C][C] 1.18[/C][/ROW]
[ROW][C]20[/C][C] 12[/C][C] 12.8[/C][C]-0.8034[/C][/ROW]
[ROW][C]21[/C][C] 12[/C][C] 11.66[/C][C] 0.3398[/C][/ROW]
[ROW][C]22[/C][C] 12[/C][C] 11.71[/C][C] 0.293[/C][/ROW]
[ROW][C]23[/C][C] 12[/C][C] 12.09[/C][C]-0.09445[/C][/ROW]
[ROW][C]24[/C][C] 12[/C][C] 11.93[/C][C] 0.06669[/C][/ROW]
[ROW][C]25[/C][C] 11[/C][C] 11.79[/C][C]-0.7907[/C][/ROW]
[ROW][C]26[/C][C] 13[/C][C] 12.56[/C][C] 0.4403[/C][/ROW]
[ROW][C]27[/C][C] 13[/C][C] 11.84[/C][C] 1.164[/C][/ROW]
[ROW][C]28[/C][C] 10[/C][C] 11.79[/C][C]-1.791[/C][/ROW]
[ROW][C]29[/C][C] 13[/C][C] 12.57[/C][C] 0.4298[/C][/ROW]
[ROW][C]30[/C][C] 5[/C][C] 10.7[/C][C]-5.696[/C][/ROW]
[ROW][C]31[/C][C] 10[/C][C] 12.66[/C][C]-2.655[/C][/ROW]
[ROW][C]32[/C][C] 12[/C][C] 12.45[/C][C]-0.4464[/C][/ROW]
[ROW][C]33[/C][C] 13[/C][C] 11.41[/C][C] 1.595[/C][/ROW]
[ROW][C]34[/C][C] 13[/C][C] 11.37[/C][C] 1.63[/C][/ROW]
[ROW][C]35[/C][C] 12[/C][C] 11.8[/C][C] 0.2002[/C][/ROW]
[ROW][C]36[/C][C] 12[/C][C] 11.89[/C][C] 0.1128[/C][/ROW]
[ROW][C]37[/C][C] 13[/C][C] 12.25[/C][C] 0.7505[/C][/ROW]
[ROW][C]38[/C][C] 14[/C][C] 12.01[/C][C] 1.994[/C][/ROW]
[ROW][C]39[/C][C] 12[/C][C] 12.75[/C][C]-0.7487[/C][/ROW]
[ROW][C]40[/C][C] 12[/C][C] 11.55[/C][C] 0.4547[/C][/ROW]
[ROW][C]41[/C][C] 10[/C][C] 11.89[/C][C]-1.886[/C][/ROW]
[ROW][C]42[/C][C] 12[/C][C] 11.7[/C][C] 0.3006[/C][/ROW]
[ROW][C]43[/C][C] 12[/C][C] 11.16[/C][C] 0.8386[/C][/ROW]
[ROW][C]44[/C][C] 12[/C][C] 11.87[/C][C] 0.1322[/C][/ROW]
[ROW][C]45[/C][C] 14[/C][C] 12.25[/C][C] 1.754[/C][/ROW]
[ROW][C]46[/C][C] 10[/C][C] 11.28[/C][C]-1.278[/C][/ROW]
[ROW][C]47[/C][C] 12[/C][C] 11.26[/C][C] 0.7438[/C][/ROW]
[ROW][C]48[/C][C] 11[/C][C] 12.02[/C][C]-1.025[/C][/ROW]
[ROW][C]49[/C][C] 12[/C][C] 11.64[/C][C] 0.3593[/C][/ROW]
[ROW][C]50[/C][C] 12[/C][C] 12.76[/C][C]-0.7583[/C][/ROW]
[ROW][C]51[/C][C] 13[/C][C] 11.84[/C][C] 1.161[/C][/ROW]
[ROW][C]52[/C][C] 12[/C][C] 11.69[/C][C] 0.3054[/C][/ROW]
[ROW][C]53[/C][C] 9[/C][C] 11.41[/C][C]-2.413[/C][/ROW]
[ROW][C]54[/C][C] 12[/C][C] 12.14[/C][C]-0.1356[/C][/ROW]
[ROW][C]55[/C][C] 14[/C][C] 11.06[/C][C] 2.942[/C][/ROW]
[ROW][C]56[/C][C] 11[/C][C] 11.27[/C][C]-0.267[/C][/ROW]
[ROW][C]57[/C][C] 12[/C][C] 11.18[/C][C] 0.8204[/C][/ROW]
[ROW][C]58[/C][C] 9[/C][C] 12.25[/C][C]-3.253[/C][/ROW]
[ROW][C]59[/C][C] 13[/C][C] 12.02[/C][C] 0.9766[/C][/ROW]
[ROW][C]60[/C][C] 10[/C][C] 11.51[/C][C]-1.507[/C][/ROW]
[ROW][C]61[/C][C] 14[/C][C] 12.37[/C][C] 1.628[/C][/ROW]
[ROW][C]62[/C][C] 10[/C][C] 12.48[/C][C]-2.485[/C][/ROW]
[ROW][C]63[/C][C] 12[/C][C] 11.8[/C][C] 0.2008[/C][/ROW]
[ROW][C]64[/C][C] 11[/C][C] 11.56[/C][C]-0.5586[/C][/ROW]
[ROW][C]65[/C][C] 14[/C][C] 12.58[/C][C] 1.416[/C][/ROW]
[ROW][C]66[/C][C] 13[/C][C] 12.66[/C][C] 0.3363[/C][/ROW]
[ROW][C]67[/C][C] 12[/C][C] 11.87[/C][C] 0.1321[/C][/ROW]
[ROW][C]68[/C][C] 10[/C][C] 11.37[/C][C]-1.372[/C][/ROW]
[ROW][C]69[/C][C] 12[/C][C] 11.96[/C][C] 0.03936[/C][/ROW]
[ROW][C]70[/C][C] 12[/C][C] 12.19[/C][C]-0.1927[/C][/ROW]
[ROW][C]71[/C][C] 15[/C][C] 12.91[/C][C] 2.093[/C][/ROW]
[ROW][C]72[/C][C] 12[/C][C] 11.96[/C][C] 0.03805[/C][/ROW]
[ROW][C]73[/C][C] 12[/C][C] 11.86[/C][C] 0.1405[/C][/ROW]
[ROW][C]74[/C][C] 12[/C][C] 12.11[/C][C]-0.109[/C][/ROW]
[ROW][C]75[/C][C] 12[/C][C] 11.97[/C][C] 0.03113[/C][/ROW]
[ROW][C]76[/C][C] 11[/C][C] 11.48[/C][C]-0.4798[/C][/ROW]
[ROW][C]77[/C][C] 13[/C][C] 11.98[/C][C] 1.017[/C][/ROW]
[ROW][C]78[/C][C] 13[/C][C] 13.08[/C][C]-0.08381[/C][/ROW]
[ROW][C]79[/C][C] 10[/C][C] 11.24[/C][C]-1.245[/C][/ROW]
[ROW][C]80[/C][C] 9[/C][C] 11.42[/C][C]-2.421[/C][/ROW]
[ROW][C]81[/C][C] 12[/C][C] 11.26[/C][C] 0.7431[/C][/ROW]
[ROW][C]82[/C][C] 10[/C][C] 10.82[/C][C]-0.8242[/C][/ROW]
[ROW][C]83[/C][C] 13[/C][C] 12.5[/C][C] 0.5032[/C][/ROW]
[ROW][C]84[/C][C] 12[/C][C] 11.9[/C][C] 0.1047[/C][/ROW]
[ROW][C]85[/C][C] 12[/C][C] 11.5[/C][C] 0.4952[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=316406&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=316406&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	11	10.74	0.2574
2	12	12.21	-0.2068
3	12	11.48	0.5154
4	12	12.8	-0.8044
5	11	11.44	-0.4412
6	12	12.09	-0.0936
7	12	12.3	-0.2972
8	15	11.6	3.397
9	13	11.52	1.478
10	12	12.42	-0.4191
11	11	11.05	-0.05408
12	12	10.87	1.134
13	12	12.49	-0.4892
14	12	12.34	-0.3418
15	14	11.85	2.146
16	12	11.13	0.871
17	9	11.76	-2.763
18	13	11.47	1.528
19	13	11.82	1.18
20	12	12.8	-0.8034
21	12	11.66	0.3398
22	12	11.71	0.293
23	12	12.09	-0.09445
24	12	11.93	0.06669
25	11	11.79	-0.7907
26	13	12.56	0.4403
27	13	11.84	1.164
28	10	11.79	-1.791
29	13	12.57	0.4298
30	5	10.7	-5.696
31	10	12.66	-2.655
32	12	12.45	-0.4464
33	13	11.41	1.595
34	13	11.37	1.63
35	12	11.8	0.2002
36	12	11.89	0.1128
37	13	12.25	0.7505
38	14	12.01	1.994
39	12	12.75	-0.7487
40	12	11.55	0.4547
41	10	11.89	-1.886
42	12	11.7	0.3006
43	12	11.16	0.8386
44	12	11.87	0.1322
45	14	12.25	1.754
46	10	11.28	-1.278
47	12	11.26	0.7438
48	11	12.02	-1.025
49	12	11.64	0.3593
50	12	12.76	-0.7583
51	13	11.84	1.161
52	12	11.69	0.3054
53	9	11.41	-2.413
54	12	12.14	-0.1356
55	14	11.06	2.942
56	11	11.27	-0.267
57	12	11.18	0.8204
58	9	12.25	-3.253
59	13	12.02	0.9766
60	10	11.51	-1.507
61	14	12.37	1.628
62	10	12.48	-2.485
63	12	11.8	0.2008
64	11	11.56	-0.5586
65	14	12.58	1.416
66	13	12.66	0.3363
67	12	11.87	0.1321
68	10	11.37	-1.372
69	12	11.96	0.03936
70	12	12.19	-0.1927
71	15	12.91	2.093
72	12	11.96	0.03805
73	12	11.86	0.1405
74	12	12.11	-0.109
75	12	11.97	0.03113
76	11	11.48	-0.4798
77	13	11.98	1.017
78	13	13.08	-0.08381
79	10	11.24	-1.245
80	9	11.42	-2.421
81	12	11.26	0.7431
82	10	10.82	-0.8242
83	13	12.5	0.5032
84	12	11.9	0.1047
85	12	11.5	0.4952

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
18	0.6436	0.7128	0.3564
19	0.533	0.934	0.467
20	0.6444	0.7113	0.3556
21	0.5836	0.8327	0.4164
22	0.4605	0.9209	0.5395
23	0.3569	0.7139	0.6431
24	0.2861	0.5722	0.7139
25	0.2138	0.4276	0.7862
26	0.2068	0.4136	0.7932
27	0.2541	0.5082	0.7459
28	0.2454	0.4909	0.7546
29	0.1893	0.3785	0.8107
30	0.9526	0.09489	0.04745
31	0.9708	0.05842	0.02921
32	0.963	0.074	0.037
33	0.9567	0.08651	0.04326
34	0.9543	0.09136	0.04568
35	0.9322	0.1356	0.06781
36	0.9073	0.1854	0.09268
37	0.8753	0.2494	0.1247
38	0.8856	0.2288	0.1144
39	0.8504	0.2993	0.1496
40	0.8119	0.3762	0.1881
41	0.8424	0.3153	0.1576
42	0.7926	0.4148	0.2074
43	0.7506	0.4988	0.2494
44	0.6899	0.6202	0.3101
45	0.7168	0.5663	0.2832
46	0.7144	0.5711	0.2856
47	0.669	0.662	0.331
48	0.7764	0.4472	0.2236
49	0.724	0.5521	0.276
50	0.6551	0.6898	0.3449
51	0.6916	0.6168	0.3084
52	0.627	0.7459	0.373
53	0.8423	0.3154	0.1577
54	0.8159	0.3681	0.184
55	0.9351	0.1297	0.06485
56	0.9175	0.1651	0.08254
57	0.9683	0.06331	0.03165
58	0.9968	0.00637	0.003185
59	0.9931	0.01387	0.006934
60	0.9874	0.02528	0.01264
61	0.9833	0.03349	0.01675
62	0.9921	0.0158	0.007898
63	0.9999	0.0001463	7.317e-05
64	0.9996	0.0007894	0.0003947
65	0.9982	0.003617	0.001808
66	0.9948	0.01039	0.005197
67	0.9765	0.04692	0.02346

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
18 &  0.6436 &  0.7128 &  0.3564 \tabularnewline
19 &  0.533 &  0.934 &  0.467 \tabularnewline
20 &  0.6444 &  0.7113 &  0.3556 \tabularnewline
21 &  0.5836 &  0.8327 &  0.4164 \tabularnewline
22 &  0.4605 &  0.9209 &  0.5395 \tabularnewline
23 &  0.3569 &  0.7139 &  0.6431 \tabularnewline
24 &  0.2861 &  0.5722 &  0.7139 \tabularnewline
25 &  0.2138 &  0.4276 &  0.7862 \tabularnewline
26 &  0.2068 &  0.4136 &  0.7932 \tabularnewline
27 &  0.2541 &  0.5082 &  0.7459 \tabularnewline
28 &  0.2454 &  0.4909 &  0.7546 \tabularnewline
29 &  0.1893 &  0.3785 &  0.8107 \tabularnewline
30 &  0.9526 &  0.09489 &  0.04745 \tabularnewline
31 &  0.9708 &  0.05842 &  0.02921 \tabularnewline
32 &  0.963 &  0.074 &  0.037 \tabularnewline
33 &  0.9567 &  0.08651 &  0.04326 \tabularnewline
34 &  0.9543 &  0.09136 &  0.04568 \tabularnewline
35 &  0.9322 &  0.1356 &  0.06781 \tabularnewline
36 &  0.9073 &  0.1854 &  0.09268 \tabularnewline
37 &  0.8753 &  0.2494 &  0.1247 \tabularnewline
38 &  0.8856 &  0.2288 &  0.1144 \tabularnewline
39 &  0.8504 &  0.2993 &  0.1496 \tabularnewline
40 &  0.8119 &  0.3762 &  0.1881 \tabularnewline
41 &  0.8424 &  0.3153 &  0.1576 \tabularnewline
42 &  0.7926 &  0.4148 &  0.2074 \tabularnewline
43 &  0.7506 &  0.4988 &  0.2494 \tabularnewline
44 &  0.6899 &  0.6202 &  0.3101 \tabularnewline
45 &  0.7168 &  0.5663 &  0.2832 \tabularnewline
46 &  0.7144 &  0.5711 &  0.2856 \tabularnewline
47 &  0.669 &  0.662 &  0.331 \tabularnewline
48 &  0.7764 &  0.4472 &  0.2236 \tabularnewline
49 &  0.724 &  0.5521 &  0.276 \tabularnewline
50 &  0.6551 &  0.6898 &  0.3449 \tabularnewline
51 &  0.6916 &  0.6168 &  0.3084 \tabularnewline
52 &  0.627 &  0.7459 &  0.373 \tabularnewline
53 &  0.8423 &  0.3154 &  0.1577 \tabularnewline
54 &  0.8159 &  0.3681 &  0.184 \tabularnewline
55 &  0.9351 &  0.1297 &  0.06485 \tabularnewline
56 &  0.9175 &  0.1651 &  0.08254 \tabularnewline
57 &  0.9683 &  0.06331 &  0.03165 \tabularnewline
58 &  0.9968 &  0.00637 &  0.003185 \tabularnewline
59 &  0.9931 &  0.01387 &  0.006934 \tabularnewline
60 &  0.9874 &  0.02528 &  0.01264 \tabularnewline
61 &  0.9833 &  0.03349 &  0.01675 \tabularnewline
62 &  0.9921 &  0.0158 &  0.007898 \tabularnewline
63 &  0.9999 &  0.0001463 &  7.317e-05 \tabularnewline
64 &  0.9996 &  0.0007894 &  0.0003947 \tabularnewline
65 &  0.9982 &  0.003617 &  0.001808 \tabularnewline
66 &  0.9948 &  0.01039 &  0.005197 \tabularnewline
67 &  0.9765 &  0.04692 &  0.02346 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=316406&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]18[/C][C] 0.6436[/C][C] 0.7128[/C][C] 0.3564[/C][/ROW]
[ROW][C]19[/C][C] 0.533[/C][C] 0.934[/C][C] 0.467[/C][/ROW]
[ROW][C]20[/C][C] 0.6444[/C][C] 0.7113[/C][C] 0.3556[/C][/ROW]
[ROW][C]21[/C][C] 0.5836[/C][C] 0.8327[/C][C] 0.4164[/C][/ROW]
[ROW][C]22[/C][C] 0.4605[/C][C] 0.9209[/C][C] 0.5395[/C][/ROW]
[ROW][C]23[/C][C] 0.3569[/C][C] 0.7139[/C][C] 0.6431[/C][/ROW]
[ROW][C]24[/C][C] 0.2861[/C][C] 0.5722[/C][C] 0.7139[/C][/ROW]
[ROW][C]25[/C][C] 0.2138[/C][C] 0.4276[/C][C] 0.7862[/C][/ROW]
[ROW][C]26[/C][C] 0.2068[/C][C] 0.4136[/C][C] 0.7932[/C][/ROW]
[ROW][C]27[/C][C] 0.2541[/C][C] 0.5082[/C][C] 0.7459[/C][/ROW]
[ROW][C]28[/C][C] 0.2454[/C][C] 0.4909[/C][C] 0.7546[/C][/ROW]
[ROW][C]29[/C][C] 0.1893[/C][C] 0.3785[/C][C] 0.8107[/C][/ROW]
[ROW][C]30[/C][C] 0.9526[/C][C] 0.09489[/C][C] 0.04745[/C][/ROW]
[ROW][C]31[/C][C] 0.9708[/C][C] 0.05842[/C][C] 0.02921[/C][/ROW]
[ROW][C]32[/C][C] 0.963[/C][C] 0.074[/C][C] 0.037[/C][/ROW]
[ROW][C]33[/C][C] 0.9567[/C][C] 0.08651[/C][C] 0.04326[/C][/ROW]
[ROW][C]34[/C][C] 0.9543[/C][C] 0.09136[/C][C] 0.04568[/C][/ROW]
[ROW][C]35[/C][C] 0.9322[/C][C] 0.1356[/C][C] 0.06781[/C][/ROW]
[ROW][C]36[/C][C] 0.9073[/C][C] 0.1854[/C][C] 0.09268[/C][/ROW]
[ROW][C]37[/C][C] 0.8753[/C][C] 0.2494[/C][C] 0.1247[/C][/ROW]
[ROW][C]38[/C][C] 0.8856[/C][C] 0.2288[/C][C] 0.1144[/C][/ROW]
[ROW][C]39[/C][C] 0.8504[/C][C] 0.2993[/C][C] 0.1496[/C][/ROW]
[ROW][C]40[/C][C] 0.8119[/C][C] 0.3762[/C][C] 0.1881[/C][/ROW]
[ROW][C]41[/C][C] 0.8424[/C][C] 0.3153[/C][C] 0.1576[/C][/ROW]
[ROW][C]42[/C][C] 0.7926[/C][C] 0.4148[/C][C] 0.2074[/C][/ROW]
[ROW][C]43[/C][C] 0.7506[/C][C] 0.4988[/C][C] 0.2494[/C][/ROW]
[ROW][C]44[/C][C] 0.6899[/C][C] 0.6202[/C][C] 0.3101[/C][/ROW]
[ROW][C]45[/C][C] 0.7168[/C][C] 0.5663[/C][C] 0.2832[/C][/ROW]
[ROW][C]46[/C][C] 0.7144[/C][C] 0.5711[/C][C] 0.2856[/C][/ROW]
[ROW][C]47[/C][C] 0.669[/C][C] 0.662[/C][C] 0.331[/C][/ROW]
[ROW][C]48[/C][C] 0.7764[/C][C] 0.4472[/C][C] 0.2236[/C][/ROW]
[ROW][C]49[/C][C] 0.724[/C][C] 0.5521[/C][C] 0.276[/C][/ROW]
[ROW][C]50[/C][C] 0.6551[/C][C] 0.6898[/C][C] 0.3449[/C][/ROW]
[ROW][C]51[/C][C] 0.6916[/C][C] 0.6168[/C][C] 0.3084[/C][/ROW]
[ROW][C]52[/C][C] 0.627[/C][C] 0.7459[/C][C] 0.373[/C][/ROW]
[ROW][C]53[/C][C] 0.8423[/C][C] 0.3154[/C][C] 0.1577[/C][/ROW]
[ROW][C]54[/C][C] 0.8159[/C][C] 0.3681[/C][C] 0.184[/C][/ROW]
[ROW][C]55[/C][C] 0.9351[/C][C] 0.1297[/C][C] 0.06485[/C][/ROW]
[ROW][C]56[/C][C] 0.9175[/C][C] 0.1651[/C][C] 0.08254[/C][/ROW]
[ROW][C]57[/C][C] 0.9683[/C][C] 0.06331[/C][C] 0.03165[/C][/ROW]
[ROW][C]58[/C][C] 0.9968[/C][C] 0.00637[/C][C] 0.003185[/C][/ROW]
[ROW][C]59[/C][C] 0.9931[/C][C] 0.01387[/C][C] 0.006934[/C][/ROW]
[ROW][C]60[/C][C] 0.9874[/C][C] 0.02528[/C][C] 0.01264[/C][/ROW]
[ROW][C]61[/C][C] 0.9833[/C][C] 0.03349[/C][C] 0.01675[/C][/ROW]
[ROW][C]62[/C][C] 0.9921[/C][C] 0.0158[/C][C] 0.007898[/C][/ROW]
[ROW][C]63[/C][C] 0.9999[/C][C] 0.0001463[/C][C] 7.317e-05[/C][/ROW]
[ROW][C]64[/C][C] 0.9996[/C][C] 0.0007894[/C][C] 0.0003947[/C][/ROW]
[ROW][C]65[/C][C] 0.9982[/C][C] 0.003617[/C][C] 0.001808[/C][/ROW]
[ROW][C]66[/C][C] 0.9948[/C][C] 0.01039[/C][C] 0.005197[/C][/ROW]
[ROW][C]67[/C][C] 0.9765[/C][C] 0.04692[/C][C] 0.02346[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=316406&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=316406&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
18	0.6436	0.7128	0.3564
19	0.533	0.934	0.467
20	0.6444	0.7113	0.3556
21	0.5836	0.8327	0.4164
22	0.4605	0.9209	0.5395
23	0.3569	0.7139	0.6431
24	0.2861	0.5722	0.7139
25	0.2138	0.4276	0.7862
26	0.2068	0.4136	0.7932
27	0.2541	0.5082	0.7459
28	0.2454	0.4909	0.7546
29	0.1893	0.3785	0.8107
30	0.9526	0.09489	0.04745
31	0.9708	0.05842	0.02921
32	0.963	0.074	0.037
33	0.9567	0.08651	0.04326
34	0.9543	0.09136	0.04568
35	0.9322	0.1356	0.06781
36	0.9073	0.1854	0.09268
37	0.8753	0.2494	0.1247
38	0.8856	0.2288	0.1144
39	0.8504	0.2993	0.1496
40	0.8119	0.3762	0.1881
41	0.8424	0.3153	0.1576
42	0.7926	0.4148	0.2074
43	0.7506	0.4988	0.2494
44	0.6899	0.6202	0.3101
45	0.7168	0.5663	0.2832
46	0.7144	0.5711	0.2856
47	0.669	0.662	0.331
48	0.7764	0.4472	0.2236
49	0.724	0.5521	0.276
50	0.6551	0.6898	0.3449
51	0.6916	0.6168	0.3084
52	0.627	0.7459	0.373
53	0.8423	0.3154	0.1577
54	0.8159	0.3681	0.184
55	0.9351	0.1297	0.06485
56	0.9175	0.1651	0.08254
57	0.9683	0.06331	0.03165
58	0.9968	0.00637	0.003185
59	0.9931	0.01387	0.006934
60	0.9874	0.02528	0.01264
61	0.9833	0.03349	0.01675
62	0.9921	0.0158	0.007898
63	0.9999	0.0001463	7.317e-05
64	0.9996	0.0007894	0.0003947
65	0.9982	0.003617	0.001808
66	0.9948	0.01039	0.005197
67	0.9765	0.04692	0.02346

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	4	0.08	NOK
5% type I error level	10	0.2	NOK
10% type I error level	16	0.32	NOK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 4 &  0.08 & NOK \tabularnewline
5% type I error level & 10 & 0.2 & NOK \tabularnewline
10% type I error level & 16 & 0.32 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=316406&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]4[/C][C] 0.08[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]10[/C][C]0.2[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]16[/C][C]0.32[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=316406&T=7

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=316406&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	4	0.08	NOK
5% type I error level	10	0.2	NOK
10% type I error level	16	0.32	NOK

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted RESET test data: mylm RESET = 2.7706, df1 = 2, df2 = 68, p-value = 0.0697
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 0.8361, df1 = 28, df2 = 42, p-value = 0.6874
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 0.10566, df1 = 2, df2 = 68, p-value = 0.8999

\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 = 2.7706, df1 = 2, df2 = 68, p-value = 0.0697
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of regressors \tabularnewline
> reset_test_regressors
	RESET test
data:  mylm
RESET = 0.8361, df1 = 28, df2 = 42, p-value = 0.6874
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of principal components \tabularnewline
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 0.10566, df1 = 2, df2 = 68, p-value = 0.8999
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=316406&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 = 2.7706, df1 = 2, df2 = 68, p-value = 0.0697
[/C][/ROW]
[ROW][C]Ramsey RESET F-Test for powers (2 and 3) of regressors[/C][/ROW]
[ROW][C]> reset_test_regressors
	RESET test
data:  mylm
RESET = 0.8361, df1 = 28, df2 = 42, p-value = 0.6874
[/C][/ROW]
[ROW][C]Ramsey RESET F-Test for powers (2 and 3) of principal components[/C][/ROW]
[ROW][C]> reset_test_principal_components
	RESET test
data:  mylm
RESET = 0.10566, df1 = 2, df2 = 68, p-value = 0.8999
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=316406&T=8

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=316406&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 = 2.7706, df1 = 2, df2 = 68, p-value = 0.0697
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 0.8361, df1 = 28, df2 = 42, p-value = 0.6874
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 0.10566, df1 = 2, df2 = 68, p-value = 0.8999

Variance Inflation Factors (Multicollinearity)

> vif
      IK1       IK2       IK3       IK4     KVDD1     KVDD2     KVDD3     KVDD4 
 1.542067  1.319036  1.705432  1.290774  1.168808  1.128414  1.220570  1.261831 
`SK/EOU1` `SK/EOU2` `SK/EOU3` `SK/EOU4` `SK/EOU5` `SK/EOU6` 
 1.360240  1.302373  1.145574  1.175605  1.143655  1.116096

\begin{tabular}{lllllllll}
\hline
Variance Inflation Factors (Multicollinearity) \tabularnewline
> vif
      IK1       IK2       IK3       IK4     KVDD1     KVDD2     KVDD3     KVDD4 
 1.542067  1.319036  1.705432  1.290774  1.168808  1.128414  1.220570  1.261831 
`SK/EOU1` `SK/EOU2` `SK/EOU3` `SK/EOU4` `SK/EOU5` `SK/EOU6` 
 1.360240  1.302373  1.145574  1.175605  1.143655  1.116096 
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=316406&T=9

[TABLE]
[ROW][C]Variance Inflation Factors (Multicollinearity)[/C][/ROW]
[ROW][C]> vif
      IK1       IK2       IK3       IK4     KVDD1     KVDD2     KVDD3     KVDD4 
 1.542067  1.319036  1.705432  1.290774  1.168808  1.128414  1.220570  1.261831 
`SK/EOU1` `SK/EOU2` `SK/EOU3` `SK/EOU4` `SK/EOU5` `SK/EOU6` 
 1.360240  1.302373  1.145574  1.175605  1.143655  1.116096 
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=316406&T=9

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=316406&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
      IK1       IK2       IK3       IK4     KVDD1     KVDD2     KVDD3     KVDD4 
 1.542067  1.319036  1.705432  1.290774  1.168808  1.128414  1.220570  1.261831 
`SK/EOU1` `SK/EOU2` `SK/EOU3` `SK/EOU4` `SK/EOU5` `SK/EOU6` 
 1.360240  1.302373  1.145574  1.175605  1.143655  1.116096

Figure 1

PNG link

Postscript link

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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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Postscript link

PDF link

Parameters (Session):

par1 = grey ; par2 = no ;

Parameters (R input):

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