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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Fri, 23 Dec 2016 09:53:05 +0100

Cite this page as follows

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2016/Dec/23/t148248334771377gze9j1btjc.htm/, Retrieved Tue, 07 May 2024 13:05:22 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=302786, Retrieved Tue, 07 May 2024 13:05:22 +0000

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Estimated Impact

100

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

-       [Multiple Regression] [] [2016-12-23 08:53:05] [361c8dad91b3f1ef2e651cd04783c23b] [Current]

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13	17	15	11	11	18	14	17	13	22	14
16	11	13	9	11	19	19	17	13	24	14
17	12	14	12	15	18	17	12	12	26	17
15	12	13	11	15	15	17	13	15	21	14
16	13	12	12	13	19	15	16	12	26	13
16	17	17	12	14	19	20	15	12	25	16
18	17	12	12	13	19	15	14	12	21	13
16	12	13	12	15	16	19	15	10	24	16
17	16	13	12	15	18	15	13	9	27	18
17	15	16	11	15	20	15	12	15	28	20
17	11	12	12	10	14	19	13	14	23	14
15	16	12	12	11	15	16	14	11	25	14
16	15	13	15	16	18	20	18	18	24	15
14	16	16	13	17	19	18	19	14	24	18
16	15	15	12	14	16	15	15	11	24	16
17	11	12	11	13	18	14	14	9	25	14
16	8	13	13	10	18	20	13	16	25	18
15	10	15	9	13	17	16	11	15	25	17
17	14	12	10	17	19	16	17	14	25	16
16	16	15	11	18	19	16	16	17	24	17
15	15	11	12	17	17	10	8	13	26	18
16	15	13	12	11	18	19	12	11	26	17
15	12	13	12	15	16	19	12	13	25	13
17	18	14	12	12	20	16	12	10	26	16
14	10	14	10	15	13	15	14	13	23	14
16	17	14	12	15	19	18	14	14	24	16
15	12	15	12	12	15	17	15	10	24	15
16	13	16	14	19	17	19	13	15	25	14
16	9	16	11	13	17	17	14	12	25	16
13	11	16	12	15	16	14	16	10	24	19
15	10	13	9	13	17	19	16	10	28	16
17	15	13	13	10	19	20	15	14	27	18
15	15	14	8	14	18	5	15	12	26	18
13	13	13	13	12	19	19	16	15	23	18
17	13	14	12	15	20	16	16	13	23	18
15	9	12	12	13	16	15	17	10	24	14
14	14	17	12	18	17	16	16	14	24	20
14	14	14	12	15	16	18	11	11	22	18
18	11	15	12	11	16	16	15	11	25	14
15	15	13	10	14	16	15	15	12	25	18
17	12	14	12	11	16	17	11	13	28	13
13	11	15	11	14	14	14	13	14	22	14
16	12	19	13	9	17	20	13	13	28	16
15	15	14	13	13	18	19	17	14	25	16
15	13	13	9	13	16	7	13	13	24	15
16	11	12	12	12	16	13	12	11	24	13
15	10	4	13	17	13	16	17	19	23	16
13	16	14	10	16	16	16	16	13	25	13
12	13	15	5	15	15	16	18	13	19	12
17	15	15	13	16	19	18	12	15	26	18
18	14	12	12	16	16	18	15	11	25	13
18	12	14	12	13	17	16	15	16	27	15
11	10	11	5	13	19	17	15	13	26	19
14	12	12	12	12	17	19	14	15	23	14
13	9	10	10	11	17	16	17	13	25	18
15	15	13	12	13	15	19	15	9	21	12
17	16	14	15	15	16	13	15	12	22	12
16	12	14	13	13	16	16	15	14	24	14
15	11	15	12	14	16	13	16	15	25	8
17	11	15	12	13	17	12	12	11	27	13
16	9	13	13	15	18	17	10	12	24	14
16	13	15	13	14	18	17	15	14	26	13
16	17	16	11	14	18	17	13	13	21	13
15	18	12	12	13	19	16	14	14	27	15
12	15	17	9	11	14	16	14	10	22	14
17	12	15	12	14	13	14	13	14	23	17
14	18	18	12	17	18	16	17	14	24	16
14	11	12	13	15	16	13	16	16	25	14
16	6	16	14	15	15	16	16	16	24	18
15	10	15	10	13	18	14	16	14	23	17
15	19	15	12	12	18	20	17	14	28	18
14	16	12	8	14	16	12	16	16	23	12
13	12	13	12	11	19	13	16	11	24	13
18	10	10	12	14	17	18	13	4	26	15
15	14	14	12	18	17	14	17	14	22	15
16	12	11	12	15	19	19	12	14	25	14
14	13	12	10	18	19	18	18	9	25	16
15	16	14	12	16	20	14	15	11	24	13
17	18	12	12	12	19	18	12	11	24	18
16	13	14	12	14	18	19	13	10	26	15
10	15	12	12	14	16	15	13	12	21	13
16	16	13	12	14	16	14	13	11	25	16
17	9	13	13	14	15	17	11	8	25	12
17	9	14	12	13	20	19	17	9	26	18
20	8	12	14	12	16	13	15	13	25	14
17	18	15	10	13	16	19	16	16	26	17
18	18	13	12	17	20	18	14	14	27	14
15	14	13	11	13	20	20	18	13	25	17
17	8	11	13	14	18	15	16	14	23	17
14	14	12	11	15	15	15	14	13	20	12
15	13	16	13	13	14	15	12	19	24	14
17	14	11	12	14	16	20	14	11	26	13
16	7	13	12	17	14	15	9	8	25	16
17	18	12	12	15	18	19	14	14	25	12
15	16	17	13	13	20	18	17	11	24	16
16	9	14	12	14	20	18	15	14	26	16
18	11	15	9	17	18	15	15	12	25	16
18	10	8	20	8	20	20	20	7	28	15
16	13	13	12	15	14	17	12	14	27	15
16	10	13	13	10	20	12	14	13	25	19
17	12	15	14	15	17	18	16	14	26	17
15	11	14	12	15	20	19	18	11	26	20
13	12	13	11	14	14	20	10	9	26	14
15	12	14	12	15	16	13	13	14	22	13
17	10	12	12	18	20	17	16	10	28	15
16	20	19	12	14	19	15	17	15	26	16
16	12	15	12	19	18	16	16	13	21	16
15	12	14	12	16	17	18	17	16	25	11
16	16	14	12	17	17	18	19	14	25	14
16	11	15	12	18	19	14	18	14	24	13
14	12	13	12	13	15	15	15	15	24	14
15	12	15	11	10	18	12	14	16	24	16
12	13	14	12	14	15	17	15	16	23	14
19	10	11	11	13	16	14	14	12	23	15
16	14	17	9	12	16	18	16	8	24	17
16	13	13	13	13	20	17	12	12	24	14
17	15	9	11	12	18	17	19	14	25	15
16	13	12	10	13	20	20	17	13	28	15
14	13	13	14	16	18	16	14	9	23	14
15	17	17	10	12	17	14	13	10	24	15
14	12	14	12	14	19	15	14	13	23	13
16	17	13	12	17	18	18	14	12	24	16
15	9	16	11	14	19	20	17	11	25	16
17	12	14	12	12	17	17	15	12	24	14
15	14	14	14	14	18	17	15	12	23	10
16	14	14	13	17	17	17	16	14	23	16
16	14	10	12	13	16	17	17	10	25	14
15	12	12	12	12	19	15	13	10	21	13
15	14	13	12	14	18	17	15	14	22	14
11	13	14	10	11	17	18	10	9	19	15
16	15	18	12	17	18	17	18	13	24	17
18	16	14	12	15	16	20	16	11	25	16
13	13	14	10	10	20	15	16	15	21	17
11	14	13	12	15	14	16	14	11	22	15
16	14	13	12	16	17	15	15	12	23	18
18	17	16	15	17	13	18	13	17	27	16
15	15	13	15	12	17	15	13	12	26	17
19	8	14	12	15	18	18	14	12	29	12
17	11	8	12	10	16	20	17	10	28	15
13	11	13	10	13	17	19	13	13	24	15
14	9	13	12	17	19	14	14	11	25	16
16	15	16	12	17	18	16	18	13	25	19
13	16	14	12	16	17	15	12	11	22	15
17	16	13	12	15	16	17	14	13	25	14
14	10	14	11	16	17	18	8	11	26	14
19	15	12	13	16	17	20	16	13	26	17
14	10	16	9	15	17	17	13	10	24	15
16	12	18	11	16	20	18	16	14	25	18
12	14	16	12	14	14	15	11	13	19	13
16	18	15	13	17	20	16	15	16	25	16
16	15	18	11	14	19	11	16	8	23	14
15	19	15	10	12	16	15	14	15	25	16
12	13	14	9	15	19	18	13	16	25	18
15	10	14	12	14	17	17	17	12	26	15
17	15	15	12	14	19	16	13	15	27	14
14	7	9	12	14	20	12	18	13	24	15
15	14	17	13	13	19	19	16	13	22	20
18	11	11	13	16	19	18	13	18	25	15
15	14	15	10	13	16	15	16	8	24	14
18	11	15	13	14	18	17	15	12	23	16
15	18	15	13	13	16	19	14	8	27	13
15	8	13	12	13	17	18	15	13	24	13
16	19	14	12	15	18	19	16	16	24	14
13	5	15	9	13	16	16	12	16	21	15
16	17	15	12	14	17	16	19	15	25	16
14	14	14	11	13	15	16	15	12	25	15
16	17	13	12	12	18	14	13	11	23	15

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	9 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 time9 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=302786&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]

9 seconds[/C][/ROW] [ROW]

R Server[/C]

Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=302786&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=302786&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	9 seconds
R Server	Big Analytics Cloud Computing Center

Multiple Linear Regression - Estimated Regression Equation
a[t] = + 2.25414 + 0.00190694b[t] -0.0287653c[t] + 0.328833d[t] + 0.0490247e[t] + 0.0744453f[t] -0.0177131g[t] + 0.00483333h[t] -0.014926i[t] + 0.363566j[t] -0.0467502k[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
a[t] =  +  2.25414 +  0.00190694b[t] -0.0287653c[t] +  0.328833d[t] +  0.0490247e[t] +  0.0744453f[t] -0.0177131g[t] +  0.00483333h[t] -0.014926i[t] +  0.363566j[t] -0.0467502k[t]  + e[t] \tabularnewline
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=302786&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]a[t] =  +  2.25414 +  0.00190694b[t] -0.0287653c[t] +  0.328833d[t] +  0.0490247e[t] +  0.0744453f[t] -0.0177131g[t] +  0.00483333h[t] -0.014926i[t] +  0.363566j[t] -0.0467502k[t]  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=302786&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=302786&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
a[t] = + 2.25414 + 0.00190694b[t] -0.0287653c[t] + 0.328833d[t] + 0.0490247e[t] + 0.0744453f[t] -0.0177131g[t] + 0.00483333h[t] -0.014926i[t] + 0.363566j[t] -0.0467502k[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)	+2.254	2.187	+1.0310e+00	0.3042	0.1521
b	+0.001907	0.03999	+4.7690e-02	0.962	0.481
c	-0.02876	0.05779	-4.9780e-01	0.6193	0.3097
d	+0.3288	0.07229	+4.5490e+00	1.078e-05	5.392e-06
e	+0.04902	0.05507	+8.9030e-01	0.3747	0.1873
f	+0.07444	0.06996	+1.0640e+00	0.2889	0.1445
g	-0.01771	0.04813	-3.6800e-01	0.7134	0.3567
h	+0.004833	0.05394	+8.9600e-02	0.9287	0.4644
i	-0.01493	0.04806	-3.1060e-01	0.7566	0.3783
j	+0.3636	0.06427	+5.6570e+00	7.175e-08	3.588e-08
k	-0.04675	0.05924	-7.8920e-01	0.4312	0.2156

\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) & +2.254 &  2.187 & +1.0310e+00 &  0.3042 &  0.1521 \tabularnewline
b & +0.001907 &  0.03999 & +4.7690e-02 &  0.962 &  0.481 \tabularnewline
c & -0.02876 &  0.05779 & -4.9780e-01 &  0.6193 &  0.3097 \tabularnewline
d & +0.3288 &  0.07229 & +4.5490e+00 &  1.078e-05 &  5.392e-06 \tabularnewline
e & +0.04902 &  0.05507 & +8.9030e-01 &  0.3747 &  0.1873 \tabularnewline
f & +0.07444 &  0.06996 & +1.0640e+00 &  0.2889 &  0.1445 \tabularnewline
g & -0.01771 &  0.04813 & -3.6800e-01 &  0.7134 &  0.3567 \tabularnewline
h & +0.004833 &  0.05394 & +8.9600e-02 &  0.9287 &  0.4644 \tabularnewline
i & -0.01493 &  0.04806 & -3.1060e-01 &  0.7566 &  0.3783 \tabularnewline
j & +0.3636 &  0.06427 & +5.6570e+00 &  7.175e-08 &  3.588e-08 \tabularnewline
k & -0.04675 &  0.05924 & -7.8920e-01 &  0.4312 &  0.2156 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=302786&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]+2.254[/C][C] 2.187[/C][C]+1.0310e+00[/C][C] 0.3042[/C][C] 0.1521[/C][/ROW]
[ROW][C]b[/C][C]+0.001907[/C][C] 0.03999[/C][C]+4.7690e-02[/C][C] 0.962[/C][C] 0.481[/C][/ROW]
[ROW][C]c[/C][C]-0.02876[/C][C] 0.05779[/C][C]-4.9780e-01[/C][C] 0.6193[/C][C] 0.3097[/C][/ROW]
[ROW][C]d[/C][C]+0.3288[/C][C] 0.07229[/C][C]+4.5490e+00[/C][C] 1.078e-05[/C][C] 5.392e-06[/C][/ROW]
[ROW][C]e[/C][C]+0.04902[/C][C] 0.05507[/C][C]+8.9030e-01[/C][C] 0.3747[/C][C] 0.1873[/C][/ROW]
[ROW][C]f[/C][C]+0.07444[/C][C] 0.06996[/C][C]+1.0640e+00[/C][C] 0.2889[/C][C] 0.1445[/C][/ROW]
[ROW][C]g[/C][C]-0.01771[/C][C] 0.04813[/C][C]-3.6800e-01[/C][C] 0.7134[/C][C] 0.3567[/C][/ROW]
[ROW][C]h[/C][C]+0.004833[/C][C] 0.05394[/C][C]+8.9600e-02[/C][C] 0.9287[/C][C] 0.4644[/C][/ROW]
[ROW][C]i[/C][C]-0.01493[/C][C] 0.04806[/C][C]-3.1060e-01[/C][C] 0.7566[/C][C] 0.3783[/C][/ROW]
[ROW][C]j[/C][C]+0.3636[/C][C] 0.06427[/C][C]+5.6570e+00[/C][C] 7.175e-08[/C][C] 3.588e-08[/C][/ROW]
[ROW][C]k[/C][C]-0.04675[/C][C] 0.05924[/C][C]-7.8920e-01[/C][C] 0.4312[/C][C] 0.2156[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=302786&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=302786&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)	+2.254	2.187	+1.0310e+00	0.3042	0.1521
b	+0.001907	0.03999	+4.7690e-02	0.962	0.481
c	-0.02876	0.05779	-4.9780e-01	0.6193	0.3097
d	+0.3288	0.07229	+4.5490e+00	1.078e-05	5.392e-06
e	+0.04902	0.05507	+8.9030e-01	0.3747	0.1873
f	+0.07444	0.06996	+1.0640e+00	0.2889	0.1445
g	-0.01771	0.04813	-3.6800e-01	0.7134	0.3567
h	+0.004833	0.05394	+8.9600e-02	0.9287	0.4644
i	-0.01493	0.04806	-3.1060e-01	0.7566	0.3783
j	+0.3636	0.06427	+5.6570e+00	7.175e-08	3.588e-08
k	-0.04675	0.05924	-7.8920e-01	0.4312	0.2156

Multiple Linear Regression - Regression Statistics
Multiple R	0.5685
R-squared	0.3232
Adjusted R-squared	0.2798
F-TEST (value)	7.448
F-TEST (DF numerator)	10
F-TEST (DF denominator)	156
p-value	1.276e-09
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.449
Sum Squared Residuals	327.4

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.5685 \tabularnewline
R-squared &  0.3232 \tabularnewline
Adjusted R-squared &  0.2798 \tabularnewline
F-TEST (value) &  7.448 \tabularnewline
F-TEST (DF numerator) & 10 \tabularnewline
F-TEST (DF denominator) & 156 \tabularnewline
p-value &  1.276e-09 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  1.449 \tabularnewline
Sum Squared Residuals &  327.4 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=302786&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.5685[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.3232[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.2798[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 7.448[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]10[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]156[/C][/ROW]
[ROW][C]p-value[/C][C] 1.276e-09[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C] 1.449[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 327.4[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=302786&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=302786&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	0.5685
R-squared	0.3232
Adjusted R-squared	0.2798
F-TEST (value)	7.448
F-TEST (DF numerator)	10
F-TEST (DF denominator)	156
p-value	1.276e-09
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.449
Sum Squared Residuals	327.4

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	13	14.34	-1.336
2	16	14.44	1.563
3	17	16.13	0.8686
4	15	13.89	1.11
5	16	16.41	-0.409
6	16	15.72	0.2754
7	18	14.59	3.411
8	16	15.34	0.6602
9	17	16.57	0.4303
10	17	16.48	0.5228
11	17	14.63	2.367
12	15	15.6	-0.5961
13	16	16.45	-0.4541
14	14	15.8	-1.795
15	16	15.29	0.7051
16	17	15.64	1.356
17	16	15.72	0.2821
18	15	14.54	0.4556
19	17	15.4	1.597
20	16	15.24	0.7618
21	15	16.29	-1.29
22	16	15.95	0.0507
23	15	15.78	-0.7844
24	17	16.24	0.761
25	14	14.18	-0.1774
26	16	15.5	0.5029
27	15	15.14	-0.143
28	16	16.56	-0.5564
29	16	15.26	0.7403
30	13	15.2	-2.205
31	15	15.79	-0.785
32	17	16.57	0.4276
33	15	14.95	0.04691
34	13	15.22	-2.22
35	17	15.17	1.833
36	15	15.44	-0.4388
37	14	15.26	-1.261
38	14	14.48	-0.4777
39	18	15.58	2.42
40	15	14.95	0.05007
41	17	16.68	0.3192
42	13	14.14	-1.139
43	16	16.66	-0.6585
44	15	16.01	-1.01
45	15	14.46	0.5379
46	16	15.44	0.5633
47	15	15.36	-0.3634
48	13	15.23	-2.227
49	12	11.3	0.7
50	17	16.45	0.5486
51	18	15.93	2.072
52	18	16.39	1.611
53	11	13.79	-2.795
54	14	14.95	-0.9464
55	13	14.93	-1.929
56	15	14.28	0.7157
57	17	15.84	1.159
58	16	15.63	0.3712
59	15	16.01	-1.005
60	17	16.58	0.4177
61	16	15.89	0.1133
62	16	16.56	-0.556
63	16	14.06	1.935
64	15	16.63	-1.631
65	12	13.31	-1.314
66	17	14.62	2.381
67	14	15.46	-1.457
68	14	16.17	-2.174
69	16	15.7	0.2998
70	15	14.29	0.705
71	15	16.59	-1.59
72	14	13.87	0.1254
73	13	15.6	-2.604
74	18	16.32	1.681
75	15	14.89	0.1055
76	16	16	-0.003564
77	14	15.49	-1.494
78	15	15.88	-0.8794
79	17	15.35	1.649
80	16	16.18	-0.1771
81	10	14.41	-4.406
82	16	15.73	0.274
83	17	16.14	0.864
84	17	16.16	0.8367
85	20	16.39	3.61
86	17	15.13	1.866
87	18	16.88	1.116
88	15	15.48	-0.4834
89	17	15.42	1.576
90	14	13.72	0.2769
91	15	15.35	-0.3529
92	17	16.18	0.8179
93	16	15.71	0.2853
94	17	16.02	0.985
95	15	15.77	-0.7734
96	16	16.24	-0.2392
97	18	14.95	3.055
98	18	19.62	-1.617
99	16	16.29	-0.2915
100	16	16.02	-0.01521
101	17	16.66	0.3424
102	15	16.15	-1.147
103	13	15.61	-2.607
104	15	14.76	0.2389
105	17	17.35	-0.3509
106	16	16.09	-0.08983
107	16	14.55	1.45
108	15	15.97	-0.9697
109	16	15.93	0.0744
110	16	15.83	0.1656
111	14	15.26	-1.257
112	15	14.89	0.1132
113	12	14.87	-2.865
114	19	14.7	4.296
115	16	14.1	1.899
116	16	15.95	0.04512
117	17	15.54	1.461
118	16	16.36	-0.3608
119	14	15.99	-1.99
120	15	14.63	0.3703
121	14	15.28	-1.283
122	16	15.58	0.4207
123	15	15.43	-0.4339
124	17	15.34	1.662
125	15	15.99	-0.9949
126	16	15.43	0.5668
127	16	15.83	0.1659
128	15	14.56	0.4444
129	15	14.79	0.2144
130	11	12.77	-1.771
131	16	15.41	0.5929
132	18	15.65	2.346
133	13	13.57	-0.5716
134	11	14.55	-3.548
135	16	15.05	0.9489
136	18	17.12	0.8815
137	15	16.97	-1.971
138	19	17.44	1.56
139	17	16.73	0.2704
140	13	14.65	-1.649
141	14	16.09	-2.088
142	16	15.75	0.2476
143	13	14.8	-1.803
144	17	15.79	1.21
145	14	15.89	-1.891
146	19	16.45	2.551
147	14	14.41	-0.4102
148	16	15.45	0.5531
149	12	13.39	-1.389
150	16	16.35	-0.3456
151	16	14.95	1.046
152	15	14.85	0.1541
153	12	14.74	-2.738
154	15	16.12	-1.122
155	17	16.62	0.3846
156	14	15.83	-1.835
157	15	14.73	0.2714
158	18	16.3	1.704
159	15	14.73	0.2706
160	18	15.35	2.649
161	15	16.78	-1.78
162	15	15.42	-0.4218
163	16	15.48	0.5179
164	13	13.09	-0.08954
165	16	15.68	0.3213
166	14	15.25	-1.247
167	16	15.1	0.9017

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 &  13 &  14.34 & -1.336 \tabularnewline
2 &  16 &  14.44 &  1.563 \tabularnewline
3 &  17 &  16.13 &  0.8686 \tabularnewline
4 &  15 &  13.89 &  1.11 \tabularnewline
5 &  16 &  16.41 & -0.409 \tabularnewline
6 &  16 &  15.72 &  0.2754 \tabularnewline
7 &  18 &  14.59 &  3.411 \tabularnewline
8 &  16 &  15.34 &  0.6602 \tabularnewline
9 &  17 &  16.57 &  0.4303 \tabularnewline
10 &  17 &  16.48 &  0.5228 \tabularnewline
11 &  17 &  14.63 &  2.367 \tabularnewline
12 &  15 &  15.6 & -0.5961 \tabularnewline
13 &  16 &  16.45 & -0.4541 \tabularnewline
14 &  14 &  15.8 & -1.795 \tabularnewline
15 &  16 &  15.29 &  0.7051 \tabularnewline
16 &  17 &  15.64 &  1.356 \tabularnewline
17 &  16 &  15.72 &  0.2821 \tabularnewline
18 &  15 &  14.54 &  0.4556 \tabularnewline
19 &  17 &  15.4 &  1.597 \tabularnewline
20 &  16 &  15.24 &  0.7618 \tabularnewline
21 &  15 &  16.29 & -1.29 \tabularnewline
22 &  16 &  15.95 &  0.0507 \tabularnewline
23 &  15 &  15.78 & -0.7844 \tabularnewline
24 &  17 &  16.24 &  0.761 \tabularnewline
25 &  14 &  14.18 & -0.1774 \tabularnewline
26 &  16 &  15.5 &  0.5029 \tabularnewline
27 &  15 &  15.14 & -0.143 \tabularnewline
28 &  16 &  16.56 & -0.5564 \tabularnewline
29 &  16 &  15.26 &  0.7403 \tabularnewline
30 &  13 &  15.2 & -2.205 \tabularnewline
31 &  15 &  15.79 & -0.785 \tabularnewline
32 &  17 &  16.57 &  0.4276 \tabularnewline
33 &  15 &  14.95 &  0.04691 \tabularnewline
34 &  13 &  15.22 & -2.22 \tabularnewline
35 &  17 &  15.17 &  1.833 \tabularnewline
36 &  15 &  15.44 & -0.4388 \tabularnewline
37 &  14 &  15.26 & -1.261 \tabularnewline
38 &  14 &  14.48 & -0.4777 \tabularnewline
39 &  18 &  15.58 &  2.42 \tabularnewline
40 &  15 &  14.95 &  0.05007 \tabularnewline
41 &  17 &  16.68 &  0.3192 \tabularnewline
42 &  13 &  14.14 & -1.139 \tabularnewline
43 &  16 &  16.66 & -0.6585 \tabularnewline
44 &  15 &  16.01 & -1.01 \tabularnewline
45 &  15 &  14.46 &  0.5379 \tabularnewline
46 &  16 &  15.44 &  0.5633 \tabularnewline
47 &  15 &  15.36 & -0.3634 \tabularnewline
48 &  13 &  15.23 & -2.227 \tabularnewline
49 &  12 &  11.3 &  0.7 \tabularnewline
50 &  17 &  16.45 &  0.5486 \tabularnewline
51 &  18 &  15.93 &  2.072 \tabularnewline
52 &  18 &  16.39 &  1.611 \tabularnewline
53 &  11 &  13.79 & -2.795 \tabularnewline
54 &  14 &  14.95 & -0.9464 \tabularnewline
55 &  13 &  14.93 & -1.929 \tabularnewline
56 &  15 &  14.28 &  0.7157 \tabularnewline
57 &  17 &  15.84 &  1.159 \tabularnewline
58 &  16 &  15.63 &  0.3712 \tabularnewline
59 &  15 &  16.01 & -1.005 \tabularnewline
60 &  17 &  16.58 &  0.4177 \tabularnewline
61 &  16 &  15.89 &  0.1133 \tabularnewline
62 &  16 &  16.56 & -0.556 \tabularnewline
63 &  16 &  14.06 &  1.935 \tabularnewline
64 &  15 &  16.63 & -1.631 \tabularnewline
65 &  12 &  13.31 & -1.314 \tabularnewline
66 &  17 &  14.62 &  2.381 \tabularnewline
67 &  14 &  15.46 & -1.457 \tabularnewline
68 &  14 &  16.17 & -2.174 \tabularnewline
69 &  16 &  15.7 &  0.2998 \tabularnewline
70 &  15 &  14.29 &  0.705 \tabularnewline
71 &  15 &  16.59 & -1.59 \tabularnewline
72 &  14 &  13.87 &  0.1254 \tabularnewline
73 &  13 &  15.6 & -2.604 \tabularnewline
74 &  18 &  16.32 &  1.681 \tabularnewline
75 &  15 &  14.89 &  0.1055 \tabularnewline
76 &  16 &  16 & -0.003564 \tabularnewline
77 &  14 &  15.49 & -1.494 \tabularnewline
78 &  15 &  15.88 & -0.8794 \tabularnewline
79 &  17 &  15.35 &  1.649 \tabularnewline
80 &  16 &  16.18 & -0.1771 \tabularnewline
81 &  10 &  14.41 & -4.406 \tabularnewline
82 &  16 &  15.73 &  0.274 \tabularnewline
83 &  17 &  16.14 &  0.864 \tabularnewline
84 &  17 &  16.16 &  0.8367 \tabularnewline
85 &  20 &  16.39 &  3.61 \tabularnewline
86 &  17 &  15.13 &  1.866 \tabularnewline
87 &  18 &  16.88 &  1.116 \tabularnewline
88 &  15 &  15.48 & -0.4834 \tabularnewline
89 &  17 &  15.42 &  1.576 \tabularnewline
90 &  14 &  13.72 &  0.2769 \tabularnewline
91 &  15 &  15.35 & -0.3529 \tabularnewline
92 &  17 &  16.18 &  0.8179 \tabularnewline
93 &  16 &  15.71 &  0.2853 \tabularnewline
94 &  17 &  16.02 &  0.985 \tabularnewline
95 &  15 &  15.77 & -0.7734 \tabularnewline
96 &  16 &  16.24 & -0.2392 \tabularnewline
97 &  18 &  14.95 &  3.055 \tabularnewline
98 &  18 &  19.62 & -1.617 \tabularnewline
99 &  16 &  16.29 & -0.2915 \tabularnewline
100 &  16 &  16.02 & -0.01521 \tabularnewline
101 &  17 &  16.66 &  0.3424 \tabularnewline
102 &  15 &  16.15 & -1.147 \tabularnewline
103 &  13 &  15.61 & -2.607 \tabularnewline
104 &  15 &  14.76 &  0.2389 \tabularnewline
105 &  17 &  17.35 & -0.3509 \tabularnewline
106 &  16 &  16.09 & -0.08983 \tabularnewline
107 &  16 &  14.55 &  1.45 \tabularnewline
108 &  15 &  15.97 & -0.9697 \tabularnewline
109 &  16 &  15.93 &  0.0744 \tabularnewline
110 &  16 &  15.83 &  0.1656 \tabularnewline
111 &  14 &  15.26 & -1.257 \tabularnewline
112 &  15 &  14.89 &  0.1132 \tabularnewline
113 &  12 &  14.87 & -2.865 \tabularnewline
114 &  19 &  14.7 &  4.296 \tabularnewline
115 &  16 &  14.1 &  1.899 \tabularnewline
116 &  16 &  15.95 &  0.04512 \tabularnewline
117 &  17 &  15.54 &  1.461 \tabularnewline
118 &  16 &  16.36 & -0.3608 \tabularnewline
119 &  14 &  15.99 & -1.99 \tabularnewline
120 &  15 &  14.63 &  0.3703 \tabularnewline
121 &  14 &  15.28 & -1.283 \tabularnewline
122 &  16 &  15.58 &  0.4207 \tabularnewline
123 &  15 &  15.43 & -0.4339 \tabularnewline
124 &  17 &  15.34 &  1.662 \tabularnewline
125 &  15 &  15.99 & -0.9949 \tabularnewline
126 &  16 &  15.43 &  0.5668 \tabularnewline
127 &  16 &  15.83 &  0.1659 \tabularnewline
128 &  15 &  14.56 &  0.4444 \tabularnewline
129 &  15 &  14.79 &  0.2144 \tabularnewline
130 &  11 &  12.77 & -1.771 \tabularnewline
131 &  16 &  15.41 &  0.5929 \tabularnewline
132 &  18 &  15.65 &  2.346 \tabularnewline
133 &  13 &  13.57 & -0.5716 \tabularnewline
134 &  11 &  14.55 & -3.548 \tabularnewline
135 &  16 &  15.05 &  0.9489 \tabularnewline
136 &  18 &  17.12 &  0.8815 \tabularnewline
137 &  15 &  16.97 & -1.971 \tabularnewline
138 &  19 &  17.44 &  1.56 \tabularnewline
139 &  17 &  16.73 &  0.2704 \tabularnewline
140 &  13 &  14.65 & -1.649 \tabularnewline
141 &  14 &  16.09 & -2.088 \tabularnewline
142 &  16 &  15.75 &  0.2476 \tabularnewline
143 &  13 &  14.8 & -1.803 \tabularnewline
144 &  17 &  15.79 &  1.21 \tabularnewline
145 &  14 &  15.89 & -1.891 \tabularnewline
146 &  19 &  16.45 &  2.551 \tabularnewline
147 &  14 &  14.41 & -0.4102 \tabularnewline
148 &  16 &  15.45 &  0.5531 \tabularnewline
149 &  12 &  13.39 & -1.389 \tabularnewline
150 &  16 &  16.35 & -0.3456 \tabularnewline
151 &  16 &  14.95 &  1.046 \tabularnewline
152 &  15 &  14.85 &  0.1541 \tabularnewline
153 &  12 &  14.74 & -2.738 \tabularnewline
154 &  15 &  16.12 & -1.122 \tabularnewline
155 &  17 &  16.62 &  0.3846 \tabularnewline
156 &  14 &  15.83 & -1.835 \tabularnewline
157 &  15 &  14.73 &  0.2714 \tabularnewline
158 &  18 &  16.3 &  1.704 \tabularnewline
159 &  15 &  14.73 &  0.2706 \tabularnewline
160 &  18 &  15.35 &  2.649 \tabularnewline
161 &  15 &  16.78 & -1.78 \tabularnewline
162 &  15 &  15.42 & -0.4218 \tabularnewline
163 &  16 &  15.48 &  0.5179 \tabularnewline
164 &  13 &  13.09 & -0.08954 \tabularnewline
165 &  16 &  15.68 &  0.3213 \tabularnewline
166 &  14 &  15.25 & -1.247 \tabularnewline
167 &  16 &  15.1 &  0.9017 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=302786&T=4

[TABLE]
[ROW][C]Multiple Linear Regression - Actuals, Interpolation, and Residuals[/C][/ROW]
[ROW][C]Time or Index[/C][C]Actuals[/C][C]InterpolationForecast[/C][C]ResidualsPrediction Error[/C][/ROW]
[ROW][C]1[/C][C] 13[/C][C] 14.34[/C][C]-1.336[/C][/ROW]
[ROW][C]2[/C][C] 16[/C][C] 14.44[/C][C] 1.563[/C][/ROW]
[ROW][C]3[/C][C] 17[/C][C] 16.13[/C][C] 0.8686[/C][/ROW]
[ROW][C]4[/C][C] 15[/C][C] 13.89[/C][C] 1.11[/C][/ROW]
[ROW][C]5[/C][C] 16[/C][C] 16.41[/C][C]-0.409[/C][/ROW]
[ROW][C]6[/C][C] 16[/C][C] 15.72[/C][C] 0.2754[/C][/ROW]
[ROW][C]7[/C][C] 18[/C][C] 14.59[/C][C] 3.411[/C][/ROW]
[ROW][C]8[/C][C] 16[/C][C] 15.34[/C][C] 0.6602[/C][/ROW]
[ROW][C]9[/C][C] 17[/C][C] 16.57[/C][C] 0.4303[/C][/ROW]
[ROW][C]10[/C][C] 17[/C][C] 16.48[/C][C] 0.5228[/C][/ROW]
[ROW][C]11[/C][C] 17[/C][C] 14.63[/C][C] 2.367[/C][/ROW]
[ROW][C]12[/C][C] 15[/C][C] 15.6[/C][C]-0.5961[/C][/ROW]
[ROW][C]13[/C][C] 16[/C][C] 16.45[/C][C]-0.4541[/C][/ROW]
[ROW][C]14[/C][C] 14[/C][C] 15.8[/C][C]-1.795[/C][/ROW]
[ROW][C]15[/C][C] 16[/C][C] 15.29[/C][C] 0.7051[/C][/ROW]
[ROW][C]16[/C][C] 17[/C][C] 15.64[/C][C] 1.356[/C][/ROW]
[ROW][C]17[/C][C] 16[/C][C] 15.72[/C][C] 0.2821[/C][/ROW]
[ROW][C]18[/C][C] 15[/C][C] 14.54[/C][C] 0.4556[/C][/ROW]
[ROW][C]19[/C][C] 17[/C][C] 15.4[/C][C] 1.597[/C][/ROW]
[ROW][C]20[/C][C] 16[/C][C] 15.24[/C][C] 0.7618[/C][/ROW]
[ROW][C]21[/C][C] 15[/C][C] 16.29[/C][C]-1.29[/C][/ROW]
[ROW][C]22[/C][C] 16[/C][C] 15.95[/C][C] 0.0507[/C][/ROW]
[ROW][C]23[/C][C] 15[/C][C] 15.78[/C][C]-0.7844[/C][/ROW]
[ROW][C]24[/C][C] 17[/C][C] 16.24[/C][C] 0.761[/C][/ROW]
[ROW][C]25[/C][C] 14[/C][C] 14.18[/C][C]-0.1774[/C][/ROW]
[ROW][C]26[/C][C] 16[/C][C] 15.5[/C][C] 0.5029[/C][/ROW]
[ROW][C]27[/C][C] 15[/C][C] 15.14[/C][C]-0.143[/C][/ROW]
[ROW][C]28[/C][C] 16[/C][C] 16.56[/C][C]-0.5564[/C][/ROW]
[ROW][C]29[/C][C] 16[/C][C] 15.26[/C][C] 0.7403[/C][/ROW]
[ROW][C]30[/C][C] 13[/C][C] 15.2[/C][C]-2.205[/C][/ROW]
[ROW][C]31[/C][C] 15[/C][C] 15.79[/C][C]-0.785[/C][/ROW]
[ROW][C]32[/C][C] 17[/C][C] 16.57[/C][C] 0.4276[/C][/ROW]
[ROW][C]33[/C][C] 15[/C][C] 14.95[/C][C] 0.04691[/C][/ROW]
[ROW][C]34[/C][C] 13[/C][C] 15.22[/C][C]-2.22[/C][/ROW]
[ROW][C]35[/C][C] 17[/C][C] 15.17[/C][C] 1.833[/C][/ROW]
[ROW][C]36[/C][C] 15[/C][C] 15.44[/C][C]-0.4388[/C][/ROW]
[ROW][C]37[/C][C] 14[/C][C] 15.26[/C][C]-1.261[/C][/ROW]
[ROW][C]38[/C][C] 14[/C][C] 14.48[/C][C]-0.4777[/C][/ROW]
[ROW][C]39[/C][C] 18[/C][C] 15.58[/C][C] 2.42[/C][/ROW]
[ROW][C]40[/C][C] 15[/C][C] 14.95[/C][C] 0.05007[/C][/ROW]
[ROW][C]41[/C][C] 17[/C][C] 16.68[/C][C] 0.3192[/C][/ROW]
[ROW][C]42[/C][C] 13[/C][C] 14.14[/C][C]-1.139[/C][/ROW]
[ROW][C]43[/C][C] 16[/C][C] 16.66[/C][C]-0.6585[/C][/ROW]
[ROW][C]44[/C][C] 15[/C][C] 16.01[/C][C]-1.01[/C][/ROW]
[ROW][C]45[/C][C] 15[/C][C] 14.46[/C][C] 0.5379[/C][/ROW]
[ROW][C]46[/C][C] 16[/C][C] 15.44[/C][C] 0.5633[/C][/ROW]
[ROW][C]47[/C][C] 15[/C][C] 15.36[/C][C]-0.3634[/C][/ROW]
[ROW][C]48[/C][C] 13[/C][C] 15.23[/C][C]-2.227[/C][/ROW]
[ROW][C]49[/C][C] 12[/C][C] 11.3[/C][C] 0.7[/C][/ROW]
[ROW][C]50[/C][C] 17[/C][C] 16.45[/C][C] 0.5486[/C][/ROW]
[ROW][C]51[/C][C] 18[/C][C] 15.93[/C][C] 2.072[/C][/ROW]
[ROW][C]52[/C][C] 18[/C][C] 16.39[/C][C] 1.611[/C][/ROW]
[ROW][C]53[/C][C] 11[/C][C] 13.79[/C][C]-2.795[/C][/ROW]
[ROW][C]54[/C][C] 14[/C][C] 14.95[/C][C]-0.9464[/C][/ROW]
[ROW][C]55[/C][C] 13[/C][C] 14.93[/C][C]-1.929[/C][/ROW]
[ROW][C]56[/C][C] 15[/C][C] 14.28[/C][C] 0.7157[/C][/ROW]
[ROW][C]57[/C][C] 17[/C][C] 15.84[/C][C] 1.159[/C][/ROW]
[ROW][C]58[/C][C] 16[/C][C] 15.63[/C][C] 0.3712[/C][/ROW]
[ROW][C]59[/C][C] 15[/C][C] 16.01[/C][C]-1.005[/C][/ROW]
[ROW][C]60[/C][C] 17[/C][C] 16.58[/C][C] 0.4177[/C][/ROW]
[ROW][C]61[/C][C] 16[/C][C] 15.89[/C][C] 0.1133[/C][/ROW]
[ROW][C]62[/C][C] 16[/C][C] 16.56[/C][C]-0.556[/C][/ROW]
[ROW][C]63[/C][C] 16[/C][C] 14.06[/C][C] 1.935[/C][/ROW]
[ROW][C]64[/C][C] 15[/C][C] 16.63[/C][C]-1.631[/C][/ROW]
[ROW][C]65[/C][C] 12[/C][C] 13.31[/C][C]-1.314[/C][/ROW]
[ROW][C]66[/C][C] 17[/C][C] 14.62[/C][C] 2.381[/C][/ROW]
[ROW][C]67[/C][C] 14[/C][C] 15.46[/C][C]-1.457[/C][/ROW]
[ROW][C]68[/C][C] 14[/C][C] 16.17[/C][C]-2.174[/C][/ROW]
[ROW][C]69[/C][C] 16[/C][C] 15.7[/C][C] 0.2998[/C][/ROW]
[ROW][C]70[/C][C] 15[/C][C] 14.29[/C][C] 0.705[/C][/ROW]
[ROW][C]71[/C][C] 15[/C][C] 16.59[/C][C]-1.59[/C][/ROW]
[ROW][C]72[/C][C] 14[/C][C] 13.87[/C][C] 0.1254[/C][/ROW]
[ROW][C]73[/C][C] 13[/C][C] 15.6[/C][C]-2.604[/C][/ROW]
[ROW][C]74[/C][C] 18[/C][C] 16.32[/C][C] 1.681[/C][/ROW]
[ROW][C]75[/C][C] 15[/C][C] 14.89[/C][C] 0.1055[/C][/ROW]
[ROW][C]76[/C][C] 16[/C][C] 16[/C][C]-0.003564[/C][/ROW]
[ROW][C]77[/C][C] 14[/C][C] 15.49[/C][C]-1.494[/C][/ROW]
[ROW][C]78[/C][C] 15[/C][C] 15.88[/C][C]-0.8794[/C][/ROW]
[ROW][C]79[/C][C] 17[/C][C] 15.35[/C][C] 1.649[/C][/ROW]
[ROW][C]80[/C][C] 16[/C][C] 16.18[/C][C]-0.1771[/C][/ROW]
[ROW][C]81[/C][C] 10[/C][C] 14.41[/C][C]-4.406[/C][/ROW]
[ROW][C]82[/C][C] 16[/C][C] 15.73[/C][C] 0.274[/C][/ROW]
[ROW][C]83[/C][C] 17[/C][C] 16.14[/C][C] 0.864[/C][/ROW]
[ROW][C]84[/C][C] 17[/C][C] 16.16[/C][C] 0.8367[/C][/ROW]
[ROW][C]85[/C][C] 20[/C][C] 16.39[/C][C] 3.61[/C][/ROW]
[ROW][C]86[/C][C] 17[/C][C] 15.13[/C][C] 1.866[/C][/ROW]
[ROW][C]87[/C][C] 18[/C][C] 16.88[/C][C] 1.116[/C][/ROW]
[ROW][C]88[/C][C] 15[/C][C] 15.48[/C][C]-0.4834[/C][/ROW]
[ROW][C]89[/C][C] 17[/C][C] 15.42[/C][C] 1.576[/C][/ROW]
[ROW][C]90[/C][C] 14[/C][C] 13.72[/C][C] 0.2769[/C][/ROW]
[ROW][C]91[/C][C] 15[/C][C] 15.35[/C][C]-0.3529[/C][/ROW]
[ROW][C]92[/C][C] 17[/C][C] 16.18[/C][C] 0.8179[/C][/ROW]
[ROW][C]93[/C][C] 16[/C][C] 15.71[/C][C] 0.2853[/C][/ROW]
[ROW][C]94[/C][C] 17[/C][C] 16.02[/C][C] 0.985[/C][/ROW]
[ROW][C]95[/C][C] 15[/C][C] 15.77[/C][C]-0.7734[/C][/ROW]
[ROW][C]96[/C][C] 16[/C][C] 16.24[/C][C]-0.2392[/C][/ROW]
[ROW][C]97[/C][C] 18[/C][C] 14.95[/C][C] 3.055[/C][/ROW]
[ROW][C]98[/C][C] 18[/C][C] 19.62[/C][C]-1.617[/C][/ROW]
[ROW][C]99[/C][C] 16[/C][C] 16.29[/C][C]-0.2915[/C][/ROW]
[ROW][C]100[/C][C] 16[/C][C] 16.02[/C][C]-0.01521[/C][/ROW]
[ROW][C]101[/C][C] 17[/C][C] 16.66[/C][C] 0.3424[/C][/ROW]
[ROW][C]102[/C][C] 15[/C][C] 16.15[/C][C]-1.147[/C][/ROW]
[ROW][C]103[/C][C] 13[/C][C] 15.61[/C][C]-2.607[/C][/ROW]
[ROW][C]104[/C][C] 15[/C][C] 14.76[/C][C] 0.2389[/C][/ROW]
[ROW][C]105[/C][C] 17[/C][C] 17.35[/C][C]-0.3509[/C][/ROW]
[ROW][C]106[/C][C] 16[/C][C] 16.09[/C][C]-0.08983[/C][/ROW]
[ROW][C]107[/C][C] 16[/C][C] 14.55[/C][C] 1.45[/C][/ROW]
[ROW][C]108[/C][C] 15[/C][C] 15.97[/C][C]-0.9697[/C][/ROW]
[ROW][C]109[/C][C] 16[/C][C] 15.93[/C][C] 0.0744[/C][/ROW]
[ROW][C]110[/C][C] 16[/C][C] 15.83[/C][C] 0.1656[/C][/ROW]
[ROW][C]111[/C][C] 14[/C][C] 15.26[/C][C]-1.257[/C][/ROW]
[ROW][C]112[/C][C] 15[/C][C] 14.89[/C][C] 0.1132[/C][/ROW]
[ROW][C]113[/C][C] 12[/C][C] 14.87[/C][C]-2.865[/C][/ROW]
[ROW][C]114[/C][C] 19[/C][C] 14.7[/C][C] 4.296[/C][/ROW]
[ROW][C]115[/C][C] 16[/C][C] 14.1[/C][C] 1.899[/C][/ROW]
[ROW][C]116[/C][C] 16[/C][C] 15.95[/C][C] 0.04512[/C][/ROW]
[ROW][C]117[/C][C] 17[/C][C] 15.54[/C][C] 1.461[/C][/ROW]
[ROW][C]118[/C][C] 16[/C][C] 16.36[/C][C]-0.3608[/C][/ROW]
[ROW][C]119[/C][C] 14[/C][C] 15.99[/C][C]-1.99[/C][/ROW]
[ROW][C]120[/C][C] 15[/C][C] 14.63[/C][C] 0.3703[/C][/ROW]
[ROW][C]121[/C][C] 14[/C][C] 15.28[/C][C]-1.283[/C][/ROW]
[ROW][C]122[/C][C] 16[/C][C] 15.58[/C][C] 0.4207[/C][/ROW]
[ROW][C]123[/C][C] 15[/C][C] 15.43[/C][C]-0.4339[/C][/ROW]
[ROW][C]124[/C][C] 17[/C][C] 15.34[/C][C] 1.662[/C][/ROW]
[ROW][C]125[/C][C] 15[/C][C] 15.99[/C][C]-0.9949[/C][/ROW]
[ROW][C]126[/C][C] 16[/C][C] 15.43[/C][C] 0.5668[/C][/ROW]
[ROW][C]127[/C][C] 16[/C][C] 15.83[/C][C] 0.1659[/C][/ROW]
[ROW][C]128[/C][C] 15[/C][C] 14.56[/C][C] 0.4444[/C][/ROW]
[ROW][C]129[/C][C] 15[/C][C] 14.79[/C][C] 0.2144[/C][/ROW]
[ROW][C]130[/C][C] 11[/C][C] 12.77[/C][C]-1.771[/C][/ROW]
[ROW][C]131[/C][C] 16[/C][C] 15.41[/C][C] 0.5929[/C][/ROW]
[ROW][C]132[/C][C] 18[/C][C] 15.65[/C][C] 2.346[/C][/ROW]
[ROW][C]133[/C][C] 13[/C][C] 13.57[/C][C]-0.5716[/C][/ROW]
[ROW][C]134[/C][C] 11[/C][C] 14.55[/C][C]-3.548[/C][/ROW]
[ROW][C]135[/C][C] 16[/C][C] 15.05[/C][C] 0.9489[/C][/ROW]
[ROW][C]136[/C][C] 18[/C][C] 17.12[/C][C] 0.8815[/C][/ROW]
[ROW][C]137[/C][C] 15[/C][C] 16.97[/C][C]-1.971[/C][/ROW]
[ROW][C]138[/C][C] 19[/C][C] 17.44[/C][C] 1.56[/C][/ROW]
[ROW][C]139[/C][C] 17[/C][C] 16.73[/C][C] 0.2704[/C][/ROW]
[ROW][C]140[/C][C] 13[/C][C] 14.65[/C][C]-1.649[/C][/ROW]
[ROW][C]141[/C][C] 14[/C][C] 16.09[/C][C]-2.088[/C][/ROW]
[ROW][C]142[/C][C] 16[/C][C] 15.75[/C][C] 0.2476[/C][/ROW]
[ROW][C]143[/C][C] 13[/C][C] 14.8[/C][C]-1.803[/C][/ROW]
[ROW][C]144[/C][C] 17[/C][C] 15.79[/C][C] 1.21[/C][/ROW]
[ROW][C]145[/C][C] 14[/C][C] 15.89[/C][C]-1.891[/C][/ROW]
[ROW][C]146[/C][C] 19[/C][C] 16.45[/C][C] 2.551[/C][/ROW]
[ROW][C]147[/C][C] 14[/C][C] 14.41[/C][C]-0.4102[/C][/ROW]
[ROW][C]148[/C][C] 16[/C][C] 15.45[/C][C] 0.5531[/C][/ROW]
[ROW][C]149[/C][C] 12[/C][C] 13.39[/C][C]-1.389[/C][/ROW]
[ROW][C]150[/C][C] 16[/C][C] 16.35[/C][C]-0.3456[/C][/ROW]
[ROW][C]151[/C][C] 16[/C][C] 14.95[/C][C] 1.046[/C][/ROW]
[ROW][C]152[/C][C] 15[/C][C] 14.85[/C][C] 0.1541[/C][/ROW]
[ROW][C]153[/C][C] 12[/C][C] 14.74[/C][C]-2.738[/C][/ROW]
[ROW][C]154[/C][C] 15[/C][C] 16.12[/C][C]-1.122[/C][/ROW]
[ROW][C]155[/C][C] 17[/C][C] 16.62[/C][C] 0.3846[/C][/ROW]
[ROW][C]156[/C][C] 14[/C][C] 15.83[/C][C]-1.835[/C][/ROW]
[ROW][C]157[/C][C] 15[/C][C] 14.73[/C][C] 0.2714[/C][/ROW]
[ROW][C]158[/C][C] 18[/C][C] 16.3[/C][C] 1.704[/C][/ROW]
[ROW][C]159[/C][C] 15[/C][C] 14.73[/C][C] 0.2706[/C][/ROW]
[ROW][C]160[/C][C] 18[/C][C] 15.35[/C][C] 2.649[/C][/ROW]
[ROW][C]161[/C][C] 15[/C][C] 16.78[/C][C]-1.78[/C][/ROW]
[ROW][C]162[/C][C] 15[/C][C] 15.42[/C][C]-0.4218[/C][/ROW]
[ROW][C]163[/C][C] 16[/C][C] 15.48[/C][C] 0.5179[/C][/ROW]
[ROW][C]164[/C][C] 13[/C][C] 13.09[/C][C]-0.08954[/C][/ROW]
[ROW][C]165[/C][C] 16[/C][C] 15.68[/C][C] 0.3213[/C][/ROW]
[ROW][C]166[/C][C] 14[/C][C] 15.25[/C][C]-1.247[/C][/ROW]
[ROW][C]167[/C][C] 16[/C][C] 15.1[/C][C] 0.9017[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=302786&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=302786&T=4

As an alternative you can also use a QR Code:

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	13	14.34	-1.336
2	16	14.44	1.563
3	17	16.13	0.8686
4	15	13.89	1.11
5	16	16.41	-0.409
6	16	15.72	0.2754
7	18	14.59	3.411
8	16	15.34	0.6602
9	17	16.57	0.4303
10	17	16.48	0.5228
11	17	14.63	2.367
12	15	15.6	-0.5961
13	16	16.45	-0.4541
14	14	15.8	-1.795
15	16	15.29	0.7051
16	17	15.64	1.356
17	16	15.72	0.2821
18	15	14.54	0.4556
19	17	15.4	1.597
20	16	15.24	0.7618
21	15	16.29	-1.29
22	16	15.95	0.0507
23	15	15.78	-0.7844
24	17	16.24	0.761
25	14	14.18	-0.1774
26	16	15.5	0.5029
27	15	15.14	-0.143
28	16	16.56	-0.5564
29	16	15.26	0.7403
30	13	15.2	-2.205
31	15	15.79	-0.785
32	17	16.57	0.4276
33	15	14.95	0.04691
34	13	15.22	-2.22
35	17	15.17	1.833
36	15	15.44	-0.4388
37	14	15.26	-1.261
38	14	14.48	-0.4777
39	18	15.58	2.42
40	15	14.95	0.05007
41	17	16.68	0.3192
42	13	14.14	-1.139
43	16	16.66	-0.6585
44	15	16.01	-1.01
45	15	14.46	0.5379
46	16	15.44	0.5633
47	15	15.36	-0.3634
48	13	15.23	-2.227
49	12	11.3	0.7
50	17	16.45	0.5486
51	18	15.93	2.072
52	18	16.39	1.611
53	11	13.79	-2.795
54	14	14.95	-0.9464
55	13	14.93	-1.929
56	15	14.28	0.7157
57	17	15.84	1.159
58	16	15.63	0.3712
59	15	16.01	-1.005
60	17	16.58	0.4177
61	16	15.89	0.1133
62	16	16.56	-0.556
63	16	14.06	1.935
64	15	16.63	-1.631
65	12	13.31	-1.314
66	17	14.62	2.381
67	14	15.46	-1.457
68	14	16.17	-2.174
69	16	15.7	0.2998
70	15	14.29	0.705
71	15	16.59	-1.59
72	14	13.87	0.1254
73	13	15.6	-2.604
74	18	16.32	1.681
75	15	14.89	0.1055
76	16	16	-0.003564
77	14	15.49	-1.494
78	15	15.88	-0.8794
79	17	15.35	1.649
80	16	16.18	-0.1771
81	10	14.41	-4.406
82	16	15.73	0.274
83	17	16.14	0.864
84	17	16.16	0.8367
85	20	16.39	3.61
86	17	15.13	1.866
87	18	16.88	1.116
88	15	15.48	-0.4834
89	17	15.42	1.576
90	14	13.72	0.2769
91	15	15.35	-0.3529
92	17	16.18	0.8179
93	16	15.71	0.2853
94	17	16.02	0.985
95	15	15.77	-0.7734
96	16	16.24	-0.2392
97	18	14.95	3.055
98	18	19.62	-1.617
99	16	16.29	-0.2915
100	16	16.02	-0.01521
101	17	16.66	0.3424
102	15	16.15	-1.147
103	13	15.61	-2.607
104	15	14.76	0.2389
105	17	17.35	-0.3509
106	16	16.09	-0.08983
107	16	14.55	1.45
108	15	15.97	-0.9697
109	16	15.93	0.0744
110	16	15.83	0.1656
111	14	15.26	-1.257
112	15	14.89	0.1132
113	12	14.87	-2.865
114	19	14.7	4.296
115	16	14.1	1.899
116	16	15.95	0.04512
117	17	15.54	1.461
118	16	16.36	-0.3608
119	14	15.99	-1.99
120	15	14.63	0.3703
121	14	15.28	-1.283
122	16	15.58	0.4207
123	15	15.43	-0.4339
124	17	15.34	1.662
125	15	15.99	-0.9949
126	16	15.43	0.5668
127	16	15.83	0.1659
128	15	14.56	0.4444
129	15	14.79	0.2144
130	11	12.77	-1.771
131	16	15.41	0.5929
132	18	15.65	2.346
133	13	13.57	-0.5716
134	11	14.55	-3.548
135	16	15.05	0.9489
136	18	17.12	0.8815
137	15	16.97	-1.971
138	19	17.44	1.56
139	17	16.73	0.2704
140	13	14.65	-1.649
141	14	16.09	-2.088
142	16	15.75	0.2476
143	13	14.8	-1.803
144	17	15.79	1.21
145	14	15.89	-1.891
146	19	16.45	2.551
147	14	14.41	-0.4102
148	16	15.45	0.5531
149	12	13.39	-1.389
150	16	16.35	-0.3456
151	16	14.95	1.046
152	15	14.85	0.1541
153	12	14.74	-2.738
154	15	16.12	-1.122
155	17	16.62	0.3846
156	14	15.83	-1.835
157	15	14.73	0.2714
158	18	16.3	1.704
159	15	14.73	0.2706
160	18	15.35	2.649
161	15	16.78	-1.78
162	15	15.42	-0.4218
163	16	15.48	0.5179
164	13	13.09	-0.08954
165	16	15.68	0.3213
166	14	15.25	-1.247
167	16	15.1	0.9017

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
14	0.03998	0.07996	0.96
15	0.3175	0.6349	0.6825
16	0.1943	0.3887	0.8057
17	0.253	0.5059	0.747
18	0.1855	0.3709	0.8145
19	0.1421	0.2842	0.8579
20	0.09381	0.1876	0.9062
21	0.2251	0.4502	0.7749
22	0.2149	0.4299	0.7851
23	0.2493	0.4987	0.7507
24	0.1851	0.3701	0.8149
25	0.1322	0.2644	0.8678
26	0.09591	0.1918	0.9041
27	0.06558	0.1312	0.9344
28	0.04527	0.09054	0.9547
29	0.0325	0.06499	0.9675
30	0.03703	0.07407	0.963
31	0.03181	0.06363	0.9682
32	0.02247	0.04495	0.9775
33	0.01932	0.03863	0.9807
34	0.07054	0.1411	0.9295
35	0.07407	0.1481	0.9259
36	0.05386	0.1077	0.9461
37	0.04049	0.08097	0.9595
38	0.03796	0.07592	0.962
39	0.08598	0.172	0.914
40	0.06661	0.1332	0.9334
41	0.04943	0.09886	0.9506
42	0.04853	0.09706	0.9515
43	0.03541	0.07082	0.9646
44	0.02795	0.05589	0.9721
45	0.02	0.03999	0.98
46	0.0143	0.02859	0.9857
47	0.01052	0.02104	0.9895
48	0.02026	0.04052	0.9797
49	0.01524	0.03047	0.9848
50	0.01138	0.02277	0.9886
51	0.01768	0.03535	0.9823
52	0.0243	0.04859	0.9757
53	0.07793	0.1559	0.9221
54	0.08436	0.1687	0.9156
55	0.09018	0.1804	0.9098
56	0.07348	0.147	0.9265
57	0.06187	0.1237	0.9381
58	0.04799	0.09598	0.952
59	0.05778	0.1156	0.9422
60	0.04517	0.09033	0.9548
61	0.03991	0.07983	0.9601
62	0.03258	0.06517	0.9674
63	0.03377	0.06753	0.9662
64	0.04094	0.08189	0.9591
65	0.04018	0.08036	0.9598
66	0.07653	0.1531	0.9235
67	0.07183	0.1437	0.9282
68	0.09302	0.186	0.907
69	0.07955	0.1591	0.9204
70	0.06545	0.1309	0.9346
71	0.06135	0.1227	0.9386
72	0.04841	0.09681	0.9516
73	0.1008	0.2015	0.8992
74	0.1019	0.2038	0.8981
75	0.0823	0.1646	0.9177
76	0.06867	0.1373	0.9313
77	0.06952	0.139	0.9305
78	0.06256	0.1251	0.9374
79	0.06455	0.1291	0.9355
80	0.05166	0.1033	0.9483
81	0.3284	0.6568	0.6716
82	0.2879	0.5758	0.7121
83	0.2705	0.5411	0.7295
84	0.2482	0.4963	0.7518
85	0.4691	0.9383	0.5309
86	0.5191	0.9617	0.4809
87	0.5022	0.9955	0.4978
88	0.4632	0.9264	0.5368
89	0.4642	0.9284	0.5358
90	0.4189	0.8379	0.5811
91	0.3767	0.7534	0.6233
92	0.3506	0.7011	0.6494
93	0.3236	0.6473	0.6764
94	0.3028	0.6056	0.6972
95	0.2729	0.5458	0.7271
96	0.2358	0.4716	0.7642
97	0.3636	0.7272	0.6364
98	0.361	0.7221	0.639
99	0.3192	0.6385	0.6808
100	0.2775	0.555	0.7225
101	0.2417	0.4834	0.7583
102	0.2434	0.4869	0.7566
103	0.3042	0.6084	0.6958
104	0.2745	0.549	0.7255
105	0.238	0.4759	0.762
106	0.2062	0.4123	0.7938
107	0.206	0.4121	0.794
108	0.1836	0.3671	0.8164
109	0.1566	0.3133	0.8434
110	0.1288	0.2576	0.8712
111	0.1184	0.2368	0.8816
112	0.09546	0.1909	0.9045
113	0.1748	0.3495	0.8252
114	0.6148	0.7704	0.3852
115	0.649	0.7019	0.351
116	0.6066	0.7868	0.3934
117	0.5961	0.8078	0.4039
118	0.5623	0.8755	0.4377
119	0.587	0.8259	0.413
120	0.5581	0.8838	0.4419
121	0.5425	0.915	0.4575
122	0.4936	0.9872	0.5064
123	0.4709	0.9418	0.5291
124	0.4838	0.9676	0.5162
125	0.5222	0.9556	0.4778
126	0.4671	0.9342	0.5329
127	0.4111	0.8223	0.5889
128	0.3795	0.7589	0.6205
129	0.3239	0.6479	0.6761
130	0.3052	0.6104	0.6948
131	0.2637	0.5275	0.7363
132	0.3127	0.6254	0.6873
133	0.2643	0.5287	0.7357
134	0.4365	0.873	0.5635
135	0.4526	0.9053	0.5474
136	0.399	0.7979	0.601
137	0.4182	0.8363	0.5818
138	0.4151	0.8302	0.5849
139	0.3544	0.7089	0.6456
140	0.3232	0.6463	0.6768
141	0.3093	0.6187	0.6907
142	0.2454	0.4909	0.7546
143	0.2559	0.5117	0.7441
144	0.2365	0.4731	0.7635
145	0.2057	0.4115	0.7943
146	0.4107	0.8215	0.5893
147	0.3614	0.7228	0.6386
148	0.2794	0.5589	0.7206
149	0.5672	0.8656	0.4328
150	0.6917	0.6166	0.3083
151	0.7279	0.5441	0.2721
152	0.7779	0.4442	0.2221
153	0.6484	0.7032	0.3516

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
14 &  0.03998 &  0.07996 &  0.96 \tabularnewline
15 &  0.3175 &  0.6349 &  0.6825 \tabularnewline
16 &  0.1943 &  0.3887 &  0.8057 \tabularnewline
17 &  0.253 &  0.5059 &  0.747 \tabularnewline
18 &  0.1855 &  0.3709 &  0.8145 \tabularnewline
19 &  0.1421 &  0.2842 &  0.8579 \tabularnewline
20 &  0.09381 &  0.1876 &  0.9062 \tabularnewline
21 &  0.2251 &  0.4502 &  0.7749 \tabularnewline
22 &  0.2149 &  0.4299 &  0.7851 \tabularnewline
23 &  0.2493 &  0.4987 &  0.7507 \tabularnewline
24 &  0.1851 &  0.3701 &  0.8149 \tabularnewline
25 &  0.1322 &  0.2644 &  0.8678 \tabularnewline
26 &  0.09591 &  0.1918 &  0.9041 \tabularnewline
27 &  0.06558 &  0.1312 &  0.9344 \tabularnewline
28 &  0.04527 &  0.09054 &  0.9547 \tabularnewline
29 &  0.0325 &  0.06499 &  0.9675 \tabularnewline
30 &  0.03703 &  0.07407 &  0.963 \tabularnewline
31 &  0.03181 &  0.06363 &  0.9682 \tabularnewline
32 &  0.02247 &  0.04495 &  0.9775 \tabularnewline
33 &  0.01932 &  0.03863 &  0.9807 \tabularnewline
34 &  0.07054 &  0.1411 &  0.9295 \tabularnewline
35 &  0.07407 &  0.1481 &  0.9259 \tabularnewline
36 &  0.05386 &  0.1077 &  0.9461 \tabularnewline
37 &  0.04049 &  0.08097 &  0.9595 \tabularnewline
38 &  0.03796 &  0.07592 &  0.962 \tabularnewline
39 &  0.08598 &  0.172 &  0.914 \tabularnewline
40 &  0.06661 &  0.1332 &  0.9334 \tabularnewline
41 &  0.04943 &  0.09886 &  0.9506 \tabularnewline
42 &  0.04853 &  0.09706 &  0.9515 \tabularnewline
43 &  0.03541 &  0.07082 &  0.9646 \tabularnewline
44 &  0.02795 &  0.05589 &  0.9721 \tabularnewline
45 &  0.02 &  0.03999 &  0.98 \tabularnewline
46 &  0.0143 &  0.02859 &  0.9857 \tabularnewline
47 &  0.01052 &  0.02104 &  0.9895 \tabularnewline
48 &  0.02026 &  0.04052 &  0.9797 \tabularnewline
49 &  0.01524 &  0.03047 &  0.9848 \tabularnewline
50 &  0.01138 &  0.02277 &  0.9886 \tabularnewline
51 &  0.01768 &  0.03535 &  0.9823 \tabularnewline
52 &  0.0243 &  0.04859 &  0.9757 \tabularnewline
53 &  0.07793 &  0.1559 &  0.9221 \tabularnewline
54 &  0.08436 &  0.1687 &  0.9156 \tabularnewline
55 &  0.09018 &  0.1804 &  0.9098 \tabularnewline
56 &  0.07348 &  0.147 &  0.9265 \tabularnewline
57 &  0.06187 &  0.1237 &  0.9381 \tabularnewline
58 &  0.04799 &  0.09598 &  0.952 \tabularnewline
59 &  0.05778 &  0.1156 &  0.9422 \tabularnewline
60 &  0.04517 &  0.09033 &  0.9548 \tabularnewline
61 &  0.03991 &  0.07983 &  0.9601 \tabularnewline
62 &  0.03258 &  0.06517 &  0.9674 \tabularnewline
63 &  0.03377 &  0.06753 &  0.9662 \tabularnewline
64 &  0.04094 &  0.08189 &  0.9591 \tabularnewline
65 &  0.04018 &  0.08036 &  0.9598 \tabularnewline
66 &  0.07653 &  0.1531 &  0.9235 \tabularnewline
67 &  0.07183 &  0.1437 &  0.9282 \tabularnewline
68 &  0.09302 &  0.186 &  0.907 \tabularnewline
69 &  0.07955 &  0.1591 &  0.9204 \tabularnewline
70 &  0.06545 &  0.1309 &  0.9346 \tabularnewline
71 &  0.06135 &  0.1227 &  0.9386 \tabularnewline
72 &  0.04841 &  0.09681 &  0.9516 \tabularnewline
73 &  0.1008 &  0.2015 &  0.8992 \tabularnewline
74 &  0.1019 &  0.2038 &  0.8981 \tabularnewline
75 &  0.0823 &  0.1646 &  0.9177 \tabularnewline
76 &  0.06867 &  0.1373 &  0.9313 \tabularnewline
77 &  0.06952 &  0.139 &  0.9305 \tabularnewline
78 &  0.06256 &  0.1251 &  0.9374 \tabularnewline
79 &  0.06455 &  0.1291 &  0.9355 \tabularnewline
80 &  0.05166 &  0.1033 &  0.9483 \tabularnewline
81 &  0.3284 &  0.6568 &  0.6716 \tabularnewline
82 &  0.2879 &  0.5758 &  0.7121 \tabularnewline
83 &  0.2705 &  0.5411 &  0.7295 \tabularnewline
84 &  0.2482 &  0.4963 &  0.7518 \tabularnewline
85 &  0.4691 &  0.9383 &  0.5309 \tabularnewline
86 &  0.5191 &  0.9617 &  0.4809 \tabularnewline
87 &  0.5022 &  0.9955 &  0.4978 \tabularnewline
88 &  0.4632 &  0.9264 &  0.5368 \tabularnewline
89 &  0.4642 &  0.9284 &  0.5358 \tabularnewline
90 &  0.4189 &  0.8379 &  0.5811 \tabularnewline
91 &  0.3767 &  0.7534 &  0.6233 \tabularnewline
92 &  0.3506 &  0.7011 &  0.6494 \tabularnewline
93 &  0.3236 &  0.6473 &  0.6764 \tabularnewline
94 &  0.3028 &  0.6056 &  0.6972 \tabularnewline
95 &  0.2729 &  0.5458 &  0.7271 \tabularnewline
96 &  0.2358 &  0.4716 &  0.7642 \tabularnewline
97 &  0.3636 &  0.7272 &  0.6364 \tabularnewline
98 &  0.361 &  0.7221 &  0.639 \tabularnewline
99 &  0.3192 &  0.6385 &  0.6808 \tabularnewline
100 &  0.2775 &  0.555 &  0.7225 \tabularnewline
101 &  0.2417 &  0.4834 &  0.7583 \tabularnewline
102 &  0.2434 &  0.4869 &  0.7566 \tabularnewline
103 &  0.3042 &  0.6084 &  0.6958 \tabularnewline
104 &  0.2745 &  0.549 &  0.7255 \tabularnewline
105 &  0.238 &  0.4759 &  0.762 \tabularnewline
106 &  0.2062 &  0.4123 &  0.7938 \tabularnewline
107 &  0.206 &  0.4121 &  0.794 \tabularnewline
108 &  0.1836 &  0.3671 &  0.8164 \tabularnewline
109 &  0.1566 &  0.3133 &  0.8434 \tabularnewline
110 &  0.1288 &  0.2576 &  0.8712 \tabularnewline
111 &  0.1184 &  0.2368 &  0.8816 \tabularnewline
112 &  0.09546 &  0.1909 &  0.9045 \tabularnewline
113 &  0.1748 &  0.3495 &  0.8252 \tabularnewline
114 &  0.6148 &  0.7704 &  0.3852 \tabularnewline
115 &  0.649 &  0.7019 &  0.351 \tabularnewline
116 &  0.6066 &  0.7868 &  0.3934 \tabularnewline
117 &  0.5961 &  0.8078 &  0.4039 \tabularnewline
118 &  0.5623 &  0.8755 &  0.4377 \tabularnewline
119 &  0.587 &  0.8259 &  0.413 \tabularnewline
120 &  0.5581 &  0.8838 &  0.4419 \tabularnewline
121 &  0.5425 &  0.915 &  0.4575 \tabularnewline
122 &  0.4936 &  0.9872 &  0.5064 \tabularnewline
123 &  0.4709 &  0.9418 &  0.5291 \tabularnewline
124 &  0.4838 &  0.9676 &  0.5162 \tabularnewline
125 &  0.5222 &  0.9556 &  0.4778 \tabularnewline
126 &  0.4671 &  0.9342 &  0.5329 \tabularnewline
127 &  0.4111 &  0.8223 &  0.5889 \tabularnewline
128 &  0.3795 &  0.7589 &  0.6205 \tabularnewline
129 &  0.3239 &  0.6479 &  0.6761 \tabularnewline
130 &  0.3052 &  0.6104 &  0.6948 \tabularnewline
131 &  0.2637 &  0.5275 &  0.7363 \tabularnewline
132 &  0.3127 &  0.6254 &  0.6873 \tabularnewline
133 &  0.2643 &  0.5287 &  0.7357 \tabularnewline
134 &  0.4365 &  0.873 &  0.5635 \tabularnewline
135 &  0.4526 &  0.9053 &  0.5474 \tabularnewline
136 &  0.399 &  0.7979 &  0.601 \tabularnewline
137 &  0.4182 &  0.8363 &  0.5818 \tabularnewline
138 &  0.4151 &  0.8302 &  0.5849 \tabularnewline
139 &  0.3544 &  0.7089 &  0.6456 \tabularnewline
140 &  0.3232 &  0.6463 &  0.6768 \tabularnewline
141 &  0.3093 &  0.6187 &  0.6907 \tabularnewline
142 &  0.2454 &  0.4909 &  0.7546 \tabularnewline
143 &  0.2559 &  0.5117 &  0.7441 \tabularnewline
144 &  0.2365 &  0.4731 &  0.7635 \tabularnewline
145 &  0.2057 &  0.4115 &  0.7943 \tabularnewline
146 &  0.4107 &  0.8215 &  0.5893 \tabularnewline
147 &  0.3614 &  0.7228 &  0.6386 \tabularnewline
148 &  0.2794 &  0.5589 &  0.7206 \tabularnewline
149 &  0.5672 &  0.8656 &  0.4328 \tabularnewline
150 &  0.6917 &  0.6166 &  0.3083 \tabularnewline
151 &  0.7279 &  0.5441 &  0.2721 \tabularnewline
152 &  0.7779 &  0.4442 &  0.2221 \tabularnewline
153 &  0.6484 &  0.7032 &  0.3516 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=302786&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]14[/C][C] 0.03998[/C][C] 0.07996[/C][C] 0.96[/C][/ROW]
[ROW][C]15[/C][C] 0.3175[/C][C] 0.6349[/C][C] 0.6825[/C][/ROW]
[ROW][C]16[/C][C] 0.1943[/C][C] 0.3887[/C][C] 0.8057[/C][/ROW]
[ROW][C]17[/C][C] 0.253[/C][C] 0.5059[/C][C] 0.747[/C][/ROW]
[ROW][C]18[/C][C] 0.1855[/C][C] 0.3709[/C][C] 0.8145[/C][/ROW]
[ROW][C]19[/C][C] 0.1421[/C][C] 0.2842[/C][C] 0.8579[/C][/ROW]
[ROW][C]20[/C][C] 0.09381[/C][C] 0.1876[/C][C] 0.9062[/C][/ROW]
[ROW][C]21[/C][C] 0.2251[/C][C] 0.4502[/C][C] 0.7749[/C][/ROW]
[ROW][C]22[/C][C] 0.2149[/C][C] 0.4299[/C][C] 0.7851[/C][/ROW]
[ROW][C]23[/C][C] 0.2493[/C][C] 0.4987[/C][C] 0.7507[/C][/ROW]
[ROW][C]24[/C][C] 0.1851[/C][C] 0.3701[/C][C] 0.8149[/C][/ROW]
[ROW][C]25[/C][C] 0.1322[/C][C] 0.2644[/C][C] 0.8678[/C][/ROW]
[ROW][C]26[/C][C] 0.09591[/C][C] 0.1918[/C][C] 0.9041[/C][/ROW]
[ROW][C]27[/C][C] 0.06558[/C][C] 0.1312[/C][C] 0.9344[/C][/ROW]
[ROW][C]28[/C][C] 0.04527[/C][C] 0.09054[/C][C] 0.9547[/C][/ROW]
[ROW][C]29[/C][C] 0.0325[/C][C] 0.06499[/C][C] 0.9675[/C][/ROW]
[ROW][C]30[/C][C] 0.03703[/C][C] 0.07407[/C][C] 0.963[/C][/ROW]
[ROW][C]31[/C][C] 0.03181[/C][C] 0.06363[/C][C] 0.9682[/C][/ROW]
[ROW][C]32[/C][C] 0.02247[/C][C] 0.04495[/C][C] 0.9775[/C][/ROW]
[ROW][C]33[/C][C] 0.01932[/C][C] 0.03863[/C][C] 0.9807[/C][/ROW]
[ROW][C]34[/C][C] 0.07054[/C][C] 0.1411[/C][C] 0.9295[/C][/ROW]
[ROW][C]35[/C][C] 0.07407[/C][C] 0.1481[/C][C] 0.9259[/C][/ROW]
[ROW][C]36[/C][C] 0.05386[/C][C] 0.1077[/C][C] 0.9461[/C][/ROW]
[ROW][C]37[/C][C] 0.04049[/C][C] 0.08097[/C][C] 0.9595[/C][/ROW]
[ROW][C]38[/C][C] 0.03796[/C][C] 0.07592[/C][C] 0.962[/C][/ROW]
[ROW][C]39[/C][C] 0.08598[/C][C] 0.172[/C][C] 0.914[/C][/ROW]
[ROW][C]40[/C][C] 0.06661[/C][C] 0.1332[/C][C] 0.9334[/C][/ROW]
[ROW][C]41[/C][C] 0.04943[/C][C] 0.09886[/C][C] 0.9506[/C][/ROW]
[ROW][C]42[/C][C] 0.04853[/C][C] 0.09706[/C][C] 0.9515[/C][/ROW]
[ROW][C]43[/C][C] 0.03541[/C][C] 0.07082[/C][C] 0.9646[/C][/ROW]
[ROW][C]44[/C][C] 0.02795[/C][C] 0.05589[/C][C] 0.9721[/C][/ROW]
[ROW][C]45[/C][C] 0.02[/C][C] 0.03999[/C][C] 0.98[/C][/ROW]
[ROW][C]46[/C][C] 0.0143[/C][C] 0.02859[/C][C] 0.9857[/C][/ROW]
[ROW][C]47[/C][C] 0.01052[/C][C] 0.02104[/C][C] 0.9895[/C][/ROW]
[ROW][C]48[/C][C] 0.02026[/C][C] 0.04052[/C][C] 0.9797[/C][/ROW]
[ROW][C]49[/C][C] 0.01524[/C][C] 0.03047[/C][C] 0.9848[/C][/ROW]
[ROW][C]50[/C][C] 0.01138[/C][C] 0.02277[/C][C] 0.9886[/C][/ROW]
[ROW][C]51[/C][C] 0.01768[/C][C] 0.03535[/C][C] 0.9823[/C][/ROW]
[ROW][C]52[/C][C] 0.0243[/C][C] 0.04859[/C][C] 0.9757[/C][/ROW]
[ROW][C]53[/C][C] 0.07793[/C][C] 0.1559[/C][C] 0.9221[/C][/ROW]
[ROW][C]54[/C][C] 0.08436[/C][C] 0.1687[/C][C] 0.9156[/C][/ROW]
[ROW][C]55[/C][C] 0.09018[/C][C] 0.1804[/C][C] 0.9098[/C][/ROW]
[ROW][C]56[/C][C] 0.07348[/C][C] 0.147[/C][C] 0.9265[/C][/ROW]
[ROW][C]57[/C][C] 0.06187[/C][C] 0.1237[/C][C] 0.9381[/C][/ROW]
[ROW][C]58[/C][C] 0.04799[/C][C] 0.09598[/C][C] 0.952[/C][/ROW]
[ROW][C]59[/C][C] 0.05778[/C][C] 0.1156[/C][C] 0.9422[/C][/ROW]
[ROW][C]60[/C][C] 0.04517[/C][C] 0.09033[/C][C] 0.9548[/C][/ROW]
[ROW][C]61[/C][C] 0.03991[/C][C] 0.07983[/C][C] 0.9601[/C][/ROW]
[ROW][C]62[/C][C] 0.03258[/C][C] 0.06517[/C][C] 0.9674[/C][/ROW]
[ROW][C]63[/C][C] 0.03377[/C][C] 0.06753[/C][C] 0.9662[/C][/ROW]
[ROW][C]64[/C][C] 0.04094[/C][C] 0.08189[/C][C] 0.9591[/C][/ROW]
[ROW][C]65[/C][C] 0.04018[/C][C] 0.08036[/C][C] 0.9598[/C][/ROW]
[ROW][C]66[/C][C] 0.07653[/C][C] 0.1531[/C][C] 0.9235[/C][/ROW]
[ROW][C]67[/C][C] 0.07183[/C][C] 0.1437[/C][C] 0.9282[/C][/ROW]
[ROW][C]68[/C][C] 0.09302[/C][C] 0.186[/C][C] 0.907[/C][/ROW]
[ROW][C]69[/C][C] 0.07955[/C][C] 0.1591[/C][C] 0.9204[/C][/ROW]
[ROW][C]70[/C][C] 0.06545[/C][C] 0.1309[/C][C] 0.9346[/C][/ROW]
[ROW][C]71[/C][C] 0.06135[/C][C] 0.1227[/C][C] 0.9386[/C][/ROW]
[ROW][C]72[/C][C] 0.04841[/C][C] 0.09681[/C][C] 0.9516[/C][/ROW]
[ROW][C]73[/C][C] 0.1008[/C][C] 0.2015[/C][C] 0.8992[/C][/ROW]
[ROW][C]74[/C][C] 0.1019[/C][C] 0.2038[/C][C] 0.8981[/C][/ROW]
[ROW][C]75[/C][C] 0.0823[/C][C] 0.1646[/C][C] 0.9177[/C][/ROW]
[ROW][C]76[/C][C] 0.06867[/C][C] 0.1373[/C][C] 0.9313[/C][/ROW]
[ROW][C]77[/C][C] 0.06952[/C][C] 0.139[/C][C] 0.9305[/C][/ROW]
[ROW][C]78[/C][C] 0.06256[/C][C] 0.1251[/C][C] 0.9374[/C][/ROW]
[ROW][C]79[/C][C] 0.06455[/C][C] 0.1291[/C][C] 0.9355[/C][/ROW]
[ROW][C]80[/C][C] 0.05166[/C][C] 0.1033[/C][C] 0.9483[/C][/ROW]
[ROW][C]81[/C][C] 0.3284[/C][C] 0.6568[/C][C] 0.6716[/C][/ROW]
[ROW][C]82[/C][C] 0.2879[/C][C] 0.5758[/C][C] 0.7121[/C][/ROW]
[ROW][C]83[/C][C] 0.2705[/C][C] 0.5411[/C][C] 0.7295[/C][/ROW]
[ROW][C]84[/C][C] 0.2482[/C][C] 0.4963[/C][C] 0.7518[/C][/ROW]
[ROW][C]85[/C][C] 0.4691[/C][C] 0.9383[/C][C] 0.5309[/C][/ROW]
[ROW][C]86[/C][C] 0.5191[/C][C] 0.9617[/C][C] 0.4809[/C][/ROW]
[ROW][C]87[/C][C] 0.5022[/C][C] 0.9955[/C][C] 0.4978[/C][/ROW]
[ROW][C]88[/C][C] 0.4632[/C][C] 0.9264[/C][C] 0.5368[/C][/ROW]
[ROW][C]89[/C][C] 0.4642[/C][C] 0.9284[/C][C] 0.5358[/C][/ROW]
[ROW][C]90[/C][C] 0.4189[/C][C] 0.8379[/C][C] 0.5811[/C][/ROW]
[ROW][C]91[/C][C] 0.3767[/C][C] 0.7534[/C][C] 0.6233[/C][/ROW]
[ROW][C]92[/C][C] 0.3506[/C][C] 0.7011[/C][C] 0.6494[/C][/ROW]
[ROW][C]93[/C][C] 0.3236[/C][C] 0.6473[/C][C] 0.6764[/C][/ROW]
[ROW][C]94[/C][C] 0.3028[/C][C] 0.6056[/C][C] 0.6972[/C][/ROW]
[ROW][C]95[/C][C] 0.2729[/C][C] 0.5458[/C][C] 0.7271[/C][/ROW]
[ROW][C]96[/C][C] 0.2358[/C][C] 0.4716[/C][C] 0.7642[/C][/ROW]
[ROW][C]97[/C][C] 0.3636[/C][C] 0.7272[/C][C] 0.6364[/C][/ROW]
[ROW][C]98[/C][C] 0.361[/C][C] 0.7221[/C][C] 0.639[/C][/ROW]
[ROW][C]99[/C][C] 0.3192[/C][C] 0.6385[/C][C] 0.6808[/C][/ROW]
[ROW][C]100[/C][C] 0.2775[/C][C] 0.555[/C][C] 0.7225[/C][/ROW]
[ROW][C]101[/C][C] 0.2417[/C][C] 0.4834[/C][C] 0.7583[/C][/ROW]
[ROW][C]102[/C][C] 0.2434[/C][C] 0.4869[/C][C] 0.7566[/C][/ROW]
[ROW][C]103[/C][C] 0.3042[/C][C] 0.6084[/C][C] 0.6958[/C][/ROW]
[ROW][C]104[/C][C] 0.2745[/C][C] 0.549[/C][C] 0.7255[/C][/ROW]
[ROW][C]105[/C][C] 0.238[/C][C] 0.4759[/C][C] 0.762[/C][/ROW]
[ROW][C]106[/C][C] 0.2062[/C][C] 0.4123[/C][C] 0.7938[/C][/ROW]
[ROW][C]107[/C][C] 0.206[/C][C] 0.4121[/C][C] 0.794[/C][/ROW]
[ROW][C]108[/C][C] 0.1836[/C][C] 0.3671[/C][C] 0.8164[/C][/ROW]
[ROW][C]109[/C][C] 0.1566[/C][C] 0.3133[/C][C] 0.8434[/C][/ROW]
[ROW][C]110[/C][C] 0.1288[/C][C] 0.2576[/C][C] 0.8712[/C][/ROW]
[ROW][C]111[/C][C] 0.1184[/C][C] 0.2368[/C][C] 0.8816[/C][/ROW]
[ROW][C]112[/C][C] 0.09546[/C][C] 0.1909[/C][C] 0.9045[/C][/ROW]
[ROW][C]113[/C][C] 0.1748[/C][C] 0.3495[/C][C] 0.8252[/C][/ROW]
[ROW][C]114[/C][C] 0.6148[/C][C] 0.7704[/C][C] 0.3852[/C][/ROW]
[ROW][C]115[/C][C] 0.649[/C][C] 0.7019[/C][C] 0.351[/C][/ROW]
[ROW][C]116[/C][C] 0.6066[/C][C] 0.7868[/C][C] 0.3934[/C][/ROW]
[ROW][C]117[/C][C] 0.5961[/C][C] 0.8078[/C][C] 0.4039[/C][/ROW]
[ROW][C]118[/C][C] 0.5623[/C][C] 0.8755[/C][C] 0.4377[/C][/ROW]
[ROW][C]119[/C][C] 0.587[/C][C] 0.8259[/C][C] 0.413[/C][/ROW]
[ROW][C]120[/C][C] 0.5581[/C][C] 0.8838[/C][C] 0.4419[/C][/ROW]
[ROW][C]121[/C][C] 0.5425[/C][C] 0.915[/C][C] 0.4575[/C][/ROW]
[ROW][C]122[/C][C] 0.4936[/C][C] 0.9872[/C][C] 0.5064[/C][/ROW]
[ROW][C]123[/C][C] 0.4709[/C][C] 0.9418[/C][C] 0.5291[/C][/ROW]
[ROW][C]124[/C][C] 0.4838[/C][C] 0.9676[/C][C] 0.5162[/C][/ROW]
[ROW][C]125[/C][C] 0.5222[/C][C] 0.9556[/C][C] 0.4778[/C][/ROW]
[ROW][C]126[/C][C] 0.4671[/C][C] 0.9342[/C][C] 0.5329[/C][/ROW]
[ROW][C]127[/C][C] 0.4111[/C][C] 0.8223[/C][C] 0.5889[/C][/ROW]
[ROW][C]128[/C][C] 0.3795[/C][C] 0.7589[/C][C] 0.6205[/C][/ROW]
[ROW][C]129[/C][C] 0.3239[/C][C] 0.6479[/C][C] 0.6761[/C][/ROW]
[ROW][C]130[/C][C] 0.3052[/C][C] 0.6104[/C][C] 0.6948[/C][/ROW]
[ROW][C]131[/C][C] 0.2637[/C][C] 0.5275[/C][C] 0.7363[/C][/ROW]
[ROW][C]132[/C][C] 0.3127[/C][C] 0.6254[/C][C] 0.6873[/C][/ROW]
[ROW][C]133[/C][C] 0.2643[/C][C] 0.5287[/C][C] 0.7357[/C][/ROW]
[ROW][C]134[/C][C] 0.4365[/C][C] 0.873[/C][C] 0.5635[/C][/ROW]
[ROW][C]135[/C][C] 0.4526[/C][C] 0.9053[/C][C] 0.5474[/C][/ROW]
[ROW][C]136[/C][C] 0.399[/C][C] 0.7979[/C][C] 0.601[/C][/ROW]
[ROW][C]137[/C][C] 0.4182[/C][C] 0.8363[/C][C] 0.5818[/C][/ROW]
[ROW][C]138[/C][C] 0.4151[/C][C] 0.8302[/C][C] 0.5849[/C][/ROW]
[ROW][C]139[/C][C] 0.3544[/C][C] 0.7089[/C][C] 0.6456[/C][/ROW]
[ROW][C]140[/C][C] 0.3232[/C][C] 0.6463[/C][C] 0.6768[/C][/ROW]
[ROW][C]141[/C][C] 0.3093[/C][C] 0.6187[/C][C] 0.6907[/C][/ROW]
[ROW][C]142[/C][C] 0.2454[/C][C] 0.4909[/C][C] 0.7546[/C][/ROW]
[ROW][C]143[/C][C] 0.2559[/C][C] 0.5117[/C][C] 0.7441[/C][/ROW]
[ROW][C]144[/C][C] 0.2365[/C][C] 0.4731[/C][C] 0.7635[/C][/ROW]
[ROW][C]145[/C][C] 0.2057[/C][C] 0.4115[/C][C] 0.7943[/C][/ROW]
[ROW][C]146[/C][C] 0.4107[/C][C] 0.8215[/C][C] 0.5893[/C][/ROW]
[ROW][C]147[/C][C] 0.3614[/C][C] 0.7228[/C][C] 0.6386[/C][/ROW]
[ROW][C]148[/C][C] 0.2794[/C][C] 0.5589[/C][C] 0.7206[/C][/ROW]
[ROW][C]149[/C][C] 0.5672[/C][C] 0.8656[/C][C] 0.4328[/C][/ROW]
[ROW][C]150[/C][C] 0.6917[/C][C] 0.6166[/C][C] 0.3083[/C][/ROW]
[ROW][C]151[/C][C] 0.7279[/C][C] 0.5441[/C][C] 0.2721[/C][/ROW]
[ROW][C]152[/C][C] 0.7779[/C][C] 0.4442[/C][C] 0.2221[/C][/ROW]
[ROW][C]153[/C][C] 0.6484[/C][C] 0.7032[/C][C] 0.3516[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=302786&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=302786&T=5

As an alternative you can also use a QR Code:

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

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
14	0.03998	0.07996	0.96
15	0.3175	0.6349	0.6825
16	0.1943	0.3887	0.8057
17	0.253	0.5059	0.747
18	0.1855	0.3709	0.8145
19	0.1421	0.2842	0.8579
20	0.09381	0.1876	0.9062
21	0.2251	0.4502	0.7749
22	0.2149	0.4299	0.7851
23	0.2493	0.4987	0.7507
24	0.1851	0.3701	0.8149
25	0.1322	0.2644	0.8678
26	0.09591	0.1918	0.9041
27	0.06558	0.1312	0.9344
28	0.04527	0.09054	0.9547
29	0.0325	0.06499	0.9675
30	0.03703	0.07407	0.963
31	0.03181	0.06363	0.9682
32	0.02247	0.04495	0.9775
33	0.01932	0.03863	0.9807
34	0.07054	0.1411	0.9295
35	0.07407	0.1481	0.9259
36	0.05386	0.1077	0.9461
37	0.04049	0.08097	0.9595
38	0.03796	0.07592	0.962
39	0.08598	0.172	0.914
40	0.06661	0.1332	0.9334
41	0.04943	0.09886	0.9506
42	0.04853	0.09706	0.9515
43	0.03541	0.07082	0.9646
44	0.02795	0.05589	0.9721
45	0.02	0.03999	0.98
46	0.0143	0.02859	0.9857
47	0.01052	0.02104	0.9895
48	0.02026	0.04052	0.9797
49	0.01524	0.03047	0.9848
50	0.01138	0.02277	0.9886
51	0.01768	0.03535	0.9823
52	0.0243	0.04859	0.9757
53	0.07793	0.1559	0.9221
54	0.08436	0.1687	0.9156
55	0.09018	0.1804	0.9098
56	0.07348	0.147	0.9265
57	0.06187	0.1237	0.9381
58	0.04799	0.09598	0.952
59	0.05778	0.1156	0.9422
60	0.04517	0.09033	0.9548
61	0.03991	0.07983	0.9601
62	0.03258	0.06517	0.9674
63	0.03377	0.06753	0.9662
64	0.04094	0.08189	0.9591
65	0.04018	0.08036	0.9598
66	0.07653	0.1531	0.9235
67	0.07183	0.1437	0.9282
68	0.09302	0.186	0.907
69	0.07955	0.1591	0.9204
70	0.06545	0.1309	0.9346
71	0.06135	0.1227	0.9386
72	0.04841	0.09681	0.9516
73	0.1008	0.2015	0.8992
74	0.1019	0.2038	0.8981
75	0.0823	0.1646	0.9177
76	0.06867	0.1373	0.9313
77	0.06952	0.139	0.9305
78	0.06256	0.1251	0.9374
79	0.06455	0.1291	0.9355
80	0.05166	0.1033	0.9483
81	0.3284	0.6568	0.6716
82	0.2879	0.5758	0.7121
83	0.2705	0.5411	0.7295
84	0.2482	0.4963	0.7518
85	0.4691	0.9383	0.5309
86	0.5191	0.9617	0.4809
87	0.5022	0.9955	0.4978
88	0.4632	0.9264	0.5368
89	0.4642	0.9284	0.5358
90	0.4189	0.8379	0.5811
91	0.3767	0.7534	0.6233
92	0.3506	0.7011	0.6494
93	0.3236	0.6473	0.6764
94	0.3028	0.6056	0.6972
95	0.2729	0.5458	0.7271
96	0.2358	0.4716	0.7642
97	0.3636	0.7272	0.6364
98	0.361	0.7221	0.639
99	0.3192	0.6385	0.6808
100	0.2775	0.555	0.7225
101	0.2417	0.4834	0.7583
102	0.2434	0.4869	0.7566
103	0.3042	0.6084	0.6958
104	0.2745	0.549	0.7255
105	0.238	0.4759	0.762
106	0.2062	0.4123	0.7938
107	0.206	0.4121	0.794
108	0.1836	0.3671	0.8164
109	0.1566	0.3133	0.8434
110	0.1288	0.2576	0.8712
111	0.1184	0.2368	0.8816
112	0.09546	0.1909	0.9045
113	0.1748	0.3495	0.8252
114	0.6148	0.7704	0.3852
115	0.649	0.7019	0.351
116	0.6066	0.7868	0.3934
117	0.5961	0.8078	0.4039
118	0.5623	0.8755	0.4377
119	0.587	0.8259	0.413
120	0.5581	0.8838	0.4419
121	0.5425	0.915	0.4575
122	0.4936	0.9872	0.5064
123	0.4709	0.9418	0.5291
124	0.4838	0.9676	0.5162
125	0.5222	0.9556	0.4778
126	0.4671	0.9342	0.5329
127	0.4111	0.8223	0.5889
128	0.3795	0.7589	0.6205
129	0.3239	0.6479	0.6761
130	0.3052	0.6104	0.6948
131	0.2637	0.5275	0.7363
132	0.3127	0.6254	0.6873
133	0.2643	0.5287	0.7357
134	0.4365	0.873	0.5635
135	0.4526	0.9053	0.5474
136	0.399	0.7979	0.601
137	0.4182	0.8363	0.5818
138	0.4151	0.8302	0.5849
139	0.3544	0.7089	0.6456
140	0.3232	0.6463	0.6768
141	0.3093	0.6187	0.6907
142	0.2454	0.4909	0.7546
143	0.2559	0.5117	0.7441
144	0.2365	0.4731	0.7635
145	0.2057	0.4115	0.7943
146	0.4107	0.8215	0.5893
147	0.3614	0.7228	0.6386
148	0.2794	0.5589	0.7206
149	0.5672	0.8656	0.4328
150	0.6917	0.6166	0.3083
151	0.7279	0.5441	0.2721
152	0.7779	0.4442	0.2221
153	0.6484	0.7032	0.3516

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	10	0.0714286	NOK
10% type I error level	29	0.207143	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 & 0 &  0 & OK \tabularnewline
5% type I error level & 10 & 0.0714286 & NOK \tabularnewline
10% type I error level & 29 & 0.207143 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=302786&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C] 0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]10[/C][C]0.0714286[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]29[/C][C]0.207143[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=302786&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=302786&T=6

As an alternative you can also use a QR Code:

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	0	0	OK
5% type I error level	10	0.0714286	NOK
10% type I error level	29	0.207143	NOK

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted RESET test data: mylm RESET = 0.92274, df1 = 2, df2 = 154, p-value = 0.3996
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 0.61326, df1 = 20, df2 = 136, p-value = 0.8976
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 1.6348, df1 = 2, df2 = 154, p-value = 0.1984

\begin{tabular}{lllllllll}
\hline
Ramsey RESET F-Test for powers (2 and 3) of fitted values \tabularnewline
> reset_test_fitted
	RESET test
data:  mylm
RESET = 0.92274, df1 = 2, df2 = 154, p-value = 0.3996
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of regressors \tabularnewline
> reset_test_regressors
	RESET test
data:  mylm
RESET = 0.61326, df1 = 20, df2 = 136, p-value = 0.8976
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of principal components \tabularnewline
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 1.6348, df1 = 2, df2 = 154, p-value = 0.1984
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=302786&T=7

[TABLE]
[ROW][C]Ramsey RESET F-Test for powers (2 and 3) of fitted values[/C][/ROW]
[ROW][C]> reset_test_fitted
	RESET test
data:  mylm
RESET = 0.92274, df1 = 2, df2 = 154, p-value = 0.3996
[/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 = 0.61326, df1 = 20, df2 = 136, p-value = 0.8976
[/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 = 1.6348, df1 = 2, df2 = 154, p-value = 0.1984
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=302786&T=7

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=302786&T=7

As an alternative you can also use a QR Code:

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

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted RESET test data: mylm RESET = 0.92274, df1 = 2, df2 = 154, p-value = 0.3996
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 0.61326, df1 = 20, df2 = 136, p-value = 0.8976
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 1.6348, df1 = 2, df2 = 154, p-value = 0.1984

Variance Inflation Factors (Multicollinearity)
> vif b c d e f g h i 1.094153 1.125516 1.086792 1.057595 1.225304 1.111554 1.112658 1.065332 j k 1.178890 1.168493

\begin{tabular}{lllllllll}
\hline
Variance Inflation Factors (Multicollinearity) \tabularnewline
> vif
       b        c        d        e        f        g        h        i 
1.094153 1.125516 1.086792 1.057595 1.225304 1.111554 1.112658 1.065332 
       j        k 
1.178890 1.168493 
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=302786&T=8

[TABLE]
[ROW][C]Variance Inflation Factors (Multicollinearity)[/C][/ROW]
[ROW][C]> vif
       b        c        d        e        f        g        h        i 
1.094153 1.125516 1.086792 1.057595 1.225304 1.111554 1.112658 1.065332 
       j        k 
1.178890 1.168493 
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=302786&T=8

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=302786&T=8

As an alternative you can also use a QR Code:

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

Variance Inflation Factors (Multicollinearity)
> vif b c d e f g h i 1.094153 1.125516 1.086792 1.057595 1.225304 1.111554 1.112658 1.065332 j k 1.178890 1.168493

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 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ; par4 = 0 ; par5 = 0 ;

Parameters (R input):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ; par4 = 0 ; par5 = 0 ;

R code (references can be found in the software module):

library(lattice)
library(lmtest)
library(car)
library(MASS)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
mywarning <- ''
par1 <- as.numeric(par1)
if(is.na(par1)) {
par1 <- 1
mywarning = 'Warning: you did not specify the column number of the endogenous series! The first column was selected by default.'
}
if (par4=='') par4 <- 0
par4 <- as.numeric(par4)
if (par5=='') par5 <- 0
par5 <- as.numeric(par5)
x <- na.omit(t(y))
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
(n <- n -1)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par3 == 'Seasonal Differences (s=12)'){
(n <- n - 12)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B12)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+12,j] - x[i,j]
}
}
x <- x2
}
if (par3 == 'First and Seasonal Differences (s=12)'){
(n <- n -1)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
(n <- n - 12)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B12)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+12,j] - x[i,j]
}
}
x <- x2
}
if(par4 > 0) {
x2 <- array(0, dim=c(n-par4,par4), dimnames=list(1:(n-par4), paste(colnames(x)[par1],'(t-',1:par4,')',sep='')))
for (i in 1:(n-par4)) {
for (j in 1:par4) {
x2[i,j] <- x[i+par4-j,par1]
}
}
x <- cbind(x[(par4+1):n,], x2)
n <- n - par4
}
if(par5 > 0) {
x2 <- array(0, dim=c(n-par5*12,par5), dimnames=list(1:(n-par5*12), paste(colnames(x)[par1],'(t-',1:par5,'s)',sep='')))
for (i in 1:(n-par5*12)) {
for (j in 1:par5) {
x2[i,j] <- x[i+par5*12-j*12,par1]
}
}
x <- cbind(x[(par5*12+1):n,], x2)
n <- n - par5*12
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
(k <- length(x[n,]))
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
print(x)
(k <- length(x[n,]))
head(x)
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
sresid <- studres(mylm)
hist(sresid, freq=FALSE, main='Distribution of Studentized Residuals')
xfit<-seq(min(sresid),max(sresid),length=40)
yfit<-dnorm(xfit)
lines(xfit, yfit)
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqPlot(mylm, main='QQ Plot')
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
print(z)
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, signif(mysum$coefficients[i,1],6), sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, mywarning)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Multiple Linear Regression - Ordinary Least Squares', 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,formatC(signif(mysum$coefficients[i,1],5),format='g',flag='+'))
a<-table.element(a,formatC(signif(mysum$coefficients[i,2],5),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$coefficients[i,3],4),format='e',flag='+'))
a<-table.element(a,formatC(signif(mysum$coefficients[i,4],4),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$coefficients[i,4]/2,4),format='g',flag=' '))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a,formatC(signif(sqrt(mysum$r.squared),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a,formatC(signif(mysum$r.squared,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a,formatC(signif(mysum$adj.r.squared,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a,formatC(signif(mysum$fstatistic[1],6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[2],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[3],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a,formatC(signif(1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a,formatC(signif(mysum$sigma,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a,formatC(signif(sum(myerror*myerror),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
myr <- as.numeric(mysum$resid)
myr
if(n < 200) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,formatC(signif(x[i],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(x[i]-mysum$resid[i],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$resid[i],6),format='g',flag=' '))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
if (n > n25) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-values',header=TRUE)
a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'breakpoint index',header=TRUE)
a<-table.element(a,'greater',header=TRUE)
a<-table.element(a,'2-sided',header=TRUE)
a<-table.element(a,'less',header=TRUE)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
a<-table.element(a,mypoint,header=TRUE)
a<-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,1],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,2],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,3],6),format='g',flag=' '))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,signif(numsignificant1,6))
a<-table.element(a,formatC(signif(numsignificant1/numgqtests,6),format='g',flag=' '))
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,signif(numsignificant5,6))
a<-table.element(a,signif(numsignificant5/numgqtests,6))
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,signif(numsignificant10,6))
a<-table.element(a,signif(numsignificant10/numgqtests,6))
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}
}
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Ramsey RESET F-Test for powers (2 and 3) of fitted values',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
reset_test_fitted <- resettest(mylm,power=2:3,type='fitted')
a<-table.element(a,paste('',RC.texteval('reset_test_fitted'),'
',sep=''))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Ramsey RESET F-Test for powers (2 and 3) of regressors',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
reset_test_regressors <- resettest(mylm,power=2:3,type='regressor')
a<-table.element(a,paste('',RC.texteval('reset_test_regressors'),'
',sep=''))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Ramsey RESET F-Test for powers (2 and 3) of principal components',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
reset_test_principal_components <- resettest(mylm,power=2:3,type='princomp')
a<-table.element(a,paste('',RC.texteval('reset_test_principal_components'),'
',sep=''))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable8.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Variance Inflation Factors (Multicollinearity)',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
vif <- vif(mylm)
a<-table.element(a,paste('',RC.texteval('vif'),'
',sep=''))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable9.tab')

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code