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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Fri, 23 Dec 2016 13:55:56 +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/2016/Dec/23/t1482498047wa3x91g7lvemlcr.htm/, Retrieved Fri, 01 Nov 2024 03:33:21 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=302923, Retrieved Fri, 01 Nov 2024 03:33:21 +0000

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Estimated Impact

113

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-       [Multiple Regression] [] [2016-12-23 12:55:56] [361c8dad91b3f1ef2e651cd04783c23b] [Current]

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10	17	15	11	11	18	14	17	13	22	14
13	11	13	9	11	19	19	17	13	24	14
14	12	14	12	15	18	17	12	12	26	17
12	12	13	11	15	15	17	13	15	21	14
12	13	12	12	13	19	15	16	12	26	13
13	17	17	12	14	19	20	15	12	25	16
13	17	12	12	13	19	15	14	12	21	13
13	12	13	12	15	16	19	15	10	24	16
13	16	13	12	15	18	15	13	9	27	18
14	15	16	11	15	20	15	12	15	28	20
14	11	12	12	10	14	19	13	14	23	14
12	16	12	12	11	15	16	14	11	25	14
12	15	13	15	16	18	20	18	18	24	15
11	16	16	13	17	19	18	19	14	24	18
12	15	15	12	14	16	15	15	11	24	16
14	11	12	11	13	18	14	14	9	25	14
12	8	13	13	10	18	20	13	16	25	18
11	10	15	9	13	17	16	11	15	25	17
13	14	12	10	17	19	16	17	14	25	16
13	16	15	11	18	19	16	16	17	24	17
12	15	11	12	17	17	10	8	13	26	18
13	15	13	12	11	18	19	12	11	26	17
12	12	13	12	15	16	19	12	13	25	13
13	18	14	12	12	20	16	12	10	26	16
11	10	14	10	15	13	15	14	13	23	14
12	17	14	12	15	19	18	14	14	24	16
12	12	15	12	12	15	17	15	10	24	15
13	13	16	14	19	17	19	13	15	25	14
13	9	16	11	13	17	17	14	12	25	16
10	11	16	12	15	16	14	16	10	24	19
12	10	13	9	13	17	19	16	10	28	16
13	15	13	13	10	19	20	15	14	27	18
13	15	14	8	14	18	5	15	12	26	18
10	13	13	13	12	19	19	16	15	23	18
14	13	14	12	15	20	16	16	13	23	18
12	9	12	12	13	16	15	17	10	24	14
10	14	17	12	18	17	16	16	14	24	20
10	14	14	12	15	16	18	11	11	22	18
14	11	15	12	11	16	16	15	11	25	14
12	15	13	10	14	16	15	15	12	25	18
14	12	14	12	11	16	17	11	13	28	13
10	11	15	11	14	14	14	13	14	22	14
13	12	19	13	9	17	20	13	13	28	16
12	15	14	13	13	18	19	17	14	25	16
12	13	13	9	13	16	7	13	13	24	15
13	11	12	12	12	16	13	12	11	24	13
12	10	4	13	17	13	16	17	19	23	16
10	16	14	10	16	16	16	16	13	25	13
9	13	15	5	15	15	16	18	13	19	12
14	15	15	13	16	19	18	12	15	26	18
15	14	12	12	16	16	18	15	11	25	13
14	12	14	12	13	17	16	15	16	27	15
8	10	11	5	13	19	17	15	13	26	19
11	12	12	12	12	17	19	14	15	23	14
10	9	10	10	11	17	16	17	13	25	18
12	15	13	12	13	15	19	15	9	21	12
14	16	14	15	15	16	13	15	12	22	12
12	12	14	13	13	16	16	15	14	24	14
12	11	15	12	14	16	13	16	15	25	8
14	11	15	12	13	17	12	12	11	27	13
13	9	13	13	15	18	17	10	12	24	14
13	13	15	13	14	18	17	15	14	26	13
13	17	16	11	14	18	17	13	13	21	13
12	18	12	12	13	19	16	14	14	27	15
10	15	17	9	11	14	16	14	10	22	14
14	12	15	12	14	13	14	13	14	23	17
11	18	18	12	17	18	16	17	14	24	16
10	11	12	13	15	16	13	16	16	25	14
13	6	16	14	15	15	16	16	16	24	18
12	10	15	10	13	18	14	16	14	23	17
12	19	15	12	12	18	20	17	14	28	18
11	16	12	8	14	16	12	16	16	23	12
10	12	13	12	11	19	13	16	11	24	13
14	10	10	12	14	17	18	13	4	26	15
12	14	14	12	18	17	14	17	14	22	15
13	12	11	12	15	19	19	12	14	25	14
11	13	12	10	18	19	18	18	9	25	16
10	16	14	12	16	20	14	15	11	24	13
14	18	12	12	12	19	18	12	11	24	18
13	13	14	12	14	18	19	13	10	26	15
7	15	12	12	14	16	15	13	12	21	13
13	16	13	12	14	16	14	13	11	25	16
13	9	13	13	14	15	17	11	8	25	12
13	9	14	12	13	20	19	17	9	26	18
15	8	12	14	12	16	13	15	13	25	14
13	18	15	10	13	16	19	16	16	26	17
14	18	13	12	17	20	18	14	14	27	14
12	14	13	11	13	20	20	18	13	25	17
13	8	11	13	14	18	15	16	14	23	17
11	14	12	11	15	15	15	14	13	20	12
12	13	16	13	13	14	15	12	19	24	14
14	14	11	12	14	16	20	14	11	26	13
13	7	13	12	17	14	15	9	8	25	16
14	18	12	12	15	18	19	14	14	25	12
12	16	17	13	13	20	18	17	11	24	16
12	9	14	12	14	20	18	15	14	26	16
13	11	15	9	17	18	15	15	12	25	16
14	10	8	20	8	20	20	20	7	28	15
13	13	13	12	15	14	17	12	14	27	15
12	10	13	13	10	20	12	14	13	25	19
13	12	15	14	15	17	18	16	14	26	17
12	11	14	12	15	20	19	18	11	26	20
10	12	13	11	14	14	20	10	9	26	14
12	12	14	12	15	16	13	13	14	22	13
13	10	12	12	18	20	17	16	10	28	15
12	20	19	12	14	19	15	17	15	26	16
13	12	15	12	19	18	16	16	13	21	16
12	12	14	12	16	17	18	17	16	25	11
12	16	14	12	17	17	18	19	14	25	14
12	11	15	12	18	19	14	18	14	24	13
11	12	13	12	13	15	15	15	15	24	14
12	12	15	11	10	18	12	14	16	24	16
9	13	14	12	14	15	17	15	16	23	14
14	10	11	11	13	16	14	14	12	23	15
12	14	17	9	12	16	18	16	8	24	17
13	13	13	13	13	20	17	12	12	24	14
13	15	9	11	12	18	17	19	14	25	15
13	13	12	10	13	20	20	17	13	28	15
11	13	13	14	16	18	16	14	9	23	14
12	17	17	10	12	17	14	13	10	24	15
11	12	14	12	14	19	15	14	13	23	13
12	17	13	12	17	18	18	14	12	24	16
12	9	16	11	14	19	20	17	11	25	16
13	12	14	12	12	17	17	15	12	24	14
12	14	14	14	14	18	17	15	12	23	10
13	14	14	13	17	17	17	16	14	23	16
13	14	10	12	13	16	17	17	10	25	14
12	12	12	12	12	19	15	13	10	21	13
12	14	13	12	14	18	17	15	14	22	14
8	13	14	10	11	17	18	10	9	19	15
12	15	18	12	17	18	17	18	13	24	17
13	16	14	12	15	16	20	16	11	25	16
10	13	14	10	10	20	15	16	15	21	17
8	14	13	12	15	14	16	14	11	22	15
12	14	13	12	16	17	15	15	12	23	18
13	17	16	15	17	13	18	13	17	27	16
12	15	13	15	12	17	15	13	12	26	17
15	8	14	12	15	18	18	14	12	29	12
14	11	8	12	10	16	20	17	10	28	15
10	11	13	10	13	17	19	13	13	24	15
11	9	13	12	17	19	14	14	11	25	16
12	15	16	12	17	18	16	18	13	25	19
10	16	14	12	16	17	15	12	11	22	15
14	16	13	12	15	16	17	14	13	25	14
10	10	14	11	16	17	18	8	11	26	14
15	15	12	13	16	17	20	16	13	26	17
11	10	16	9	15	17	17	13	10	24	15
12	12	18	11	16	20	18	16	14	25	18
9	14	16	12	14	14	15	11	13	19	13
12	18	15	13	17	20	16	15	16	25	16
13	15	18	11	14	19	11	16	8	23	14
12	19	15	10	12	16	15	14	15	25	16
9	13	14	9	15	19	18	13	16	25	18
12	10	14	12	14	17	17	17	12	26	15
14	15	15	12	14	19	16	13	15	27	14
11	7	9	12	14	20	12	18	13	24	15
12	14	17	13	13	19	19	16	13	22	20
14	11	11	13	16	19	18	13	18	25	15
12	14	15	10	13	16	15	16	8	24	14
15	11	15	13	14	18	17	15	12	23	16
11	18	15	13	13	16	19	14	8	27	13
12	8	13	12	13	17	18	15	13	24	13
12	19	14	12	15	18	19	16	16	24	14
10	5	15	9	13	16	16	12	16	21	15
12	17	15	12	14	17	16	19	15	25	16
11	14	14	11	13	15	16	15	12	25	15
11	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	8 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 time8 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=302923&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]

8 seconds[/C][/ROW] [ROW]

R Server[/C]

Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=302923&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=302923&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	8 seconds
R Server	Big Analytics Cloud Computing Center

Multiple Linear Regression - Estimated Regression Equation
TVDC[t] = + 1.92886 -0.00806943ECSUM[t] -0.0186152EPSUM[t] + 0.26005GWSUM[t] + 0.0179689IVHBSUM[t] + 0.0495554IKSUM[t] -0.0205343ITHSUM[t] -0.010683KVDDSUM[t] -0.0130042SNSUM[t] + 0.325409SKEOUSUM[t] -0.0591037VSUM[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
TVDC[t] =  +  1.92886 -0.00806943ECSUM[t] -0.0186152EPSUM[t] +  0.26005GWSUM[t] +  0.0179689IVHBSUM[t] +  0.0495554IKSUM[t] -0.0205343ITHSUM[t] -0.010683KVDDSUM[t] -0.0130042SNSUM[t] +  0.325409SKEOUSUM[t] -0.0591037VSUM[t]  + e[t] \tabularnewline
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=302923&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]TVDC[t] =  +  1.92886 -0.00806943ECSUM[t] -0.0186152EPSUM[t] +  0.26005GWSUM[t] +  0.0179689IVHBSUM[t] +  0.0495554IKSUM[t] -0.0205343ITHSUM[t] -0.010683KVDDSUM[t] -0.0130042SNSUM[t] +  0.325409SKEOUSUM[t] -0.0591037VSUM[t]  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=302923&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=302923&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
TVDC[t] = + 1.92886 -0.00806943ECSUM[t] -0.0186152EPSUM[t] + 0.26005GWSUM[t] + 0.0179689IVHBSUM[t] + 0.0495554IKSUM[t] -0.0205343ITHSUM[t] -0.010683KVDDSUM[t] -0.0130042SNSUM[t] + 0.325409SKEOUSUM[t] -0.0591037VSUM[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)	+1.929	1.963	+9.8250e-01	0.3274	0.1637
ECSUM	-0.008069	0.0359	-2.2470e-01	0.8225	0.4112
EPSUM	-0.01861	0.05188	-3.5880e-01	0.7202	0.3601
GWSUM	+0.2601	0.06491	+4.0070e+00	9.516e-05	4.758e-05
IVHBSUM	+0.01797	0.04944	+3.6340e-01	0.7168	0.3584
IKSUM	+0.04956	0.06282	+7.8890e-01	0.4314	0.2157
ITHSUM	-0.02053	0.04322	-4.7510e-01	0.6353	0.3177
KVDDSUM	-0.01068	0.04843	-2.2060e-01	0.8257	0.4129
SNSUM	-0.013	0.04315	-3.0140e-01	0.7635	0.3818
SKEOUSUM	+0.3254	0.0577	+5.6390e+00	7.813e-08	3.907e-08
VSUM	-0.0591	0.05319	-1.1110e+00	0.2682	0.1341

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Ordinary Least Squares \tabularnewline
Variable & Parameter & S.D. & T-STATH0: parameter = 0 & 2-tail p-value & 1-tail p-value \tabularnewline
(Intercept) & +1.929 &  1.963 & +9.8250e-01 &  0.3274 &  0.1637 \tabularnewline
ECSUM & -0.008069 &  0.0359 & -2.2470e-01 &  0.8225 &  0.4112 \tabularnewline
EPSUM & -0.01861 &  0.05188 & -3.5880e-01 &  0.7202 &  0.3601 \tabularnewline
GWSUM & +0.2601 &  0.06491 & +4.0070e+00 &  9.516e-05 &  4.758e-05 \tabularnewline
IVHBSUM & +0.01797 &  0.04944 & +3.6340e-01 &  0.7168 &  0.3584 \tabularnewline
IKSUM & +0.04956 &  0.06282 & +7.8890e-01 &  0.4314 &  0.2157 \tabularnewline
ITHSUM & -0.02053 &  0.04322 & -4.7510e-01 &  0.6353 &  0.3177 \tabularnewline
KVDDSUM & -0.01068 &  0.04843 & -2.2060e-01 &  0.8257 &  0.4129 \tabularnewline
SNSUM & -0.013 &  0.04315 & -3.0140e-01 &  0.7635 &  0.3818 \tabularnewline
SKEOUSUM & +0.3254 &  0.0577 & +5.6390e+00 &  7.813e-08 &  3.907e-08 \tabularnewline
VSUM & -0.0591 &  0.05319 & -1.1110e+00 &  0.2682 &  0.1341 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=302923&T=2

[TABLE]
[ROW][C]Multiple Linear Regression - Ordinary Least Squares[/C][/ROW]
[ROW][C]Variable[/C][C]Parameter[/C][C]S.D.[/C][C]T-STATH0: parameter = 0[/C][C]2-tail p-value[/C][C]1-tail p-value[/C][/ROW]
[ROW][C](Intercept)[/C][C]+1.929[/C][C] 1.963[/C][C]+9.8250e-01[/C][C] 0.3274[/C][C] 0.1637[/C][/ROW]
[ROW][C]ECSUM[/C][C]-0.008069[/C][C] 0.0359[/C][C]-2.2470e-01[/C][C] 0.8225[/C][C] 0.4112[/C][/ROW]
[ROW][C]EPSUM[/C][C]-0.01861[/C][C] 0.05188[/C][C]-3.5880e-01[/C][C] 0.7202[/C][C] 0.3601[/C][/ROW]
[ROW][C]GWSUM[/C][C]+0.2601[/C][C] 0.06491[/C][C]+4.0070e+00[/C][C] 9.516e-05[/C][C] 4.758e-05[/C][/ROW]
[ROW][C]IVHBSUM[/C][C]+0.01797[/C][C] 0.04944[/C][C]+3.6340e-01[/C][C] 0.7168[/C][C] 0.3584[/C][/ROW]
[ROW][C]IKSUM[/C][C]+0.04956[/C][C] 0.06282[/C][C]+7.8890e-01[/C][C] 0.4314[/C][C] 0.2157[/C][/ROW]
[ROW][C]ITHSUM[/C][C]-0.02053[/C][C] 0.04322[/C][C]-4.7510e-01[/C][C] 0.6353[/C][C] 0.3177[/C][/ROW]
[ROW][C]KVDDSUM[/C][C]-0.01068[/C][C] 0.04843[/C][C]-2.2060e-01[/C][C] 0.8257[/C][C] 0.4129[/C][/ROW]
[ROW][C]SNSUM[/C][C]-0.013[/C][C] 0.04315[/C][C]-3.0140e-01[/C][C] 0.7635[/C][C] 0.3818[/C][/ROW]
[ROW][C]SKEOUSUM[/C][C]+0.3254[/C][C] 0.0577[/C][C]+5.6390e+00[/C][C] 7.813e-08[/C][C] 3.907e-08[/C][/ROW]
[ROW][C]VSUM[/C][C]-0.0591[/C][C] 0.05319[/C][C]-1.1110e+00[/C][C] 0.2682[/C][C] 0.1341[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=302923&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=302923&T=2

As an alternative you can also use a QR Code:

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

Multiple Linear Regression - Ordinary Least Squares
Variable	Parameter	S.D.	T-STATH0: parameter = 0	2-tail p-value	1-tail p-value
(Intercept)	+1.929	1.963	+9.8250e-01	0.3274	0.1637
ECSUM	-0.008069	0.0359	-2.2470e-01	0.8225	0.4112
EPSUM	-0.01861	0.05188	-3.5880e-01	0.7202	0.3601
GWSUM	+0.2601	0.06491	+4.0070e+00	9.516e-05	4.758e-05
IVHBSUM	+0.01797	0.04944	+3.6340e-01	0.7168	0.3584
IKSUM	+0.04956	0.06282	+7.8890e-01	0.4314	0.2157
ITHSUM	-0.02053	0.04322	-4.7510e-01	0.6353	0.3177
KVDDSUM	-0.01068	0.04843	-2.2060e-01	0.8257	0.4129
SNSUM	-0.013	0.04315	-3.0140e-01	0.7635	0.3818
SKEOUSUM	+0.3254	0.0577	+5.6390e+00	7.813e-08	3.907e-08
VSUM	-0.0591	0.05319	-1.1110e+00	0.2682	0.1341

Multiple Linear Regression - Regression Statistics
Multiple R	0.5419
R-squared	0.2936
Adjusted R-squared	0.2483
F-TEST (value)	6.484
F-TEST (DF numerator)	10
F-TEST (DF denominator)	156
p-value	2.474e-08
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.301
Sum Squared Residuals	263.9

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.5419 \tabularnewline
R-squared &  0.2936 \tabularnewline
Adjusted R-squared &  0.2483 \tabularnewline
F-TEST (value) &  6.484 \tabularnewline
F-TEST (DF numerator) & 10 \tabularnewline
F-TEST (DF denominator) & 156 \tabularnewline
p-value &  2.474e-08 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  1.301 \tabularnewline
Sum Squared Residuals &  263.9 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=302923&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.5419[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.2936[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.2483[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 6.484[/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] 2.474e-08[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C] 1.301[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 263.9[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=302923&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=302923&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	0.5419
R-squared	0.2936
Adjusted R-squared	0.2483
F-TEST (value)	6.484
F-TEST (DF numerator)	10
F-TEST (DF denominator)	156
p-value	2.474e-08
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.301
Sum Squared Residuals	263.9

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	10	11.16	-1.156
2	13	11.32	1.681
3	14	12.68	1.324
4	12	10.79	1.213
5	12	12.95	-0.9536
6	13	12.25	0.7485
7	13	11.32	1.684
8	13	11.96	1.043
9	13	13	0.001882
10	14	12.93	1.071
11	14	11.56	2.443
12	12	12.32	-0.3246
13	12	12.73	-0.7323
14	11	12.12	-1.121
15	12	11.95	0.05352
16	14	12.36	1.643
17	12	12.39	-0.3884
18	11	11.47	-0.4748
19	13	11.94	1.063
20	13	11.73	1.269
21	12	12.81	-0.8085
22	13	12.57	0.4295
23	12	12.45	-0.4525
24	13	12.78	0.2215
25	11	11.13	-0.1321
26	12	12.03	-0.02566
27	12	11.92	0.08376
28	13	12.93	0.06566
29	13	12.03	0.9702
30	10	11.82	-1.824
31	12	12.5	-0.4973
32	13	13.04	-0.03689
33	13	11.75	1.251
34	10	11.78	-1.784
35	14	11.7	2.303
36	12	12.14	-0.1426
37	10	11.73	-1.732
38	10	11.2	-1.203
39	14	12.35	1.652
40	12	11.66	0.3422
41	14	13.39	0.61
42	10	11.09	-1.09
43	13	13.31	-0.3103
44	12	12.49	-0.4892
45	12	11.42	0.5795
46	13	12.25	0.7509
47	12	11.89	0.1145
48	10	11.92	-1.918
49	9	8.641	0.3585
50	14	12.84	1.158
51	15	12.49	2.513
52	14	12.97	1.029
53	8	10.78	-2.778
54	11	11.71	-0.7095
55	10	11.7	-1.703
56	12	11.12	0.8798
57	14	12.37	1.631
58	12	12.29	-0.29
59	12	12.76	-0.7554
60	14	13.26	0.7425
61	13	12.53	0.4732
62	13	13.07	-0.06983
63	13	10.91	2.094
64	12	13.1	-1.095
65	10	10.45	-0.4466
66	14	11.44	2.56
67	11	11.94	-0.9385
68	10	12.72	-2.722
69	13	12.27	0.7254
70	12	11.13	0.8658
71	12	13	-0.9978
72	11	10.85	0.149
73	10	12.31	-2.31
74	14	12.89	1.11
75	12	11.46	0.537
76	13	12.57	0.4337
77	11	11.98	-0.9767
78	10	12.39	-2.389
79	14	11.94	2.057
80	13	12.74	0.2575
81	7	11.21	-4.212
82	13	12.34	0.657
83	13	12.85	0.1549
84	13	12.65	0.3511
85	15	13	1.998
86	13	11.82	1.182
87	14	13.22	0.7839
88	12	12.02	-0.01756
89	13	12	0.9976
90	11	10.64	0.3618
91	12	12.13	-0.1332
92	14	12.77	1.235
93	13	12.43	0.5684
94	14	12.55	1.453
95	12	12.26	-0.2585
96	12	12.76	-0.7619
97	13	11.66	1.336
98	14	15.54	-1.545
99	13	12.91	0.09399
100	12	12.6	-0.6048
101	13	13.04	-0.03873
102	12	12.51	-0.5138
103	10	12.39	-2.395
104	12	11.56	0.4428
105	13	13.63	-0.6348
106	12	12.56	-0.5578
107	13	11.13	1.874
108	12	12.55	-0.5477
109	12	12.36	-0.3608
110	12	12.33	-0.3261
111	11	12.01	-1.007
112	12	11.75	0.2549
113	9	11.62	-2.618
114	14	11.54	2.465
115	12	11.01	0.9911
116	13	12.54	0.4637
117	13	12.12	0.877
118	13	12.89	0.1107
119	11	12.46	-1.464
120	12	11.5	0.4994
121	11	11.97	-0.9746
122	12	12.06	-0.05667
123	12	12.07	-0.06627
124	13	12.07	0.9329
125	12	12.57	-0.5675
126	13	11.92	1.079
127	13	12.42	0.5762
128	12	11.37	0.6253
129	12	11.48	0.5218
130	8	9.907	-1.907
131	12	11.89	0.1146
132	13	12.19	0.8129
133	10	10.49	-0.4895
134	8	11.31	-3.309
135	12	11.62	0.3793
136	13	13.46	-0.4551
137	12	13.38	-1.378
138	15	13.94	1.062
139	14	13.29	0.7131
140	10	11.5	-1.5
141	11	12.59	-1.591
142	12	12.15	-0.1504
143	10	11.48	-1.483
144	14	12.38	1.619
145	10	12.61	-2.613
146	15	12.8	2.2
147	11	11.31	-0.308
148	12	11.98	0.01514
149	9	10.4	-1.404
150	12	12.67	-0.6743
151	13	11.68	1.317
152	12	11.64	0.3578
153	9	11.47	-2.47
154	12	12.69	-0.6895
155	14	13.14	0.8616
156	11	12.38	-1.384
157	12	11.3	0.698
158	14	12.75	1.249
159	12	11.56	0.437
160	15	11.96	3.042
161	11	13.29	-2.285
162	12	12.16	-0.1615
163	12	12.01	-0.01027
164	10	10.26	-0.2584
165	12	12.19	-0.19
166	11	12	-0.9966
167	11	11.81	-0.8063

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 &  10 &  11.16 & -1.156 \tabularnewline
2 &  13 &  11.32 &  1.681 \tabularnewline
3 &  14 &  12.68 &  1.324 \tabularnewline
4 &  12 &  10.79 &  1.213 \tabularnewline
5 &  12 &  12.95 & -0.9536 \tabularnewline
6 &  13 &  12.25 &  0.7485 \tabularnewline
7 &  13 &  11.32 &  1.684 \tabularnewline
8 &  13 &  11.96 &  1.043 \tabularnewline
9 &  13 &  13 &  0.001882 \tabularnewline
10 &  14 &  12.93 &  1.071 \tabularnewline
11 &  14 &  11.56 &  2.443 \tabularnewline
12 &  12 &  12.32 & -0.3246 \tabularnewline
13 &  12 &  12.73 & -0.7323 \tabularnewline
14 &  11 &  12.12 & -1.121 \tabularnewline
15 &  12 &  11.95 &  0.05352 \tabularnewline
16 &  14 &  12.36 &  1.643 \tabularnewline
17 &  12 &  12.39 & -0.3884 \tabularnewline
18 &  11 &  11.47 & -0.4748 \tabularnewline
19 &  13 &  11.94 &  1.063 \tabularnewline
20 &  13 &  11.73 &  1.269 \tabularnewline
21 &  12 &  12.81 & -0.8085 \tabularnewline
22 &  13 &  12.57 &  0.4295 \tabularnewline
23 &  12 &  12.45 & -0.4525 \tabularnewline
24 &  13 &  12.78 &  0.2215 \tabularnewline
25 &  11 &  11.13 & -0.1321 \tabularnewline
26 &  12 &  12.03 & -0.02566 \tabularnewline
27 &  12 &  11.92 &  0.08376 \tabularnewline
28 &  13 &  12.93 &  0.06566 \tabularnewline
29 &  13 &  12.03 &  0.9702 \tabularnewline
30 &  10 &  11.82 & -1.824 \tabularnewline
31 &  12 &  12.5 & -0.4973 \tabularnewline
32 &  13 &  13.04 & -0.03689 \tabularnewline
33 &  13 &  11.75 &  1.251 \tabularnewline
34 &  10 &  11.78 & -1.784 \tabularnewline
35 &  14 &  11.7 &  2.303 \tabularnewline
36 &  12 &  12.14 & -0.1426 \tabularnewline
37 &  10 &  11.73 & -1.732 \tabularnewline
38 &  10 &  11.2 & -1.203 \tabularnewline
39 &  14 &  12.35 &  1.652 \tabularnewline
40 &  12 &  11.66 &  0.3422 \tabularnewline
41 &  14 &  13.39 &  0.61 \tabularnewline
42 &  10 &  11.09 & -1.09 \tabularnewline
43 &  13 &  13.31 & -0.3103 \tabularnewline
44 &  12 &  12.49 & -0.4892 \tabularnewline
45 &  12 &  11.42 &  0.5795 \tabularnewline
46 &  13 &  12.25 &  0.7509 \tabularnewline
47 &  12 &  11.89 &  0.1145 \tabularnewline
48 &  10 &  11.92 & -1.918 \tabularnewline
49 &  9 &  8.641 &  0.3585 \tabularnewline
50 &  14 &  12.84 &  1.158 \tabularnewline
51 &  15 &  12.49 &  2.513 \tabularnewline
52 &  14 &  12.97 &  1.029 \tabularnewline
53 &  8 &  10.78 & -2.778 \tabularnewline
54 &  11 &  11.71 & -0.7095 \tabularnewline
55 &  10 &  11.7 & -1.703 \tabularnewline
56 &  12 &  11.12 &  0.8798 \tabularnewline
57 &  14 &  12.37 &  1.631 \tabularnewline
58 &  12 &  12.29 & -0.29 \tabularnewline
59 &  12 &  12.76 & -0.7554 \tabularnewline
60 &  14 &  13.26 &  0.7425 \tabularnewline
61 &  13 &  12.53 &  0.4732 \tabularnewline
62 &  13 &  13.07 & -0.06983 \tabularnewline
63 &  13 &  10.91 &  2.094 \tabularnewline
64 &  12 &  13.1 & -1.095 \tabularnewline
65 &  10 &  10.45 & -0.4466 \tabularnewline
66 &  14 &  11.44 &  2.56 \tabularnewline
67 &  11 &  11.94 & -0.9385 \tabularnewline
68 &  10 &  12.72 & -2.722 \tabularnewline
69 &  13 &  12.27 &  0.7254 \tabularnewline
70 &  12 &  11.13 &  0.8658 \tabularnewline
71 &  12 &  13 & -0.9978 \tabularnewline
72 &  11 &  10.85 &  0.149 \tabularnewline
73 &  10 &  12.31 & -2.31 \tabularnewline
74 &  14 &  12.89 &  1.11 \tabularnewline
75 &  12 &  11.46 &  0.537 \tabularnewline
76 &  13 &  12.57 &  0.4337 \tabularnewline
77 &  11 &  11.98 & -0.9767 \tabularnewline
78 &  10 &  12.39 & -2.389 \tabularnewline
79 &  14 &  11.94 &  2.057 \tabularnewline
80 &  13 &  12.74 &  0.2575 \tabularnewline
81 &  7 &  11.21 & -4.212 \tabularnewline
82 &  13 &  12.34 &  0.657 \tabularnewline
83 &  13 &  12.85 &  0.1549 \tabularnewline
84 &  13 &  12.65 &  0.3511 \tabularnewline
85 &  15 &  13 &  1.998 \tabularnewline
86 &  13 &  11.82 &  1.182 \tabularnewline
87 &  14 &  13.22 &  0.7839 \tabularnewline
88 &  12 &  12.02 & -0.01756 \tabularnewline
89 &  13 &  12 &  0.9976 \tabularnewline
90 &  11 &  10.64 &  0.3618 \tabularnewline
91 &  12 &  12.13 & -0.1332 \tabularnewline
92 &  14 &  12.77 &  1.235 \tabularnewline
93 &  13 &  12.43 &  0.5684 \tabularnewline
94 &  14 &  12.55 &  1.453 \tabularnewline
95 &  12 &  12.26 & -0.2585 \tabularnewline
96 &  12 &  12.76 & -0.7619 \tabularnewline
97 &  13 &  11.66 &  1.336 \tabularnewline
98 &  14 &  15.54 & -1.545 \tabularnewline
99 &  13 &  12.91 &  0.09399 \tabularnewline
100 &  12 &  12.6 & -0.6048 \tabularnewline
101 &  13 &  13.04 & -0.03873 \tabularnewline
102 &  12 &  12.51 & -0.5138 \tabularnewline
103 &  10 &  12.39 & -2.395 \tabularnewline
104 &  12 &  11.56 &  0.4428 \tabularnewline
105 &  13 &  13.63 & -0.6348 \tabularnewline
106 &  12 &  12.56 & -0.5578 \tabularnewline
107 &  13 &  11.13 &  1.874 \tabularnewline
108 &  12 &  12.55 & -0.5477 \tabularnewline
109 &  12 &  12.36 & -0.3608 \tabularnewline
110 &  12 &  12.33 & -0.3261 \tabularnewline
111 &  11 &  12.01 & -1.007 \tabularnewline
112 &  12 &  11.75 &  0.2549 \tabularnewline
113 &  9 &  11.62 & -2.618 \tabularnewline
114 &  14 &  11.54 &  2.465 \tabularnewline
115 &  12 &  11.01 &  0.9911 \tabularnewline
116 &  13 &  12.54 &  0.4637 \tabularnewline
117 &  13 &  12.12 &  0.877 \tabularnewline
118 &  13 &  12.89 &  0.1107 \tabularnewline
119 &  11 &  12.46 & -1.464 \tabularnewline
120 &  12 &  11.5 &  0.4994 \tabularnewline
121 &  11 &  11.97 & -0.9746 \tabularnewline
122 &  12 &  12.06 & -0.05667 \tabularnewline
123 &  12 &  12.07 & -0.06627 \tabularnewline
124 &  13 &  12.07 &  0.9329 \tabularnewline
125 &  12 &  12.57 & -0.5675 \tabularnewline
126 &  13 &  11.92 &  1.079 \tabularnewline
127 &  13 &  12.42 &  0.5762 \tabularnewline
128 &  12 &  11.37 &  0.6253 \tabularnewline
129 &  12 &  11.48 &  0.5218 \tabularnewline
130 &  8 &  9.907 & -1.907 \tabularnewline
131 &  12 &  11.89 &  0.1146 \tabularnewline
132 &  13 &  12.19 &  0.8129 \tabularnewline
133 &  10 &  10.49 & -0.4895 \tabularnewline
134 &  8 &  11.31 & -3.309 \tabularnewline
135 &  12 &  11.62 &  0.3793 \tabularnewline
136 &  13 &  13.46 & -0.4551 \tabularnewline
137 &  12 &  13.38 & -1.378 \tabularnewline
138 &  15 &  13.94 &  1.062 \tabularnewline
139 &  14 &  13.29 &  0.7131 \tabularnewline
140 &  10 &  11.5 & -1.5 \tabularnewline
141 &  11 &  12.59 & -1.591 \tabularnewline
142 &  12 &  12.15 & -0.1504 \tabularnewline
143 &  10 &  11.48 & -1.483 \tabularnewline
144 &  14 &  12.38 &  1.619 \tabularnewline
145 &  10 &  12.61 & -2.613 \tabularnewline
146 &  15 &  12.8 &  2.2 \tabularnewline
147 &  11 &  11.31 & -0.308 \tabularnewline
148 &  12 &  11.98 &  0.01514 \tabularnewline
149 &  9 &  10.4 & -1.404 \tabularnewline
150 &  12 &  12.67 & -0.6743 \tabularnewline
151 &  13 &  11.68 &  1.317 \tabularnewline
152 &  12 &  11.64 &  0.3578 \tabularnewline
153 &  9 &  11.47 & -2.47 \tabularnewline
154 &  12 &  12.69 & -0.6895 \tabularnewline
155 &  14 &  13.14 &  0.8616 \tabularnewline
156 &  11 &  12.38 & -1.384 \tabularnewline
157 &  12 &  11.3 &  0.698 \tabularnewline
158 &  14 &  12.75 &  1.249 \tabularnewline
159 &  12 &  11.56 &  0.437 \tabularnewline
160 &  15 &  11.96 &  3.042 \tabularnewline
161 &  11 &  13.29 & -2.285 \tabularnewline
162 &  12 &  12.16 & -0.1615 \tabularnewline
163 &  12 &  12.01 & -0.01027 \tabularnewline
164 &  10 &  10.26 & -0.2584 \tabularnewline
165 &  12 &  12.19 & -0.19 \tabularnewline
166 &  11 &  12 & -0.9966 \tabularnewline
167 &  11 &  11.81 & -0.8063 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=302923&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] 10[/C][C] 11.16[/C][C]-1.156[/C][/ROW]
[ROW][C]2[/C][C] 13[/C][C] 11.32[/C][C] 1.681[/C][/ROW]
[ROW][C]3[/C][C] 14[/C][C] 12.68[/C][C] 1.324[/C][/ROW]
[ROW][C]4[/C][C] 12[/C][C] 10.79[/C][C] 1.213[/C][/ROW]
[ROW][C]5[/C][C] 12[/C][C] 12.95[/C][C]-0.9536[/C][/ROW]
[ROW][C]6[/C][C] 13[/C][C] 12.25[/C][C] 0.7485[/C][/ROW]
[ROW][C]7[/C][C] 13[/C][C] 11.32[/C][C] 1.684[/C][/ROW]
[ROW][C]8[/C][C] 13[/C][C] 11.96[/C][C] 1.043[/C][/ROW]
[ROW][C]9[/C][C] 13[/C][C] 13[/C][C] 0.001882[/C][/ROW]
[ROW][C]10[/C][C] 14[/C][C] 12.93[/C][C] 1.071[/C][/ROW]
[ROW][C]11[/C][C] 14[/C][C] 11.56[/C][C] 2.443[/C][/ROW]
[ROW][C]12[/C][C] 12[/C][C] 12.32[/C][C]-0.3246[/C][/ROW]
[ROW][C]13[/C][C] 12[/C][C] 12.73[/C][C]-0.7323[/C][/ROW]
[ROW][C]14[/C][C] 11[/C][C] 12.12[/C][C]-1.121[/C][/ROW]
[ROW][C]15[/C][C] 12[/C][C] 11.95[/C][C] 0.05352[/C][/ROW]
[ROW][C]16[/C][C] 14[/C][C] 12.36[/C][C] 1.643[/C][/ROW]
[ROW][C]17[/C][C] 12[/C][C] 12.39[/C][C]-0.3884[/C][/ROW]
[ROW][C]18[/C][C] 11[/C][C] 11.47[/C][C]-0.4748[/C][/ROW]
[ROW][C]19[/C][C] 13[/C][C] 11.94[/C][C] 1.063[/C][/ROW]
[ROW][C]20[/C][C] 13[/C][C] 11.73[/C][C] 1.269[/C][/ROW]
[ROW][C]21[/C][C] 12[/C][C] 12.81[/C][C]-0.8085[/C][/ROW]
[ROW][C]22[/C][C] 13[/C][C] 12.57[/C][C] 0.4295[/C][/ROW]
[ROW][C]23[/C][C] 12[/C][C] 12.45[/C][C]-0.4525[/C][/ROW]
[ROW][C]24[/C][C] 13[/C][C] 12.78[/C][C] 0.2215[/C][/ROW]
[ROW][C]25[/C][C] 11[/C][C] 11.13[/C][C]-0.1321[/C][/ROW]
[ROW][C]26[/C][C] 12[/C][C] 12.03[/C][C]-0.02566[/C][/ROW]
[ROW][C]27[/C][C] 12[/C][C] 11.92[/C][C] 0.08376[/C][/ROW]
[ROW][C]28[/C][C] 13[/C][C] 12.93[/C][C] 0.06566[/C][/ROW]
[ROW][C]29[/C][C] 13[/C][C] 12.03[/C][C] 0.9702[/C][/ROW]
[ROW][C]30[/C][C] 10[/C][C] 11.82[/C][C]-1.824[/C][/ROW]
[ROW][C]31[/C][C] 12[/C][C] 12.5[/C][C]-0.4973[/C][/ROW]
[ROW][C]32[/C][C] 13[/C][C] 13.04[/C][C]-0.03689[/C][/ROW]
[ROW][C]33[/C][C] 13[/C][C] 11.75[/C][C] 1.251[/C][/ROW]
[ROW][C]34[/C][C] 10[/C][C] 11.78[/C][C]-1.784[/C][/ROW]
[ROW][C]35[/C][C] 14[/C][C] 11.7[/C][C] 2.303[/C][/ROW]
[ROW][C]36[/C][C] 12[/C][C] 12.14[/C][C]-0.1426[/C][/ROW]
[ROW][C]37[/C][C] 10[/C][C] 11.73[/C][C]-1.732[/C][/ROW]
[ROW][C]38[/C][C] 10[/C][C] 11.2[/C][C]-1.203[/C][/ROW]
[ROW][C]39[/C][C] 14[/C][C] 12.35[/C][C] 1.652[/C][/ROW]
[ROW][C]40[/C][C] 12[/C][C] 11.66[/C][C] 0.3422[/C][/ROW]
[ROW][C]41[/C][C] 14[/C][C] 13.39[/C][C] 0.61[/C][/ROW]
[ROW][C]42[/C][C] 10[/C][C] 11.09[/C][C]-1.09[/C][/ROW]
[ROW][C]43[/C][C] 13[/C][C] 13.31[/C][C]-0.3103[/C][/ROW]
[ROW][C]44[/C][C] 12[/C][C] 12.49[/C][C]-0.4892[/C][/ROW]
[ROW][C]45[/C][C] 12[/C][C] 11.42[/C][C] 0.5795[/C][/ROW]
[ROW][C]46[/C][C] 13[/C][C] 12.25[/C][C] 0.7509[/C][/ROW]
[ROW][C]47[/C][C] 12[/C][C] 11.89[/C][C] 0.1145[/C][/ROW]
[ROW][C]48[/C][C] 10[/C][C] 11.92[/C][C]-1.918[/C][/ROW]
[ROW][C]49[/C][C] 9[/C][C] 8.641[/C][C] 0.3585[/C][/ROW]
[ROW][C]50[/C][C] 14[/C][C] 12.84[/C][C] 1.158[/C][/ROW]
[ROW][C]51[/C][C] 15[/C][C] 12.49[/C][C] 2.513[/C][/ROW]
[ROW][C]52[/C][C] 14[/C][C] 12.97[/C][C] 1.029[/C][/ROW]
[ROW][C]53[/C][C] 8[/C][C] 10.78[/C][C]-2.778[/C][/ROW]
[ROW][C]54[/C][C] 11[/C][C] 11.71[/C][C]-0.7095[/C][/ROW]
[ROW][C]55[/C][C] 10[/C][C] 11.7[/C][C]-1.703[/C][/ROW]
[ROW][C]56[/C][C] 12[/C][C] 11.12[/C][C] 0.8798[/C][/ROW]
[ROW][C]57[/C][C] 14[/C][C] 12.37[/C][C] 1.631[/C][/ROW]
[ROW][C]58[/C][C] 12[/C][C] 12.29[/C][C]-0.29[/C][/ROW]
[ROW][C]59[/C][C] 12[/C][C] 12.76[/C][C]-0.7554[/C][/ROW]
[ROW][C]60[/C][C] 14[/C][C] 13.26[/C][C] 0.7425[/C][/ROW]
[ROW][C]61[/C][C] 13[/C][C] 12.53[/C][C] 0.4732[/C][/ROW]
[ROW][C]62[/C][C] 13[/C][C] 13.07[/C][C]-0.06983[/C][/ROW]
[ROW][C]63[/C][C] 13[/C][C] 10.91[/C][C] 2.094[/C][/ROW]
[ROW][C]64[/C][C] 12[/C][C] 13.1[/C][C]-1.095[/C][/ROW]
[ROW][C]65[/C][C] 10[/C][C] 10.45[/C][C]-0.4466[/C][/ROW]
[ROW][C]66[/C][C] 14[/C][C] 11.44[/C][C] 2.56[/C][/ROW]
[ROW][C]67[/C][C] 11[/C][C] 11.94[/C][C]-0.9385[/C][/ROW]
[ROW][C]68[/C][C] 10[/C][C] 12.72[/C][C]-2.722[/C][/ROW]
[ROW][C]69[/C][C] 13[/C][C] 12.27[/C][C] 0.7254[/C][/ROW]
[ROW][C]70[/C][C] 12[/C][C] 11.13[/C][C] 0.8658[/C][/ROW]
[ROW][C]71[/C][C] 12[/C][C] 13[/C][C]-0.9978[/C][/ROW]
[ROW][C]72[/C][C] 11[/C][C] 10.85[/C][C] 0.149[/C][/ROW]
[ROW][C]73[/C][C] 10[/C][C] 12.31[/C][C]-2.31[/C][/ROW]
[ROW][C]74[/C][C] 14[/C][C] 12.89[/C][C] 1.11[/C][/ROW]
[ROW][C]75[/C][C] 12[/C][C] 11.46[/C][C] 0.537[/C][/ROW]
[ROW][C]76[/C][C] 13[/C][C] 12.57[/C][C] 0.4337[/C][/ROW]
[ROW][C]77[/C][C] 11[/C][C] 11.98[/C][C]-0.9767[/C][/ROW]
[ROW][C]78[/C][C] 10[/C][C] 12.39[/C][C]-2.389[/C][/ROW]
[ROW][C]79[/C][C] 14[/C][C] 11.94[/C][C] 2.057[/C][/ROW]
[ROW][C]80[/C][C] 13[/C][C] 12.74[/C][C] 0.2575[/C][/ROW]
[ROW][C]81[/C][C] 7[/C][C] 11.21[/C][C]-4.212[/C][/ROW]
[ROW][C]82[/C][C] 13[/C][C] 12.34[/C][C] 0.657[/C][/ROW]
[ROW][C]83[/C][C] 13[/C][C] 12.85[/C][C] 0.1549[/C][/ROW]
[ROW][C]84[/C][C] 13[/C][C] 12.65[/C][C] 0.3511[/C][/ROW]
[ROW][C]85[/C][C] 15[/C][C] 13[/C][C] 1.998[/C][/ROW]
[ROW][C]86[/C][C] 13[/C][C] 11.82[/C][C] 1.182[/C][/ROW]
[ROW][C]87[/C][C] 14[/C][C] 13.22[/C][C] 0.7839[/C][/ROW]
[ROW][C]88[/C][C] 12[/C][C] 12.02[/C][C]-0.01756[/C][/ROW]
[ROW][C]89[/C][C] 13[/C][C] 12[/C][C] 0.9976[/C][/ROW]
[ROW][C]90[/C][C] 11[/C][C] 10.64[/C][C] 0.3618[/C][/ROW]
[ROW][C]91[/C][C] 12[/C][C] 12.13[/C][C]-0.1332[/C][/ROW]
[ROW][C]92[/C][C] 14[/C][C] 12.77[/C][C] 1.235[/C][/ROW]
[ROW][C]93[/C][C] 13[/C][C] 12.43[/C][C] 0.5684[/C][/ROW]
[ROW][C]94[/C][C] 14[/C][C] 12.55[/C][C] 1.453[/C][/ROW]
[ROW][C]95[/C][C] 12[/C][C] 12.26[/C][C]-0.2585[/C][/ROW]
[ROW][C]96[/C][C] 12[/C][C] 12.76[/C][C]-0.7619[/C][/ROW]
[ROW][C]97[/C][C] 13[/C][C] 11.66[/C][C] 1.336[/C][/ROW]
[ROW][C]98[/C][C] 14[/C][C] 15.54[/C][C]-1.545[/C][/ROW]
[ROW][C]99[/C][C] 13[/C][C] 12.91[/C][C] 0.09399[/C][/ROW]
[ROW][C]100[/C][C] 12[/C][C] 12.6[/C][C]-0.6048[/C][/ROW]
[ROW][C]101[/C][C] 13[/C][C] 13.04[/C][C]-0.03873[/C][/ROW]
[ROW][C]102[/C][C] 12[/C][C] 12.51[/C][C]-0.5138[/C][/ROW]
[ROW][C]103[/C][C] 10[/C][C] 12.39[/C][C]-2.395[/C][/ROW]
[ROW][C]104[/C][C] 12[/C][C] 11.56[/C][C] 0.4428[/C][/ROW]
[ROW][C]105[/C][C] 13[/C][C] 13.63[/C][C]-0.6348[/C][/ROW]
[ROW][C]106[/C][C] 12[/C][C] 12.56[/C][C]-0.5578[/C][/ROW]
[ROW][C]107[/C][C] 13[/C][C] 11.13[/C][C] 1.874[/C][/ROW]
[ROW][C]108[/C][C] 12[/C][C] 12.55[/C][C]-0.5477[/C][/ROW]
[ROW][C]109[/C][C] 12[/C][C] 12.36[/C][C]-0.3608[/C][/ROW]
[ROW][C]110[/C][C] 12[/C][C] 12.33[/C][C]-0.3261[/C][/ROW]
[ROW][C]111[/C][C] 11[/C][C] 12.01[/C][C]-1.007[/C][/ROW]
[ROW][C]112[/C][C] 12[/C][C] 11.75[/C][C] 0.2549[/C][/ROW]
[ROW][C]113[/C][C] 9[/C][C] 11.62[/C][C]-2.618[/C][/ROW]
[ROW][C]114[/C][C] 14[/C][C] 11.54[/C][C] 2.465[/C][/ROW]
[ROW][C]115[/C][C] 12[/C][C] 11.01[/C][C] 0.9911[/C][/ROW]
[ROW][C]116[/C][C] 13[/C][C] 12.54[/C][C] 0.4637[/C][/ROW]
[ROW][C]117[/C][C] 13[/C][C] 12.12[/C][C] 0.877[/C][/ROW]
[ROW][C]118[/C][C] 13[/C][C] 12.89[/C][C] 0.1107[/C][/ROW]
[ROW][C]119[/C][C] 11[/C][C] 12.46[/C][C]-1.464[/C][/ROW]
[ROW][C]120[/C][C] 12[/C][C] 11.5[/C][C] 0.4994[/C][/ROW]
[ROW][C]121[/C][C] 11[/C][C] 11.97[/C][C]-0.9746[/C][/ROW]
[ROW][C]122[/C][C] 12[/C][C] 12.06[/C][C]-0.05667[/C][/ROW]
[ROW][C]123[/C][C] 12[/C][C] 12.07[/C][C]-0.06627[/C][/ROW]
[ROW][C]124[/C][C] 13[/C][C] 12.07[/C][C] 0.9329[/C][/ROW]
[ROW][C]125[/C][C] 12[/C][C] 12.57[/C][C]-0.5675[/C][/ROW]
[ROW][C]126[/C][C] 13[/C][C] 11.92[/C][C] 1.079[/C][/ROW]
[ROW][C]127[/C][C] 13[/C][C] 12.42[/C][C] 0.5762[/C][/ROW]
[ROW][C]128[/C][C] 12[/C][C] 11.37[/C][C] 0.6253[/C][/ROW]
[ROW][C]129[/C][C] 12[/C][C] 11.48[/C][C] 0.5218[/C][/ROW]
[ROW][C]130[/C][C] 8[/C][C] 9.907[/C][C]-1.907[/C][/ROW]
[ROW][C]131[/C][C] 12[/C][C] 11.89[/C][C] 0.1146[/C][/ROW]
[ROW][C]132[/C][C] 13[/C][C] 12.19[/C][C] 0.8129[/C][/ROW]
[ROW][C]133[/C][C] 10[/C][C] 10.49[/C][C]-0.4895[/C][/ROW]
[ROW][C]134[/C][C] 8[/C][C] 11.31[/C][C]-3.309[/C][/ROW]
[ROW][C]135[/C][C] 12[/C][C] 11.62[/C][C] 0.3793[/C][/ROW]
[ROW][C]136[/C][C] 13[/C][C] 13.46[/C][C]-0.4551[/C][/ROW]
[ROW][C]137[/C][C] 12[/C][C] 13.38[/C][C]-1.378[/C][/ROW]
[ROW][C]138[/C][C] 15[/C][C] 13.94[/C][C] 1.062[/C][/ROW]
[ROW][C]139[/C][C] 14[/C][C] 13.29[/C][C] 0.7131[/C][/ROW]
[ROW][C]140[/C][C] 10[/C][C] 11.5[/C][C]-1.5[/C][/ROW]
[ROW][C]141[/C][C] 11[/C][C] 12.59[/C][C]-1.591[/C][/ROW]
[ROW][C]142[/C][C] 12[/C][C] 12.15[/C][C]-0.1504[/C][/ROW]
[ROW][C]143[/C][C] 10[/C][C] 11.48[/C][C]-1.483[/C][/ROW]
[ROW][C]144[/C][C] 14[/C][C] 12.38[/C][C] 1.619[/C][/ROW]
[ROW][C]145[/C][C] 10[/C][C] 12.61[/C][C]-2.613[/C][/ROW]
[ROW][C]146[/C][C] 15[/C][C] 12.8[/C][C] 2.2[/C][/ROW]
[ROW][C]147[/C][C] 11[/C][C] 11.31[/C][C]-0.308[/C][/ROW]
[ROW][C]148[/C][C] 12[/C][C] 11.98[/C][C] 0.01514[/C][/ROW]
[ROW][C]149[/C][C] 9[/C][C] 10.4[/C][C]-1.404[/C][/ROW]
[ROW][C]150[/C][C] 12[/C][C] 12.67[/C][C]-0.6743[/C][/ROW]
[ROW][C]151[/C][C] 13[/C][C] 11.68[/C][C] 1.317[/C][/ROW]
[ROW][C]152[/C][C] 12[/C][C] 11.64[/C][C] 0.3578[/C][/ROW]
[ROW][C]153[/C][C] 9[/C][C] 11.47[/C][C]-2.47[/C][/ROW]
[ROW][C]154[/C][C] 12[/C][C] 12.69[/C][C]-0.6895[/C][/ROW]
[ROW][C]155[/C][C] 14[/C][C] 13.14[/C][C] 0.8616[/C][/ROW]
[ROW][C]156[/C][C] 11[/C][C] 12.38[/C][C]-1.384[/C][/ROW]
[ROW][C]157[/C][C] 12[/C][C] 11.3[/C][C] 0.698[/C][/ROW]
[ROW][C]158[/C][C] 14[/C][C] 12.75[/C][C] 1.249[/C][/ROW]
[ROW][C]159[/C][C] 12[/C][C] 11.56[/C][C] 0.437[/C][/ROW]
[ROW][C]160[/C][C] 15[/C][C] 11.96[/C][C] 3.042[/C][/ROW]
[ROW][C]161[/C][C] 11[/C][C] 13.29[/C][C]-2.285[/C][/ROW]
[ROW][C]162[/C][C] 12[/C][C] 12.16[/C][C]-0.1615[/C][/ROW]
[ROW][C]163[/C][C] 12[/C][C] 12.01[/C][C]-0.01027[/C][/ROW]
[ROW][C]164[/C][C] 10[/C][C] 10.26[/C][C]-0.2584[/C][/ROW]
[ROW][C]165[/C][C] 12[/C][C] 12.19[/C][C]-0.19[/C][/ROW]
[ROW][C]166[/C][C] 11[/C][C] 12[/C][C]-0.9966[/C][/ROW]
[ROW][C]167[/C][C] 11[/C][C] 11.81[/C][C]-0.8063[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=302923&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=302923&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	10	11.16	-1.156
2	13	11.32	1.681
3	14	12.68	1.324
4	12	10.79	1.213
5	12	12.95	-0.9536
6	13	12.25	0.7485
7	13	11.32	1.684
8	13	11.96	1.043
9	13	13	0.001882
10	14	12.93	1.071
11	14	11.56	2.443
12	12	12.32	-0.3246
13	12	12.73	-0.7323
14	11	12.12	-1.121
15	12	11.95	0.05352
16	14	12.36	1.643
17	12	12.39	-0.3884
18	11	11.47	-0.4748
19	13	11.94	1.063
20	13	11.73	1.269
21	12	12.81	-0.8085
22	13	12.57	0.4295
23	12	12.45	-0.4525
24	13	12.78	0.2215
25	11	11.13	-0.1321
26	12	12.03	-0.02566
27	12	11.92	0.08376
28	13	12.93	0.06566
29	13	12.03	0.9702
30	10	11.82	-1.824
31	12	12.5	-0.4973
32	13	13.04	-0.03689
33	13	11.75	1.251
34	10	11.78	-1.784
35	14	11.7	2.303
36	12	12.14	-0.1426
37	10	11.73	-1.732
38	10	11.2	-1.203
39	14	12.35	1.652
40	12	11.66	0.3422
41	14	13.39	0.61
42	10	11.09	-1.09
43	13	13.31	-0.3103
44	12	12.49	-0.4892
45	12	11.42	0.5795
46	13	12.25	0.7509
47	12	11.89	0.1145
48	10	11.92	-1.918
49	9	8.641	0.3585
50	14	12.84	1.158
51	15	12.49	2.513
52	14	12.97	1.029
53	8	10.78	-2.778
54	11	11.71	-0.7095
55	10	11.7	-1.703
56	12	11.12	0.8798
57	14	12.37	1.631
58	12	12.29	-0.29
59	12	12.76	-0.7554
60	14	13.26	0.7425
61	13	12.53	0.4732
62	13	13.07	-0.06983
63	13	10.91	2.094
64	12	13.1	-1.095
65	10	10.45	-0.4466
66	14	11.44	2.56
67	11	11.94	-0.9385
68	10	12.72	-2.722
69	13	12.27	0.7254
70	12	11.13	0.8658
71	12	13	-0.9978
72	11	10.85	0.149
73	10	12.31	-2.31
74	14	12.89	1.11
75	12	11.46	0.537
76	13	12.57	0.4337
77	11	11.98	-0.9767
78	10	12.39	-2.389
79	14	11.94	2.057
80	13	12.74	0.2575
81	7	11.21	-4.212
82	13	12.34	0.657
83	13	12.85	0.1549
84	13	12.65	0.3511
85	15	13	1.998
86	13	11.82	1.182
87	14	13.22	0.7839
88	12	12.02	-0.01756
89	13	12	0.9976
90	11	10.64	0.3618
91	12	12.13	-0.1332
92	14	12.77	1.235
93	13	12.43	0.5684
94	14	12.55	1.453
95	12	12.26	-0.2585
96	12	12.76	-0.7619
97	13	11.66	1.336
98	14	15.54	-1.545
99	13	12.91	0.09399
100	12	12.6	-0.6048
101	13	13.04	-0.03873
102	12	12.51	-0.5138
103	10	12.39	-2.395
104	12	11.56	0.4428
105	13	13.63	-0.6348
106	12	12.56	-0.5578
107	13	11.13	1.874
108	12	12.55	-0.5477
109	12	12.36	-0.3608
110	12	12.33	-0.3261
111	11	12.01	-1.007
112	12	11.75	0.2549
113	9	11.62	-2.618
114	14	11.54	2.465
115	12	11.01	0.9911
116	13	12.54	0.4637
117	13	12.12	0.877
118	13	12.89	0.1107
119	11	12.46	-1.464
120	12	11.5	0.4994
121	11	11.97	-0.9746
122	12	12.06	-0.05667
123	12	12.07	-0.06627
124	13	12.07	0.9329
125	12	12.57	-0.5675
126	13	11.92	1.079
127	13	12.42	0.5762
128	12	11.37	0.6253
129	12	11.48	0.5218
130	8	9.907	-1.907
131	12	11.89	0.1146
132	13	12.19	0.8129
133	10	10.49	-0.4895
134	8	11.31	-3.309
135	12	11.62	0.3793
136	13	13.46	-0.4551
137	12	13.38	-1.378
138	15	13.94	1.062
139	14	13.29	0.7131
140	10	11.5	-1.5
141	11	12.59	-1.591
142	12	12.15	-0.1504
143	10	11.48	-1.483
144	14	12.38	1.619
145	10	12.61	-2.613
146	15	12.8	2.2
147	11	11.31	-0.308
148	12	11.98	0.01514
149	9	10.4	-1.404
150	12	12.67	-0.6743
151	13	11.68	1.317
152	12	11.64	0.3578
153	9	11.47	-2.47
154	12	12.69	-0.6895
155	14	13.14	0.8616
156	11	12.38	-1.384
157	12	11.3	0.698
158	14	12.75	1.249
159	12	11.56	0.437
160	15	11.96	3.042
161	11	13.29	-2.285
162	12	12.16	-0.1615
163	12	12.01	-0.01027
164	10	10.26	-0.2584
165	12	12.19	-0.19
166	11	12	-0.9966
167	11	11.81	-0.8063

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
14	0.01632	0.03263	0.9837
15	0.04293	0.08586	0.9571
16	0.03953	0.07906	0.9605
17	0.2402	0.4803	0.7598
18	0.4125	0.8251	0.5875
19	0.3193	0.6385	0.6807
20	0.2933	0.5866	0.7067
21	0.2901	0.5802	0.7099
22	0.2408	0.4816	0.7592
23	0.2856	0.5713	0.7144
24	0.2172	0.4343	0.7828
25	0.1619	0.3239	0.8381
26	0.1351	0.2703	0.8649
27	0.09599	0.192	0.904
28	0.07053	0.1411	0.9295
29	0.05436	0.1087	0.9456
30	0.06161	0.1232	0.9384
31	0.06257	0.1251	0.9374
32	0.04344	0.08688	0.9566
33	0.07213	0.1443	0.9279
34	0.1062	0.2123	0.8938
35	0.1638	0.3275	0.8362
36	0.1263	0.2527	0.8737
37	0.129	0.2581	0.871
38	0.1496	0.2993	0.8504
39	0.1729	0.3458	0.8271
40	0.1433	0.2866	0.8567
41	0.1139	0.2279	0.8861
42	0.1152	0.2305	0.8848
43	0.08885	0.1777	0.9111
44	0.06837	0.1367	0.9316
45	0.05262	0.1052	0.9474
46	0.04045	0.0809	0.9596
47	0.03222	0.06445	0.9678
48	0.05883	0.1177	0.9412
49	0.04657	0.09313	0.9534
50	0.04443	0.08887	0.9556
51	0.08663	0.1733	0.9134
52	0.07918	0.1584	0.9208
53	0.2179	0.4357	0.7821
54	0.2085	0.4169	0.7915
55	0.2162	0.4324	0.7838
56	0.1876	0.3752	0.8124
57	0.1807	0.3615	0.8193
58	0.1556	0.3112	0.8444
59	0.1806	0.3612	0.8194
60	0.157	0.3141	0.843
61	0.138	0.276	0.862
62	0.1154	0.2307	0.8846
63	0.1364	0.2729	0.8636
64	0.1375	0.2749	0.8625
65	0.115	0.23	0.885
66	0.2193	0.4385	0.7808
67	0.1994	0.3988	0.8006
68	0.3298	0.6595	0.6702
69	0.3097	0.6193	0.6903
70	0.2831	0.5661	0.7169
71	0.2587	0.5175	0.7413
72	0.2225	0.4449	0.7775
73	0.3276	0.6553	0.6724
74	0.3083	0.6167	0.6917
75	0.2724	0.5449	0.7276
76	0.2391	0.4782	0.7609
77	0.2253	0.4506	0.7747
78	0.3352	0.6705	0.6648
79	0.3955	0.791	0.6045
80	0.356	0.7119	0.644
81	0.808	0.3841	0.192
82	0.7826	0.4347	0.2174
83	0.7583	0.4834	0.2417
84	0.7254	0.5492	0.2746
85	0.7817	0.4366	0.2183
86	0.7774	0.4452	0.2226
87	0.7551	0.4899	0.2449
88	0.7185	0.5629	0.2815
89	0.7007	0.5986	0.2993
90	0.6599	0.6801	0.3401
91	0.6222	0.7556	0.3778
92	0.6238	0.7523	0.3762
93	0.624	0.752	0.376
94	0.6382	0.7236	0.3618
95	0.5943	0.8114	0.4057
96	0.5603	0.8795	0.4397
97	0.5595	0.881	0.4405
98	0.5824	0.8351	0.4176
99	0.5562	0.8877	0.4438
100	0.5193	0.9615	0.4807
101	0.4713	0.9426	0.5287
102	0.4497	0.8995	0.5503
103	0.5239	0.9521	0.4761
104	0.5011	0.9978	0.4989
105	0.4641	0.9282	0.5359
106	0.43	0.86	0.57
107	0.4982	0.9964	0.5018
108	0.4538	0.9075	0.5462
109	0.4115	0.823	0.5885
110	0.3675	0.735	0.6325
111	0.3402	0.6804	0.6598
112	0.2958	0.5916	0.7042
113	0.4354	0.8709	0.5646
114	0.642	0.716	0.358
115	0.6253	0.7494	0.3747
116	0.5896	0.8209	0.4104
117	0.5535	0.893	0.4465
118	0.5076	0.9849	0.4924
119	0.5055	0.989	0.4945
120	0.4971	0.9943	0.5029
121	0.4733	0.9466	0.5267
122	0.4232	0.8464	0.5768
123	0.3884	0.7768	0.6116
124	0.3552	0.7104	0.6448
125	0.3672	0.7343	0.6328
126	0.3397	0.6795	0.6603
127	0.2994	0.5989	0.7006
128	0.2721	0.5442	0.7279
129	0.2291	0.4582	0.7709
130	0.2255	0.4509	0.7745
131	0.1917	0.3834	0.8083
132	0.1644	0.3287	0.8356
133	0.1379	0.2757	0.8621
134	0.2579	0.5158	0.7421
135	0.2393	0.4786	0.7607
136	0.1938	0.3876	0.8062
137	0.1857	0.3715	0.8143
138	0.1613	0.3226	0.8387
139	0.135	0.2699	0.865
140	0.1138	0.2275	0.8862
141	0.09717	0.1943	0.9028
142	0.07555	0.1511	0.9244
143	0.06395	0.1279	0.9361
144	0.08669	0.1734	0.9133
145	0.09517	0.1903	0.9048
146	0.2418	0.4835	0.7582
147	0.2075	0.415	0.7925
148	0.1546	0.3092	0.8454
149	0.2228	0.4456	0.7772
150	0.3141	0.6282	0.6859
151	0.3197	0.6393	0.6803
152	0.6033	0.7933	0.3967
153	0.4471	0.8943	0.5529

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
14 &  0.01632 &  0.03263 &  0.9837 \tabularnewline
15 &  0.04293 &  0.08586 &  0.9571 \tabularnewline
16 &  0.03953 &  0.07906 &  0.9605 \tabularnewline
17 &  0.2402 &  0.4803 &  0.7598 \tabularnewline
18 &  0.4125 &  0.8251 &  0.5875 \tabularnewline
19 &  0.3193 &  0.6385 &  0.6807 \tabularnewline
20 &  0.2933 &  0.5866 &  0.7067 \tabularnewline
21 &  0.2901 &  0.5802 &  0.7099 \tabularnewline
22 &  0.2408 &  0.4816 &  0.7592 \tabularnewline
23 &  0.2856 &  0.5713 &  0.7144 \tabularnewline
24 &  0.2172 &  0.4343 &  0.7828 \tabularnewline
25 &  0.1619 &  0.3239 &  0.8381 \tabularnewline
26 &  0.1351 &  0.2703 &  0.8649 \tabularnewline
27 &  0.09599 &  0.192 &  0.904 \tabularnewline
28 &  0.07053 &  0.1411 &  0.9295 \tabularnewline
29 &  0.05436 &  0.1087 &  0.9456 \tabularnewline
30 &  0.06161 &  0.1232 &  0.9384 \tabularnewline
31 &  0.06257 &  0.1251 &  0.9374 \tabularnewline
32 &  0.04344 &  0.08688 &  0.9566 \tabularnewline
33 &  0.07213 &  0.1443 &  0.9279 \tabularnewline
34 &  0.1062 &  0.2123 &  0.8938 \tabularnewline
35 &  0.1638 &  0.3275 &  0.8362 \tabularnewline
36 &  0.1263 &  0.2527 &  0.8737 \tabularnewline
37 &  0.129 &  0.2581 &  0.871 \tabularnewline
38 &  0.1496 &  0.2993 &  0.8504 \tabularnewline
39 &  0.1729 &  0.3458 &  0.8271 \tabularnewline
40 &  0.1433 &  0.2866 &  0.8567 \tabularnewline
41 &  0.1139 &  0.2279 &  0.8861 \tabularnewline
42 &  0.1152 &  0.2305 &  0.8848 \tabularnewline
43 &  0.08885 &  0.1777 &  0.9111 \tabularnewline
44 &  0.06837 &  0.1367 &  0.9316 \tabularnewline
45 &  0.05262 &  0.1052 &  0.9474 \tabularnewline
46 &  0.04045 &  0.0809 &  0.9596 \tabularnewline
47 &  0.03222 &  0.06445 &  0.9678 \tabularnewline
48 &  0.05883 &  0.1177 &  0.9412 \tabularnewline
49 &  0.04657 &  0.09313 &  0.9534 \tabularnewline
50 &  0.04443 &  0.08887 &  0.9556 \tabularnewline
51 &  0.08663 &  0.1733 &  0.9134 \tabularnewline
52 &  0.07918 &  0.1584 &  0.9208 \tabularnewline
53 &  0.2179 &  0.4357 &  0.7821 \tabularnewline
54 &  0.2085 &  0.4169 &  0.7915 \tabularnewline
55 &  0.2162 &  0.4324 &  0.7838 \tabularnewline
56 &  0.1876 &  0.3752 &  0.8124 \tabularnewline
57 &  0.1807 &  0.3615 &  0.8193 \tabularnewline
58 &  0.1556 &  0.3112 &  0.8444 \tabularnewline
59 &  0.1806 &  0.3612 &  0.8194 \tabularnewline
60 &  0.157 &  0.3141 &  0.843 \tabularnewline
61 &  0.138 &  0.276 &  0.862 \tabularnewline
62 &  0.1154 &  0.2307 &  0.8846 \tabularnewline
63 &  0.1364 &  0.2729 &  0.8636 \tabularnewline
64 &  0.1375 &  0.2749 &  0.8625 \tabularnewline
65 &  0.115 &  0.23 &  0.885 \tabularnewline
66 &  0.2193 &  0.4385 &  0.7808 \tabularnewline
67 &  0.1994 &  0.3988 &  0.8006 \tabularnewline
68 &  0.3298 &  0.6595 &  0.6702 \tabularnewline
69 &  0.3097 &  0.6193 &  0.6903 \tabularnewline
70 &  0.2831 &  0.5661 &  0.7169 \tabularnewline
71 &  0.2587 &  0.5175 &  0.7413 \tabularnewline
72 &  0.2225 &  0.4449 &  0.7775 \tabularnewline
73 &  0.3276 &  0.6553 &  0.6724 \tabularnewline
74 &  0.3083 &  0.6167 &  0.6917 \tabularnewline
75 &  0.2724 &  0.5449 &  0.7276 \tabularnewline
76 &  0.2391 &  0.4782 &  0.7609 \tabularnewline
77 &  0.2253 &  0.4506 &  0.7747 \tabularnewline
78 &  0.3352 &  0.6705 &  0.6648 \tabularnewline
79 &  0.3955 &  0.791 &  0.6045 \tabularnewline
80 &  0.356 &  0.7119 &  0.644 \tabularnewline
81 &  0.808 &  0.3841 &  0.192 \tabularnewline
82 &  0.7826 &  0.4347 &  0.2174 \tabularnewline
83 &  0.7583 &  0.4834 &  0.2417 \tabularnewline
84 &  0.7254 &  0.5492 &  0.2746 \tabularnewline
85 &  0.7817 &  0.4366 &  0.2183 \tabularnewline
86 &  0.7774 &  0.4452 &  0.2226 \tabularnewline
87 &  0.7551 &  0.4899 &  0.2449 \tabularnewline
88 &  0.7185 &  0.5629 &  0.2815 \tabularnewline
89 &  0.7007 &  0.5986 &  0.2993 \tabularnewline
90 &  0.6599 &  0.6801 &  0.3401 \tabularnewline
91 &  0.6222 &  0.7556 &  0.3778 \tabularnewline
92 &  0.6238 &  0.7523 &  0.3762 \tabularnewline
93 &  0.624 &  0.752 &  0.376 \tabularnewline
94 &  0.6382 &  0.7236 &  0.3618 \tabularnewline
95 &  0.5943 &  0.8114 &  0.4057 \tabularnewline
96 &  0.5603 &  0.8795 &  0.4397 \tabularnewline
97 &  0.5595 &  0.881 &  0.4405 \tabularnewline
98 &  0.5824 &  0.8351 &  0.4176 \tabularnewline
99 &  0.5562 &  0.8877 &  0.4438 \tabularnewline
100 &  0.5193 &  0.9615 &  0.4807 \tabularnewline
101 &  0.4713 &  0.9426 &  0.5287 \tabularnewline
102 &  0.4497 &  0.8995 &  0.5503 \tabularnewline
103 &  0.5239 &  0.9521 &  0.4761 \tabularnewline
104 &  0.5011 &  0.9978 &  0.4989 \tabularnewline
105 &  0.4641 &  0.9282 &  0.5359 \tabularnewline
106 &  0.43 &  0.86 &  0.57 \tabularnewline
107 &  0.4982 &  0.9964 &  0.5018 \tabularnewline
108 &  0.4538 &  0.9075 &  0.5462 \tabularnewline
109 &  0.4115 &  0.823 &  0.5885 \tabularnewline
110 &  0.3675 &  0.735 &  0.6325 \tabularnewline
111 &  0.3402 &  0.6804 &  0.6598 \tabularnewline
112 &  0.2958 &  0.5916 &  0.7042 \tabularnewline
113 &  0.4354 &  0.8709 &  0.5646 \tabularnewline
114 &  0.642 &  0.716 &  0.358 \tabularnewline
115 &  0.6253 &  0.7494 &  0.3747 \tabularnewline
116 &  0.5896 &  0.8209 &  0.4104 \tabularnewline
117 &  0.5535 &  0.893 &  0.4465 \tabularnewline
118 &  0.5076 &  0.9849 &  0.4924 \tabularnewline
119 &  0.5055 &  0.989 &  0.4945 \tabularnewline
120 &  0.4971 &  0.9943 &  0.5029 \tabularnewline
121 &  0.4733 &  0.9466 &  0.5267 \tabularnewline
122 &  0.4232 &  0.8464 &  0.5768 \tabularnewline
123 &  0.3884 &  0.7768 &  0.6116 \tabularnewline
124 &  0.3552 &  0.7104 &  0.6448 \tabularnewline
125 &  0.3672 &  0.7343 &  0.6328 \tabularnewline
126 &  0.3397 &  0.6795 &  0.6603 \tabularnewline
127 &  0.2994 &  0.5989 &  0.7006 \tabularnewline
128 &  0.2721 &  0.5442 &  0.7279 \tabularnewline
129 &  0.2291 &  0.4582 &  0.7709 \tabularnewline
130 &  0.2255 &  0.4509 &  0.7745 \tabularnewline
131 &  0.1917 &  0.3834 &  0.8083 \tabularnewline
132 &  0.1644 &  0.3287 &  0.8356 \tabularnewline
133 &  0.1379 &  0.2757 &  0.8621 \tabularnewline
134 &  0.2579 &  0.5158 &  0.7421 \tabularnewline
135 &  0.2393 &  0.4786 &  0.7607 \tabularnewline
136 &  0.1938 &  0.3876 &  0.8062 \tabularnewline
137 &  0.1857 &  0.3715 &  0.8143 \tabularnewline
138 &  0.1613 &  0.3226 &  0.8387 \tabularnewline
139 &  0.135 &  0.2699 &  0.865 \tabularnewline
140 &  0.1138 &  0.2275 &  0.8862 \tabularnewline
141 &  0.09717 &  0.1943 &  0.9028 \tabularnewline
142 &  0.07555 &  0.1511 &  0.9244 \tabularnewline
143 &  0.06395 &  0.1279 &  0.9361 \tabularnewline
144 &  0.08669 &  0.1734 &  0.9133 \tabularnewline
145 &  0.09517 &  0.1903 &  0.9048 \tabularnewline
146 &  0.2418 &  0.4835 &  0.7582 \tabularnewline
147 &  0.2075 &  0.415 &  0.7925 \tabularnewline
148 &  0.1546 &  0.3092 &  0.8454 \tabularnewline
149 &  0.2228 &  0.4456 &  0.7772 \tabularnewline
150 &  0.3141 &  0.6282 &  0.6859 \tabularnewline
151 &  0.3197 &  0.6393 &  0.6803 \tabularnewline
152 &  0.6033 &  0.7933 &  0.3967 \tabularnewline
153 &  0.4471 &  0.8943 &  0.5529 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=302923&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] 0.01632[/C][C] 0.03263[/C][C] 0.9837[/C][/ROW]
[ROW][C]15[/C][C] 0.04293[/C][C] 0.08586[/C][C] 0.9571[/C][/ROW]
[ROW][C]16[/C][C] 0.03953[/C][C] 0.07906[/C][C] 0.9605[/C][/ROW]
[ROW][C]17[/C][C] 0.2402[/C][C] 0.4803[/C][C] 0.7598[/C][/ROW]
[ROW][C]18[/C][C] 0.4125[/C][C] 0.8251[/C][C] 0.5875[/C][/ROW]
[ROW][C]19[/C][C] 0.3193[/C][C] 0.6385[/C][C] 0.6807[/C][/ROW]
[ROW][C]20[/C][C] 0.2933[/C][C] 0.5866[/C][C] 0.7067[/C][/ROW]
[ROW][C]21[/C][C] 0.2901[/C][C] 0.5802[/C][C] 0.7099[/C][/ROW]
[ROW][C]22[/C][C] 0.2408[/C][C] 0.4816[/C][C] 0.7592[/C][/ROW]
[ROW][C]23[/C][C] 0.2856[/C][C] 0.5713[/C][C] 0.7144[/C][/ROW]
[ROW][C]24[/C][C] 0.2172[/C][C] 0.4343[/C][C] 0.7828[/C][/ROW]
[ROW][C]25[/C][C] 0.1619[/C][C] 0.3239[/C][C] 0.8381[/C][/ROW]
[ROW][C]26[/C][C] 0.1351[/C][C] 0.2703[/C][C] 0.8649[/C][/ROW]
[ROW][C]27[/C][C] 0.09599[/C][C] 0.192[/C][C] 0.904[/C][/ROW]
[ROW][C]28[/C][C] 0.07053[/C][C] 0.1411[/C][C] 0.9295[/C][/ROW]
[ROW][C]29[/C][C] 0.05436[/C][C] 0.1087[/C][C] 0.9456[/C][/ROW]
[ROW][C]30[/C][C] 0.06161[/C][C] 0.1232[/C][C] 0.9384[/C][/ROW]
[ROW][C]31[/C][C] 0.06257[/C][C] 0.1251[/C][C] 0.9374[/C][/ROW]
[ROW][C]32[/C][C] 0.04344[/C][C] 0.08688[/C][C] 0.9566[/C][/ROW]
[ROW][C]33[/C][C] 0.07213[/C][C] 0.1443[/C][C] 0.9279[/C][/ROW]
[ROW][C]34[/C][C] 0.1062[/C][C] 0.2123[/C][C] 0.8938[/C][/ROW]
[ROW][C]35[/C][C] 0.1638[/C][C] 0.3275[/C][C] 0.8362[/C][/ROW]
[ROW][C]36[/C][C] 0.1263[/C][C] 0.2527[/C][C] 0.8737[/C][/ROW]
[ROW][C]37[/C][C] 0.129[/C][C] 0.2581[/C][C] 0.871[/C][/ROW]
[ROW][C]38[/C][C] 0.1496[/C][C] 0.2993[/C][C] 0.8504[/C][/ROW]
[ROW][C]39[/C][C] 0.1729[/C][C] 0.3458[/C][C] 0.8271[/C][/ROW]
[ROW][C]40[/C][C] 0.1433[/C][C] 0.2866[/C][C] 0.8567[/C][/ROW]
[ROW][C]41[/C][C] 0.1139[/C][C] 0.2279[/C][C] 0.8861[/C][/ROW]
[ROW][C]42[/C][C] 0.1152[/C][C] 0.2305[/C][C] 0.8848[/C][/ROW]
[ROW][C]43[/C][C] 0.08885[/C][C] 0.1777[/C][C] 0.9111[/C][/ROW]
[ROW][C]44[/C][C] 0.06837[/C][C] 0.1367[/C][C] 0.9316[/C][/ROW]
[ROW][C]45[/C][C] 0.05262[/C][C] 0.1052[/C][C] 0.9474[/C][/ROW]
[ROW][C]46[/C][C] 0.04045[/C][C] 0.0809[/C][C] 0.9596[/C][/ROW]
[ROW][C]47[/C][C] 0.03222[/C][C] 0.06445[/C][C] 0.9678[/C][/ROW]
[ROW][C]48[/C][C] 0.05883[/C][C] 0.1177[/C][C] 0.9412[/C][/ROW]
[ROW][C]49[/C][C] 0.04657[/C][C] 0.09313[/C][C] 0.9534[/C][/ROW]
[ROW][C]50[/C][C] 0.04443[/C][C] 0.08887[/C][C] 0.9556[/C][/ROW]
[ROW][C]51[/C][C] 0.08663[/C][C] 0.1733[/C][C] 0.9134[/C][/ROW]
[ROW][C]52[/C][C] 0.07918[/C][C] 0.1584[/C][C] 0.9208[/C][/ROW]
[ROW][C]53[/C][C] 0.2179[/C][C] 0.4357[/C][C] 0.7821[/C][/ROW]
[ROW][C]54[/C][C] 0.2085[/C][C] 0.4169[/C][C] 0.7915[/C][/ROW]
[ROW][C]55[/C][C] 0.2162[/C][C] 0.4324[/C][C] 0.7838[/C][/ROW]
[ROW][C]56[/C][C] 0.1876[/C][C] 0.3752[/C][C] 0.8124[/C][/ROW]
[ROW][C]57[/C][C] 0.1807[/C][C] 0.3615[/C][C] 0.8193[/C][/ROW]
[ROW][C]58[/C][C] 0.1556[/C][C] 0.3112[/C][C] 0.8444[/C][/ROW]
[ROW][C]59[/C][C] 0.1806[/C][C] 0.3612[/C][C] 0.8194[/C][/ROW]
[ROW][C]60[/C][C] 0.157[/C][C] 0.3141[/C][C] 0.843[/C][/ROW]
[ROW][C]61[/C][C] 0.138[/C][C] 0.276[/C][C] 0.862[/C][/ROW]
[ROW][C]62[/C][C] 0.1154[/C][C] 0.2307[/C][C] 0.8846[/C][/ROW]
[ROW][C]63[/C][C] 0.1364[/C][C] 0.2729[/C][C] 0.8636[/C][/ROW]
[ROW][C]64[/C][C] 0.1375[/C][C] 0.2749[/C][C] 0.8625[/C][/ROW]
[ROW][C]65[/C][C] 0.115[/C][C] 0.23[/C][C] 0.885[/C][/ROW]
[ROW][C]66[/C][C] 0.2193[/C][C] 0.4385[/C][C] 0.7808[/C][/ROW]
[ROW][C]67[/C][C] 0.1994[/C][C] 0.3988[/C][C] 0.8006[/C][/ROW]
[ROW][C]68[/C][C] 0.3298[/C][C] 0.6595[/C][C] 0.6702[/C][/ROW]
[ROW][C]69[/C][C] 0.3097[/C][C] 0.6193[/C][C] 0.6903[/C][/ROW]
[ROW][C]70[/C][C] 0.2831[/C][C] 0.5661[/C][C] 0.7169[/C][/ROW]
[ROW][C]71[/C][C] 0.2587[/C][C] 0.5175[/C][C] 0.7413[/C][/ROW]
[ROW][C]72[/C][C] 0.2225[/C][C] 0.4449[/C][C] 0.7775[/C][/ROW]
[ROW][C]73[/C][C] 0.3276[/C][C] 0.6553[/C][C] 0.6724[/C][/ROW]
[ROW][C]74[/C][C] 0.3083[/C][C] 0.6167[/C][C] 0.6917[/C][/ROW]
[ROW][C]75[/C][C] 0.2724[/C][C] 0.5449[/C][C] 0.7276[/C][/ROW]
[ROW][C]76[/C][C] 0.2391[/C][C] 0.4782[/C][C] 0.7609[/C][/ROW]
[ROW][C]77[/C][C] 0.2253[/C][C] 0.4506[/C][C] 0.7747[/C][/ROW]
[ROW][C]78[/C][C] 0.3352[/C][C] 0.6705[/C][C] 0.6648[/C][/ROW]
[ROW][C]79[/C][C] 0.3955[/C][C] 0.791[/C][C] 0.6045[/C][/ROW]
[ROW][C]80[/C][C] 0.356[/C][C] 0.7119[/C][C] 0.644[/C][/ROW]
[ROW][C]81[/C][C] 0.808[/C][C] 0.3841[/C][C] 0.192[/C][/ROW]
[ROW][C]82[/C][C] 0.7826[/C][C] 0.4347[/C][C] 0.2174[/C][/ROW]
[ROW][C]83[/C][C] 0.7583[/C][C] 0.4834[/C][C] 0.2417[/C][/ROW]
[ROW][C]84[/C][C] 0.7254[/C][C] 0.5492[/C][C] 0.2746[/C][/ROW]
[ROW][C]85[/C][C] 0.7817[/C][C] 0.4366[/C][C] 0.2183[/C][/ROW]
[ROW][C]86[/C][C] 0.7774[/C][C] 0.4452[/C][C] 0.2226[/C][/ROW]
[ROW][C]87[/C][C] 0.7551[/C][C] 0.4899[/C][C] 0.2449[/C][/ROW]
[ROW][C]88[/C][C] 0.7185[/C][C] 0.5629[/C][C] 0.2815[/C][/ROW]
[ROW][C]89[/C][C] 0.7007[/C][C] 0.5986[/C][C] 0.2993[/C][/ROW]
[ROW][C]90[/C][C] 0.6599[/C][C] 0.6801[/C][C] 0.3401[/C][/ROW]
[ROW][C]91[/C][C] 0.6222[/C][C] 0.7556[/C][C] 0.3778[/C][/ROW]
[ROW][C]92[/C][C] 0.6238[/C][C] 0.7523[/C][C] 0.3762[/C][/ROW]
[ROW][C]93[/C][C] 0.624[/C][C] 0.752[/C][C] 0.376[/C][/ROW]
[ROW][C]94[/C][C] 0.6382[/C][C] 0.7236[/C][C] 0.3618[/C][/ROW]
[ROW][C]95[/C][C] 0.5943[/C][C] 0.8114[/C][C] 0.4057[/C][/ROW]
[ROW][C]96[/C][C] 0.5603[/C][C] 0.8795[/C][C] 0.4397[/C][/ROW]
[ROW][C]97[/C][C] 0.5595[/C][C] 0.881[/C][C] 0.4405[/C][/ROW]
[ROW][C]98[/C][C] 0.5824[/C][C] 0.8351[/C][C] 0.4176[/C][/ROW]
[ROW][C]99[/C][C] 0.5562[/C][C] 0.8877[/C][C] 0.4438[/C][/ROW]
[ROW][C]100[/C][C] 0.5193[/C][C] 0.9615[/C][C] 0.4807[/C][/ROW]
[ROW][C]101[/C][C] 0.4713[/C][C] 0.9426[/C][C] 0.5287[/C][/ROW]
[ROW][C]102[/C][C] 0.4497[/C][C] 0.8995[/C][C] 0.5503[/C][/ROW]
[ROW][C]103[/C][C] 0.5239[/C][C] 0.9521[/C][C] 0.4761[/C][/ROW]
[ROW][C]104[/C][C] 0.5011[/C][C] 0.9978[/C][C] 0.4989[/C][/ROW]
[ROW][C]105[/C][C] 0.4641[/C][C] 0.9282[/C][C] 0.5359[/C][/ROW]
[ROW][C]106[/C][C] 0.43[/C][C] 0.86[/C][C] 0.57[/C][/ROW]
[ROW][C]107[/C][C] 0.4982[/C][C] 0.9964[/C][C] 0.5018[/C][/ROW]
[ROW][C]108[/C][C] 0.4538[/C][C] 0.9075[/C][C] 0.5462[/C][/ROW]
[ROW][C]109[/C][C] 0.4115[/C][C] 0.823[/C][C] 0.5885[/C][/ROW]
[ROW][C]110[/C][C] 0.3675[/C][C] 0.735[/C][C] 0.6325[/C][/ROW]
[ROW][C]111[/C][C] 0.3402[/C][C] 0.6804[/C][C] 0.6598[/C][/ROW]
[ROW][C]112[/C][C] 0.2958[/C][C] 0.5916[/C][C] 0.7042[/C][/ROW]
[ROW][C]113[/C][C] 0.4354[/C][C] 0.8709[/C][C] 0.5646[/C][/ROW]
[ROW][C]114[/C][C] 0.642[/C][C] 0.716[/C][C] 0.358[/C][/ROW]
[ROW][C]115[/C][C] 0.6253[/C][C] 0.7494[/C][C] 0.3747[/C][/ROW]
[ROW][C]116[/C][C] 0.5896[/C][C] 0.8209[/C][C] 0.4104[/C][/ROW]
[ROW][C]117[/C][C] 0.5535[/C][C] 0.893[/C][C] 0.4465[/C][/ROW]
[ROW][C]118[/C][C] 0.5076[/C][C] 0.9849[/C][C] 0.4924[/C][/ROW]
[ROW][C]119[/C][C] 0.5055[/C][C] 0.989[/C][C] 0.4945[/C][/ROW]
[ROW][C]120[/C][C] 0.4971[/C][C] 0.9943[/C][C] 0.5029[/C][/ROW]
[ROW][C]121[/C][C] 0.4733[/C][C] 0.9466[/C][C] 0.5267[/C][/ROW]
[ROW][C]122[/C][C] 0.4232[/C][C] 0.8464[/C][C] 0.5768[/C][/ROW]
[ROW][C]123[/C][C] 0.3884[/C][C] 0.7768[/C][C] 0.6116[/C][/ROW]
[ROW][C]124[/C][C] 0.3552[/C][C] 0.7104[/C][C] 0.6448[/C][/ROW]
[ROW][C]125[/C][C] 0.3672[/C][C] 0.7343[/C][C] 0.6328[/C][/ROW]
[ROW][C]126[/C][C] 0.3397[/C][C] 0.6795[/C][C] 0.6603[/C][/ROW]
[ROW][C]127[/C][C] 0.2994[/C][C] 0.5989[/C][C] 0.7006[/C][/ROW]
[ROW][C]128[/C][C] 0.2721[/C][C] 0.5442[/C][C] 0.7279[/C][/ROW]
[ROW][C]129[/C][C] 0.2291[/C][C] 0.4582[/C][C] 0.7709[/C][/ROW]
[ROW][C]130[/C][C] 0.2255[/C][C] 0.4509[/C][C] 0.7745[/C][/ROW]
[ROW][C]131[/C][C] 0.1917[/C][C] 0.3834[/C][C] 0.8083[/C][/ROW]
[ROW][C]132[/C][C] 0.1644[/C][C] 0.3287[/C][C] 0.8356[/C][/ROW]
[ROW][C]133[/C][C] 0.1379[/C][C] 0.2757[/C][C] 0.8621[/C][/ROW]
[ROW][C]134[/C][C] 0.2579[/C][C] 0.5158[/C][C] 0.7421[/C][/ROW]
[ROW][C]135[/C][C] 0.2393[/C][C] 0.4786[/C][C] 0.7607[/C][/ROW]
[ROW][C]136[/C][C] 0.1938[/C][C] 0.3876[/C][C] 0.8062[/C][/ROW]
[ROW][C]137[/C][C] 0.1857[/C][C] 0.3715[/C][C] 0.8143[/C][/ROW]
[ROW][C]138[/C][C] 0.1613[/C][C] 0.3226[/C][C] 0.8387[/C][/ROW]
[ROW][C]139[/C][C] 0.135[/C][C] 0.2699[/C][C] 0.865[/C][/ROW]
[ROW][C]140[/C][C] 0.1138[/C][C] 0.2275[/C][C] 0.8862[/C][/ROW]
[ROW][C]141[/C][C] 0.09717[/C][C] 0.1943[/C][C] 0.9028[/C][/ROW]
[ROW][C]142[/C][C] 0.07555[/C][C] 0.1511[/C][C] 0.9244[/C][/ROW]
[ROW][C]143[/C][C] 0.06395[/C][C] 0.1279[/C][C] 0.9361[/C][/ROW]
[ROW][C]144[/C][C] 0.08669[/C][C] 0.1734[/C][C] 0.9133[/C][/ROW]
[ROW][C]145[/C][C] 0.09517[/C][C] 0.1903[/C][C] 0.9048[/C][/ROW]
[ROW][C]146[/C][C] 0.2418[/C][C] 0.4835[/C][C] 0.7582[/C][/ROW]
[ROW][C]147[/C][C] 0.2075[/C][C] 0.415[/C][C] 0.7925[/C][/ROW]
[ROW][C]148[/C][C] 0.1546[/C][C] 0.3092[/C][C] 0.8454[/C][/ROW]
[ROW][C]149[/C][C] 0.2228[/C][C] 0.4456[/C][C] 0.7772[/C][/ROW]
[ROW][C]150[/C][C] 0.3141[/C][C] 0.6282[/C][C] 0.6859[/C][/ROW]
[ROW][C]151[/C][C] 0.3197[/C][C] 0.6393[/C][C] 0.6803[/C][/ROW]
[ROW][C]152[/C][C] 0.6033[/C][C] 0.7933[/C][C] 0.3967[/C][/ROW]
[ROW][C]153[/C][C] 0.4471[/C][C] 0.8943[/C][C] 0.5529[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=302923&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=302923&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	0.01632	0.03263	0.9837
15	0.04293	0.08586	0.9571
16	0.03953	0.07906	0.9605
17	0.2402	0.4803	0.7598
18	0.4125	0.8251	0.5875
19	0.3193	0.6385	0.6807
20	0.2933	0.5866	0.7067
21	0.2901	0.5802	0.7099
22	0.2408	0.4816	0.7592
23	0.2856	0.5713	0.7144
24	0.2172	0.4343	0.7828
25	0.1619	0.3239	0.8381
26	0.1351	0.2703	0.8649
27	0.09599	0.192	0.904
28	0.07053	0.1411	0.9295
29	0.05436	0.1087	0.9456
30	0.06161	0.1232	0.9384
31	0.06257	0.1251	0.9374
32	0.04344	0.08688	0.9566
33	0.07213	0.1443	0.9279
34	0.1062	0.2123	0.8938
35	0.1638	0.3275	0.8362
36	0.1263	0.2527	0.8737
37	0.129	0.2581	0.871
38	0.1496	0.2993	0.8504
39	0.1729	0.3458	0.8271
40	0.1433	0.2866	0.8567
41	0.1139	0.2279	0.8861
42	0.1152	0.2305	0.8848
43	0.08885	0.1777	0.9111
44	0.06837	0.1367	0.9316
45	0.05262	0.1052	0.9474
46	0.04045	0.0809	0.9596
47	0.03222	0.06445	0.9678
48	0.05883	0.1177	0.9412
49	0.04657	0.09313	0.9534
50	0.04443	0.08887	0.9556
51	0.08663	0.1733	0.9134
52	0.07918	0.1584	0.9208
53	0.2179	0.4357	0.7821
54	0.2085	0.4169	0.7915
55	0.2162	0.4324	0.7838
56	0.1876	0.3752	0.8124
57	0.1807	0.3615	0.8193
58	0.1556	0.3112	0.8444
59	0.1806	0.3612	0.8194
60	0.157	0.3141	0.843
61	0.138	0.276	0.862
62	0.1154	0.2307	0.8846
63	0.1364	0.2729	0.8636
64	0.1375	0.2749	0.8625
65	0.115	0.23	0.885
66	0.2193	0.4385	0.7808
67	0.1994	0.3988	0.8006
68	0.3298	0.6595	0.6702
69	0.3097	0.6193	0.6903
70	0.2831	0.5661	0.7169
71	0.2587	0.5175	0.7413
72	0.2225	0.4449	0.7775
73	0.3276	0.6553	0.6724
74	0.3083	0.6167	0.6917
75	0.2724	0.5449	0.7276
76	0.2391	0.4782	0.7609
77	0.2253	0.4506	0.7747
78	0.3352	0.6705	0.6648
79	0.3955	0.791	0.6045
80	0.356	0.7119	0.644
81	0.808	0.3841	0.192
82	0.7826	0.4347	0.2174
83	0.7583	0.4834	0.2417
84	0.7254	0.5492	0.2746
85	0.7817	0.4366	0.2183
86	0.7774	0.4452	0.2226
87	0.7551	0.4899	0.2449
88	0.7185	0.5629	0.2815
89	0.7007	0.5986	0.2993
90	0.6599	0.6801	0.3401
91	0.6222	0.7556	0.3778
92	0.6238	0.7523	0.3762
93	0.624	0.752	0.376
94	0.6382	0.7236	0.3618
95	0.5943	0.8114	0.4057
96	0.5603	0.8795	0.4397
97	0.5595	0.881	0.4405
98	0.5824	0.8351	0.4176
99	0.5562	0.8877	0.4438
100	0.5193	0.9615	0.4807
101	0.4713	0.9426	0.5287
102	0.4497	0.8995	0.5503
103	0.5239	0.9521	0.4761
104	0.5011	0.9978	0.4989
105	0.4641	0.9282	0.5359
106	0.43	0.86	0.57
107	0.4982	0.9964	0.5018
108	0.4538	0.9075	0.5462
109	0.4115	0.823	0.5885
110	0.3675	0.735	0.6325
111	0.3402	0.6804	0.6598
112	0.2958	0.5916	0.7042
113	0.4354	0.8709	0.5646
114	0.642	0.716	0.358
115	0.6253	0.7494	0.3747
116	0.5896	0.8209	0.4104
117	0.5535	0.893	0.4465
118	0.5076	0.9849	0.4924
119	0.5055	0.989	0.4945
120	0.4971	0.9943	0.5029
121	0.4733	0.9466	0.5267
122	0.4232	0.8464	0.5768
123	0.3884	0.7768	0.6116
124	0.3552	0.7104	0.6448
125	0.3672	0.7343	0.6328
126	0.3397	0.6795	0.6603
127	0.2994	0.5989	0.7006
128	0.2721	0.5442	0.7279
129	0.2291	0.4582	0.7709
130	0.2255	0.4509	0.7745
131	0.1917	0.3834	0.8083
132	0.1644	0.3287	0.8356
133	0.1379	0.2757	0.8621
134	0.2579	0.5158	0.7421
135	0.2393	0.4786	0.7607
136	0.1938	0.3876	0.8062
137	0.1857	0.3715	0.8143
138	0.1613	0.3226	0.8387
139	0.135	0.2699	0.865
140	0.1138	0.2275	0.8862
141	0.09717	0.1943	0.9028
142	0.07555	0.1511	0.9244
143	0.06395	0.1279	0.9361
144	0.08669	0.1734	0.9133
145	0.09517	0.1903	0.9048
146	0.2418	0.4835	0.7582
147	0.2075	0.415	0.7925
148	0.1546	0.3092	0.8454
149	0.2228	0.4456	0.7772
150	0.3141	0.6282	0.6859
151	0.3197	0.6393	0.6803
152	0.6033	0.7933	0.3967
153	0.4471	0.8943	0.5529

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	1	0.00714286	OK
10% type I error level	8	0.0571429	OK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 &  0 & OK \tabularnewline
5% type I error level & 1 & 0.00714286 & OK \tabularnewline
10% type I error level & 8 & 0.0571429 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=302923&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]1[/C][C]0.00714286[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]8[/C][C]0.0571429[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=302923&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=302923&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	1	0.00714286	OK
10% type I error level	8	0.0571429	OK

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted RESET test data: mylm RESET = 1.127, df1 = 2, df2 = 154, p-value = 0.3267
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 0.76489, df1 = 20, df2 = 136, p-value = 0.751
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 1.1689, df1 = 2, df2 = 154, p-value = 0.3135

\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 = 1.127, df1 = 2, df2 = 154, p-value = 0.3267
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of regressors \tabularnewline
> reset_test_regressors
	RESET test
data:  mylm
RESET = 0.76489, df1 = 20, df2 = 136, p-value = 0.751
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of principal components \tabularnewline
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 1.1689, df1 = 2, df2 = 154, p-value = 0.3135
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=302923&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 = 1.127, df1 = 2, df2 = 154, p-value = 0.3267
[/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 = 0.76489, df1 = 20, df2 = 136, p-value = 0.751
[/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 = 1.1689, df1 = 2, df2 = 154, p-value = 0.3135
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=302923&T=7

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=302923&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 = 1.127, df1 = 2, df2 = 154, p-value = 0.3267
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 0.76489, df1 = 20, df2 = 136, p-value = 0.751
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 1.1689, df1 = 2, df2 = 154, p-value = 0.3135

Variance Inflation Factors (Multicollinearity)
> vif ECSUM EPSUM GWSUM IVHBSUM IKSUM ITHSUM KVDDSUM SNSUM 1.094153 1.125516 1.086792 1.057595 1.225304 1.111554 1.112658 1.065332 SKEOUSUM VSUM 1.178890 1.168493

\begin{tabular}{lllllllll}
\hline
Variance Inflation Factors (Multicollinearity) \tabularnewline
> vif
   ECSUM    EPSUM    GWSUM  IVHBSUM    IKSUM   ITHSUM  KVDDSUM    SNSUM 
1.094153 1.125516 1.086792 1.057595 1.225304 1.111554 1.112658 1.065332 
SKEOUSUM     VSUM 
1.178890 1.168493 
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=302923&T=8

[TABLE]
[ROW][C]Variance Inflation Factors (Multicollinearity)[/C][/ROW]
[ROW][C]> vif
   ECSUM    EPSUM    GWSUM  IVHBSUM    IKSUM   ITHSUM  KVDDSUM    SNSUM 
1.094153 1.125516 1.086792 1.057595 1.225304 1.111554 1.112658 1.065332 
SKEOUSUM     VSUM 
1.178890 1.168493 
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=302923&T=8

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=302923&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 ECSUM EPSUM GWSUM IVHBSUM IKSUM ITHSUM KVDDSUM SNSUM 1.094153 1.125516 1.086792 1.057595 1.225304 1.111554 1.112658 1.065332 SKEOUSUM VSUM 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):

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):

par5 <- '0'
par4 <- '0'
par3 <- 'No Linear Trend'
par2 <- 'Do not include Seasonal Dummies'
par1 <- '1'
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