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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Wed, 05 Dec 2018 10:16:59 +0100

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2018/Dec/05/t1544002784raxx98cso7q0nij.htm/, Retrieved Sat, 04 May 2024 04:50:07 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=315767, Retrieved Sat, 04 May 2024 04:50:07 +0000

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Estimated Impact

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

-       [Multiple Regression] [Probleemstelling ...] [2018-12-05 09:16:59] [e402f482a79f42b301acfa5359f31da7] [Current]

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

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	14 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 time14 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=315767&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]

14 seconds[/C][/ROW] [ROW]

R Server[/C]

Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=315767&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=315767&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	14 seconds
R Server	Big Analytics Cloud Computing Center

Multiple Linear Regression - Estimated Regression Equation
TVDCSUM[t] = -3.06225 + 0.0882605ECSUM[t] -0.0310463EPSUM[t] -0.014804IKSUM[t] -0.00582557KVDDSUM[t] + 0.501122SKEOUSUM[t] + 0.484291GWSUM[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
TVDCSUM[t] =  -3.06225 +  0.0882605ECSUM[t] -0.0310463EPSUM[t] -0.014804IKSUM[t] -0.00582557KVDDSUM[t] +  0.501122SKEOUSUM[t] +  0.484291GWSUM[t]  + e[t] \tabularnewline
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=315767&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]TVDCSUM[t] =  -3.06225 +  0.0882605ECSUM[t] -0.0310463EPSUM[t] -0.014804IKSUM[t] -0.00582557KVDDSUM[t] +  0.501122SKEOUSUM[t] +  0.484291GWSUM[t]  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=315767&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=315767&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
TVDCSUM[t] = -3.06225 + 0.0882605ECSUM[t] -0.0310463EPSUM[t] -0.014804IKSUM[t] -0.00582557KVDDSUM[t] + 0.501122SKEOUSUM[t] + 0.484291GWSUM[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)	-3.062	3.569	-8.5790e-01	0.3939	0.1969
ECSUM	+0.08826	0.06452	+1.3680e+00	0.1757	0.08786
EPSUM	-0.03105	0.09878	-3.1430e-01	0.7542	0.3771
IKSUM	-0.0148	0.09784	-1.5130e-01	0.8802	0.4401
KVDDSUM	-0.005826	0.08128	-7.1670e-02	0.9431	0.4715
SKEOUSUM	+0.5011	0.09813	+5.1070e+00	2.71e-06	1.355e-06
GWSUM	+0.4843	0.1143	+4.2370e+00	6.791e-05	3.396e-05

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Ordinary Least Squares \tabularnewline
Variable & Parameter & S.D. & T-STATH0: parameter = 0 & 2-tail p-value & 1-tail p-value \tabularnewline
(Intercept) & -3.062 &  3.569 & -8.5790e-01 &  0.3939 &  0.1969 \tabularnewline
ECSUM & +0.08826 &  0.06452 & +1.3680e+00 &  0.1757 &  0.08786 \tabularnewline
EPSUM & -0.03105 &  0.09878 & -3.1430e-01 &  0.7542 &  0.3771 \tabularnewline
IKSUM & -0.0148 &  0.09784 & -1.5130e-01 &  0.8802 &  0.4401 \tabularnewline
KVDDSUM & -0.005826 &  0.08128 & -7.1670e-02 &  0.9431 &  0.4715 \tabularnewline
SKEOUSUM & +0.5011 &  0.09813 & +5.1070e+00 &  2.71e-06 &  1.355e-06 \tabularnewline
GWSUM & +0.4843 &  0.1143 & +4.2370e+00 &  6.791e-05 &  3.396e-05 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=315767&T=2

[TABLE]
[ROW][C]Multiple Linear Regression - Ordinary Least Squares[/C][/ROW]
[ROW][C]Variable[/C][C]Parameter[/C][C]S.D.[/C][C]T-STATH0: parameter = 0[/C][C]2-tail p-value[/C][C]1-tail p-value[/C][/ROW]
[ROW][C](Intercept)[/C][C]-3.062[/C][C] 3.569[/C][C]-8.5790e-01[/C][C] 0.3939[/C][C] 0.1969[/C][/ROW]
[ROW][C]ECSUM[/C][C]+0.08826[/C][C] 0.06452[/C][C]+1.3680e+00[/C][C] 0.1757[/C][C] 0.08786[/C][/ROW]
[ROW][C]EPSUM[/C][C]-0.03105[/C][C] 0.09878[/C][C]-3.1430e-01[/C][C] 0.7542[/C][C] 0.3771[/C][/ROW]
[ROW][C]IKSUM[/C][C]-0.0148[/C][C] 0.09784[/C][C]-1.5130e-01[/C][C] 0.8802[/C][C] 0.4401[/C][/ROW]
[ROW][C]KVDDSUM[/C][C]-0.005826[/C][C] 0.08128[/C][C]-7.1670e-02[/C][C] 0.9431[/C][C] 0.4715[/C][/ROW]
[ROW][C]SKEOUSUM[/C][C]+0.5011[/C][C] 0.09813[/C][C]+5.1070e+00[/C][C] 2.71e-06[/C][C] 1.355e-06[/C][/ROW]
[ROW][C]GWSUM[/C][C]+0.4843[/C][C] 0.1143[/C][C]+4.2370e+00[/C][C] 6.791e-05[/C][C] 3.396e-05[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=315767&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=315767&T=2

As an alternative you can also use a QR Code:

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

Multiple Linear Regression - Ordinary Least Squares
Variable	Parameter	S.D.	T-STATH0: parameter = 0	2-tail p-value	1-tail p-value
(Intercept)	-3.062	3.569	-8.5790e-01	0.3939	0.1969
ECSUM	+0.08826	0.06452	+1.3680e+00	0.1757	0.08786
EPSUM	-0.03105	0.09878	-3.1430e-01	0.7542	0.3771
IKSUM	-0.0148	0.09784	-1.5130e-01	0.8802	0.4401
KVDDSUM	-0.005826	0.08128	-7.1670e-02	0.9431	0.4715
SKEOUSUM	+0.5011	0.09813	+5.1070e+00	2.71e-06	1.355e-06
GWSUM	+0.4843	0.1143	+4.2370e+00	6.791e-05	3.396e-05

Multiple Linear Regression - Regression Statistics
Multiple R	0.6259
R-squared	0.3918
Adjusted R-squared	0.3397
F-TEST (value)	7.516
F-TEST (DF numerator)	6
F-TEST (DF denominator)	70
p-value	3.078e-06
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.506
Sum Squared Residuals	158.8

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.6259 \tabularnewline
R-squared &  0.3918 \tabularnewline
Adjusted R-squared &  0.3397 \tabularnewline
F-TEST (value) &  7.516 \tabularnewline
F-TEST (DF numerator) & 6 \tabularnewline
F-TEST (DF denominator) & 70 \tabularnewline
p-value &  3.078e-06 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  1.506 \tabularnewline
Sum Squared Residuals &  158.8 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=315767&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.6259[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.3918[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.3397[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 7.516[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]6[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]70[/C][/ROW]
[ROW][C]p-value[/C][C] 3.078e-06[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C] 1.506[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 158.8[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=315767&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=315767&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	0.6259
R-squared	0.3918
Adjusted R-squared	0.3397
F-TEST (value)	7.516
F-TEST (DF numerator)	6
F-TEST (DF denominator)	70
p-value	3.078e-06
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.506
Sum Squared Residuals	158.8

Menu of Residual Diagnostics
Description	Link
Histogram	Compute
Central Tendency	Compute
QQ Plot	Compute
Kernel Density Plot	Compute
Skewness/Kurtosis Test	Compute
Skewness-Kurtosis Plot	Compute
Harrell-Davis Plot	Compute
Bootstrap Plot -- Central Tendency	Compute
Blocked Bootstrap Plot -- Central Tendency	Compute
(Partial) Autocorrelation Plot	Compute
Spectral Analysis	Compute
Tukey lambda PPCC Plot	Compute
Box-Cox Normality Plot	Compute
Summary Statistics	Compute

\begin{tabular}{lllllllll}
\hline
Menu of Residual Diagnostics \tabularnewline
Description & Link \tabularnewline
Histogram & Compute \tabularnewline
Central Tendency & Compute \tabularnewline
QQ Plot & Compute \tabularnewline
Kernel Density Plot & Compute \tabularnewline
Skewness/Kurtosis Test & Compute \tabularnewline
Skewness-Kurtosis Plot & Compute \tabularnewline
Harrell-Davis Plot & Compute \tabularnewline
Bootstrap Plot -- Central Tendency & Compute \tabularnewline
Blocked Bootstrap Plot -- Central Tendency & Compute \tabularnewline
(Partial) Autocorrelation Plot & Compute \tabularnewline
Spectral Analysis & Compute \tabularnewline
Tukey lambda PPCC Plot & Compute \tabularnewline
Box-Cox Normality Plot & Compute \tabularnewline
Summary Statistics & Compute \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=315767&T=4

[TABLE]
[ROW][C]Menu of Residual Diagnostics[/C][/ROW]
[ROW][C]Description[/C][C]Link[/C][/ROW]
[ROW][C]Histogram[/C][C]Compute[/C][/ROW]
[ROW][C]Central Tendency[/C][C]Compute[/C][/ROW]
[ROW][C]QQ Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Kernel Density Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Skewness/Kurtosis Test[/C][C]Compute[/C][/ROW]
[ROW][C]Skewness-Kurtosis Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Harrell-Davis Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Bootstrap Plot -- Central Tendency[/C][C]Compute[/C][/ROW]
[ROW][C]Blocked Bootstrap Plot -- Central Tendency[/C][C]Compute[/C][/ROW]
[ROW][C](Partial) Autocorrelation Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Spectral Analysis[/C][C]Compute[/C][/ROW]
[ROW][C]Tukey lambda PPCC Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Box-Cox Normality Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Summary Statistics[/C][C]Compute[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=315767&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=315767&T=4

As an alternative you can also use a QR Code:

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

Menu of Residual Diagnostics
Description	Link
Histogram	Compute
Central Tendency	Compute
QQ Plot	Compute
Kernel Density Plot	Compute
Skewness/Kurtosis Test	Compute
Skewness-Kurtosis Plot	Compute
Harrell-Davis Plot	Compute
Bootstrap Plot -- Central Tendency	Compute
Blocked Bootstrap Plot -- Central Tendency	Compute
(Partial) Autocorrelation Plot	Compute
Spectral Analysis	Compute
Tukey lambda PPCC Plot	Compute
Box-Cox Normality Plot	Compute
Summary Statistics	Compute

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	13	13.96	-0.9589
2	17	16.07	0.9335
3	16	15.88	0.1187
4	17	16.76	0.2425
5	17	14.59	2.41
6	15	16.01	-1.013
7	16	16.78	-0.778
8	14	15.78	-1.784
9	16	15.31	0.6899
10	17	15.04	1.957
11	16	16.36	-0.3623
12	16	15.48	0.5209
13	15	15.06	-0.06015
14	16	16.57	-0.5691
15	13	14.92	-1.92
16	15	15.46	-0.4619
17	17	17.32	-0.3155
18	13	15.13	-2.129
19	17	14.6	2.402
20	14	15.14	-1.139
21	14	14.27	-0.274
22	18	15.46	2.542
23	17	17.1	-0.1042
24	13	13.51	-0.5118
25	16	17.41	-1.407
26	15	16.29	-1.285
27	13	14.96	-1.956
28	17	16.77	0.2303
29	11	12.56	-1.561
30	13	14.44	-1.442
31	17	16.46	0.5369
32	16	15.33	0.6735
33	16	16.59	-0.5905
34	17	14.6	2.4
35	14	16.03	-2.03
36	16	15.46	0.5377
37	15	14.22	0.7758
38	16	15.64	0.3563
39	14	14.7	-0.6974
40	15	15.37	-0.3702
41	17	15.64	1.359
42	20	16.26	3.745
43	17	15.6	1.397
44	18	17.09	0.913
45	14	12.85	1.153
46	17	16.35	0.6459
47	17	16.15	0.8546
48	16	15.75	0.2454
49	18	13.98	4.024
50	16	16.75	-0.7461
51	13	15.68	-2.684
52	12	14.68	-2.678
53	16	13.7	2.299
54	16	15.64	0.3618
55	16	16.19	-0.1918
56	14	15.64	-1.639
57	15	14.45	0.5472
58	14	14.54	-0.5367
59	15	14.71	0.2897
60	15	15.69	-0.6908
61	16	15.22	0.7845
62	11	11.7	-0.7048
63	18	15.92	2.075
64	11	14.32	-3.317
65	18	18.47	-0.4679
66	17	17.17	-0.1672
67	14	15.31	-1.305
68	17	15.97	1.033
69	14	15.44	-1.444
70	19	16.87	2.131
71	16	16.5	-0.5011
72	15	15.2	-0.2015
73	12	14.18	-2.18
74	17	16.78	0.2193
75	15	14.25	0.7525
76	16	15.95	0.05034
77	16	15.03	0.9703

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 &  13 &  13.96 & -0.9589 \tabularnewline
2 &  17 &  16.07 &  0.9335 \tabularnewline
3 &  16 &  15.88 &  0.1187 \tabularnewline
4 &  17 &  16.76 &  0.2425 \tabularnewline
5 &  17 &  14.59 &  2.41 \tabularnewline
6 &  15 &  16.01 & -1.013 \tabularnewline
7 &  16 &  16.78 & -0.778 \tabularnewline
8 &  14 &  15.78 & -1.784 \tabularnewline
9 &  16 &  15.31 &  0.6899 \tabularnewline
10 &  17 &  15.04 &  1.957 \tabularnewline
11 &  16 &  16.36 & -0.3623 \tabularnewline
12 &  16 &  15.48 &  0.5209 \tabularnewline
13 &  15 &  15.06 & -0.06015 \tabularnewline
14 &  16 &  16.57 & -0.5691 \tabularnewline
15 &  13 &  14.92 & -1.92 \tabularnewline
16 &  15 &  15.46 & -0.4619 \tabularnewline
17 &  17 &  17.32 & -0.3155 \tabularnewline
18 &  13 &  15.13 & -2.129 \tabularnewline
19 &  17 &  14.6 &  2.402 \tabularnewline
20 &  14 &  15.14 & -1.139 \tabularnewline
21 &  14 &  14.27 & -0.274 \tabularnewline
22 &  18 &  15.46 &  2.542 \tabularnewline
23 &  17 &  17.1 & -0.1042 \tabularnewline
24 &  13 &  13.51 & -0.5118 \tabularnewline
25 &  16 &  17.41 & -1.407 \tabularnewline
26 &  15 &  16.29 & -1.285 \tabularnewline
27 &  13 &  14.96 & -1.956 \tabularnewline
28 &  17 &  16.77 &  0.2303 \tabularnewline
29 &  11 &  12.56 & -1.561 \tabularnewline
30 &  13 &  14.44 & -1.442 \tabularnewline
31 &  17 &  16.46 &  0.5369 \tabularnewline
32 &  16 &  15.33 &  0.6735 \tabularnewline
33 &  16 &  16.59 & -0.5905 \tabularnewline
34 &  17 &  14.6 &  2.4 \tabularnewline
35 &  14 &  16.03 & -2.03 \tabularnewline
36 &  16 &  15.46 &  0.5377 \tabularnewline
37 &  15 &  14.22 &  0.7758 \tabularnewline
38 &  16 &  15.64 &  0.3563 \tabularnewline
39 &  14 &  14.7 & -0.6974 \tabularnewline
40 &  15 &  15.37 & -0.3702 \tabularnewline
41 &  17 &  15.64 &  1.359 \tabularnewline
42 &  20 &  16.26 &  3.745 \tabularnewline
43 &  17 &  15.6 &  1.397 \tabularnewline
44 &  18 &  17.09 &  0.913 \tabularnewline
45 &  14 &  12.85 &  1.153 \tabularnewline
46 &  17 &  16.35 &  0.6459 \tabularnewline
47 &  17 &  16.15 &  0.8546 \tabularnewline
48 &  16 &  15.75 &  0.2454 \tabularnewline
49 &  18 &  13.98 &  4.024 \tabularnewline
50 &  16 &  16.75 & -0.7461 \tabularnewline
51 &  13 &  15.68 & -2.684 \tabularnewline
52 &  12 &  14.68 & -2.678 \tabularnewline
53 &  16 &  13.7 &  2.299 \tabularnewline
54 &  16 &  15.64 &  0.3618 \tabularnewline
55 &  16 &  16.19 & -0.1918 \tabularnewline
56 &  14 &  15.64 & -1.639 \tabularnewline
57 &  15 &  14.45 &  0.5472 \tabularnewline
58 &  14 &  14.54 & -0.5367 \tabularnewline
59 &  15 &  14.71 &  0.2897 \tabularnewline
60 &  15 &  15.69 & -0.6908 \tabularnewline
61 &  16 &  15.22 &  0.7845 \tabularnewline
62 &  11 &  11.7 & -0.7048 \tabularnewline
63 &  18 &  15.92 &  2.075 \tabularnewline
64 &  11 &  14.32 & -3.317 \tabularnewline
65 &  18 &  18.47 & -0.4679 \tabularnewline
66 &  17 &  17.17 & -0.1672 \tabularnewline
67 &  14 &  15.31 & -1.305 \tabularnewline
68 &  17 &  15.97 &  1.033 \tabularnewline
69 &  14 &  15.44 & -1.444 \tabularnewline
70 &  19 &  16.87 &  2.131 \tabularnewline
71 &  16 &  16.5 & -0.5011 \tabularnewline
72 &  15 &  15.2 & -0.2015 \tabularnewline
73 &  12 &  14.18 & -2.18 \tabularnewline
74 &  17 &  16.78 &  0.2193 \tabularnewline
75 &  15 &  14.25 &  0.7525 \tabularnewline
76 &  16 &  15.95 &  0.05034 \tabularnewline
77 &  16 &  15.03 &  0.9703 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=315767&T=5

[TABLE]
[ROW][C]Multiple Linear Regression - Actuals, Interpolation, and Residuals[/C][/ROW]
[ROW][C]Time or Index[/C][C]Actuals[/C][C]InterpolationForecast[/C][C]ResidualsPrediction Error[/C][/ROW]
[ROW][C]1[/C][C] 13[/C][C] 13.96[/C][C]-0.9589[/C][/ROW]
[ROW][C]2[/C][C] 17[/C][C] 16.07[/C][C] 0.9335[/C][/ROW]
[ROW][C]3[/C][C] 16[/C][C] 15.88[/C][C] 0.1187[/C][/ROW]
[ROW][C]4[/C][C] 17[/C][C] 16.76[/C][C] 0.2425[/C][/ROW]
[ROW][C]5[/C][C] 17[/C][C] 14.59[/C][C] 2.41[/C][/ROW]
[ROW][C]6[/C][C] 15[/C][C] 16.01[/C][C]-1.013[/C][/ROW]
[ROW][C]7[/C][C] 16[/C][C] 16.78[/C][C]-0.778[/C][/ROW]
[ROW][C]8[/C][C] 14[/C][C] 15.78[/C][C]-1.784[/C][/ROW]
[ROW][C]9[/C][C] 16[/C][C] 15.31[/C][C] 0.6899[/C][/ROW]
[ROW][C]10[/C][C] 17[/C][C] 15.04[/C][C] 1.957[/C][/ROW]
[ROW][C]11[/C][C] 16[/C][C] 16.36[/C][C]-0.3623[/C][/ROW]
[ROW][C]12[/C][C] 16[/C][C] 15.48[/C][C] 0.5209[/C][/ROW]
[ROW][C]13[/C][C] 15[/C][C] 15.06[/C][C]-0.06015[/C][/ROW]
[ROW][C]14[/C][C] 16[/C][C] 16.57[/C][C]-0.5691[/C][/ROW]
[ROW][C]15[/C][C] 13[/C][C] 14.92[/C][C]-1.92[/C][/ROW]
[ROW][C]16[/C][C] 15[/C][C] 15.46[/C][C]-0.4619[/C][/ROW]
[ROW][C]17[/C][C] 17[/C][C] 17.32[/C][C]-0.3155[/C][/ROW]
[ROW][C]18[/C][C] 13[/C][C] 15.13[/C][C]-2.129[/C][/ROW]
[ROW][C]19[/C][C] 17[/C][C] 14.6[/C][C] 2.402[/C][/ROW]
[ROW][C]20[/C][C] 14[/C][C] 15.14[/C][C]-1.139[/C][/ROW]
[ROW][C]21[/C][C] 14[/C][C] 14.27[/C][C]-0.274[/C][/ROW]
[ROW][C]22[/C][C] 18[/C][C] 15.46[/C][C] 2.542[/C][/ROW]
[ROW][C]23[/C][C] 17[/C][C] 17.1[/C][C]-0.1042[/C][/ROW]
[ROW][C]24[/C][C] 13[/C][C] 13.51[/C][C]-0.5118[/C][/ROW]
[ROW][C]25[/C][C] 16[/C][C] 17.41[/C][C]-1.407[/C][/ROW]
[ROW][C]26[/C][C] 15[/C][C] 16.29[/C][C]-1.285[/C][/ROW]
[ROW][C]27[/C][C] 13[/C][C] 14.96[/C][C]-1.956[/C][/ROW]
[ROW][C]28[/C][C] 17[/C][C] 16.77[/C][C] 0.2303[/C][/ROW]
[ROW][C]29[/C][C] 11[/C][C] 12.56[/C][C]-1.561[/C][/ROW]
[ROW][C]30[/C][C] 13[/C][C] 14.44[/C][C]-1.442[/C][/ROW]
[ROW][C]31[/C][C] 17[/C][C] 16.46[/C][C] 0.5369[/C][/ROW]
[ROW][C]32[/C][C] 16[/C][C] 15.33[/C][C] 0.6735[/C][/ROW]
[ROW][C]33[/C][C] 16[/C][C] 16.59[/C][C]-0.5905[/C][/ROW]
[ROW][C]34[/C][C] 17[/C][C] 14.6[/C][C] 2.4[/C][/ROW]
[ROW][C]35[/C][C] 14[/C][C] 16.03[/C][C]-2.03[/C][/ROW]
[ROW][C]36[/C][C] 16[/C][C] 15.46[/C][C] 0.5377[/C][/ROW]
[ROW][C]37[/C][C] 15[/C][C] 14.22[/C][C] 0.7758[/C][/ROW]
[ROW][C]38[/C][C] 16[/C][C] 15.64[/C][C] 0.3563[/C][/ROW]
[ROW][C]39[/C][C] 14[/C][C] 14.7[/C][C]-0.6974[/C][/ROW]
[ROW][C]40[/C][C] 15[/C][C] 15.37[/C][C]-0.3702[/C][/ROW]
[ROW][C]41[/C][C] 17[/C][C] 15.64[/C][C] 1.359[/C][/ROW]
[ROW][C]42[/C][C] 20[/C][C] 16.26[/C][C] 3.745[/C][/ROW]
[ROW][C]43[/C][C] 17[/C][C] 15.6[/C][C] 1.397[/C][/ROW]
[ROW][C]44[/C][C] 18[/C][C] 17.09[/C][C] 0.913[/C][/ROW]
[ROW][C]45[/C][C] 14[/C][C] 12.85[/C][C] 1.153[/C][/ROW]
[ROW][C]46[/C][C] 17[/C][C] 16.35[/C][C] 0.6459[/C][/ROW]
[ROW][C]47[/C][C] 17[/C][C] 16.15[/C][C] 0.8546[/C][/ROW]
[ROW][C]48[/C][C] 16[/C][C] 15.75[/C][C] 0.2454[/C][/ROW]
[ROW][C]49[/C][C] 18[/C][C] 13.98[/C][C] 4.024[/C][/ROW]
[ROW][C]50[/C][C] 16[/C][C] 16.75[/C][C]-0.7461[/C][/ROW]
[ROW][C]51[/C][C] 13[/C][C] 15.68[/C][C]-2.684[/C][/ROW]
[ROW][C]52[/C][C] 12[/C][C] 14.68[/C][C]-2.678[/C][/ROW]
[ROW][C]53[/C][C] 16[/C][C] 13.7[/C][C] 2.299[/C][/ROW]
[ROW][C]54[/C][C] 16[/C][C] 15.64[/C][C] 0.3618[/C][/ROW]
[ROW][C]55[/C][C] 16[/C][C] 16.19[/C][C]-0.1918[/C][/ROW]
[ROW][C]56[/C][C] 14[/C][C] 15.64[/C][C]-1.639[/C][/ROW]
[ROW][C]57[/C][C] 15[/C][C] 14.45[/C][C] 0.5472[/C][/ROW]
[ROW][C]58[/C][C] 14[/C][C] 14.54[/C][C]-0.5367[/C][/ROW]
[ROW][C]59[/C][C] 15[/C][C] 14.71[/C][C] 0.2897[/C][/ROW]
[ROW][C]60[/C][C] 15[/C][C] 15.69[/C][C]-0.6908[/C][/ROW]
[ROW][C]61[/C][C] 16[/C][C] 15.22[/C][C] 0.7845[/C][/ROW]
[ROW][C]62[/C][C] 11[/C][C] 11.7[/C][C]-0.7048[/C][/ROW]
[ROW][C]63[/C][C] 18[/C][C] 15.92[/C][C] 2.075[/C][/ROW]
[ROW][C]64[/C][C] 11[/C][C] 14.32[/C][C]-3.317[/C][/ROW]
[ROW][C]65[/C][C] 18[/C][C] 18.47[/C][C]-0.4679[/C][/ROW]
[ROW][C]66[/C][C] 17[/C][C] 17.17[/C][C]-0.1672[/C][/ROW]
[ROW][C]67[/C][C] 14[/C][C] 15.31[/C][C]-1.305[/C][/ROW]
[ROW][C]68[/C][C] 17[/C][C] 15.97[/C][C] 1.033[/C][/ROW]
[ROW][C]69[/C][C] 14[/C][C] 15.44[/C][C]-1.444[/C][/ROW]
[ROW][C]70[/C][C] 19[/C][C] 16.87[/C][C] 2.131[/C][/ROW]
[ROW][C]71[/C][C] 16[/C][C] 16.5[/C][C]-0.5011[/C][/ROW]
[ROW][C]72[/C][C] 15[/C][C] 15.2[/C][C]-0.2015[/C][/ROW]
[ROW][C]73[/C][C] 12[/C][C] 14.18[/C][C]-2.18[/C][/ROW]
[ROW][C]74[/C][C] 17[/C][C] 16.78[/C][C] 0.2193[/C][/ROW]
[ROW][C]75[/C][C] 15[/C][C] 14.25[/C][C] 0.7525[/C][/ROW]
[ROW][C]76[/C][C] 16[/C][C] 15.95[/C][C] 0.05034[/C][/ROW]
[ROW][C]77[/C][C] 16[/C][C] 15.03[/C][C] 0.9703[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=315767&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=315767&T=5

As an alternative you can also use a QR Code:

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	13	13.96	-0.9589
2	17	16.07	0.9335
3	16	15.88	0.1187
4	17	16.76	0.2425
5	17	14.59	2.41
6	15	16.01	-1.013
7	16	16.78	-0.778
8	14	15.78	-1.784
9	16	15.31	0.6899
10	17	15.04	1.957
11	16	16.36	-0.3623
12	16	15.48	0.5209
13	15	15.06	-0.06015
14	16	16.57	-0.5691
15	13	14.92	-1.92
16	15	15.46	-0.4619
17	17	17.32	-0.3155
18	13	15.13	-2.129
19	17	14.6	2.402
20	14	15.14	-1.139
21	14	14.27	-0.274
22	18	15.46	2.542
23	17	17.1	-0.1042
24	13	13.51	-0.5118
25	16	17.41	-1.407
26	15	16.29	-1.285
27	13	14.96	-1.956
28	17	16.77	0.2303
29	11	12.56	-1.561
30	13	14.44	-1.442
31	17	16.46	0.5369
32	16	15.33	0.6735
33	16	16.59	-0.5905
34	17	14.6	2.4
35	14	16.03	-2.03
36	16	15.46	0.5377
37	15	14.22	0.7758
38	16	15.64	0.3563
39	14	14.7	-0.6974
40	15	15.37	-0.3702
41	17	15.64	1.359
42	20	16.26	3.745
43	17	15.6	1.397
44	18	17.09	0.913
45	14	12.85	1.153
46	17	16.35	0.6459
47	17	16.15	0.8546
48	16	15.75	0.2454
49	18	13.98	4.024
50	16	16.75	-0.7461
51	13	15.68	-2.684
52	12	14.68	-2.678
53	16	13.7	2.299
54	16	15.64	0.3618
55	16	16.19	-0.1918
56	14	15.64	-1.639
57	15	14.45	0.5472
58	14	14.54	-0.5367
59	15	14.71	0.2897
60	15	15.69	-0.6908
61	16	15.22	0.7845
62	11	11.7	-0.7048
63	18	15.92	2.075
64	11	14.32	-3.317
65	18	18.47	-0.4679
66	17	17.17	-0.1672
67	14	15.31	-1.305
68	17	15.97	1.033
69	14	15.44	-1.444
70	19	16.87	2.131
71	16	16.5	-0.5011
72	15	15.2	-0.2015
73	12	14.18	-2.18
74	17	16.78	0.2193
75	15	14.25	0.7525
76	16	15.95	0.05034
77	16	15.03	0.9703

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
10	0.1697	0.3393	0.8303
11	0.07977	0.1595	0.9202
12	0.064	0.128	0.936
13	0.05331	0.1066	0.9467
14	0.05202	0.104	0.948
15	0.1153	0.2306	0.8847
16	0.06746	0.1349	0.9325
17	0.03845	0.0769	0.9616
18	0.1273	0.2546	0.8727
19	0.2128	0.4256	0.7872
20	0.1616	0.3232	0.8384
21	0.1764	0.3528	0.8236
22	0.3268	0.6536	0.6732
23	0.2617	0.5234	0.7383
24	0.2292	0.4585	0.7708
25	0.1946	0.3892	0.8054
26	0.1643	0.3286	0.8357
27	0.1623	0.3246	0.8377
28	0.1183	0.2366	0.8817
29	0.1551	0.3102	0.8449
30	0.1539	0.3077	0.8461
31	0.115	0.23	0.885
32	0.1072	0.2144	0.8928
33	0.08018	0.1604	0.9198
34	0.1351	0.2703	0.8649
35	0.1674	0.3348	0.8326
36	0.1293	0.2586	0.8707
37	0.1087	0.2174	0.8913
38	0.08156	0.1631	0.9184
39	0.07309	0.1462	0.9269
40	0.05444	0.1089	0.9456
41	0.05038	0.1008	0.9496
42	0.3105	0.6209	0.6895
43	0.3561	0.7122	0.6439
44	0.3134	0.6269	0.6866
45	0.3078	0.6155	0.6922
46	0.2684	0.5367	0.7316
47	0.2237	0.4474	0.7763
48	0.1748	0.3496	0.8252
49	0.6283	0.7433	0.3717
50	0.5726	0.8549	0.4274
51	0.6435	0.713	0.3565
52	0.7546	0.4908	0.2454
53	0.8267	0.3466	0.1733
54	0.7914	0.4173	0.2086
55	0.7406	0.5188	0.2594
56	0.726	0.5481	0.274
57	0.6625	0.675	0.3375
58	0.582	0.8361	0.418
59	0.5239	0.9523	0.4761
60	0.4459	0.8919	0.5541
61	0.3802	0.7605	0.6198
62	0.3627	0.7255	0.6373
63	0.4394	0.8789	0.5606
64	0.7702	0.4596	0.2298
65	0.8526	0.2948	0.1474
66	0.8163	0.3673	0.1837
67	0.7173	0.5654	0.2827

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
10 &  0.1697 &  0.3393 &  0.8303 \tabularnewline
11 &  0.07977 &  0.1595 &  0.9202 \tabularnewline
12 &  0.064 &  0.128 &  0.936 \tabularnewline
13 &  0.05331 &  0.1066 &  0.9467 \tabularnewline
14 &  0.05202 &  0.104 &  0.948 \tabularnewline
15 &  0.1153 &  0.2306 &  0.8847 \tabularnewline
16 &  0.06746 &  0.1349 &  0.9325 \tabularnewline
17 &  0.03845 &  0.0769 &  0.9616 \tabularnewline
18 &  0.1273 &  0.2546 &  0.8727 \tabularnewline
19 &  0.2128 &  0.4256 &  0.7872 \tabularnewline
20 &  0.1616 &  0.3232 &  0.8384 \tabularnewline
21 &  0.1764 &  0.3528 &  0.8236 \tabularnewline
22 &  0.3268 &  0.6536 &  0.6732 \tabularnewline
23 &  0.2617 &  0.5234 &  0.7383 \tabularnewline
24 &  0.2292 &  0.4585 &  0.7708 \tabularnewline
25 &  0.1946 &  0.3892 &  0.8054 \tabularnewline
26 &  0.1643 &  0.3286 &  0.8357 \tabularnewline
27 &  0.1623 &  0.3246 &  0.8377 \tabularnewline
28 &  0.1183 &  0.2366 &  0.8817 \tabularnewline
29 &  0.1551 &  0.3102 &  0.8449 \tabularnewline
30 &  0.1539 &  0.3077 &  0.8461 \tabularnewline
31 &  0.115 &  0.23 &  0.885 \tabularnewline
32 &  0.1072 &  0.2144 &  0.8928 \tabularnewline
33 &  0.08018 &  0.1604 &  0.9198 \tabularnewline
34 &  0.1351 &  0.2703 &  0.8649 \tabularnewline
35 &  0.1674 &  0.3348 &  0.8326 \tabularnewline
36 &  0.1293 &  0.2586 &  0.8707 \tabularnewline
37 &  0.1087 &  0.2174 &  0.8913 \tabularnewline
38 &  0.08156 &  0.1631 &  0.9184 \tabularnewline
39 &  0.07309 &  0.1462 &  0.9269 \tabularnewline
40 &  0.05444 &  0.1089 &  0.9456 \tabularnewline
41 &  0.05038 &  0.1008 &  0.9496 \tabularnewline
42 &  0.3105 &  0.6209 &  0.6895 \tabularnewline
43 &  0.3561 &  0.7122 &  0.6439 \tabularnewline
44 &  0.3134 &  0.6269 &  0.6866 \tabularnewline
45 &  0.3078 &  0.6155 &  0.6922 \tabularnewline
46 &  0.2684 &  0.5367 &  0.7316 \tabularnewline
47 &  0.2237 &  0.4474 &  0.7763 \tabularnewline
48 &  0.1748 &  0.3496 &  0.8252 \tabularnewline
49 &  0.6283 &  0.7433 &  0.3717 \tabularnewline
50 &  0.5726 &  0.8549 &  0.4274 \tabularnewline
51 &  0.6435 &  0.713 &  0.3565 \tabularnewline
52 &  0.7546 &  0.4908 &  0.2454 \tabularnewline
53 &  0.8267 &  0.3466 &  0.1733 \tabularnewline
54 &  0.7914 &  0.4173 &  0.2086 \tabularnewline
55 &  0.7406 &  0.5188 &  0.2594 \tabularnewline
56 &  0.726 &  0.5481 &  0.274 \tabularnewline
57 &  0.6625 &  0.675 &  0.3375 \tabularnewline
58 &  0.582 &  0.8361 &  0.418 \tabularnewline
59 &  0.5239 &  0.9523 &  0.4761 \tabularnewline
60 &  0.4459 &  0.8919 &  0.5541 \tabularnewline
61 &  0.3802 &  0.7605 &  0.6198 \tabularnewline
62 &  0.3627 &  0.7255 &  0.6373 \tabularnewline
63 &  0.4394 &  0.8789 &  0.5606 \tabularnewline
64 &  0.7702 &  0.4596 &  0.2298 \tabularnewline
65 &  0.8526 &  0.2948 &  0.1474 \tabularnewline
66 &  0.8163 &  0.3673 &  0.1837 \tabularnewline
67 &  0.7173 &  0.5654 &  0.2827 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=315767&T=6

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]10[/C][C] 0.1697[/C][C] 0.3393[/C][C] 0.8303[/C][/ROW]
[ROW][C]11[/C][C] 0.07977[/C][C] 0.1595[/C][C] 0.9202[/C][/ROW]
[ROW][C]12[/C][C] 0.064[/C][C] 0.128[/C][C] 0.936[/C][/ROW]
[ROW][C]13[/C][C] 0.05331[/C][C] 0.1066[/C][C] 0.9467[/C][/ROW]
[ROW][C]14[/C][C] 0.05202[/C][C] 0.104[/C][C] 0.948[/C][/ROW]
[ROW][C]15[/C][C] 0.1153[/C][C] 0.2306[/C][C] 0.8847[/C][/ROW]
[ROW][C]16[/C][C] 0.06746[/C][C] 0.1349[/C][C] 0.9325[/C][/ROW]
[ROW][C]17[/C][C] 0.03845[/C][C] 0.0769[/C][C] 0.9616[/C][/ROW]
[ROW][C]18[/C][C] 0.1273[/C][C] 0.2546[/C][C] 0.8727[/C][/ROW]
[ROW][C]19[/C][C] 0.2128[/C][C] 0.4256[/C][C] 0.7872[/C][/ROW]
[ROW][C]20[/C][C] 0.1616[/C][C] 0.3232[/C][C] 0.8384[/C][/ROW]
[ROW][C]21[/C][C] 0.1764[/C][C] 0.3528[/C][C] 0.8236[/C][/ROW]
[ROW][C]22[/C][C] 0.3268[/C][C] 0.6536[/C][C] 0.6732[/C][/ROW]
[ROW][C]23[/C][C] 0.2617[/C][C] 0.5234[/C][C] 0.7383[/C][/ROW]
[ROW][C]24[/C][C] 0.2292[/C][C] 0.4585[/C][C] 0.7708[/C][/ROW]
[ROW][C]25[/C][C] 0.1946[/C][C] 0.3892[/C][C] 0.8054[/C][/ROW]
[ROW][C]26[/C][C] 0.1643[/C][C] 0.3286[/C][C] 0.8357[/C][/ROW]
[ROW][C]27[/C][C] 0.1623[/C][C] 0.3246[/C][C] 0.8377[/C][/ROW]
[ROW][C]28[/C][C] 0.1183[/C][C] 0.2366[/C][C] 0.8817[/C][/ROW]
[ROW][C]29[/C][C] 0.1551[/C][C] 0.3102[/C][C] 0.8449[/C][/ROW]
[ROW][C]30[/C][C] 0.1539[/C][C] 0.3077[/C][C] 0.8461[/C][/ROW]
[ROW][C]31[/C][C] 0.115[/C][C] 0.23[/C][C] 0.885[/C][/ROW]
[ROW][C]32[/C][C] 0.1072[/C][C] 0.2144[/C][C] 0.8928[/C][/ROW]
[ROW][C]33[/C][C] 0.08018[/C][C] 0.1604[/C][C] 0.9198[/C][/ROW]
[ROW][C]34[/C][C] 0.1351[/C][C] 0.2703[/C][C] 0.8649[/C][/ROW]
[ROW][C]35[/C][C] 0.1674[/C][C] 0.3348[/C][C] 0.8326[/C][/ROW]
[ROW][C]36[/C][C] 0.1293[/C][C] 0.2586[/C][C] 0.8707[/C][/ROW]
[ROW][C]37[/C][C] 0.1087[/C][C] 0.2174[/C][C] 0.8913[/C][/ROW]
[ROW][C]38[/C][C] 0.08156[/C][C] 0.1631[/C][C] 0.9184[/C][/ROW]
[ROW][C]39[/C][C] 0.07309[/C][C] 0.1462[/C][C] 0.9269[/C][/ROW]
[ROW][C]40[/C][C] 0.05444[/C][C] 0.1089[/C][C] 0.9456[/C][/ROW]
[ROW][C]41[/C][C] 0.05038[/C][C] 0.1008[/C][C] 0.9496[/C][/ROW]
[ROW][C]42[/C][C] 0.3105[/C][C] 0.6209[/C][C] 0.6895[/C][/ROW]
[ROW][C]43[/C][C] 0.3561[/C][C] 0.7122[/C][C] 0.6439[/C][/ROW]
[ROW][C]44[/C][C] 0.3134[/C][C] 0.6269[/C][C] 0.6866[/C][/ROW]
[ROW][C]45[/C][C] 0.3078[/C][C] 0.6155[/C][C] 0.6922[/C][/ROW]
[ROW][C]46[/C][C] 0.2684[/C][C] 0.5367[/C][C] 0.7316[/C][/ROW]
[ROW][C]47[/C][C] 0.2237[/C][C] 0.4474[/C][C] 0.7763[/C][/ROW]
[ROW][C]48[/C][C] 0.1748[/C][C] 0.3496[/C][C] 0.8252[/C][/ROW]
[ROW][C]49[/C][C] 0.6283[/C][C] 0.7433[/C][C] 0.3717[/C][/ROW]
[ROW][C]50[/C][C] 0.5726[/C][C] 0.8549[/C][C] 0.4274[/C][/ROW]
[ROW][C]51[/C][C] 0.6435[/C][C] 0.713[/C][C] 0.3565[/C][/ROW]
[ROW][C]52[/C][C] 0.7546[/C][C] 0.4908[/C][C] 0.2454[/C][/ROW]
[ROW][C]53[/C][C] 0.8267[/C][C] 0.3466[/C][C] 0.1733[/C][/ROW]
[ROW][C]54[/C][C] 0.7914[/C][C] 0.4173[/C][C] 0.2086[/C][/ROW]
[ROW][C]55[/C][C] 0.7406[/C][C] 0.5188[/C][C] 0.2594[/C][/ROW]
[ROW][C]56[/C][C] 0.726[/C][C] 0.5481[/C][C] 0.274[/C][/ROW]
[ROW][C]57[/C][C] 0.6625[/C][C] 0.675[/C][C] 0.3375[/C][/ROW]
[ROW][C]58[/C][C] 0.582[/C][C] 0.8361[/C][C] 0.418[/C][/ROW]
[ROW][C]59[/C][C] 0.5239[/C][C] 0.9523[/C][C] 0.4761[/C][/ROW]
[ROW][C]60[/C][C] 0.4459[/C][C] 0.8919[/C][C] 0.5541[/C][/ROW]
[ROW][C]61[/C][C] 0.3802[/C][C] 0.7605[/C][C] 0.6198[/C][/ROW]
[ROW][C]62[/C][C] 0.3627[/C][C] 0.7255[/C][C] 0.6373[/C][/ROW]
[ROW][C]63[/C][C] 0.4394[/C][C] 0.8789[/C][C] 0.5606[/C][/ROW]
[ROW][C]64[/C][C] 0.7702[/C][C] 0.4596[/C][C] 0.2298[/C][/ROW]
[ROW][C]65[/C][C] 0.8526[/C][C] 0.2948[/C][C] 0.1474[/C][/ROW]
[ROW][C]66[/C][C] 0.8163[/C][C] 0.3673[/C][C] 0.1837[/C][/ROW]
[ROW][C]67[/C][C] 0.7173[/C][C] 0.5654[/C][C] 0.2827[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=315767&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=315767&T=6

As an alternative you can also use a QR Code:

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

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
10	0.1697	0.3393	0.8303
11	0.07977	0.1595	0.9202
12	0.064	0.128	0.936
13	0.05331	0.1066	0.9467
14	0.05202	0.104	0.948
15	0.1153	0.2306	0.8847
16	0.06746	0.1349	0.9325
17	0.03845	0.0769	0.9616
18	0.1273	0.2546	0.8727
19	0.2128	0.4256	0.7872
20	0.1616	0.3232	0.8384
21	0.1764	0.3528	0.8236
22	0.3268	0.6536	0.6732
23	0.2617	0.5234	0.7383
24	0.2292	0.4585	0.7708
25	0.1946	0.3892	0.8054
26	0.1643	0.3286	0.8357
27	0.1623	0.3246	0.8377
28	0.1183	0.2366	0.8817
29	0.1551	0.3102	0.8449
30	0.1539	0.3077	0.8461
31	0.115	0.23	0.885
32	0.1072	0.2144	0.8928
33	0.08018	0.1604	0.9198
34	0.1351	0.2703	0.8649
35	0.1674	0.3348	0.8326
36	0.1293	0.2586	0.8707
37	0.1087	0.2174	0.8913
38	0.08156	0.1631	0.9184
39	0.07309	0.1462	0.9269
40	0.05444	0.1089	0.9456
41	0.05038	0.1008	0.9496
42	0.3105	0.6209	0.6895
43	0.3561	0.7122	0.6439
44	0.3134	0.6269	0.6866
45	0.3078	0.6155	0.6922
46	0.2684	0.5367	0.7316
47	0.2237	0.4474	0.7763
48	0.1748	0.3496	0.8252
49	0.6283	0.7433	0.3717
50	0.5726	0.8549	0.4274
51	0.6435	0.713	0.3565
52	0.7546	0.4908	0.2454
53	0.8267	0.3466	0.1733
54	0.7914	0.4173	0.2086
55	0.7406	0.5188	0.2594
56	0.726	0.5481	0.274
57	0.6625	0.675	0.3375
58	0.582	0.8361	0.418
59	0.5239	0.9523	0.4761
60	0.4459	0.8919	0.5541
61	0.3802	0.7605	0.6198
62	0.3627	0.7255	0.6373
63	0.4394	0.8789	0.5606
64	0.7702	0.4596	0.2298
65	0.8526	0.2948	0.1474
66	0.8163	0.3673	0.1837
67	0.7173	0.5654	0.2827

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	0	0	OK
5% type I error level	0	0	OK
10% type I error level	1	0.0172414	OK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 &  0 & OK \tabularnewline
5% type I error level & 0 & 0 & OK \tabularnewline
10% type I error level & 1 & 0.0172414 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=315767&T=7

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C] 0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]1[/C][C]0.0172414[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=315767&T=7

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=315767&T=7

As an alternative you can also use a QR Code:

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	0	0	OK
5% type I error level	0	0	OK
10% type I error level	1	0.0172414	OK

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted RESET test data: mylm RESET = 0.12967, df1 = 2, df2 = 68, p-value = 0.8786
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 0.74707, df1 = 12, df2 = 58, p-value = 0.7001
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 0.76048, df1 = 2, df2 = 68, p-value = 0.4714

\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 = 0.12967, df1 = 2, df2 = 68, p-value = 0.8786
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of regressors \tabularnewline
> reset_test_regressors
	RESET test
data:  mylm
RESET = 0.74707, df1 = 12, df2 = 58, p-value = 0.7001
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of principal components \tabularnewline
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 0.76048, df1 = 2, df2 = 68, p-value = 0.4714
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=315767&T=8

[TABLE]
[ROW][C]Ramsey RESET F-Test for powers (2 and 3) of fitted values[/C][/ROW]
[ROW][C]> reset_test_fitted
	RESET test
data:  mylm
RESET = 0.12967, df1 = 2, df2 = 68, p-value = 0.8786
[/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 = 0.74707, df1 = 12, df2 = 58, p-value = 0.7001
[/C][/ROW]
[ROW][C]Ramsey RESET F-Test for powers (2 and 3) of principal components[/C][/ROW]
[ROW][C]> reset_test_principal_components
	RESET test
data:  mylm
RESET = 0.76048, df1 = 2, df2 = 68, p-value = 0.4714
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=315767&T=8

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=315767&T=8

As an alternative you can also use a QR Code:

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

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted RESET test data: mylm RESET = 0.12967, df1 = 2, df2 = 68, p-value = 0.8786
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 0.74707, df1 = 12, df2 = 58, p-value = 0.7001
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 0.76048, df1 = 2, df2 = 68, p-value = 0.4714

Variance Inflation Factors (Multicollinearity)
> vif ECSUM EPSUM IKSUM KVDDSUM SKEOUSUM GWSUM 1.062707 1.040427 1.070636 1.025101 1.038950 1.017289

\begin{tabular}{lllllllll}
\hline
Variance Inflation Factors (Multicollinearity) \tabularnewline
> vif
   ECSUM    EPSUM    IKSUM  KVDDSUM SKEOUSUM    GWSUM 
1.062707 1.040427 1.070636 1.025101 1.038950 1.017289 
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=315767&T=9

[TABLE]
[ROW][C]Variance Inflation Factors (Multicollinearity)[/C][/ROW]
[ROW][C]> vif
   ECSUM    EPSUM    IKSUM  KVDDSUM SKEOUSUM    GWSUM 
1.062707 1.040427 1.070636 1.025101 1.038950 1.017289 
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=315767&T=9

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=315767&T=9

As an alternative you can also use a QR Code:

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

Variance Inflation Factors (Multicollinearity)
> vif ECSUM EPSUM IKSUM KVDDSUM SKEOUSUM GWSUM 1.062707 1.040427 1.070636 1.025101 1.038950 1.017289

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Parameters (Session):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ; par6 = 12 ;

Parameters (R input):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ; par4 = ; par5 = ; par6 = 12 ;

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

library(lattice)
library(lmtest)
library(car)
library(MASS)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
mywarning <- ''
par6 <- as.numeric(par6)
if(is.na(par6)) {
par6 <- 12
mywarning = 'Warning: you did not specify the seasonality. The seasonal period was set to s = 12.'
}
par1 <- as.numeric(par1)
if(is.na(par1)) {
par1 <- 1
mywarning = 'Warning: you did not specify the column number of the endogenous series! The first column was selected by default.'
}
if (par4=='') par4 <- 0
par4 <- as.numeric(par4)
if (!is.numeric(par4)) par4 <- 0
if (par5=='') par5 <- 0
par5 <- as.numeric(par5)
if (!is.numeric(par5)) par5 <- 0
x <- na.omit(t(y))
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
(n <- n -1)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par3 == 'Seasonal Differences (s)'){
(n <- n - par6)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-Bs)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+par6,j] - x[i,j]
}
}
x <- x2
}
if (par3 == 'First and Seasonal Differences (s)'){
(n <- n -1)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
(n <- n - par6)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-Bs)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+par6,j] - x[i,j]
}
}
x <- x2
}
if(par4 > 0) {
x2 <- array(0, dim=c(n-par4,par4), dimnames=list(1:(n-par4), paste(colnames(x)[par1],'(t-',1:par4,')',sep='')))
for (i in 1:(n-par4)) {
for (j in 1:par4) {
x2[i,j] <- x[i+par4-j,par1]
}
}
x <- cbind(x[(par4+1):n,], x2)
n <- n - par4
}
if(par5 > 0) {
x2 <- array(0, dim=c(n-par5*par6,par5), dimnames=list(1:(n-par5*par6), paste(colnames(x)[par1],'(t-',1:par5,'s)',sep='')))
for (i in 1:(n-par5*par6)) {
for (j in 1:par5) {
x2[i,j] <- x[i+par5*par6-j*par6,par1]
}
}
x <- cbind(x[(par5*par6+1):n,], x2)
n <- n - par5*par6
}
if (par2 == 'Include Seasonal Dummies'){
x2 <- array(0, dim=c(n,par6-1), dimnames=list(1:n, paste('M', seq(1:(par6-1)), sep ='')))
for (i in 1:(par6-1)){
x2[seq(i,n,par6),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
(k <- length(x[n,]))
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
print(x)
(k <- length(x[n,]))
head(x)
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
sresid <- studres(mylm)
hist(sresid, freq=FALSE, main='Distribution of Studentized Residuals')
xfit<-seq(min(sresid),max(sresid),length=40)
yfit<-dnorm(xfit)
lines(xfit, yfit)
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqPlot(mylm, main='QQ Plot')
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
print(z)
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, signif(mysum$coefficients[i,1],6), sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, mywarning)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Multiple Linear Regression - Ordinary Least Squares', 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,formatC(signif(mysum$coefficients[i,1],5),format='g',flag='+'))
a<-table.element(a,formatC(signif(mysum$coefficients[i,2],5),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$coefficients[i,3],4),format='e',flag='+'))
a<-table.element(a,formatC(signif(mysum$coefficients[i,4],4),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$coefficients[i,4]/2,4),format='g',flag=' '))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a,formatC(signif(sqrt(mysum$r.squared),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a,formatC(signif(mysum$r.squared,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a,formatC(signif(mysum$adj.r.squared,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a,formatC(signif(mysum$fstatistic[1],6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[2],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[3],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a,formatC(signif(1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a,formatC(signif(mysum$sigma,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a,formatC(signif(sum(myerror*myerror),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
myr <- as.numeric(mysum$resid)
myr
a <-table.start()
a <- table.row.start(a)
a <- table.element(a,'Menu of Residual Diagnostics',2,TRUE)
a <- table.row.end(a)
a <- table.row.start(a)
a <- table.element(a,'Description',1,TRUE)
a <- table.element(a,'Link',1,TRUE)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Histogram',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_histogram.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Central Tendency',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_centraltendency.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'QQ Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_fitdistrnorm.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Kernel Density Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_density.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Skewness/Kurtosis Test',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_skewness_kurtosis.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Skewness-Kurtosis Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_skewness_kurtosis_plot.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Harrell-Davis Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_harrell_davis.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Bootstrap Plot -- Central Tendency',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_bootstrapplot1.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Blocked Bootstrap Plot -- Central Tendency',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_bootstrapplot.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'(Partial) Autocorrelation Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_autocorrelation.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Spectral Analysis',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_spectrum.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Tukey lambda PPCC Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_tukeylambda.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Box-Cox Normality Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_boxcoxnorm.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <- table.element(a,'Summary Statistics',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_summary1.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable7.tab')
if(n < 200) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,formatC(signif(x[i],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(x[i]-mysum$resid[i],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$resid[i],6),format='g',flag=' '))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
if (n > n25) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-values',header=TRUE)
a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'breakpoint index',header=TRUE)
a<-table.element(a,'greater',header=TRUE)
a<-table.element(a,'2-sided',header=TRUE)
a<-table.element(a,'less',header=TRUE)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
a<-table.element(a,mypoint,header=TRUE)
a<-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,1],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,2],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,3],6),format='g',flag=' '))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,signif(numsignificant1,6))
a<-table.element(a,formatC(signif(numsignificant1/numgqtests,6),format='g',flag=' '))
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,signif(numsignificant5,6))
a<-table.element(a,signif(numsignificant5/numgqtests,6))
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,signif(numsignificant10,6))
a<-table.element(a,signif(numsignificant10/numgqtests,6))
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}
}
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Ramsey RESET F-Test for powers (2 and 3) of fitted values',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
reset_test_fitted <- resettest(mylm,power=2:3,type='fitted')
a<-table.element(a,paste('',RC.texteval('reset_test_fitted'),'
',sep=''))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Ramsey RESET F-Test for powers (2 and 3) of regressors',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
reset_test_regressors <- resettest(mylm,power=2:3,type='regressor')
a<-table.element(a,paste('',RC.texteval('reset_test_regressors'),'
',sep=''))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Ramsey RESET F-Test for powers (2 and 3) of principal components',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
reset_test_principal_components <- resettest(mylm,power=2:3,type='princomp')
a<-table.element(a,paste('',RC.texteval('reset_test_principal_components'),'
',sep=''))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable8.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Variance Inflation Factors (Multicollinearity)',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
vif <- vif(mylm)
a<-table.element(a,paste('',RC.texteval('vif'),'
',sep=''))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable9.tab')

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code