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R Software Module

rwasp_multipleregression.wasp

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Multiple Regression

Date of computation

Mon, 23 Jan 2017 10:41:43 +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/2017/Jan/23/t1485164580zqdqolrs5ygl1sb.htm/, Retrieved Thu, 16 May 2024 03:18:42 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=304585, Retrieved Thu, 16 May 2024 03:18:42 +0000

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-       [Multiple Regression] [Vraag 11] [2017-01-23 09:41:43] [e302b41a790d997d9c99fd21b0cdfda2] [Current]

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22 13 4 2 4 3 5 4 14
24 16 5 3 3 4 5 4 19
21 17 4 4 5 4 5 4 17
21 NA 3 4 3 3 4 4 17
24 NA 4 4 5 4 5 4 15
20 16 3 4 4 4 5 5 20
22 NA 3 4 4 3 3 4 15
20 NA 3 4 5 4 4 4 19
19 NA 4 5 4 4 5 5 15
23 17 4 5 5 4 5 5 15
21 17 4 4 2 4 5 4 19
19 15 4 4 5 3 5 4 NA
19 16 4 4 4 3 4 5 20
21 14 3 3 5 4 4 5 18
21 16 4 4 5 4 2 5 15
22 17 3 4 5 4 4 5 14
22 NA 3 4 5 4 4 5 20
19 NA NA NA 5 NA 5 5 NA
21 NA 5 5 4 3 4 4 16
21 NA 4 4 4 4 5 4 16
21 16 3 4 5 3 4 5 16
20 NA 4 4 4 4 5 5 10
22 16 4 4 5 4 4 5 19
22 NA 4 4 5 4 4 4 19
24 NA 4 4 5 4 4 5 16
21 NA 3 4 4 4 4 4 15
19 16 3 4 4 3 5 5 18
19 15 4 4 4 4 4 4 17
23 16 2 4 5 4 5 5 19
21 16 5 4 4 4 4 4 17
21 13 4 3 5 4 4 4 NA
19 15 4 5 5 4 5 5 19
21 17 5 4 5 4 4 5 20
19 NA 4 3 5 4 NA 5 5
21 13 2 3 5 4 5 4 19
21 17 4 5 2 4 4 4 16
23 NA 3 4 5 4 4 4 15
19 14 4 3 5 3 4 5 16
19 14 4 3 3 4 4 4 18
19 18 4 4 5 4 4 4 16
18 NA 5 4 4 4 4 4 15
22 17 4 5 5 4 5 5 17
18 13 3 3 4 4 4 4 NA
22 16 5 5 5 3 5 5 20
18 15 5 4 5 3 4 4 19
22 15 4 4 4 3 4 5 7
22 NA 4 4 4 4 4 4 13
19 15 3 5 5 3 3 4 16
22 13 4 4 4 4 5 4 16
25 NA 2 3 4 2 NA 4 NA
19 17 4 5 5 4 4 4 18
19 NA 5 5 2 4 5 4 18
19 NA 5 5 5 4 4 4 16
19 11 4 3 5 4 5 5 17
21 14 4 3 4 3 4 5 19
21 13 4 4 5 4 4 4 16
20 NA 3 4 4 3 3 4 19
19 17 3 4 4 4 4 3 13
19 16 4 4 4 3 5 4 16
22 NA 4 4 4 4 5 4 13
26 17 5 5 3 4 5 5 12
19 16 2 4 4 4 5 5 17
21 16 4 4 4 4 5 5 17
21 16 3 4 4 4 2 4 17
20 15 4 4 5 4 5 5 16
23 12 4 2 4 4 4 4 16
22 17 4 4 4 3 5 3 14
22 14 4 4 4 3 5 4 16
22 14 5 4 5 3 3 5 13
21 16 3 4 4 3 5 5 16
21 NA 3 4 4 3 4 5 14
22 NA 4 5 5 5 5 4 20
23 NA 4 4 3 4 NA 4 12
18 NA 4 4 4 4 4 4 13
24 NA 4 4 4 5 5 4 18
22 15 3 4 3 4 4 4 14
21 16 4 4 4 4 5 4 19
21 14 3 4 5 3 5 5 18
21 15 3 3 5 4 4 5 14
23 17 4 3 5 4 4 4 18
21 NA 4 4 5 4 4 5 19
23 10 3 3 3 4 4 4 15
21 NA 4 4 4 4 5 4 14
19 17 4 4 3 4 5 5 17
21 NA 4 4 4 4 5 5 19
21 20 5 4 4 4 4 4 13
21 17 5 4 3 5 4 5 19
23 18 4 4 5 4 5 5 18
23 NA 3 4 5 4 4 5 20
20 17 3 NA 4 4 4 4 15
20 14 4 2 3 3 4 4 15
19 NA 4 4 5 4 4 3 15
23 17 4 4 5 4 4 5 20
22 NA 4 4 4 4 5 4 15
19 17 4 5 4 4 5 3 19
23 NA 3 4 4 3 5 5 18
22 16 4 4 5 4 4 5 18
22 18 5 4 3 4 4 5 15
21 18 5 4 5 5 4 5 20
21 16 4 5 4 4 5 5 17
21 NA 3 4 5 4 4 5 12
21 NA 5 3 4 4 5 5 18
22 15 4 4 5 4 4 5 19
25 13 5 4 4 4 4 5 20
21 NA 3 4 4 3 NA 4 NA
23 NA 5 4 4 5 5 5 17
19 NA 4 4 5 3 NA 5 15
22 NA 4 4 3 3 4 3 16
20 NA 4 4 5 4 4 4 18
21 16 4 4 5 4 4 4 18
25 NA 3 4 5 4 5 3 14
21 NA 4 4 4 4 4 4 15
19 NA 4 4 4 3 4 5 12
23 12 3 3 4 3 5 5 17
22 NA 4 4 4 3 4 4 14
21 16 3 4 5 4 4 4 18
24 16 4 4 5 4 3 4 17
21 NA 5 4 5 1 5 5 17
19 16 5 4 5 4 5 5 20
18 14 4 4 4 4 4 3 16
19 15 4 4 5 3 4 4 14
20 14 3 4 4 3 4 5 15
19 NA 4 4 4 4 4 4 18
22 15 4 4 4 4 5 4 20
21 NA 4 5 3 4 4 4 17
22 15 3 4 4 4 4 4 17
24 16 4 4 4 3 4 4 17
28 NA 4 4 4 4 4 5 17
19 NA 3 4 3 3 4 4 15
18 NA 4 4 4 3 4 3 17
23 11 3 2 4 2 4 4 18
19 NA 4 4 4 3 5 4 17
23 18 5 4 4 3 5 4 20
19 NA 2 4 4 3 3 5 15
22 11 3 3 4 4 4 4 16
21 NA 4 4 4 3 4 4 15
19 18 5 5 4 4 5 4 18
22 NA NA NA 2 NA NA NA 11
21 15 4 5 5 4 4 4 15
23 19 5 5 5 5 5 4 18
22 17 4 5 5 4 5 5 20
19 NA 4 4 4 3 4 5 19
19 14 3 4 5 4 5 4 14
21 NA 4 4 5 4 4 4 16
22 13 4 4 2 4 4 4 15
21 17 4 4 3 4 5 5 17
20 14 4 4 4 4 5 5 18
23 19 5 4 5 3 5 4 20
22 14 4 3 5 4 4 4 17
23 NA 4 4 5 4 4 4 18
22 NA 3 3 2 3 4 4 15
21 16 4 5 5 4 4 3 16
20 16 4 4 4 3 4 4 11
18 15 4 4 4 4 4 5 15
18 12 3 4 5 3 5 5 18
20 NA 4 4 5 4 4 5 17
19 17 5 4 5 4 5 4 16
21 NA 4 4 5 4 3 4 12
24 NA 2 3 5 4 4 4 19
19 18 4 4 4 4 4 5 18
20 15 4 3 4 3 5 5 15
19 18 4 4 4 4 4 3 17
23 15 4 5 5 5 4 4 19
22 NA 5 4 3 4 4 4 18
21 NA 5 4 4 3 4 4 19
24 NA 3 3 1 4 5 5 16
21 16 4 4 4 4 4 5 16
21 NA 4 4 4 4 5 4 16
22 16 2 3 4 5 5 4 14

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=304585&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=304585&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=304585&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
Bevr_Leeftijd[t] = + 19.4791 -0.0223107TVDC[t] + 0.31301SKEOU1[t] -0.299735SKEOU2[t] -0.109554SKEOU3[t] + 0.386353SKEOU4[t] -0.00533069SKEOU5[t] + 0.254054SKEOU6[t] -0.0139994ITHSUM[t] + e[t]
Warning: you did not specify the column number of the endogenous series! The first column was selected by default.

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Bevr_Leeftijd[t] =  +  19.4791 -0.0223107TVDC[t] +  0.31301SKEOU1[t] -0.299735SKEOU2[t] -0.109554SKEOU3[t] +  0.386353SKEOU4[t] -0.00533069SKEOU5[t] +  0.254054SKEOU6[t] -0.0139994ITHSUM[t]  + e[t] \tabularnewline
Warning: you did not specify the column number of the endogenous series! The first column was selected by default. \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=304585&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Bevr_Leeftijd[t] =  +  19.4791 -0.0223107TVDC[t] +  0.31301SKEOU1[t] -0.299735SKEOU2[t] -0.109554SKEOU3[t] +  0.386353SKEOU4[t] -0.00533069SKEOU5[t] +  0.254054SKEOU6[t] -0.0139994ITHSUM[t]  + e[t][/C][/ROW]
[ROW][C]Warning: you did not specify the column number of the endogenous series! The first column was selected by default.[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=304585&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=304585&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
Bevr_Leeftijd[t] = + 19.4791 -0.0223107TVDC[t] + 0.31301SKEOU1[t] -0.299735SKEOU2[t] -0.109554SKEOU3[t] + 0.386353SKEOU4[t] -0.00533069SKEOU5[t] + 0.254054SKEOU6[t] -0.0139994ITHSUM[t] + e[t]
Warning: you did not specify the column number of the endogenous series! The first column was selected by default.

Multiple Linear Regression - Ordinary Least Squares
Variable	Parameter	S.D.	T-STATH0: parameter = 0	2-tail p-value	1-tail p-value
(Intercept)	+19.48	2.536	+7.6810e+00	1.841e-11	9.203e-12
TVDC	-0.02231	0.115	-1.9400e-01	0.8466	0.4233
SKEOU1	+0.313	0.2538	+1.2330e+00	0.2207	0.1104
SKEOU2	-0.2997	0.3008	-9.9650e-01	0.3217	0.1608
SKEOU3	-0.1095	0.2341	-4.6800e-01	0.6409	0.3205
SKEOU4	+0.3864	0.3335	+1.1590e+00	0.2497	0.1248
SKEOU5	-0.005331	0.2769	-1.9250e-02	0.9847	0.4923
SKEOU6	+0.254	0.29	+8.7610e-01	0.3833	0.1917
ITHSUM	-0.014	0.08096	-1.7290e-01	0.8631	0.4316

\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) & +19.48 &  2.536 & +7.6810e+00 &  1.841e-11 &  9.203e-12 \tabularnewline
TVDC & -0.02231 &  0.115 & -1.9400e-01 &  0.8466 &  0.4233 \tabularnewline
SKEOU1 & +0.313 &  0.2538 & +1.2330e+00 &  0.2207 &  0.1104 \tabularnewline
SKEOU2 & -0.2997 &  0.3008 & -9.9650e-01 &  0.3217 &  0.1608 \tabularnewline
SKEOU3 & -0.1095 &  0.2341 & -4.6800e-01 &  0.6409 &  0.3205 \tabularnewline
SKEOU4 & +0.3864 &  0.3335 & +1.1590e+00 &  0.2497 &  0.1248 \tabularnewline
SKEOU5 & -0.005331 &  0.2769 & -1.9250e-02 &  0.9847 &  0.4923 \tabularnewline
SKEOU6 & +0.254 &  0.29 & +8.7610e-01 &  0.3833 &  0.1917 \tabularnewline
ITHSUM & -0.014 &  0.08096 & -1.7290e-01 &  0.8631 &  0.4316 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=304585&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]+19.48[/C][C] 2.536[/C][C]+7.6810e+00[/C][C] 1.841e-11[/C][C] 9.203e-12[/C][/ROW]
[ROW][C]TVDC[/C][C]-0.02231[/C][C] 0.115[/C][C]-1.9400e-01[/C][C] 0.8466[/C][C] 0.4233[/C][/ROW]
[ROW][C]SKEOU1[/C][C]+0.313[/C][C] 0.2538[/C][C]+1.2330e+00[/C][C] 0.2207[/C][C] 0.1104[/C][/ROW]
[ROW][C]SKEOU2[/C][C]-0.2997[/C][C] 0.3008[/C][C]-9.9650e-01[/C][C] 0.3217[/C][C] 0.1608[/C][/ROW]
[ROW][C]SKEOU3[/C][C]-0.1095[/C][C] 0.2341[/C][C]-4.6800e-01[/C][C] 0.6409[/C][C] 0.3205[/C][/ROW]
[ROW][C]SKEOU4[/C][C]+0.3864[/C][C] 0.3335[/C][C]+1.1590e+00[/C][C] 0.2497[/C][C] 0.1248[/C][/ROW]
[ROW][C]SKEOU5[/C][C]-0.005331[/C][C] 0.2769[/C][C]-1.9250e-02[/C][C] 0.9847[/C][C] 0.4923[/C][/ROW]
[ROW][C]SKEOU6[/C][C]+0.254[/C][C] 0.29[/C][C]+8.7610e-01[/C][C] 0.3833[/C][C] 0.1917[/C][/ROW]
[ROW][C]ITHSUM[/C][C]-0.014[/C][C] 0.08096[/C][C]-1.7290e-01[/C][C] 0.8631[/C][C] 0.4316[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=304585&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=304585&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)	+19.48	2.536	+7.6810e+00	1.841e-11	9.203e-12
TVDC	-0.02231	0.115	-1.9400e-01	0.8466	0.4233
SKEOU1	+0.313	0.2538	+1.2330e+00	0.2207	0.1104
SKEOU2	-0.2997	0.3008	-9.9650e-01	0.3217	0.1608
SKEOU3	-0.1095	0.2341	-4.6800e-01	0.6409	0.3205
SKEOU4	+0.3864	0.3335	+1.1590e+00	0.2497	0.1248
SKEOU5	-0.005331	0.2769	-1.9250e-02	0.9847	0.4923
SKEOU6	+0.254	0.29	+8.7610e-01	0.3833	0.1917
ITHSUM	-0.014	0.08096	-1.7290e-01	0.8631	0.4316

Multiple Linear Regression - Regression Statistics
Multiple R	0.2173
R-squared	0.0472
Adjusted R-squared	-0.03749
F-TEST (value)	0.5574
F-TEST (DF numerator)	8
F-TEST (DF denominator)	90
p-value	0.8099
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.697
Sum Squared Residuals	259.1

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.2173 \tabularnewline
R-squared &  0.0472 \tabularnewline
Adjusted R-squared & -0.03749 \tabularnewline
F-TEST (value) &  0.5574 \tabularnewline
F-TEST (DF numerator) & 8 \tabularnewline
F-TEST (DF denominator) & 90 \tabularnewline
p-value &  0.8099 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  1.697 \tabularnewline
Sum Squared Residuals &  259.1 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=304585&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.2173[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.0472[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]-0.03749[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 0.5574[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]8[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]90[/C][/ROW]
[ROW][C]p-value[/C][C] 0.8099[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C] 1.697[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 259.1[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=304585&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=304585&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	0.2173
R-squared	0.0472
Adjusted R-squared	-0.03749
F-TEST (value)	0.5574
F-TEST (DF numerator)	8
F-TEST (DF denominator)	90
p-value	0.8099
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.697
Sum Squared Residuals	259.1

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	22	21.36	0.644
2	24	21.73	2.272
3	21	20.9	0.09791
4	20	20.93	-0.933
5	23	20.88	2.116
6	21	21.2	-0.2028
7	19	20.86	-1.865
8	21	21.2	-0.2011
9	21	21.22	-0.2224
10	22	20.89	1.11
11	21	20.5	0.5016
12	22	21.16	0.8442
13	19	20.57	-1.575
14	19	21.06	-2.062
15	23	20.52	2.476
16	21	21.35	-0.3523
17	19	20.87	-1.873
18	21	21.43	-0.4325
19	21	20.64	0.363
20	21	20.95	0.04965
21	19	21.16	-2.156
22	19	21.48	-2.479
23	19	20.9	-1.899
24	22	20.86	1.144
25	22	20.76	1.237
26	18	20.85	-2.851
27	22	21.07	0.9307
28	19	19.97	-0.9723
29	22	21.11	0.8851
30	19	20.59	-1.594
31	19	21.59	-2.59
32	21	21.22	-0.2233
33	21	21.01	-0.01066
34	19	20.51	-1.506
35	19	20.66	-1.662
36	26	21.46	4.541
37	19	20.66	-1.662
38	21	21.29	-0.288
39	21	20.74	0.2631
40	20	21.21	-1.215
41	23	21.74	1.258
42	22	20.41	1.587
43	22	20.71	1.294
44	22	21.22	0.7836
45	21	20.6	0.3974
46	22	20.9	1.1
47	21	21.01	-0.005953
48	21	20.51	0.4903
49	21	21.23	-0.2348
50	23	21.19	1.807
51	23	21.3	1.703
52	19	21.38	-2.375
53	21	21.32	-0.319
54	21	22.05	-1.052
55	23	21.12	1.88
56	20	21.43	-1.435
57	23	21.12	1.881
58	19	20.43	-1.43
59	22	21.17	0.8302
60	22	21.7	0.3007
61	21	21.8	-0.7965
62	21	20.99	0.01173
63	22	21.18	0.8219
64	25	21.63	3.369
65	21	20.92	0.08427
66	23	20.98	2.022
67	21	20.6	0.3973
68	24	20.94	3.065
69	19	21.45	-2.449
70	18	20.84	-2.844
71	19	20.61	-1.608
72	20	20.67	-0.6666
73	22	21.01	0.9857
74	22	20.75	1.251
75	24	20.65	3.347
76	23	20.65	2.349
77	23	20.87	2.126
78	22	21.15	0.8484
79	19	20.99	-1.989
80	21	20.68	0.3197
81	23	21.24	1.757
82	22	20.81	1.186
83	19	20.7	-1.698
84	22	21.35	0.6467
85	21	21.38	-0.3752
86	20	21.32	-1.319
87	23	20.74	2.258
88	22	21.27	0.7259
89	21	20.39	0.6101
90	20	20.74	-0.7369
91	18	21.34	-3.344
92	18	20.55	-2.554
93	19	21.23	-2.229
94	19	21.23	-2.235
95	20	21.25	-1.252
96	19	20.74	-1.741
97	23	21.01	1.989
98	21	21.31	-0.3073
99	22	21.14	0.864

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 &  22 &  21.36 &  0.644 \tabularnewline
2 &  24 &  21.73 &  2.272 \tabularnewline
3 &  21 &  20.9 &  0.09791 \tabularnewline
4 &  20 &  20.93 & -0.933 \tabularnewline
5 &  23 &  20.88 &  2.116 \tabularnewline
6 &  21 &  21.2 & -0.2028 \tabularnewline
7 &  19 &  20.86 & -1.865 \tabularnewline
8 &  21 &  21.2 & -0.2011 \tabularnewline
9 &  21 &  21.22 & -0.2224 \tabularnewline
10 &  22 &  20.89 &  1.11 \tabularnewline
11 &  21 &  20.5 &  0.5016 \tabularnewline
12 &  22 &  21.16 &  0.8442 \tabularnewline
13 &  19 &  20.57 & -1.575 \tabularnewline
14 &  19 &  21.06 & -2.062 \tabularnewline
15 &  23 &  20.52 &  2.476 \tabularnewline
16 &  21 &  21.35 & -0.3523 \tabularnewline
17 &  19 &  20.87 & -1.873 \tabularnewline
18 &  21 &  21.43 & -0.4325 \tabularnewline
19 &  21 &  20.64 &  0.363 \tabularnewline
20 &  21 &  20.95 &  0.04965 \tabularnewline
21 &  19 &  21.16 & -2.156 \tabularnewline
22 &  19 &  21.48 & -2.479 \tabularnewline
23 &  19 &  20.9 & -1.899 \tabularnewline
24 &  22 &  20.86 &  1.144 \tabularnewline
25 &  22 &  20.76 &  1.237 \tabularnewline
26 &  18 &  20.85 & -2.851 \tabularnewline
27 &  22 &  21.07 &  0.9307 \tabularnewline
28 &  19 &  19.97 & -0.9723 \tabularnewline
29 &  22 &  21.11 &  0.8851 \tabularnewline
30 &  19 &  20.59 & -1.594 \tabularnewline
31 &  19 &  21.59 & -2.59 \tabularnewline
32 &  21 &  21.22 & -0.2233 \tabularnewline
33 &  21 &  21.01 & -0.01066 \tabularnewline
34 &  19 &  20.51 & -1.506 \tabularnewline
35 &  19 &  20.66 & -1.662 \tabularnewline
36 &  26 &  21.46 &  4.541 \tabularnewline
37 &  19 &  20.66 & -1.662 \tabularnewline
38 &  21 &  21.29 & -0.288 \tabularnewline
39 &  21 &  20.74 &  0.2631 \tabularnewline
40 &  20 &  21.21 & -1.215 \tabularnewline
41 &  23 &  21.74 &  1.258 \tabularnewline
42 &  22 &  20.41 &  1.587 \tabularnewline
43 &  22 &  20.71 &  1.294 \tabularnewline
44 &  22 &  21.22 &  0.7836 \tabularnewline
45 &  21 &  20.6 &  0.3974 \tabularnewline
46 &  22 &  20.9 &  1.1 \tabularnewline
47 &  21 &  21.01 & -0.005953 \tabularnewline
48 &  21 &  20.51 &  0.4903 \tabularnewline
49 &  21 &  21.23 & -0.2348 \tabularnewline
50 &  23 &  21.19 &  1.807 \tabularnewline
51 &  23 &  21.3 &  1.703 \tabularnewline
52 &  19 &  21.38 & -2.375 \tabularnewline
53 &  21 &  21.32 & -0.319 \tabularnewline
54 &  21 &  22.05 & -1.052 \tabularnewline
55 &  23 &  21.12 &  1.88 \tabularnewline
56 &  20 &  21.43 & -1.435 \tabularnewline
57 &  23 &  21.12 &  1.881 \tabularnewline
58 &  19 &  20.43 & -1.43 \tabularnewline
59 &  22 &  21.17 &  0.8302 \tabularnewline
60 &  22 &  21.7 &  0.3007 \tabularnewline
61 &  21 &  21.8 & -0.7965 \tabularnewline
62 &  21 &  20.99 &  0.01173 \tabularnewline
63 &  22 &  21.18 &  0.8219 \tabularnewline
64 &  25 &  21.63 &  3.369 \tabularnewline
65 &  21 &  20.92 &  0.08427 \tabularnewline
66 &  23 &  20.98 &  2.022 \tabularnewline
67 &  21 &  20.6 &  0.3973 \tabularnewline
68 &  24 &  20.94 &  3.065 \tabularnewline
69 &  19 &  21.45 & -2.449 \tabularnewline
70 &  18 &  20.84 & -2.844 \tabularnewline
71 &  19 &  20.61 & -1.608 \tabularnewline
72 &  20 &  20.67 & -0.6666 \tabularnewline
73 &  22 &  21.01 &  0.9857 \tabularnewline
74 &  22 &  20.75 &  1.251 \tabularnewline
75 &  24 &  20.65 &  3.347 \tabularnewline
76 &  23 &  20.65 &  2.349 \tabularnewline
77 &  23 &  20.87 &  2.126 \tabularnewline
78 &  22 &  21.15 &  0.8484 \tabularnewline
79 &  19 &  20.99 & -1.989 \tabularnewline
80 &  21 &  20.68 &  0.3197 \tabularnewline
81 &  23 &  21.24 &  1.757 \tabularnewline
82 &  22 &  20.81 &  1.186 \tabularnewline
83 &  19 &  20.7 & -1.698 \tabularnewline
84 &  22 &  21.35 &  0.6467 \tabularnewline
85 &  21 &  21.38 & -0.3752 \tabularnewline
86 &  20 &  21.32 & -1.319 \tabularnewline
87 &  23 &  20.74 &  2.258 \tabularnewline
88 &  22 &  21.27 &  0.7259 \tabularnewline
89 &  21 &  20.39 &  0.6101 \tabularnewline
90 &  20 &  20.74 & -0.7369 \tabularnewline
91 &  18 &  21.34 & -3.344 \tabularnewline
92 &  18 &  20.55 & -2.554 \tabularnewline
93 &  19 &  21.23 & -2.229 \tabularnewline
94 &  19 &  21.23 & -2.235 \tabularnewline
95 &  20 &  21.25 & -1.252 \tabularnewline
96 &  19 &  20.74 & -1.741 \tabularnewline
97 &  23 &  21.01 &  1.989 \tabularnewline
98 &  21 &  21.31 & -0.3073 \tabularnewline
99 &  22 &  21.14 &  0.864 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=304585&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] 22[/C][C] 21.36[/C][C] 0.644[/C][/ROW]
[ROW][C]2[/C][C] 24[/C][C] 21.73[/C][C] 2.272[/C][/ROW]
[ROW][C]3[/C][C] 21[/C][C] 20.9[/C][C] 0.09791[/C][/ROW]
[ROW][C]4[/C][C] 20[/C][C] 20.93[/C][C]-0.933[/C][/ROW]
[ROW][C]5[/C][C] 23[/C][C] 20.88[/C][C] 2.116[/C][/ROW]
[ROW][C]6[/C][C] 21[/C][C] 21.2[/C][C]-0.2028[/C][/ROW]
[ROW][C]7[/C][C] 19[/C][C] 20.86[/C][C]-1.865[/C][/ROW]
[ROW][C]8[/C][C] 21[/C][C] 21.2[/C][C]-0.2011[/C][/ROW]
[ROW][C]9[/C][C] 21[/C][C] 21.22[/C][C]-0.2224[/C][/ROW]
[ROW][C]10[/C][C] 22[/C][C] 20.89[/C][C] 1.11[/C][/ROW]
[ROW][C]11[/C][C] 21[/C][C] 20.5[/C][C] 0.5016[/C][/ROW]
[ROW][C]12[/C][C] 22[/C][C] 21.16[/C][C] 0.8442[/C][/ROW]
[ROW][C]13[/C][C] 19[/C][C] 20.57[/C][C]-1.575[/C][/ROW]
[ROW][C]14[/C][C] 19[/C][C] 21.06[/C][C]-2.062[/C][/ROW]
[ROW][C]15[/C][C] 23[/C][C] 20.52[/C][C] 2.476[/C][/ROW]
[ROW][C]16[/C][C] 21[/C][C] 21.35[/C][C]-0.3523[/C][/ROW]
[ROW][C]17[/C][C] 19[/C][C] 20.87[/C][C]-1.873[/C][/ROW]
[ROW][C]18[/C][C] 21[/C][C] 21.43[/C][C]-0.4325[/C][/ROW]
[ROW][C]19[/C][C] 21[/C][C] 20.64[/C][C] 0.363[/C][/ROW]
[ROW][C]20[/C][C] 21[/C][C] 20.95[/C][C] 0.04965[/C][/ROW]
[ROW][C]21[/C][C] 19[/C][C] 21.16[/C][C]-2.156[/C][/ROW]
[ROW][C]22[/C][C] 19[/C][C] 21.48[/C][C]-2.479[/C][/ROW]
[ROW][C]23[/C][C] 19[/C][C] 20.9[/C][C]-1.899[/C][/ROW]
[ROW][C]24[/C][C] 22[/C][C] 20.86[/C][C] 1.144[/C][/ROW]
[ROW][C]25[/C][C] 22[/C][C] 20.76[/C][C] 1.237[/C][/ROW]
[ROW][C]26[/C][C] 18[/C][C] 20.85[/C][C]-2.851[/C][/ROW]
[ROW][C]27[/C][C] 22[/C][C] 21.07[/C][C] 0.9307[/C][/ROW]
[ROW][C]28[/C][C] 19[/C][C] 19.97[/C][C]-0.9723[/C][/ROW]
[ROW][C]29[/C][C] 22[/C][C] 21.11[/C][C] 0.8851[/C][/ROW]
[ROW][C]30[/C][C] 19[/C][C] 20.59[/C][C]-1.594[/C][/ROW]
[ROW][C]31[/C][C] 19[/C][C] 21.59[/C][C]-2.59[/C][/ROW]
[ROW][C]32[/C][C] 21[/C][C] 21.22[/C][C]-0.2233[/C][/ROW]
[ROW][C]33[/C][C] 21[/C][C] 21.01[/C][C]-0.01066[/C][/ROW]
[ROW][C]34[/C][C] 19[/C][C] 20.51[/C][C]-1.506[/C][/ROW]
[ROW][C]35[/C][C] 19[/C][C] 20.66[/C][C]-1.662[/C][/ROW]
[ROW][C]36[/C][C] 26[/C][C] 21.46[/C][C] 4.541[/C][/ROW]
[ROW][C]37[/C][C] 19[/C][C] 20.66[/C][C]-1.662[/C][/ROW]
[ROW][C]38[/C][C] 21[/C][C] 21.29[/C][C]-0.288[/C][/ROW]
[ROW][C]39[/C][C] 21[/C][C] 20.74[/C][C] 0.2631[/C][/ROW]
[ROW][C]40[/C][C] 20[/C][C] 21.21[/C][C]-1.215[/C][/ROW]
[ROW][C]41[/C][C] 23[/C][C] 21.74[/C][C] 1.258[/C][/ROW]
[ROW][C]42[/C][C] 22[/C][C] 20.41[/C][C] 1.587[/C][/ROW]
[ROW][C]43[/C][C] 22[/C][C] 20.71[/C][C] 1.294[/C][/ROW]
[ROW][C]44[/C][C] 22[/C][C] 21.22[/C][C] 0.7836[/C][/ROW]
[ROW][C]45[/C][C] 21[/C][C] 20.6[/C][C] 0.3974[/C][/ROW]
[ROW][C]46[/C][C] 22[/C][C] 20.9[/C][C] 1.1[/C][/ROW]
[ROW][C]47[/C][C] 21[/C][C] 21.01[/C][C]-0.005953[/C][/ROW]
[ROW][C]48[/C][C] 21[/C][C] 20.51[/C][C] 0.4903[/C][/ROW]
[ROW][C]49[/C][C] 21[/C][C] 21.23[/C][C]-0.2348[/C][/ROW]
[ROW][C]50[/C][C] 23[/C][C] 21.19[/C][C] 1.807[/C][/ROW]
[ROW][C]51[/C][C] 23[/C][C] 21.3[/C][C] 1.703[/C][/ROW]
[ROW][C]52[/C][C] 19[/C][C] 21.38[/C][C]-2.375[/C][/ROW]
[ROW][C]53[/C][C] 21[/C][C] 21.32[/C][C]-0.319[/C][/ROW]
[ROW][C]54[/C][C] 21[/C][C] 22.05[/C][C]-1.052[/C][/ROW]
[ROW][C]55[/C][C] 23[/C][C] 21.12[/C][C] 1.88[/C][/ROW]
[ROW][C]56[/C][C] 20[/C][C] 21.43[/C][C]-1.435[/C][/ROW]
[ROW][C]57[/C][C] 23[/C][C] 21.12[/C][C] 1.881[/C][/ROW]
[ROW][C]58[/C][C] 19[/C][C] 20.43[/C][C]-1.43[/C][/ROW]
[ROW][C]59[/C][C] 22[/C][C] 21.17[/C][C] 0.8302[/C][/ROW]
[ROW][C]60[/C][C] 22[/C][C] 21.7[/C][C] 0.3007[/C][/ROW]
[ROW][C]61[/C][C] 21[/C][C] 21.8[/C][C]-0.7965[/C][/ROW]
[ROW][C]62[/C][C] 21[/C][C] 20.99[/C][C] 0.01173[/C][/ROW]
[ROW][C]63[/C][C] 22[/C][C] 21.18[/C][C] 0.8219[/C][/ROW]
[ROW][C]64[/C][C] 25[/C][C] 21.63[/C][C] 3.369[/C][/ROW]
[ROW][C]65[/C][C] 21[/C][C] 20.92[/C][C] 0.08427[/C][/ROW]
[ROW][C]66[/C][C] 23[/C][C] 20.98[/C][C] 2.022[/C][/ROW]
[ROW][C]67[/C][C] 21[/C][C] 20.6[/C][C] 0.3973[/C][/ROW]
[ROW][C]68[/C][C] 24[/C][C] 20.94[/C][C] 3.065[/C][/ROW]
[ROW][C]69[/C][C] 19[/C][C] 21.45[/C][C]-2.449[/C][/ROW]
[ROW][C]70[/C][C] 18[/C][C] 20.84[/C][C]-2.844[/C][/ROW]
[ROW][C]71[/C][C] 19[/C][C] 20.61[/C][C]-1.608[/C][/ROW]
[ROW][C]72[/C][C] 20[/C][C] 20.67[/C][C]-0.6666[/C][/ROW]
[ROW][C]73[/C][C] 22[/C][C] 21.01[/C][C] 0.9857[/C][/ROW]
[ROW][C]74[/C][C] 22[/C][C] 20.75[/C][C] 1.251[/C][/ROW]
[ROW][C]75[/C][C] 24[/C][C] 20.65[/C][C] 3.347[/C][/ROW]
[ROW][C]76[/C][C] 23[/C][C] 20.65[/C][C] 2.349[/C][/ROW]
[ROW][C]77[/C][C] 23[/C][C] 20.87[/C][C] 2.126[/C][/ROW]
[ROW][C]78[/C][C] 22[/C][C] 21.15[/C][C] 0.8484[/C][/ROW]
[ROW][C]79[/C][C] 19[/C][C] 20.99[/C][C]-1.989[/C][/ROW]
[ROW][C]80[/C][C] 21[/C][C] 20.68[/C][C] 0.3197[/C][/ROW]
[ROW][C]81[/C][C] 23[/C][C] 21.24[/C][C] 1.757[/C][/ROW]
[ROW][C]82[/C][C] 22[/C][C] 20.81[/C][C] 1.186[/C][/ROW]
[ROW][C]83[/C][C] 19[/C][C] 20.7[/C][C]-1.698[/C][/ROW]
[ROW][C]84[/C][C] 22[/C][C] 21.35[/C][C] 0.6467[/C][/ROW]
[ROW][C]85[/C][C] 21[/C][C] 21.38[/C][C]-0.3752[/C][/ROW]
[ROW][C]86[/C][C] 20[/C][C] 21.32[/C][C]-1.319[/C][/ROW]
[ROW][C]87[/C][C] 23[/C][C] 20.74[/C][C] 2.258[/C][/ROW]
[ROW][C]88[/C][C] 22[/C][C] 21.27[/C][C] 0.7259[/C][/ROW]
[ROW][C]89[/C][C] 21[/C][C] 20.39[/C][C] 0.6101[/C][/ROW]
[ROW][C]90[/C][C] 20[/C][C] 20.74[/C][C]-0.7369[/C][/ROW]
[ROW][C]91[/C][C] 18[/C][C] 21.34[/C][C]-3.344[/C][/ROW]
[ROW][C]92[/C][C] 18[/C][C] 20.55[/C][C]-2.554[/C][/ROW]
[ROW][C]93[/C][C] 19[/C][C] 21.23[/C][C]-2.229[/C][/ROW]
[ROW][C]94[/C][C] 19[/C][C] 21.23[/C][C]-2.235[/C][/ROW]
[ROW][C]95[/C][C] 20[/C][C] 21.25[/C][C]-1.252[/C][/ROW]
[ROW][C]96[/C][C] 19[/C][C] 20.74[/C][C]-1.741[/C][/ROW]
[ROW][C]97[/C][C] 23[/C][C] 21.01[/C][C] 1.989[/C][/ROW]
[ROW][C]98[/C][C] 21[/C][C] 21.31[/C][C]-0.3073[/C][/ROW]
[ROW][C]99[/C][C] 22[/C][C] 21.14[/C][C] 0.864[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=304585&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=304585&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	22	21.36	0.644
2	24	21.73	2.272
3	21	20.9	0.09791
4	20	20.93	-0.933
5	23	20.88	2.116
6	21	21.2	-0.2028
7	19	20.86	-1.865
8	21	21.2	-0.2011
9	21	21.22	-0.2224
10	22	20.89	1.11
11	21	20.5	0.5016
12	22	21.16	0.8442
13	19	20.57	-1.575
14	19	21.06	-2.062
15	23	20.52	2.476
16	21	21.35	-0.3523
17	19	20.87	-1.873
18	21	21.43	-0.4325
19	21	20.64	0.363
20	21	20.95	0.04965
21	19	21.16	-2.156
22	19	21.48	-2.479
23	19	20.9	-1.899
24	22	20.86	1.144
25	22	20.76	1.237
26	18	20.85	-2.851
27	22	21.07	0.9307
28	19	19.97	-0.9723
29	22	21.11	0.8851
30	19	20.59	-1.594
31	19	21.59	-2.59
32	21	21.22	-0.2233
33	21	21.01	-0.01066
34	19	20.51	-1.506
35	19	20.66	-1.662
36	26	21.46	4.541
37	19	20.66	-1.662
38	21	21.29	-0.288
39	21	20.74	0.2631
40	20	21.21	-1.215
41	23	21.74	1.258
42	22	20.41	1.587
43	22	20.71	1.294
44	22	21.22	0.7836
45	21	20.6	0.3974
46	22	20.9	1.1
47	21	21.01	-0.005953
48	21	20.51	0.4903
49	21	21.23	-0.2348
50	23	21.19	1.807
51	23	21.3	1.703
52	19	21.38	-2.375
53	21	21.32	-0.319
54	21	22.05	-1.052
55	23	21.12	1.88
56	20	21.43	-1.435
57	23	21.12	1.881
58	19	20.43	-1.43
59	22	21.17	0.8302
60	22	21.7	0.3007
61	21	21.8	-0.7965
62	21	20.99	0.01173
63	22	21.18	0.8219
64	25	21.63	3.369
65	21	20.92	0.08427
66	23	20.98	2.022
67	21	20.6	0.3973
68	24	20.94	3.065
69	19	21.45	-2.449
70	18	20.84	-2.844
71	19	20.61	-1.608
72	20	20.67	-0.6666
73	22	21.01	0.9857
74	22	20.75	1.251
75	24	20.65	3.347
76	23	20.65	2.349
77	23	20.87	2.126
78	22	21.15	0.8484
79	19	20.99	-1.989
80	21	20.68	0.3197
81	23	21.24	1.757
82	22	20.81	1.186
83	19	20.7	-1.698
84	22	21.35	0.6467
85	21	21.38	-0.3752
86	20	21.32	-1.319
87	23	20.74	2.258
88	22	21.27	0.7259
89	21	20.39	0.6101
90	20	20.74	-0.7369
91	18	21.34	-3.344
92	18	20.55	-2.554
93	19	21.23	-2.229
94	19	21.23	-2.235
95	20	21.25	-1.252
96	19	20.74	-1.741
97	23	21.01	1.989
98	21	21.31	-0.3073
99	22	21.14	0.864

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
12	0.2454	0.4909	0.7546
13	0.1532	0.3065	0.8468
14	0.07823	0.1565	0.9218
15	0.3196	0.6393	0.6804
16	0.2151	0.4302	0.7849
17	0.1493	0.2985	0.8507
18	0.1303	0.2605	0.8697
19	0.09487	0.1897	0.9051
20	0.07817	0.1563	0.9218
21	0.07126	0.1425	0.9287
22	0.08101	0.162	0.919
23	0.1769	0.3538	0.8231
24	0.133	0.2661	0.867
25	0.2162	0.4325	0.7838
26	0.1997	0.3994	0.8003
27	0.1555	0.3111	0.8445
28	0.1633	0.3267	0.8367
29	0.1346	0.2692	0.8654
30	0.1176	0.2351	0.8824
31	0.1662	0.3324	0.8338
32	0.1461	0.2922	0.8539
33	0.1271	0.2543	0.8729
34	0.1193	0.2386	0.8807
35	0.1124	0.2248	0.8876
36	0.327	0.654	0.673
37	0.3902	0.7804	0.6098
38	0.3525	0.705	0.6475
39	0.3895	0.7789	0.6105
40	0.3777	0.7553	0.6223
41	0.3884	0.7768	0.6116
42	0.4282	0.8564	0.5718
43	0.4429	0.8858	0.5571
44	0.404	0.808	0.596
45	0.3471	0.6942	0.6529
46	0.3138	0.6276	0.6862
47	0.261	0.5221	0.739
48	0.2245	0.4491	0.7755
49	0.1831	0.3663	0.8169
50	0.1916	0.3833	0.8084
51	0.2133	0.4266	0.7867
52	0.2807	0.5614	0.7193
53	0.2492	0.4985	0.7508
54	0.2247	0.4495	0.7753
55	0.247	0.494	0.753
56	0.2354	0.4708	0.7646
57	0.2413	0.4827	0.7587
58	0.2443	0.4886	0.7557
59	0.2062	0.4124	0.7938
60	0.1758	0.3517	0.8242
61	0.1521	0.3042	0.8479
62	0.1235	0.247	0.8765
63	0.09894	0.1979	0.9011
64	0.1997	0.3993	0.8003
65	0.1606	0.3212	0.8394
66	0.1972	0.3945	0.8028
67	0.173	0.346	0.827
68	0.2343	0.4686	0.7657
69	0.2611	0.5222	0.7389
70	0.4546	0.9093	0.5454
71	0.4122	0.8244	0.5878
72	0.3461	0.6921	0.6539
73	0.2912	0.5824	0.7088
74	0.2385	0.477	0.7615
75	0.3871	0.7742	0.6129
76	0.38	0.7599	0.62
77	0.39	0.78	0.61
78	0.3287	0.6574	0.6713
79	0.3921	0.7842	0.6079
80	0.325	0.6499	0.675
81	0.268	0.536	0.732
82	0.2482	0.4965	0.7518
83	0.1917	0.3833	0.8083
84	0.1528	0.3057	0.8472
85	0.1266	0.2531	0.8734
86	0.09623	0.1925	0.9038
87	0.2008	0.4017	0.7992

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
12 &  0.2454 &  0.4909 &  0.7546 \tabularnewline
13 &  0.1532 &  0.3065 &  0.8468 \tabularnewline
14 &  0.07823 &  0.1565 &  0.9218 \tabularnewline
15 &  0.3196 &  0.6393 &  0.6804 \tabularnewline
16 &  0.2151 &  0.4302 &  0.7849 \tabularnewline
17 &  0.1493 &  0.2985 &  0.8507 \tabularnewline
18 &  0.1303 &  0.2605 &  0.8697 \tabularnewline
19 &  0.09487 &  0.1897 &  0.9051 \tabularnewline
20 &  0.07817 &  0.1563 &  0.9218 \tabularnewline
21 &  0.07126 &  0.1425 &  0.9287 \tabularnewline
22 &  0.08101 &  0.162 &  0.919 \tabularnewline
23 &  0.1769 &  0.3538 &  0.8231 \tabularnewline
24 &  0.133 &  0.2661 &  0.867 \tabularnewline
25 &  0.2162 &  0.4325 &  0.7838 \tabularnewline
26 &  0.1997 &  0.3994 &  0.8003 \tabularnewline
27 &  0.1555 &  0.3111 &  0.8445 \tabularnewline
28 &  0.1633 &  0.3267 &  0.8367 \tabularnewline
29 &  0.1346 &  0.2692 &  0.8654 \tabularnewline
30 &  0.1176 &  0.2351 &  0.8824 \tabularnewline
31 &  0.1662 &  0.3324 &  0.8338 \tabularnewline
32 &  0.1461 &  0.2922 &  0.8539 \tabularnewline
33 &  0.1271 &  0.2543 &  0.8729 \tabularnewline
34 &  0.1193 &  0.2386 &  0.8807 \tabularnewline
35 &  0.1124 &  0.2248 &  0.8876 \tabularnewline
36 &  0.327 &  0.654 &  0.673 \tabularnewline
37 &  0.3902 &  0.7804 &  0.6098 \tabularnewline
38 &  0.3525 &  0.705 &  0.6475 \tabularnewline
39 &  0.3895 &  0.7789 &  0.6105 \tabularnewline
40 &  0.3777 &  0.7553 &  0.6223 \tabularnewline
41 &  0.3884 &  0.7768 &  0.6116 \tabularnewline
42 &  0.4282 &  0.8564 &  0.5718 \tabularnewline
43 &  0.4429 &  0.8858 &  0.5571 \tabularnewline
44 &  0.404 &  0.808 &  0.596 \tabularnewline
45 &  0.3471 &  0.6942 &  0.6529 \tabularnewline
46 &  0.3138 &  0.6276 &  0.6862 \tabularnewline
47 &  0.261 &  0.5221 &  0.739 \tabularnewline
48 &  0.2245 &  0.4491 &  0.7755 \tabularnewline
49 &  0.1831 &  0.3663 &  0.8169 \tabularnewline
50 &  0.1916 &  0.3833 &  0.8084 \tabularnewline
51 &  0.2133 &  0.4266 &  0.7867 \tabularnewline
52 &  0.2807 &  0.5614 &  0.7193 \tabularnewline
53 &  0.2492 &  0.4985 &  0.7508 \tabularnewline
54 &  0.2247 &  0.4495 &  0.7753 \tabularnewline
55 &  0.247 &  0.494 &  0.753 \tabularnewline
56 &  0.2354 &  0.4708 &  0.7646 \tabularnewline
57 &  0.2413 &  0.4827 &  0.7587 \tabularnewline
58 &  0.2443 &  0.4886 &  0.7557 \tabularnewline
59 &  0.2062 &  0.4124 &  0.7938 \tabularnewline
60 &  0.1758 &  0.3517 &  0.8242 \tabularnewline
61 &  0.1521 &  0.3042 &  0.8479 \tabularnewline
62 &  0.1235 &  0.247 &  0.8765 \tabularnewline
63 &  0.09894 &  0.1979 &  0.9011 \tabularnewline
64 &  0.1997 &  0.3993 &  0.8003 \tabularnewline
65 &  0.1606 &  0.3212 &  0.8394 \tabularnewline
66 &  0.1972 &  0.3945 &  0.8028 \tabularnewline
67 &  0.173 &  0.346 &  0.827 \tabularnewline
68 &  0.2343 &  0.4686 &  0.7657 \tabularnewline
69 &  0.2611 &  0.5222 &  0.7389 \tabularnewline
70 &  0.4546 &  0.9093 &  0.5454 \tabularnewline
71 &  0.4122 &  0.8244 &  0.5878 \tabularnewline
72 &  0.3461 &  0.6921 &  0.6539 \tabularnewline
73 &  0.2912 &  0.5824 &  0.7088 \tabularnewline
74 &  0.2385 &  0.477 &  0.7615 \tabularnewline
75 &  0.3871 &  0.7742 &  0.6129 \tabularnewline
76 &  0.38 &  0.7599 &  0.62 \tabularnewline
77 &  0.39 &  0.78 &  0.61 \tabularnewline
78 &  0.3287 &  0.6574 &  0.6713 \tabularnewline
79 &  0.3921 &  0.7842 &  0.6079 \tabularnewline
80 &  0.325 &  0.6499 &  0.675 \tabularnewline
81 &  0.268 &  0.536 &  0.732 \tabularnewline
82 &  0.2482 &  0.4965 &  0.7518 \tabularnewline
83 &  0.1917 &  0.3833 &  0.8083 \tabularnewline
84 &  0.1528 &  0.3057 &  0.8472 \tabularnewline
85 &  0.1266 &  0.2531 &  0.8734 \tabularnewline
86 &  0.09623 &  0.1925 &  0.9038 \tabularnewline
87 &  0.2008 &  0.4017 &  0.7992 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=304585&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]12[/C][C] 0.2454[/C][C] 0.4909[/C][C] 0.7546[/C][/ROW]
[ROW][C]13[/C][C] 0.1532[/C][C] 0.3065[/C][C] 0.8468[/C][/ROW]
[ROW][C]14[/C][C] 0.07823[/C][C] 0.1565[/C][C] 0.9218[/C][/ROW]
[ROW][C]15[/C][C] 0.3196[/C][C] 0.6393[/C][C] 0.6804[/C][/ROW]
[ROW][C]16[/C][C] 0.2151[/C][C] 0.4302[/C][C] 0.7849[/C][/ROW]
[ROW][C]17[/C][C] 0.1493[/C][C] 0.2985[/C][C] 0.8507[/C][/ROW]
[ROW][C]18[/C][C] 0.1303[/C][C] 0.2605[/C][C] 0.8697[/C][/ROW]
[ROW][C]19[/C][C] 0.09487[/C][C] 0.1897[/C][C] 0.9051[/C][/ROW]
[ROW][C]20[/C][C] 0.07817[/C][C] 0.1563[/C][C] 0.9218[/C][/ROW]
[ROW][C]21[/C][C] 0.07126[/C][C] 0.1425[/C][C] 0.9287[/C][/ROW]
[ROW][C]22[/C][C] 0.08101[/C][C] 0.162[/C][C] 0.919[/C][/ROW]
[ROW][C]23[/C][C] 0.1769[/C][C] 0.3538[/C][C] 0.8231[/C][/ROW]
[ROW][C]24[/C][C] 0.133[/C][C] 0.2661[/C][C] 0.867[/C][/ROW]
[ROW][C]25[/C][C] 0.2162[/C][C] 0.4325[/C][C] 0.7838[/C][/ROW]
[ROW][C]26[/C][C] 0.1997[/C][C] 0.3994[/C][C] 0.8003[/C][/ROW]
[ROW][C]27[/C][C] 0.1555[/C][C] 0.3111[/C][C] 0.8445[/C][/ROW]
[ROW][C]28[/C][C] 0.1633[/C][C] 0.3267[/C][C] 0.8367[/C][/ROW]
[ROW][C]29[/C][C] 0.1346[/C][C] 0.2692[/C][C] 0.8654[/C][/ROW]
[ROW][C]30[/C][C] 0.1176[/C][C] 0.2351[/C][C] 0.8824[/C][/ROW]
[ROW][C]31[/C][C] 0.1662[/C][C] 0.3324[/C][C] 0.8338[/C][/ROW]
[ROW][C]32[/C][C] 0.1461[/C][C] 0.2922[/C][C] 0.8539[/C][/ROW]
[ROW][C]33[/C][C] 0.1271[/C][C] 0.2543[/C][C] 0.8729[/C][/ROW]
[ROW][C]34[/C][C] 0.1193[/C][C] 0.2386[/C][C] 0.8807[/C][/ROW]
[ROW][C]35[/C][C] 0.1124[/C][C] 0.2248[/C][C] 0.8876[/C][/ROW]
[ROW][C]36[/C][C] 0.327[/C][C] 0.654[/C][C] 0.673[/C][/ROW]
[ROW][C]37[/C][C] 0.3902[/C][C] 0.7804[/C][C] 0.6098[/C][/ROW]
[ROW][C]38[/C][C] 0.3525[/C][C] 0.705[/C][C] 0.6475[/C][/ROW]
[ROW][C]39[/C][C] 0.3895[/C][C] 0.7789[/C][C] 0.6105[/C][/ROW]
[ROW][C]40[/C][C] 0.3777[/C][C] 0.7553[/C][C] 0.6223[/C][/ROW]
[ROW][C]41[/C][C] 0.3884[/C][C] 0.7768[/C][C] 0.6116[/C][/ROW]
[ROW][C]42[/C][C] 0.4282[/C][C] 0.8564[/C][C] 0.5718[/C][/ROW]
[ROW][C]43[/C][C] 0.4429[/C][C] 0.8858[/C][C] 0.5571[/C][/ROW]
[ROW][C]44[/C][C] 0.404[/C][C] 0.808[/C][C] 0.596[/C][/ROW]
[ROW][C]45[/C][C] 0.3471[/C][C] 0.6942[/C][C] 0.6529[/C][/ROW]
[ROW][C]46[/C][C] 0.3138[/C][C] 0.6276[/C][C] 0.6862[/C][/ROW]
[ROW][C]47[/C][C] 0.261[/C][C] 0.5221[/C][C] 0.739[/C][/ROW]
[ROW][C]48[/C][C] 0.2245[/C][C] 0.4491[/C][C] 0.7755[/C][/ROW]
[ROW][C]49[/C][C] 0.1831[/C][C] 0.3663[/C][C] 0.8169[/C][/ROW]
[ROW][C]50[/C][C] 0.1916[/C][C] 0.3833[/C][C] 0.8084[/C][/ROW]
[ROW][C]51[/C][C] 0.2133[/C][C] 0.4266[/C][C] 0.7867[/C][/ROW]
[ROW][C]52[/C][C] 0.2807[/C][C] 0.5614[/C][C] 0.7193[/C][/ROW]
[ROW][C]53[/C][C] 0.2492[/C][C] 0.4985[/C][C] 0.7508[/C][/ROW]
[ROW][C]54[/C][C] 0.2247[/C][C] 0.4495[/C][C] 0.7753[/C][/ROW]
[ROW][C]55[/C][C] 0.247[/C][C] 0.494[/C][C] 0.753[/C][/ROW]
[ROW][C]56[/C][C] 0.2354[/C][C] 0.4708[/C][C] 0.7646[/C][/ROW]
[ROW][C]57[/C][C] 0.2413[/C][C] 0.4827[/C][C] 0.7587[/C][/ROW]
[ROW][C]58[/C][C] 0.2443[/C][C] 0.4886[/C][C] 0.7557[/C][/ROW]
[ROW][C]59[/C][C] 0.2062[/C][C] 0.4124[/C][C] 0.7938[/C][/ROW]
[ROW][C]60[/C][C] 0.1758[/C][C] 0.3517[/C][C] 0.8242[/C][/ROW]
[ROW][C]61[/C][C] 0.1521[/C][C] 0.3042[/C][C] 0.8479[/C][/ROW]
[ROW][C]62[/C][C] 0.1235[/C][C] 0.247[/C][C] 0.8765[/C][/ROW]
[ROW][C]63[/C][C] 0.09894[/C][C] 0.1979[/C][C] 0.9011[/C][/ROW]
[ROW][C]64[/C][C] 0.1997[/C][C] 0.3993[/C][C] 0.8003[/C][/ROW]
[ROW][C]65[/C][C] 0.1606[/C][C] 0.3212[/C][C] 0.8394[/C][/ROW]
[ROW][C]66[/C][C] 0.1972[/C][C] 0.3945[/C][C] 0.8028[/C][/ROW]
[ROW][C]67[/C][C] 0.173[/C][C] 0.346[/C][C] 0.827[/C][/ROW]
[ROW][C]68[/C][C] 0.2343[/C][C] 0.4686[/C][C] 0.7657[/C][/ROW]
[ROW][C]69[/C][C] 0.2611[/C][C] 0.5222[/C][C] 0.7389[/C][/ROW]
[ROW][C]70[/C][C] 0.4546[/C][C] 0.9093[/C][C] 0.5454[/C][/ROW]
[ROW][C]71[/C][C] 0.4122[/C][C] 0.8244[/C][C] 0.5878[/C][/ROW]
[ROW][C]72[/C][C] 0.3461[/C][C] 0.6921[/C][C] 0.6539[/C][/ROW]
[ROW][C]73[/C][C] 0.2912[/C][C] 0.5824[/C][C] 0.7088[/C][/ROW]
[ROW][C]74[/C][C] 0.2385[/C][C] 0.477[/C][C] 0.7615[/C][/ROW]
[ROW][C]75[/C][C] 0.3871[/C][C] 0.7742[/C][C] 0.6129[/C][/ROW]
[ROW][C]76[/C][C] 0.38[/C][C] 0.7599[/C][C] 0.62[/C][/ROW]
[ROW][C]77[/C][C] 0.39[/C][C] 0.78[/C][C] 0.61[/C][/ROW]
[ROW][C]78[/C][C] 0.3287[/C][C] 0.6574[/C][C] 0.6713[/C][/ROW]
[ROW][C]79[/C][C] 0.3921[/C][C] 0.7842[/C][C] 0.6079[/C][/ROW]
[ROW][C]80[/C][C] 0.325[/C][C] 0.6499[/C][C] 0.675[/C][/ROW]
[ROW][C]81[/C][C] 0.268[/C][C] 0.536[/C][C] 0.732[/C][/ROW]
[ROW][C]82[/C][C] 0.2482[/C][C] 0.4965[/C][C] 0.7518[/C][/ROW]
[ROW][C]83[/C][C] 0.1917[/C][C] 0.3833[/C][C] 0.8083[/C][/ROW]
[ROW][C]84[/C][C] 0.1528[/C][C] 0.3057[/C][C] 0.8472[/C][/ROW]
[ROW][C]85[/C][C] 0.1266[/C][C] 0.2531[/C][C] 0.8734[/C][/ROW]
[ROW][C]86[/C][C] 0.09623[/C][C] 0.1925[/C][C] 0.9038[/C][/ROW]
[ROW][C]87[/C][C] 0.2008[/C][C] 0.4017[/C][C] 0.7992[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=304585&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=304585&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
12	0.2454	0.4909	0.7546
13	0.1532	0.3065	0.8468
14	0.07823	0.1565	0.9218
15	0.3196	0.6393	0.6804
16	0.2151	0.4302	0.7849
17	0.1493	0.2985	0.8507
18	0.1303	0.2605	0.8697
19	0.09487	0.1897	0.9051
20	0.07817	0.1563	0.9218
21	0.07126	0.1425	0.9287
22	0.08101	0.162	0.919
23	0.1769	0.3538	0.8231
24	0.133	0.2661	0.867
25	0.2162	0.4325	0.7838
26	0.1997	0.3994	0.8003
27	0.1555	0.3111	0.8445
28	0.1633	0.3267	0.8367
29	0.1346	0.2692	0.8654
30	0.1176	0.2351	0.8824
31	0.1662	0.3324	0.8338
32	0.1461	0.2922	0.8539
33	0.1271	0.2543	0.8729
34	0.1193	0.2386	0.8807
35	0.1124	0.2248	0.8876
36	0.327	0.654	0.673
37	0.3902	0.7804	0.6098
38	0.3525	0.705	0.6475
39	0.3895	0.7789	0.6105
40	0.3777	0.7553	0.6223
41	0.3884	0.7768	0.6116
42	0.4282	0.8564	0.5718
43	0.4429	0.8858	0.5571
44	0.404	0.808	0.596
45	0.3471	0.6942	0.6529
46	0.3138	0.6276	0.6862
47	0.261	0.5221	0.739
48	0.2245	0.4491	0.7755
49	0.1831	0.3663	0.8169
50	0.1916	0.3833	0.8084
51	0.2133	0.4266	0.7867
52	0.2807	0.5614	0.7193
53	0.2492	0.4985	0.7508
54	0.2247	0.4495	0.7753
55	0.247	0.494	0.753
56	0.2354	0.4708	0.7646
57	0.2413	0.4827	0.7587
58	0.2443	0.4886	0.7557
59	0.2062	0.4124	0.7938
60	0.1758	0.3517	0.8242
61	0.1521	0.3042	0.8479
62	0.1235	0.247	0.8765
63	0.09894	0.1979	0.9011
64	0.1997	0.3993	0.8003
65	0.1606	0.3212	0.8394
66	0.1972	0.3945	0.8028
67	0.173	0.346	0.827
68	0.2343	0.4686	0.7657
69	0.2611	0.5222	0.7389
70	0.4546	0.9093	0.5454
71	0.4122	0.8244	0.5878
72	0.3461	0.6921	0.6539
73	0.2912	0.5824	0.7088
74	0.2385	0.477	0.7615
75	0.3871	0.7742	0.6129
76	0.38	0.7599	0.62
77	0.39	0.78	0.61
78	0.3287	0.6574	0.6713
79	0.3921	0.7842	0.6079
80	0.325	0.6499	0.675
81	0.268	0.536	0.732
82	0.2482	0.4965	0.7518
83	0.1917	0.3833	0.8083
84	0.1528	0.3057	0.8472
85	0.1266	0.2531	0.8734
86	0.09623	0.1925	0.9038
87	0.2008	0.4017	0.7992

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	0	0	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 & 0 & 0 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=304585&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]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=304585&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=304585&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	0	0	OK
10% type I error level	0	0	OK

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted RESET test data: mylm RESET = 0.21216, df1 = 2, df2 = 88, p-value = 0.8092
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 0.85279, df1 = 16, df2 = 74, p-value = 0.6234
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 1.7704, df1 = 2, df2 = 88, p-value = 0.1763

\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 = 0.21216, df1 = 2, df2 = 88, p-value = 0.8092
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of regressors \tabularnewline
> reset_test_regressors
	RESET test
data:  mylm
RESET = 0.85279, df1 = 16, df2 = 74, p-value = 0.6234
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of principal components \tabularnewline
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 1.7704, df1 = 2, df2 = 88, p-value = 0.1763
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=304585&T=7

[TABLE]
[ROW][C]Ramsey RESET F-Test for powers (2 and 3) of fitted values[/C][/ROW]
[ROW][C]> reset_test_fitted
	RESET test
data:  mylm
RESET = 0.21216, df1 = 2, df2 = 88, p-value = 0.8092
[/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 = 0.85279, df1 = 16, df2 = 74, p-value = 0.6234
[/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 = 1.7704, df1 = 2, df2 = 88, p-value = 0.1763
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=304585&T=7

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=304585&T=7

As an alternative you can also use a QR Code:

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

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted RESET test data: mylm RESET = 0.21216, df1 = 2, df2 = 88, p-value = 0.8092
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 0.85279, df1 = 16, df2 = 74, p-value = 0.6234
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 1.7704, df1 = 2, df2 = 88, p-value = 0.1763

Variance Inflation Factors (Multicollinearity)
> vif TVDC SKEOU1 SKEOU2 SKEOU3 SKEOU4 SKEOU5 SKEOU6 ITHSUM 1.574062 1.203430 1.461443 1.113750 1.145463 1.089735 1.052037 1.164214

\begin{tabular}{lllllllll}
\hline
Variance Inflation Factors (Multicollinearity) \tabularnewline
> vif
    TVDC   SKEOU1   SKEOU2   SKEOU3   SKEOU4   SKEOU5   SKEOU6   ITHSUM 
1.574062 1.203430 1.461443 1.113750 1.145463 1.089735 1.052037 1.164214 
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=304585&T=8

[TABLE]
[ROW][C]Variance Inflation Factors (Multicollinearity)[/C][/ROW]
[ROW][C]> vif
    TVDC   SKEOU1   SKEOU2   SKEOU3   SKEOU4   SKEOU5   SKEOU6   ITHSUM 
1.574062 1.203430 1.461443 1.113750 1.145463 1.089735 1.052037 1.164214 
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=304585&T=8

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=304585&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 TVDC SKEOU1 SKEOU2 SKEOU3 SKEOU4 SKEOU5 SKEOU6 ITHSUM 1.574062 1.203430 1.461443 1.113750 1.145463 1.089735 1.052037 1.164214

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):

par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

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

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

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

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code