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rwasp_multipleregression.wasp

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Multiple Regression

Date of computation

Wed, 25 Jan 2017 09:30:57 +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/25/t1485333413ij7ag92knrxy922.htm/, Retrieved Tue, 14 May 2024 12:29:30 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=305628, Retrieved Tue, 14 May 2024 12:29:30 +0000

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-       [Multiple Regression] [] [2017-01-25 08:30:57] [df90c754990be6fd2b18fcd529010a59] [Current]

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13 22 14 22 4 2 4 3
16 24 19 24 5 3 3 4
17 21 17 26 4 4 5 4
NA 21 17 21 3 4 3 3
NA 24 15 26 4 4 5 4
16 20 20 25 3 4 4 4
NA 22 15 21 3 4 4 3
NA 20 19 24 3 4 5 4
NA 19 15 27 4 5 4 4
17 23 15 28 4 5 5 4
17 21 19 23 4 4 2 4
15 19 NA 25 4 4 5 3
16 19 20 24 4 4 4 3
14 21 18 24 3 3 5 4
16 21 15 24 4 4 5 4
17 22 14 25 3 4 5 4
NA 22 20 25 3 4 5 4
NA 19 NA NA NA NA 5 NA
NA 21 16 25 5 5 4 3
NA 21 16 25 4 4 4 4
16 21 16 24 3 4 5 3
NA 20 10 26 4 4 4 4
16 22 19 26 4 4 5 4
NA 22 19 25 4 4 5 4
NA 24 16 26 4 4 5 4
NA 21 15 23 3 4 4 4
16 19 18 24 3 4 4 3
15 19 17 24 4 4 4 4
16 23 19 25 2 4 5 4
16 21 17 25 5 4 4 4
13 21 NA 24 4 3 5 4
15 19 19 28 4 5 5 4
17 21 20 27 5 4 5 4
NA 19 5 NA 4 3 5 4
13 21 19 23 2 3 5 4
17 21 16 23 4 5 2 4
NA 23 15 24 3 4 5 4
14 19 16 24 4 3 5 3
14 19 18 22 4 3 3 4
18 19 16 25 4 4 5 4
NA 18 15 25 5 4 4 4
17 22 17 28 4 5 5 4
13 18 NA 22 3 3 4 4
16 22 20 28 5 5 5 3
15 18 19 25 5 4 5 3
15 22 7 24 4 4 4 3
NA 22 13 24 4 4 4 4
15 19 16 23 3 5 5 3
13 22 16 25 4 4 4 4
NA 25 NA NA 2 3 4 2
17 19 18 26 4 5 5 4
NA 19 18 25 5 5 2 4
NA 19 16 27 5 5 5 4
11 19 17 26 4 3 5 4
14 21 19 23 4 3 4 3
13 21 16 25 4 4 5 4
NA 20 19 21 3 4 4 3
17 19 13 22 3 4 4 4
16 19 16 24 4 4 4 3
NA 22 13 25 4 4 4 4
17 26 12 27 5 5 3 4
16 19 17 24 2 4 4 4
16 21 17 26 4 4 4 4
16 21 17 21 3 4 4 4
15 20 16 27 4 4 5 4
12 23 16 22 4 2 4 4
17 22 14 23 4 4 4 3
14 22 16 24 4 4 4 3
14 22 13 25 5 4 5 3
16 21 16 24 3 4 4 3
NA 21 14 23 3 4 4 3
NA 22 20 28 4 5 5 5
NA 23 12 NA 4 4 3 4
NA 18 13 24 4 4 4 4
NA 24 18 26 4 4 4 5
15 22 14 22 3 4 3 4
16 21 19 25 4 4 4 4
14 21 18 25 3 4 5 3
15 21 14 24 3 3 5 4
17 23 18 24 4 3 5 4
NA 21 19 26 4 4 5 4
10 23 15 21 3 3 3 4
NA 21 14 25 4 4 4 4
17 19 17 25 4 4 3 4
NA 21 19 26 4 4 4 4
20 21 13 25 5 4 4 4
17 21 19 26 5 4 3 5
18 23 18 27 4 4 5 4
NA 23 20 25 3 4 5 4
17 20 15 NA 3 NA 4 4
14 20 15 20 4 2 3 3
NA 19 15 24 4 4 5 4
17 23 20 26 4 4 5 4
NA 22 15 25 4 4 4 4
17 19 19 25 4 5 4 4
NA 23 18 24 3 4 4 3
16 22 18 26 4 4 5 4
18 22 15 25 5 4 3 4
18 21 20 28 5 4 5 5
16 21 17 27 4 5 4 4
NA 21 12 25 3 4 5 4
NA 21 18 26 5 3 4 4
15 22 19 26 4 4 5 4
13 25 20 26 5 4 4 4
NA 21 NA NA 3 4 4 3
NA 23 17 28 5 4 4 5
NA 19 15 NA 4 4 5 3
NA 22 16 21 4 4 3 3
NA 20 18 25 4 4 5 4
16 21 18 25 4 4 5 4
NA 25 14 24 3 4 5 4
NA 21 15 24 4 4 4 4
NA 19 12 24 4 4 4 3
12 23 17 23 3 3 4 3
NA 22 14 23 4 4 4 3
16 21 18 24 3 4 5 4
16 24 17 24 4 4 5 4
NA 21 17 25 5 4 5 1
16 19 20 28 5 4 5 4
14 18 16 23 4 4 4 4
15 19 14 24 4 4 5 3
14 20 15 23 3 4 4 3
NA 19 18 24 4 4 4 4
15 22 20 25 4 4 4 4
NA 21 17 24 4 5 3 4
15 22 17 23 3 4 4 4
16 24 17 23 4 4 4 3
NA 28 17 25 4 4 4 4
NA 19 15 21 3 4 3 3
NA 18 17 22 4 4 4 3
11 23 18 19 3 2 4 2
NA 19 17 24 4 4 4 3
18 23 20 25 5 4 4 3
NA 19 15 21 2 4 4 3
11 22 16 22 3 3 4 4
NA 21 15 23 4 4 4 3
18 19 18 27 5 5 4 4
NA 22 11 NA NA NA 2 NA
15 21 15 26 4 5 5 4
19 23 18 29 5 5 5 5
17 22 20 28 4 5 5 4
NA 19 19 24 4 4 4 3
14 19 14 25 3 4 5 4
NA 21 16 25 4 4 5 4
13 22 15 22 4 4 2 4
17 21 17 25 4 4 3 4
14 20 18 26 4 4 4 4
19 23 20 26 5 4 5 3
14 22 17 24 4 3 5 4
NA 23 18 25 4 4 5 4
NA 22 15 19 3 3 2 3
16 21 16 25 4 5 5 4
16 20 11 23 4 4 4 3
15 18 15 25 4 4 4 4
12 18 18 25 3 4 5 3
NA 20 17 26 4 4 5 4
17 19 16 27 5 4 5 4
NA 21 12 24 4 4 5 4
NA 24 19 22 2 3 5 4
18 19 18 25 4 4 4 4
15 20 15 24 4 3 4 3
18 19 17 23 4 4 4 4
15 23 19 27 4 5 5 5
NA 22 18 24 5 4 3 4
NA 21 19 24 5 4 4 3
NA 24 16 21 3 3 1 4
16 21 16 25 4 4 4 4
NA 21 16 25 4 4 4 4
16 22 14 23 2 3 4 5

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
R Framework error message	Warning: there are blank lines in the 'Data X' field. Please, use NA for missing data - blank lines are simply deleted and are NOT treated as missing values.

\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
R Framework error message & Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values. \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=305628&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] [ROW]

R Framework error message[/C][C]

Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.

[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=305628&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=305628&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
R Framework error message	Warning: there are blank lines in the 'Data X' field. Please, use NA for missing data - blank lines are simply deleted and are NOT treated as missing values.

Multiple Linear Regression - Estimated Regression Equation
TVDC[t] = + 6.6289 -0.0224251Bevr_Leeftijd[t] + 0.00621574ITHSUM[t] + 0.0406222SKEOUSUM[t] + 0.609789SKEOU1[t] + 1.11128SKEOU2[t] -0.054243SKEOU3[t] + 0.463491SKEOU4[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
TVDC[t] =  +  6.6289 -0.0224251Bevr_Leeftijd[t] +  0.00621574ITHSUM[t] +  0.0406222SKEOUSUM[t] +  0.609789SKEOU1[t] +  1.11128SKEOU2[t] -0.054243SKEOU3[t] +  0.463491SKEOU4[t]  + e[t] \tabularnewline
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=305628&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]TVDC[t] =  +  6.6289 -0.0224251Bevr_Leeftijd[t] +  0.00621574ITHSUM[t] +  0.0406222SKEOUSUM[t] +  0.609789SKEOU1[t] +  1.11128SKEOU2[t] -0.054243SKEOU3[t] +  0.463491SKEOU4[t]  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=305628&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=305628&T=1

As an alternative you can also use a QR Code:

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

Multiple Linear Regression - Estimated Regression Equation
TVDC[t] = + 6.6289 -0.0224251Bevr_Leeftijd[t] + 0.00621574ITHSUM[t] + 0.0406222SKEOUSUM[t] + 0.609789SKEOU1[t] + 1.11128SKEOU2[t] -0.054243SKEOU3[t] + 0.463491SKEOU4[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)	+6.629	2.897	+2.2880e+00	0.02443	0.01222
Bevr_Leeftijd	-0.02243	0.09602	-2.3360e-01	0.8159	0.4079
ITHSUM	+0.006216	0.07372	+8.4320e-02	0.933	0.4665
SKEOUSUM	+0.04062	0.1744	+2.3290e-01	0.8163	0.4082
SKEOU1	+0.6098	0.2745	+2.2220e+00	0.02878	0.01439
SKEOU2	+1.111	0.3162	+3.5140e+00	0.0006893	0.0003446
SKEOU3	-0.05424	0.2716	-1.9970e-01	0.8421	0.4211
SKEOU4	+0.4635	0.3321	+1.3960e+00	0.1663	0.08313

\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) & +6.629 &  2.897 & +2.2880e+00 &  0.02443 &  0.01222 \tabularnewline
Bevr_Leeftijd & -0.02243 &  0.09602 & -2.3360e-01 &  0.8159 &  0.4079 \tabularnewline
ITHSUM & +0.006216 &  0.07372 & +8.4320e-02 &  0.933 &  0.4665 \tabularnewline
SKEOUSUM & +0.04062 &  0.1744 & +2.3290e-01 &  0.8163 &  0.4082 \tabularnewline
SKEOU1 & +0.6098 &  0.2745 & +2.2220e+00 &  0.02878 &  0.01439 \tabularnewline
SKEOU2 & +1.111 &  0.3162 & +3.5140e+00 &  0.0006893 &  0.0003446 \tabularnewline
SKEOU3 & -0.05424 &  0.2716 & -1.9970e-01 &  0.8421 &  0.4211 \tabularnewline
SKEOU4 & +0.4635 &  0.3321 & +1.3960e+00 &  0.1663 &  0.08313 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=305628&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]+6.629[/C][C] 2.897[/C][C]+2.2880e+00[/C][C] 0.02443[/C][C] 0.01222[/C][/ROW]
[ROW][C]Bevr_Leeftijd[/C][C]-0.02243[/C][C] 0.09602[/C][C]-2.3360e-01[/C][C] 0.8159[/C][C] 0.4079[/C][/ROW]
[ROW][C]ITHSUM[/C][C]+0.006216[/C][C] 0.07372[/C][C]+8.4320e-02[/C][C] 0.933[/C][C] 0.4665[/C][/ROW]
[ROW][C]SKEOUSUM[/C][C]+0.04062[/C][C] 0.1744[/C][C]+2.3290e-01[/C][C] 0.8163[/C][C] 0.4082[/C][/ROW]
[ROW][C]SKEOU1[/C][C]+0.6098[/C][C] 0.2745[/C][C]+2.2220e+00[/C][C] 0.02878[/C][C] 0.01439[/C][/ROW]
[ROW][C]SKEOU2[/C][C]+1.111[/C][C] 0.3162[/C][C]+3.5140e+00[/C][C] 0.0006893[/C][C] 0.0003446[/C][/ROW]
[ROW][C]SKEOU3[/C][C]-0.05424[/C][C] 0.2716[/C][C]-1.9970e-01[/C][C] 0.8421[/C][C] 0.4211[/C][/ROW]
[ROW][C]SKEOU4[/C][C]+0.4635[/C][C] 0.3321[/C][C]+1.3960e+00[/C][C] 0.1663[/C][C] 0.08313[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=305628&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=305628&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)	+6.629	2.897	+2.2880e+00	0.02443	0.01222
Bevr_Leeftijd	-0.02243	0.09602	-2.3360e-01	0.8159	0.4079
ITHSUM	+0.006216	0.07372	+8.4320e-02	0.933	0.4665
SKEOUSUM	+0.04062	0.1744	+2.3290e-01	0.8163	0.4082
SKEOU1	+0.6098	0.2745	+2.2220e+00	0.02878	0.01439
SKEOU2	+1.111	0.3162	+3.5140e+00	0.0006893	0.0003446
SKEOU3	-0.05424	0.2716	-1.9970e-01	0.8421	0.4211
SKEOU4	+0.4635	0.3321	+1.3960e+00	0.1663	0.08313

Multiple Linear Regression - Regression Statistics
Multiple R	0.6022
R-squared	0.3626
Adjusted R-squared	0.3136
F-TEST (value)	7.397
F-TEST (DF numerator)	7
F-TEST (DF denominator)	91
p-value	5.057e-07
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.549
Sum Squared Residuals	218.4

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.6022 \tabularnewline
R-squared &  0.3626 \tabularnewline
Adjusted R-squared &  0.3136 \tabularnewline
F-TEST (value) &  7.397 \tabularnewline
F-TEST (DF numerator) & 7 \tabularnewline
F-TEST (DF denominator) & 91 \tabularnewline
p-value &  5.057e-07 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  1.549 \tabularnewline
Sum Squared Residuals &  218.4 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=305628&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.6022[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.3626[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.3136[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 7.397[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]7[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]91[/C][/ROW]
[ROW][C]p-value[/C][C] 5.057e-07[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C] 1.549[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 218.4[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=305628&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=305628&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	0.6022
R-squared	0.3626
Adjusted R-squared	0.3136
F-TEST (value)	7.397
F-TEST (DF numerator)	7
F-TEST (DF denominator)	91
p-value	5.057e-07
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.549
Sum Squared Residuals	218.4

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	13	12.95	0.04853
2	16	15.26	0.7423
3	17	15.79	1.213
4	16	15.23	0.7683
5	17	16.92	0.07792
6	17	15.84	1.16
7	16	15.36	0.6402
8	14	13.99	0.009258
9	16	15.69	0.3068
10	17	15.1	1.905
11	16	14.63	1.374
12	16	15.78	0.2232
13	16	14.74	1.262
14	15	15.8	-0.8047
15	16	14.49	1.506
16	16	16.41	-0.4102
17	15	17.04	-2.037
18	17	16.46	0.5441
19	13	13.35	-0.3465
20	17	16.93	0.06724
21	14	14.17	-0.1695
22	14	14.67	-0.6726
23	18	15.78	2.215
24	17	16.96	0.04306
25	16	17.12	-1.122
26	15	15.97	-0.9722
27	15	15.21	-0.2118
28	15	15.74	-0.7416
29	13	15.77	-2.772
30	17	16.95	0.05082
31	11	14.72	-3.72
32	14	14.16	-0.1569
33	13	15.74	-2.74
34	17	15.09	1.911
35	16	15.34	0.665
36	17	17.51	-0.5138
37	16	14.59	1.415
38	16	15.84	0.1589
39	16	15.03	0.9718
40	15	15.84	-0.8437
41	12	13.4	-1.405
42	17	15.21	1.785
43	14	15.27	-1.268
44	14	15.85	-1.845
45	16	14.68	1.32
46	15	15.08	-0.08197
47	16	15.81	0.1871
48	14	14.68	-0.6792
49	15	13.97	1.034
50	17	14.56	2.444
51	10	13.91	-3.914
52	17	15.9	1.1
53	20	16.39	3.615
54	17	16.98	0.01896
55	18	15.79	2.211
56	14	12.98	1.024
57	17	15.76	1.239
58	17	16.97	0.03098
59	16	15.77	0.2294
60	18	16.43	1.57
61	18	16.96	1.04
62	16	16.99	-0.993
63	15	15.78	-0.7768
64	13	16.38	-3.38
65	16	15.75	0.2476
66	12	13.49	-1.49
67	16	15.1	0.898
68	16	15.64	0.3617
69	16	16.54	-0.5414
70	14	15.78	-1.78
71	15	15.27	-0.2683
72	14	14.66	-0.6559
73	15	15.8	-0.7967
74	15	15.09	-0.087
75	16	15.19	0.8116
76	11	11.76	-0.7588
77	18	15.92	2.079
78	11	13.93	-2.929
79	18	17.65	0.3462
80	15	16.89	-1.886
81	19	18.05	0.9454
82	17	16.98	0.02442
83	14	15.16	-1.163
84	13	15.75	-2.752
85	17	15.85	1.145
86	14	15.87	-1.87
87	19	15.91	3.093
88	14	14.57	-0.5719
89	16	16.85	-0.8513
90	16	15.24	0.7591
91	15	15.86	-0.8553
92	12	14.75	-2.746
93	17	16.48	0.5241
94	18	15.85	2.148
95	15	14.2	0.8049
96	18	15.76	2.236
97	15	17.37	-2.37
98	16	15.79	0.2058
99	16	13.81	2.189

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 &  13 &  12.95 &  0.04853 \tabularnewline
2 &  16 &  15.26 &  0.7423 \tabularnewline
3 &  17 &  15.79 &  1.213 \tabularnewline
4 &  16 &  15.23 &  0.7683 \tabularnewline
5 &  17 &  16.92 &  0.07792 \tabularnewline
6 &  17 &  15.84 &  1.16 \tabularnewline
7 &  16 &  15.36 &  0.6402 \tabularnewline
8 &  14 &  13.99 &  0.009258 \tabularnewline
9 &  16 &  15.69 &  0.3068 \tabularnewline
10 &  17 &  15.1 &  1.905 \tabularnewline
11 &  16 &  14.63 &  1.374 \tabularnewline
12 &  16 &  15.78 &  0.2232 \tabularnewline
13 &  16 &  14.74 &  1.262 \tabularnewline
14 &  15 &  15.8 & -0.8047 \tabularnewline
15 &  16 &  14.49 &  1.506 \tabularnewline
16 &  16 &  16.41 & -0.4102 \tabularnewline
17 &  15 &  17.04 & -2.037 \tabularnewline
18 &  17 &  16.46 &  0.5441 \tabularnewline
19 &  13 &  13.35 & -0.3465 \tabularnewline
20 &  17 &  16.93 &  0.06724 \tabularnewline
21 &  14 &  14.17 & -0.1695 \tabularnewline
22 &  14 &  14.67 & -0.6726 \tabularnewline
23 &  18 &  15.78 &  2.215 \tabularnewline
24 &  17 &  16.96 &  0.04306 \tabularnewline
25 &  16 &  17.12 & -1.122 \tabularnewline
26 &  15 &  15.97 & -0.9722 \tabularnewline
27 &  15 &  15.21 & -0.2118 \tabularnewline
28 &  15 &  15.74 & -0.7416 \tabularnewline
29 &  13 &  15.77 & -2.772 \tabularnewline
30 &  17 &  16.95 &  0.05082 \tabularnewline
31 &  11 &  14.72 & -3.72 \tabularnewline
32 &  14 &  14.16 & -0.1569 \tabularnewline
33 &  13 &  15.74 & -2.74 \tabularnewline
34 &  17 &  15.09 &  1.911 \tabularnewline
35 &  16 &  15.34 &  0.665 \tabularnewline
36 &  17 &  17.51 & -0.5138 \tabularnewline
37 &  16 &  14.59 &  1.415 \tabularnewline
38 &  16 &  15.84 &  0.1589 \tabularnewline
39 &  16 &  15.03 &  0.9718 \tabularnewline
40 &  15 &  15.84 & -0.8437 \tabularnewline
41 &  12 &  13.4 & -1.405 \tabularnewline
42 &  17 &  15.21 &  1.785 \tabularnewline
43 &  14 &  15.27 & -1.268 \tabularnewline
44 &  14 &  15.85 & -1.845 \tabularnewline
45 &  16 &  14.68 &  1.32 \tabularnewline
46 &  15 &  15.08 & -0.08197 \tabularnewline
47 &  16 &  15.81 &  0.1871 \tabularnewline
48 &  14 &  14.68 & -0.6792 \tabularnewline
49 &  15 &  13.97 &  1.034 \tabularnewline
50 &  17 &  14.56 &  2.444 \tabularnewline
51 &  10 &  13.91 & -3.914 \tabularnewline
52 &  17 &  15.9 &  1.1 \tabularnewline
53 &  20 &  16.39 &  3.615 \tabularnewline
54 &  17 &  16.98 &  0.01896 \tabularnewline
55 &  18 &  15.79 &  2.211 \tabularnewline
56 &  14 &  12.98 &  1.024 \tabularnewline
57 &  17 &  15.76 &  1.239 \tabularnewline
58 &  17 &  16.97 &  0.03098 \tabularnewline
59 &  16 &  15.77 &  0.2294 \tabularnewline
60 &  18 &  16.43 &  1.57 \tabularnewline
61 &  18 &  16.96 &  1.04 \tabularnewline
62 &  16 &  16.99 & -0.993 \tabularnewline
63 &  15 &  15.78 & -0.7768 \tabularnewline
64 &  13 &  16.38 & -3.38 \tabularnewline
65 &  16 &  15.75 &  0.2476 \tabularnewline
66 &  12 &  13.49 & -1.49 \tabularnewline
67 &  16 &  15.1 &  0.898 \tabularnewline
68 &  16 &  15.64 &  0.3617 \tabularnewline
69 &  16 &  16.54 & -0.5414 \tabularnewline
70 &  14 &  15.78 & -1.78 \tabularnewline
71 &  15 &  15.27 & -0.2683 \tabularnewline
72 &  14 &  14.66 & -0.6559 \tabularnewline
73 &  15 &  15.8 & -0.7967 \tabularnewline
74 &  15 &  15.09 & -0.087 \tabularnewline
75 &  16 &  15.19 &  0.8116 \tabularnewline
76 &  11 &  11.76 & -0.7588 \tabularnewline
77 &  18 &  15.92 &  2.079 \tabularnewline
78 &  11 &  13.93 & -2.929 \tabularnewline
79 &  18 &  17.65 &  0.3462 \tabularnewline
80 &  15 &  16.89 & -1.886 \tabularnewline
81 &  19 &  18.05 &  0.9454 \tabularnewline
82 &  17 &  16.98 &  0.02442 \tabularnewline
83 &  14 &  15.16 & -1.163 \tabularnewline
84 &  13 &  15.75 & -2.752 \tabularnewline
85 &  17 &  15.85 &  1.145 \tabularnewline
86 &  14 &  15.87 & -1.87 \tabularnewline
87 &  19 &  15.91 &  3.093 \tabularnewline
88 &  14 &  14.57 & -0.5719 \tabularnewline
89 &  16 &  16.85 & -0.8513 \tabularnewline
90 &  16 &  15.24 &  0.7591 \tabularnewline
91 &  15 &  15.86 & -0.8553 \tabularnewline
92 &  12 &  14.75 & -2.746 \tabularnewline
93 &  17 &  16.48 &  0.5241 \tabularnewline
94 &  18 &  15.85 &  2.148 \tabularnewline
95 &  15 &  14.2 &  0.8049 \tabularnewline
96 &  18 &  15.76 &  2.236 \tabularnewline
97 &  15 &  17.37 & -2.37 \tabularnewline
98 &  16 &  15.79 &  0.2058 \tabularnewline
99 &  16 &  13.81 &  2.189 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=305628&T=4

[TABLE]
[ROW][C]Multiple Linear Regression - Actuals, Interpolation, and Residuals[/C][/ROW]
[ROW][C]Time or Index[/C][C]Actuals[/C][C]InterpolationForecast[/C][C]ResidualsPrediction Error[/C][/ROW]
[ROW][C]1[/C][C] 13[/C][C] 12.95[/C][C] 0.04853[/C][/ROW]
[ROW][C]2[/C][C] 16[/C][C] 15.26[/C][C] 0.7423[/C][/ROW]
[ROW][C]3[/C][C] 17[/C][C] 15.79[/C][C] 1.213[/C][/ROW]
[ROW][C]4[/C][C] 16[/C][C] 15.23[/C][C] 0.7683[/C][/ROW]
[ROW][C]5[/C][C] 17[/C][C] 16.92[/C][C] 0.07792[/C][/ROW]
[ROW][C]6[/C][C] 17[/C][C] 15.84[/C][C] 1.16[/C][/ROW]
[ROW][C]7[/C][C] 16[/C][C] 15.36[/C][C] 0.6402[/C][/ROW]
[ROW][C]8[/C][C] 14[/C][C] 13.99[/C][C] 0.009258[/C][/ROW]
[ROW][C]9[/C][C] 16[/C][C] 15.69[/C][C] 0.3068[/C][/ROW]
[ROW][C]10[/C][C] 17[/C][C] 15.1[/C][C] 1.905[/C][/ROW]
[ROW][C]11[/C][C] 16[/C][C] 14.63[/C][C] 1.374[/C][/ROW]
[ROW][C]12[/C][C] 16[/C][C] 15.78[/C][C] 0.2232[/C][/ROW]
[ROW][C]13[/C][C] 16[/C][C] 14.74[/C][C] 1.262[/C][/ROW]
[ROW][C]14[/C][C] 15[/C][C] 15.8[/C][C]-0.8047[/C][/ROW]
[ROW][C]15[/C][C] 16[/C][C] 14.49[/C][C] 1.506[/C][/ROW]
[ROW][C]16[/C][C] 16[/C][C] 16.41[/C][C]-0.4102[/C][/ROW]
[ROW][C]17[/C][C] 15[/C][C] 17.04[/C][C]-2.037[/C][/ROW]
[ROW][C]18[/C][C] 17[/C][C] 16.46[/C][C] 0.5441[/C][/ROW]
[ROW][C]19[/C][C] 13[/C][C] 13.35[/C][C]-0.3465[/C][/ROW]
[ROW][C]20[/C][C] 17[/C][C] 16.93[/C][C] 0.06724[/C][/ROW]
[ROW][C]21[/C][C] 14[/C][C] 14.17[/C][C]-0.1695[/C][/ROW]
[ROW][C]22[/C][C] 14[/C][C] 14.67[/C][C]-0.6726[/C][/ROW]
[ROW][C]23[/C][C] 18[/C][C] 15.78[/C][C] 2.215[/C][/ROW]
[ROW][C]24[/C][C] 17[/C][C] 16.96[/C][C] 0.04306[/C][/ROW]
[ROW][C]25[/C][C] 16[/C][C] 17.12[/C][C]-1.122[/C][/ROW]
[ROW][C]26[/C][C] 15[/C][C] 15.97[/C][C]-0.9722[/C][/ROW]
[ROW][C]27[/C][C] 15[/C][C] 15.21[/C][C]-0.2118[/C][/ROW]
[ROW][C]28[/C][C] 15[/C][C] 15.74[/C][C]-0.7416[/C][/ROW]
[ROW][C]29[/C][C] 13[/C][C] 15.77[/C][C]-2.772[/C][/ROW]
[ROW][C]30[/C][C] 17[/C][C] 16.95[/C][C] 0.05082[/C][/ROW]
[ROW][C]31[/C][C] 11[/C][C] 14.72[/C][C]-3.72[/C][/ROW]
[ROW][C]32[/C][C] 14[/C][C] 14.16[/C][C]-0.1569[/C][/ROW]
[ROW][C]33[/C][C] 13[/C][C] 15.74[/C][C]-2.74[/C][/ROW]
[ROW][C]34[/C][C] 17[/C][C] 15.09[/C][C] 1.911[/C][/ROW]
[ROW][C]35[/C][C] 16[/C][C] 15.34[/C][C] 0.665[/C][/ROW]
[ROW][C]36[/C][C] 17[/C][C] 17.51[/C][C]-0.5138[/C][/ROW]
[ROW][C]37[/C][C] 16[/C][C] 14.59[/C][C] 1.415[/C][/ROW]
[ROW][C]38[/C][C] 16[/C][C] 15.84[/C][C] 0.1589[/C][/ROW]
[ROW][C]39[/C][C] 16[/C][C] 15.03[/C][C] 0.9718[/C][/ROW]
[ROW][C]40[/C][C] 15[/C][C] 15.84[/C][C]-0.8437[/C][/ROW]
[ROW][C]41[/C][C] 12[/C][C] 13.4[/C][C]-1.405[/C][/ROW]
[ROW][C]42[/C][C] 17[/C][C] 15.21[/C][C] 1.785[/C][/ROW]
[ROW][C]43[/C][C] 14[/C][C] 15.27[/C][C]-1.268[/C][/ROW]
[ROW][C]44[/C][C] 14[/C][C] 15.85[/C][C]-1.845[/C][/ROW]
[ROW][C]45[/C][C] 16[/C][C] 14.68[/C][C] 1.32[/C][/ROW]
[ROW][C]46[/C][C] 15[/C][C] 15.08[/C][C]-0.08197[/C][/ROW]
[ROW][C]47[/C][C] 16[/C][C] 15.81[/C][C] 0.1871[/C][/ROW]
[ROW][C]48[/C][C] 14[/C][C] 14.68[/C][C]-0.6792[/C][/ROW]
[ROW][C]49[/C][C] 15[/C][C] 13.97[/C][C] 1.034[/C][/ROW]
[ROW][C]50[/C][C] 17[/C][C] 14.56[/C][C] 2.444[/C][/ROW]
[ROW][C]51[/C][C] 10[/C][C] 13.91[/C][C]-3.914[/C][/ROW]
[ROW][C]52[/C][C] 17[/C][C] 15.9[/C][C] 1.1[/C][/ROW]
[ROW][C]53[/C][C] 20[/C][C] 16.39[/C][C] 3.615[/C][/ROW]
[ROW][C]54[/C][C] 17[/C][C] 16.98[/C][C] 0.01896[/C][/ROW]
[ROW][C]55[/C][C] 18[/C][C] 15.79[/C][C] 2.211[/C][/ROW]
[ROW][C]56[/C][C] 14[/C][C] 12.98[/C][C] 1.024[/C][/ROW]
[ROW][C]57[/C][C] 17[/C][C] 15.76[/C][C] 1.239[/C][/ROW]
[ROW][C]58[/C][C] 17[/C][C] 16.97[/C][C] 0.03098[/C][/ROW]
[ROW][C]59[/C][C] 16[/C][C] 15.77[/C][C] 0.2294[/C][/ROW]
[ROW][C]60[/C][C] 18[/C][C] 16.43[/C][C] 1.57[/C][/ROW]
[ROW][C]61[/C][C] 18[/C][C] 16.96[/C][C] 1.04[/C][/ROW]
[ROW][C]62[/C][C] 16[/C][C] 16.99[/C][C]-0.993[/C][/ROW]
[ROW][C]63[/C][C] 15[/C][C] 15.78[/C][C]-0.7768[/C][/ROW]
[ROW][C]64[/C][C] 13[/C][C] 16.38[/C][C]-3.38[/C][/ROW]
[ROW][C]65[/C][C] 16[/C][C] 15.75[/C][C] 0.2476[/C][/ROW]
[ROW][C]66[/C][C] 12[/C][C] 13.49[/C][C]-1.49[/C][/ROW]
[ROW][C]67[/C][C] 16[/C][C] 15.1[/C][C] 0.898[/C][/ROW]
[ROW][C]68[/C][C] 16[/C][C] 15.64[/C][C] 0.3617[/C][/ROW]
[ROW][C]69[/C][C] 16[/C][C] 16.54[/C][C]-0.5414[/C][/ROW]
[ROW][C]70[/C][C] 14[/C][C] 15.78[/C][C]-1.78[/C][/ROW]
[ROW][C]71[/C][C] 15[/C][C] 15.27[/C][C]-0.2683[/C][/ROW]
[ROW][C]72[/C][C] 14[/C][C] 14.66[/C][C]-0.6559[/C][/ROW]
[ROW][C]73[/C][C] 15[/C][C] 15.8[/C][C]-0.7967[/C][/ROW]
[ROW][C]74[/C][C] 15[/C][C] 15.09[/C][C]-0.087[/C][/ROW]
[ROW][C]75[/C][C] 16[/C][C] 15.19[/C][C] 0.8116[/C][/ROW]
[ROW][C]76[/C][C] 11[/C][C] 11.76[/C][C]-0.7588[/C][/ROW]
[ROW][C]77[/C][C] 18[/C][C] 15.92[/C][C] 2.079[/C][/ROW]
[ROW][C]78[/C][C] 11[/C][C] 13.93[/C][C]-2.929[/C][/ROW]
[ROW][C]79[/C][C] 18[/C][C] 17.65[/C][C] 0.3462[/C][/ROW]
[ROW][C]80[/C][C] 15[/C][C] 16.89[/C][C]-1.886[/C][/ROW]
[ROW][C]81[/C][C] 19[/C][C] 18.05[/C][C] 0.9454[/C][/ROW]
[ROW][C]82[/C][C] 17[/C][C] 16.98[/C][C] 0.02442[/C][/ROW]
[ROW][C]83[/C][C] 14[/C][C] 15.16[/C][C]-1.163[/C][/ROW]
[ROW][C]84[/C][C] 13[/C][C] 15.75[/C][C]-2.752[/C][/ROW]
[ROW][C]85[/C][C] 17[/C][C] 15.85[/C][C] 1.145[/C][/ROW]
[ROW][C]86[/C][C] 14[/C][C] 15.87[/C][C]-1.87[/C][/ROW]
[ROW][C]87[/C][C] 19[/C][C] 15.91[/C][C] 3.093[/C][/ROW]
[ROW][C]88[/C][C] 14[/C][C] 14.57[/C][C]-0.5719[/C][/ROW]
[ROW][C]89[/C][C] 16[/C][C] 16.85[/C][C]-0.8513[/C][/ROW]
[ROW][C]90[/C][C] 16[/C][C] 15.24[/C][C] 0.7591[/C][/ROW]
[ROW][C]91[/C][C] 15[/C][C] 15.86[/C][C]-0.8553[/C][/ROW]
[ROW][C]92[/C][C] 12[/C][C] 14.75[/C][C]-2.746[/C][/ROW]
[ROW][C]93[/C][C] 17[/C][C] 16.48[/C][C] 0.5241[/C][/ROW]
[ROW][C]94[/C][C] 18[/C][C] 15.85[/C][C] 2.148[/C][/ROW]
[ROW][C]95[/C][C] 15[/C][C] 14.2[/C][C] 0.8049[/C][/ROW]
[ROW][C]96[/C][C] 18[/C][C] 15.76[/C][C] 2.236[/C][/ROW]
[ROW][C]97[/C][C] 15[/C][C] 17.37[/C][C]-2.37[/C][/ROW]
[ROW][C]98[/C][C] 16[/C][C] 15.79[/C][C] 0.2058[/C][/ROW]
[ROW][C]99[/C][C] 16[/C][C] 13.81[/C][C] 2.189[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=305628&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=305628&T=4

As an alternative you can also use a QR Code:

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	13	12.95	0.04853
2	16	15.26	0.7423
3	17	15.79	1.213
4	16	15.23	0.7683
5	17	16.92	0.07792
6	17	15.84	1.16
7	16	15.36	0.6402
8	14	13.99	0.009258
9	16	15.69	0.3068
10	17	15.1	1.905
11	16	14.63	1.374
12	16	15.78	0.2232
13	16	14.74	1.262
14	15	15.8	-0.8047
15	16	14.49	1.506
16	16	16.41	-0.4102
17	15	17.04	-2.037
18	17	16.46	0.5441
19	13	13.35	-0.3465
20	17	16.93	0.06724
21	14	14.17	-0.1695
22	14	14.67	-0.6726
23	18	15.78	2.215
24	17	16.96	0.04306
25	16	17.12	-1.122
26	15	15.97	-0.9722
27	15	15.21	-0.2118
28	15	15.74	-0.7416
29	13	15.77	-2.772
30	17	16.95	0.05082
31	11	14.72	-3.72
32	14	14.16	-0.1569
33	13	15.74	-2.74
34	17	15.09	1.911
35	16	15.34	0.665
36	17	17.51	-0.5138
37	16	14.59	1.415
38	16	15.84	0.1589
39	16	15.03	0.9718
40	15	15.84	-0.8437
41	12	13.4	-1.405
42	17	15.21	1.785
43	14	15.27	-1.268
44	14	15.85	-1.845
45	16	14.68	1.32
46	15	15.08	-0.08197
47	16	15.81	0.1871
48	14	14.68	-0.6792
49	15	13.97	1.034
50	17	14.56	2.444
51	10	13.91	-3.914
52	17	15.9	1.1
53	20	16.39	3.615
54	17	16.98	0.01896
55	18	15.79	2.211
56	14	12.98	1.024
57	17	15.76	1.239
58	17	16.97	0.03098
59	16	15.77	0.2294
60	18	16.43	1.57
61	18	16.96	1.04
62	16	16.99	-0.993
63	15	15.78	-0.7768
64	13	16.38	-3.38
65	16	15.75	0.2476
66	12	13.49	-1.49
67	16	15.1	0.898
68	16	15.64	0.3617
69	16	16.54	-0.5414
70	14	15.78	-1.78
71	15	15.27	-0.2683
72	14	14.66	-0.6559
73	15	15.8	-0.7967
74	15	15.09	-0.087
75	16	15.19	0.8116
76	11	11.76	-0.7588
77	18	15.92	2.079
78	11	13.93	-2.929
79	18	17.65	0.3462
80	15	16.89	-1.886
81	19	18.05	0.9454
82	17	16.98	0.02442
83	14	15.16	-1.163
84	13	15.75	-2.752
85	17	15.85	1.145
86	14	15.87	-1.87
87	19	15.91	3.093
88	14	14.57	-0.5719
89	16	16.85	-0.8513
90	16	15.24	0.7591
91	15	15.86	-0.8553
92	12	14.75	-2.746
93	17	16.48	0.5241
94	18	15.85	2.148
95	15	14.2	0.8049
96	18	15.76	2.236
97	15	17.37	-2.37
98	16	15.79	0.2058
99	16	13.81	2.189

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
11	0.2074	0.4149	0.7926
12	0.09781	0.1956	0.9022
13	0.04298	0.08596	0.957
14	0.03413	0.06826	0.9659
15	0.01894	0.03788	0.9811
16	0.008196	0.01639	0.9918
17	0.0126	0.0252	0.9874
18	0.01644	0.03288	0.9836
19	0.01597	0.03195	0.984
20	0.01249	0.02497	0.9875
21	0.006234	0.01247	0.9938
22	0.003034	0.006069	0.997
23	0.02419	0.04839	0.9758
24	0.01468	0.02936	0.9853
25	0.01467	0.02935	0.9853
26	0.009241	0.01848	0.9908
27	0.00677	0.01354	0.9932
28	0.005535	0.01107	0.9945
29	0.03821	0.07643	0.9618
30	0.02525	0.05049	0.9748
31	0.1016	0.2031	0.8984
32	0.07518	0.1504	0.9248
33	0.1523	0.3046	0.8477
34	0.1652	0.3304	0.8348
35	0.1359	0.2718	0.8641
36	0.1056	0.2112	0.8944
37	0.09731	0.1946	0.9027
38	0.07354	0.1471	0.9265
39	0.06115	0.1223	0.9389
40	0.04677	0.09353	0.9532
41	0.04768	0.09535	0.9523
42	0.04829	0.09657	0.9517
43	0.0502	0.1004	0.9498
44	0.06516	0.1303	0.9348
45	0.05966	0.1193	0.9403
46	0.05291	0.1058	0.9471
47	0.03873	0.07745	0.9613
48	0.03199	0.06398	0.968
49	0.02622	0.05243	0.9738
50	0.0448	0.08961	0.9552
51	0.2441	0.4882	0.7559
52	0.2354	0.4708	0.7646
53	0.4461	0.8923	0.5539
54	0.3861	0.7721	0.6139
55	0.4431	0.8861	0.5569
56	0.4001	0.8002	0.5999
57	0.3802	0.7603	0.6198
58	0.3305	0.6609	0.6695
59	0.2762	0.5524	0.7238
60	0.2648	0.5297	0.7352
61	0.2283	0.4567	0.7717
62	0.1938	0.3875	0.8063
63	0.1604	0.3209	0.8396
64	0.3802	0.7604	0.6198
65	0.3201	0.6402	0.6799
66	0.3101	0.6203	0.6899
67	0.3136	0.6272	0.6864
68	0.2572	0.5144	0.7428
69	0.2283	0.4565	0.7717
70	0.2206	0.4412	0.7794
71	0.172	0.344	0.828
72	0.1356	0.2712	0.8644
73	0.1078	0.2156	0.8922
74	0.08682	0.1736	0.9132
75	0.071	0.142	0.929
76	0.0517	0.1034	0.9483
77	0.05027	0.1005	0.9497
78	0.1122	0.2244	0.8878
79	0.08281	0.1656	0.9172
80	0.06773	0.1355	0.9323
81	0.04856	0.09712	0.9514
82	0.03764	0.07529	0.9624
83	0.02355	0.0471	0.9765
84	0.204	0.4081	0.796
85	0.1406	0.2812	0.8594
86	0.1893	0.3786	0.8107
87	0.4472	0.8944	0.5528
88	0.5079	0.9842	0.4921

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
11 &  0.2074 &  0.4149 &  0.7926 \tabularnewline
12 &  0.09781 &  0.1956 &  0.9022 \tabularnewline
13 &  0.04298 &  0.08596 &  0.957 \tabularnewline
14 &  0.03413 &  0.06826 &  0.9659 \tabularnewline
15 &  0.01894 &  0.03788 &  0.9811 \tabularnewline
16 &  0.008196 &  0.01639 &  0.9918 \tabularnewline
17 &  0.0126 &  0.0252 &  0.9874 \tabularnewline
18 &  0.01644 &  0.03288 &  0.9836 \tabularnewline
19 &  0.01597 &  0.03195 &  0.984 \tabularnewline
20 &  0.01249 &  0.02497 &  0.9875 \tabularnewline
21 &  0.006234 &  0.01247 &  0.9938 \tabularnewline
22 &  0.003034 &  0.006069 &  0.997 \tabularnewline
23 &  0.02419 &  0.04839 &  0.9758 \tabularnewline
24 &  0.01468 &  0.02936 &  0.9853 \tabularnewline
25 &  0.01467 &  0.02935 &  0.9853 \tabularnewline
26 &  0.009241 &  0.01848 &  0.9908 \tabularnewline
27 &  0.00677 &  0.01354 &  0.9932 \tabularnewline
28 &  0.005535 &  0.01107 &  0.9945 \tabularnewline
29 &  0.03821 &  0.07643 &  0.9618 \tabularnewline
30 &  0.02525 &  0.05049 &  0.9748 \tabularnewline
31 &  0.1016 &  0.2031 &  0.8984 \tabularnewline
32 &  0.07518 &  0.1504 &  0.9248 \tabularnewline
33 &  0.1523 &  0.3046 &  0.8477 \tabularnewline
34 &  0.1652 &  0.3304 &  0.8348 \tabularnewline
35 &  0.1359 &  0.2718 &  0.8641 \tabularnewline
36 &  0.1056 &  0.2112 &  0.8944 \tabularnewline
37 &  0.09731 &  0.1946 &  0.9027 \tabularnewline
38 &  0.07354 &  0.1471 &  0.9265 \tabularnewline
39 &  0.06115 &  0.1223 &  0.9389 \tabularnewline
40 &  0.04677 &  0.09353 &  0.9532 \tabularnewline
41 &  0.04768 &  0.09535 &  0.9523 \tabularnewline
42 &  0.04829 &  0.09657 &  0.9517 \tabularnewline
43 &  0.0502 &  0.1004 &  0.9498 \tabularnewline
44 &  0.06516 &  0.1303 &  0.9348 \tabularnewline
45 &  0.05966 &  0.1193 &  0.9403 \tabularnewline
46 &  0.05291 &  0.1058 &  0.9471 \tabularnewline
47 &  0.03873 &  0.07745 &  0.9613 \tabularnewline
48 &  0.03199 &  0.06398 &  0.968 \tabularnewline
49 &  0.02622 &  0.05243 &  0.9738 \tabularnewline
50 &  0.0448 &  0.08961 &  0.9552 \tabularnewline
51 &  0.2441 &  0.4882 &  0.7559 \tabularnewline
52 &  0.2354 &  0.4708 &  0.7646 \tabularnewline
53 &  0.4461 &  0.8923 &  0.5539 \tabularnewline
54 &  0.3861 &  0.7721 &  0.6139 \tabularnewline
55 &  0.4431 &  0.8861 &  0.5569 \tabularnewline
56 &  0.4001 &  0.8002 &  0.5999 \tabularnewline
57 &  0.3802 &  0.7603 &  0.6198 \tabularnewline
58 &  0.3305 &  0.6609 &  0.6695 \tabularnewline
59 &  0.2762 &  0.5524 &  0.7238 \tabularnewline
60 &  0.2648 &  0.5297 &  0.7352 \tabularnewline
61 &  0.2283 &  0.4567 &  0.7717 \tabularnewline
62 &  0.1938 &  0.3875 &  0.8063 \tabularnewline
63 &  0.1604 &  0.3209 &  0.8396 \tabularnewline
64 &  0.3802 &  0.7604 &  0.6198 \tabularnewline
65 &  0.3201 &  0.6402 &  0.6799 \tabularnewline
66 &  0.3101 &  0.6203 &  0.6899 \tabularnewline
67 &  0.3136 &  0.6272 &  0.6864 \tabularnewline
68 &  0.2572 &  0.5144 &  0.7428 \tabularnewline
69 &  0.2283 &  0.4565 &  0.7717 \tabularnewline
70 &  0.2206 &  0.4412 &  0.7794 \tabularnewline
71 &  0.172 &  0.344 &  0.828 \tabularnewline
72 &  0.1356 &  0.2712 &  0.8644 \tabularnewline
73 &  0.1078 &  0.2156 &  0.8922 \tabularnewline
74 &  0.08682 &  0.1736 &  0.9132 \tabularnewline
75 &  0.071 &  0.142 &  0.929 \tabularnewline
76 &  0.0517 &  0.1034 &  0.9483 \tabularnewline
77 &  0.05027 &  0.1005 &  0.9497 \tabularnewline
78 &  0.1122 &  0.2244 &  0.8878 \tabularnewline
79 &  0.08281 &  0.1656 &  0.9172 \tabularnewline
80 &  0.06773 &  0.1355 &  0.9323 \tabularnewline
81 &  0.04856 &  0.09712 &  0.9514 \tabularnewline
82 &  0.03764 &  0.07529 &  0.9624 \tabularnewline
83 &  0.02355 &  0.0471 &  0.9765 \tabularnewline
84 &  0.204 &  0.4081 &  0.796 \tabularnewline
85 &  0.1406 &  0.2812 &  0.8594 \tabularnewline
86 &  0.1893 &  0.3786 &  0.8107 \tabularnewline
87 &  0.4472 &  0.8944 &  0.5528 \tabularnewline
88 &  0.5079 &  0.9842 &  0.4921 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=305628&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]11[/C][C] 0.2074[/C][C] 0.4149[/C][C] 0.7926[/C][/ROW]
[ROW][C]12[/C][C] 0.09781[/C][C] 0.1956[/C][C] 0.9022[/C][/ROW]
[ROW][C]13[/C][C] 0.04298[/C][C] 0.08596[/C][C] 0.957[/C][/ROW]
[ROW][C]14[/C][C] 0.03413[/C][C] 0.06826[/C][C] 0.9659[/C][/ROW]
[ROW][C]15[/C][C] 0.01894[/C][C] 0.03788[/C][C] 0.9811[/C][/ROW]
[ROW][C]16[/C][C] 0.008196[/C][C] 0.01639[/C][C] 0.9918[/C][/ROW]
[ROW][C]17[/C][C] 0.0126[/C][C] 0.0252[/C][C] 0.9874[/C][/ROW]
[ROW][C]18[/C][C] 0.01644[/C][C] 0.03288[/C][C] 0.9836[/C][/ROW]
[ROW][C]19[/C][C] 0.01597[/C][C] 0.03195[/C][C] 0.984[/C][/ROW]
[ROW][C]20[/C][C] 0.01249[/C][C] 0.02497[/C][C] 0.9875[/C][/ROW]
[ROW][C]21[/C][C] 0.006234[/C][C] 0.01247[/C][C] 0.9938[/C][/ROW]
[ROW][C]22[/C][C] 0.003034[/C][C] 0.006069[/C][C] 0.997[/C][/ROW]
[ROW][C]23[/C][C] 0.02419[/C][C] 0.04839[/C][C] 0.9758[/C][/ROW]
[ROW][C]24[/C][C] 0.01468[/C][C] 0.02936[/C][C] 0.9853[/C][/ROW]
[ROW][C]25[/C][C] 0.01467[/C][C] 0.02935[/C][C] 0.9853[/C][/ROW]
[ROW][C]26[/C][C] 0.009241[/C][C] 0.01848[/C][C] 0.9908[/C][/ROW]
[ROW][C]27[/C][C] 0.00677[/C][C] 0.01354[/C][C] 0.9932[/C][/ROW]
[ROW][C]28[/C][C] 0.005535[/C][C] 0.01107[/C][C] 0.9945[/C][/ROW]
[ROW][C]29[/C][C] 0.03821[/C][C] 0.07643[/C][C] 0.9618[/C][/ROW]
[ROW][C]30[/C][C] 0.02525[/C][C] 0.05049[/C][C] 0.9748[/C][/ROW]
[ROW][C]31[/C][C] 0.1016[/C][C] 0.2031[/C][C] 0.8984[/C][/ROW]
[ROW][C]32[/C][C] 0.07518[/C][C] 0.1504[/C][C] 0.9248[/C][/ROW]
[ROW][C]33[/C][C] 0.1523[/C][C] 0.3046[/C][C] 0.8477[/C][/ROW]
[ROW][C]34[/C][C] 0.1652[/C][C] 0.3304[/C][C] 0.8348[/C][/ROW]
[ROW][C]35[/C][C] 0.1359[/C][C] 0.2718[/C][C] 0.8641[/C][/ROW]
[ROW][C]36[/C][C] 0.1056[/C][C] 0.2112[/C][C] 0.8944[/C][/ROW]
[ROW][C]37[/C][C] 0.09731[/C][C] 0.1946[/C][C] 0.9027[/C][/ROW]
[ROW][C]38[/C][C] 0.07354[/C][C] 0.1471[/C][C] 0.9265[/C][/ROW]
[ROW][C]39[/C][C] 0.06115[/C][C] 0.1223[/C][C] 0.9389[/C][/ROW]
[ROW][C]40[/C][C] 0.04677[/C][C] 0.09353[/C][C] 0.9532[/C][/ROW]
[ROW][C]41[/C][C] 0.04768[/C][C] 0.09535[/C][C] 0.9523[/C][/ROW]
[ROW][C]42[/C][C] 0.04829[/C][C] 0.09657[/C][C] 0.9517[/C][/ROW]
[ROW][C]43[/C][C] 0.0502[/C][C] 0.1004[/C][C] 0.9498[/C][/ROW]
[ROW][C]44[/C][C] 0.06516[/C][C] 0.1303[/C][C] 0.9348[/C][/ROW]
[ROW][C]45[/C][C] 0.05966[/C][C] 0.1193[/C][C] 0.9403[/C][/ROW]
[ROW][C]46[/C][C] 0.05291[/C][C] 0.1058[/C][C] 0.9471[/C][/ROW]
[ROW][C]47[/C][C] 0.03873[/C][C] 0.07745[/C][C] 0.9613[/C][/ROW]
[ROW][C]48[/C][C] 0.03199[/C][C] 0.06398[/C][C] 0.968[/C][/ROW]
[ROW][C]49[/C][C] 0.02622[/C][C] 0.05243[/C][C] 0.9738[/C][/ROW]
[ROW][C]50[/C][C] 0.0448[/C][C] 0.08961[/C][C] 0.9552[/C][/ROW]
[ROW][C]51[/C][C] 0.2441[/C][C] 0.4882[/C][C] 0.7559[/C][/ROW]
[ROW][C]52[/C][C] 0.2354[/C][C] 0.4708[/C][C] 0.7646[/C][/ROW]
[ROW][C]53[/C][C] 0.4461[/C][C] 0.8923[/C][C] 0.5539[/C][/ROW]
[ROW][C]54[/C][C] 0.3861[/C][C] 0.7721[/C][C] 0.6139[/C][/ROW]
[ROW][C]55[/C][C] 0.4431[/C][C] 0.8861[/C][C] 0.5569[/C][/ROW]
[ROW][C]56[/C][C] 0.4001[/C][C] 0.8002[/C][C] 0.5999[/C][/ROW]
[ROW][C]57[/C][C] 0.3802[/C][C] 0.7603[/C][C] 0.6198[/C][/ROW]
[ROW][C]58[/C][C] 0.3305[/C][C] 0.6609[/C][C] 0.6695[/C][/ROW]
[ROW][C]59[/C][C] 0.2762[/C][C] 0.5524[/C][C] 0.7238[/C][/ROW]
[ROW][C]60[/C][C] 0.2648[/C][C] 0.5297[/C][C] 0.7352[/C][/ROW]
[ROW][C]61[/C][C] 0.2283[/C][C] 0.4567[/C][C] 0.7717[/C][/ROW]
[ROW][C]62[/C][C] 0.1938[/C][C] 0.3875[/C][C] 0.8063[/C][/ROW]
[ROW][C]63[/C][C] 0.1604[/C][C] 0.3209[/C][C] 0.8396[/C][/ROW]
[ROW][C]64[/C][C] 0.3802[/C][C] 0.7604[/C][C] 0.6198[/C][/ROW]
[ROW][C]65[/C][C] 0.3201[/C][C] 0.6402[/C][C] 0.6799[/C][/ROW]
[ROW][C]66[/C][C] 0.3101[/C][C] 0.6203[/C][C] 0.6899[/C][/ROW]
[ROW][C]67[/C][C] 0.3136[/C][C] 0.6272[/C][C] 0.6864[/C][/ROW]
[ROW][C]68[/C][C] 0.2572[/C][C] 0.5144[/C][C] 0.7428[/C][/ROW]
[ROW][C]69[/C][C] 0.2283[/C][C] 0.4565[/C][C] 0.7717[/C][/ROW]
[ROW][C]70[/C][C] 0.2206[/C][C] 0.4412[/C][C] 0.7794[/C][/ROW]
[ROW][C]71[/C][C] 0.172[/C][C] 0.344[/C][C] 0.828[/C][/ROW]
[ROW][C]72[/C][C] 0.1356[/C][C] 0.2712[/C][C] 0.8644[/C][/ROW]
[ROW][C]73[/C][C] 0.1078[/C][C] 0.2156[/C][C] 0.8922[/C][/ROW]
[ROW][C]74[/C][C] 0.08682[/C][C] 0.1736[/C][C] 0.9132[/C][/ROW]
[ROW][C]75[/C][C] 0.071[/C][C] 0.142[/C][C] 0.929[/C][/ROW]
[ROW][C]76[/C][C] 0.0517[/C][C] 0.1034[/C][C] 0.9483[/C][/ROW]
[ROW][C]77[/C][C] 0.05027[/C][C] 0.1005[/C][C] 0.9497[/C][/ROW]
[ROW][C]78[/C][C] 0.1122[/C][C] 0.2244[/C][C] 0.8878[/C][/ROW]
[ROW][C]79[/C][C] 0.08281[/C][C] 0.1656[/C][C] 0.9172[/C][/ROW]
[ROW][C]80[/C][C] 0.06773[/C][C] 0.1355[/C][C] 0.9323[/C][/ROW]
[ROW][C]81[/C][C] 0.04856[/C][C] 0.09712[/C][C] 0.9514[/C][/ROW]
[ROW][C]82[/C][C] 0.03764[/C][C] 0.07529[/C][C] 0.9624[/C][/ROW]
[ROW][C]83[/C][C] 0.02355[/C][C] 0.0471[/C][C] 0.9765[/C][/ROW]
[ROW][C]84[/C][C] 0.204[/C][C] 0.4081[/C][C] 0.796[/C][/ROW]
[ROW][C]85[/C][C] 0.1406[/C][C] 0.2812[/C][C] 0.8594[/C][/ROW]
[ROW][C]86[/C][C] 0.1893[/C][C] 0.3786[/C][C] 0.8107[/C][/ROW]
[ROW][C]87[/C][C] 0.4472[/C][C] 0.8944[/C][C] 0.5528[/C][/ROW]
[ROW][C]88[/C][C] 0.5079[/C][C] 0.9842[/C][C] 0.4921[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=305628&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=305628&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
11	0.2074	0.4149	0.7926
12	0.09781	0.1956	0.9022
13	0.04298	0.08596	0.957
14	0.03413	0.06826	0.9659
15	0.01894	0.03788	0.9811
16	0.008196	0.01639	0.9918
17	0.0126	0.0252	0.9874
18	0.01644	0.03288	0.9836
19	0.01597	0.03195	0.984
20	0.01249	0.02497	0.9875
21	0.006234	0.01247	0.9938
22	0.003034	0.006069	0.997
23	0.02419	0.04839	0.9758
24	0.01468	0.02936	0.9853
25	0.01467	0.02935	0.9853
26	0.009241	0.01848	0.9908
27	0.00677	0.01354	0.9932
28	0.005535	0.01107	0.9945
29	0.03821	0.07643	0.9618
30	0.02525	0.05049	0.9748
31	0.1016	0.2031	0.8984
32	0.07518	0.1504	0.9248
33	0.1523	0.3046	0.8477
34	0.1652	0.3304	0.8348
35	0.1359	0.2718	0.8641
36	0.1056	0.2112	0.8944
37	0.09731	0.1946	0.9027
38	0.07354	0.1471	0.9265
39	0.06115	0.1223	0.9389
40	0.04677	0.09353	0.9532
41	0.04768	0.09535	0.9523
42	0.04829	0.09657	0.9517
43	0.0502	0.1004	0.9498
44	0.06516	0.1303	0.9348
45	0.05966	0.1193	0.9403
46	0.05291	0.1058	0.9471
47	0.03873	0.07745	0.9613
48	0.03199	0.06398	0.968
49	0.02622	0.05243	0.9738
50	0.0448	0.08961	0.9552
51	0.2441	0.4882	0.7559
52	0.2354	0.4708	0.7646
53	0.4461	0.8923	0.5539
54	0.3861	0.7721	0.6139
55	0.4431	0.8861	0.5569
56	0.4001	0.8002	0.5999
57	0.3802	0.7603	0.6198
58	0.3305	0.6609	0.6695
59	0.2762	0.5524	0.7238
60	0.2648	0.5297	0.7352
61	0.2283	0.4567	0.7717
62	0.1938	0.3875	0.8063
63	0.1604	0.3209	0.8396
64	0.3802	0.7604	0.6198
65	0.3201	0.6402	0.6799
66	0.3101	0.6203	0.6899
67	0.3136	0.6272	0.6864
68	0.2572	0.5144	0.7428
69	0.2283	0.4565	0.7717
70	0.2206	0.4412	0.7794
71	0.172	0.344	0.828
72	0.1356	0.2712	0.8644
73	0.1078	0.2156	0.8922
74	0.08682	0.1736	0.9132
75	0.071	0.142	0.929
76	0.0517	0.1034	0.9483
77	0.05027	0.1005	0.9497
78	0.1122	0.2244	0.8878
79	0.08281	0.1656	0.9172
80	0.06773	0.1355	0.9323
81	0.04856	0.09712	0.9514
82	0.03764	0.07529	0.9624
83	0.02355	0.0471	0.9765
84	0.204	0.4081	0.796
85	0.1406	0.2812	0.8594
86	0.1893	0.3786	0.8107
87	0.4472	0.8944	0.5528
88	0.5079	0.9842	0.4921

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	1	0.01282	NOK
5% type I error level	15	0.192308	NOK
10% type I error level	28	0.358974	NOK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 1 &  0.01282 & NOK \tabularnewline
5% type I error level & 15 & 0.192308 & NOK \tabularnewline
10% type I error level & 28 & 0.358974 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=305628&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]1[/C][C] 0.01282[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]15[/C][C]0.192308[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]28[/C][C]0.358974[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=305628&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=305628&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	1	0.01282	NOK
5% type I error level	15	0.192308	NOK
10% type I error level	28	0.358974	NOK

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted RESET test data: mylm RESET = 0.75868, df1 = 2, df2 = 89, p-value = 0.4713
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 1.2847, df1 = 14, df2 = 77, p-value = 0.2364
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 0.0426, df1 = 2, df2 = 89, p-value = 0.9583

\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 = 0.75868, df1 = 2, df2 = 89, p-value = 0.4713
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of regressors \tabularnewline
> reset_test_regressors
	RESET test
data:  mylm
RESET = 1.2847, df1 = 14, df2 = 77, p-value = 0.2364
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of principal components \tabularnewline
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 0.0426, df1 = 2, df2 = 89, p-value = 0.9583
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=305628&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 = 0.75868, df1 = 2, df2 = 89, p-value = 0.4713
[/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 = 1.2847, df1 = 14, df2 = 77, p-value = 0.2364
[/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 = 0.0426, df1 = 2, df2 = 89, p-value = 0.9583
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=305628&T=7

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=305628&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 = 0.75868, df1 = 2, df2 = 89, p-value = 0.4713
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 1.2847, df1 = 14, df2 = 77, p-value = 0.2364
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 0.0426, df1 = 2, df2 = 89, p-value = 0.9583

Variance Inflation Factors (Multicollinearity)
> vif Bevr_Leeftijd ITHSUM SKEOUSUM SKEOU1 SKEOU2 1.044475 1.157690 4.363017 1.687615 1.937273 SKEOU3 SKEOU4 1.797334 1.362840

\begin{tabular}{lllllllll}
\hline
Variance Inflation Factors (Multicollinearity) \tabularnewline
> vif
Bevr_Leeftijd        ITHSUM      SKEOUSUM        SKEOU1        SKEOU2 
     1.044475      1.157690      4.363017      1.687615      1.937273 
       SKEOU3        SKEOU4 
     1.797334      1.362840 
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=305628&T=8

[TABLE]
[ROW][C]Variance Inflation Factors (Multicollinearity)[/C][/ROW]
[ROW][C]> vif
Bevr_Leeftijd        ITHSUM      SKEOUSUM        SKEOU1        SKEOU2 
     1.044475      1.157690      4.363017      1.687615      1.937273 
       SKEOU3        SKEOU4 
     1.797334      1.362840 
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=305628&T=8

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=305628&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 Bevr_Leeftijd ITHSUM SKEOUSUM SKEOU1 SKEOU2 1.044475 1.157690 4.363017 1.687615 1.937273 SKEOU3 SKEOU4 1.797334 1.362840

Figure 1

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Figure 2

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Figure 3

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Figure 4

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Figure 5

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Figure 6

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Figure 7

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Figure 8

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Figure 9

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Figure 10

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Parameters (Session):

Parameters (R input):

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

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

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