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rwasp_multipleregression.wasp

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Multiple Regression

Date of computation

Mon, 23 Jan 2017 11:11:20 +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/t1485166286zjczsioi715ucgp.htm/, Retrieved Wed, 15 May 2024 16:33:17 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=304819, Retrieved Wed, 15 May 2024 16:33:17 +0000

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-       [Multiple Regression] [] [2017-01-23 10:11:20] [2e11ca31a00cf8de75c33c1af2d59434] [Current]

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

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	7 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 time7 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=304819&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]

7 seconds[/C][/ROW] [ROW]

R Server[/C]

Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=304819&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=304819&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	7 seconds
R Server	Big Analytics Cloud Computing Center

Multiple Linear Regression - Estimated Regression Equation
ITHSUM[t] = + 8.11988 -0.0377059Bevr_Leeftijd[t] + 0.00760815TVDC[t] + 0.413666SKEOU1[t] -0.0133935SKEOU2[t] + 0.716207SKEOU4[t] + 0.690623SKEOU5[t] + 0.481387SKEOU6[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
ITHSUM[t] =  +  8.11988 -0.0377059Bevr_Leeftijd[t] +  0.00760815TVDC[t] +  0.413666SKEOU1[t] -0.0133935SKEOU2[t] +  0.716207SKEOU4[t] +  0.690623SKEOU5[t] +  0.481387SKEOU6[t]  + e[t] \tabularnewline
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=304819&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]ITHSUM[t] =  +  8.11988 -0.0377059Bevr_Leeftijd[t] +  0.00760815TVDC[t] +  0.413666SKEOU1[t] -0.0133935SKEOU2[t] +  0.716207SKEOU4[t] +  0.690623SKEOU5[t] +  0.481387SKEOU6[t]  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=304819&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=304819&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
ITHSUM[t] = + 8.11988 -0.0377059Bevr_Leeftijd[t] + 0.00760815TVDC[t] + 0.413666SKEOU1[t] -0.0133935SKEOU2[t] + 0.716207SKEOU4[t] + 0.690623SKEOU5[t] + 0.481387SKEOU6[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)	+8.12	4.072	+1.9940e+00	0.04916	0.02458
Bevr_Leeftijd	-0.03771	0.1389	-2.7140e-01	0.7867	0.3933
TVDC	+0.007608	0.1518	+5.0110e-02	0.9601	0.4801
SKEOU1	+0.4137	0.3331	+1.2420e+00	0.2175	0.1088
SKEOU2	-0.01339	0.3942	-3.3980e-02	0.973	0.4865
SKEOU4	+0.7162	0.436	+1.6430e+00	0.1039	0.05195
SKEOU5	+0.6906	0.3543	+1.9490e+00	0.05435	0.02717
SKEOU6	+0.4814	0.3774	+1.2760e+00	0.2053	0.1027

\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) & +8.12 &  4.072 & +1.9940e+00 &  0.04916 &  0.02458 \tabularnewline
Bevr_Leeftijd & -0.03771 &  0.1389 & -2.7140e-01 &  0.7867 &  0.3933 \tabularnewline
TVDC & +0.007608 &  0.1518 & +5.0110e-02 &  0.9601 &  0.4801 \tabularnewline
SKEOU1 & +0.4137 &  0.3331 & +1.2420e+00 &  0.2175 &  0.1088 \tabularnewline
SKEOU2 & -0.01339 &  0.3942 & -3.3980e-02 &  0.973 &  0.4865 \tabularnewline
SKEOU4 & +0.7162 &  0.436 & +1.6430e+00 &  0.1039 &  0.05195 \tabularnewline
SKEOU5 & +0.6906 &  0.3543 & +1.9490e+00 &  0.05435 &  0.02717 \tabularnewline
SKEOU6 & +0.4814 &  0.3774 & +1.2760e+00 &  0.2053 &  0.1027 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=304819&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]+8.12[/C][C] 4.072[/C][C]+1.9940e+00[/C][C] 0.04916[/C][C] 0.02458[/C][/ROW]
[ROW][C]Bevr_Leeftijd[/C][C]-0.03771[/C][C] 0.1389[/C][C]-2.7140e-01[/C][C] 0.7867[/C][C] 0.3933[/C][/ROW]
[ROW][C]TVDC[/C][C]+0.007608[/C][C] 0.1518[/C][C]+5.0110e-02[/C][C] 0.9601[/C][C] 0.4801[/C][/ROW]
[ROW][C]SKEOU1[/C][C]+0.4137[/C][C] 0.3331[/C][C]+1.2420e+00[/C][C] 0.2175[/C][C] 0.1088[/C][/ROW]
[ROW][C]SKEOU2[/C][C]-0.01339[/C][C] 0.3942[/C][C]-3.3980e-02[/C][C] 0.973[/C][C] 0.4865[/C][/ROW]
[ROW][C]SKEOU4[/C][C]+0.7162[/C][C] 0.436[/C][C]+1.6430e+00[/C][C] 0.1039[/C][C] 0.05195[/C][/ROW]
[ROW][C]SKEOU5[/C][C]+0.6906[/C][C] 0.3543[/C][C]+1.9490e+00[/C][C] 0.05435[/C][C] 0.02717[/C][/ROW]
[ROW][C]SKEOU6[/C][C]+0.4814[/C][C] 0.3774[/C][C]+1.2760e+00[/C][C] 0.2053[/C][C] 0.1027[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=304819&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=304819&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)	+8.12	4.072	+1.9940e+00	0.04916	0.02458
Bevr_Leeftijd	-0.03771	0.1389	-2.7140e-01	0.7867	0.3933
TVDC	+0.007608	0.1518	+5.0110e-02	0.9601	0.4801
SKEOU1	+0.4137	0.3331	+1.2420e+00	0.2175	0.1088
SKEOU2	-0.01339	0.3942	-3.3980e-02	0.973	0.4865
SKEOU4	+0.7162	0.436	+1.6430e+00	0.1039	0.05195
SKEOU5	+0.6906	0.3543	+1.9490e+00	0.05435	0.02717
SKEOU6	+0.4814	0.3774	+1.2760e+00	0.2053	0.1027

Multiple Linear Regression - Regression Statistics
Multiple R	0.3274
R-squared	0.1072
Adjusted R-squared	0.03853
F-TEST (value)	1.561
F-TEST (DF numerator)	7
F-TEST (DF denominator)	91
p-value	0.1571
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	2.24
Sum Squared Residuals	456.6

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.3274 \tabularnewline
R-squared &  0.1072 \tabularnewline
Adjusted R-squared &  0.03853 \tabularnewline
F-TEST (value) &  1.561 \tabularnewline
F-TEST (DF numerator) & 7 \tabularnewline
F-TEST (DF denominator) & 91 \tabularnewline
p-value &  0.1571 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  2.24 \tabularnewline
Sum Squared Residuals &  456.6 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=304819&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.3274[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.1072[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.03853[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 1.561[/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] 0.1571[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C] 2.24[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 456.6[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=304819&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=304819&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	0.3274
R-squared	0.1072
Adjusted R-squared	0.03853
F-TEST (value)	1.561
F-TEST (DF numerator)	7
F-TEST (DF denominator)	91
p-value	0.1571
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	2.24
Sum Squared Residuals	456.6

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	14	16.54	-2.544
2	19	17.61	1.392
3	17	17.3	-0.302
4	20	17.4	2.6
5	15	17.69	-2.695
6	19	17.3	1.698
7	20	16.44	3.556
8	18	16.67	1.33
9	15	15.7	-0.7039
10	14	16.64	-2.641
11	16	15.96	0.04474
12	19	17.05	1.953
13	18	16.72	1.279
14	17	16.67	0.3285
15	19	16.87	2.127
16	17	17.02	-0.01741
17	19	17.83	1.17
18	20	17.51	2.494
19	19	16.46	2.542
20	16	16.6	-0.598
21	16	16.44	-0.4425
22	18	16.68	1.323
23	16	16.69	-0.6944
24	17	17.73	-0.7323
25	20	17.42	2.578
26	19	16.41	2.593
27	7	16.32	-9.324
28	16	14.84	1.162
29	16	17.23	-1.234
30	18	16.67	1.327
31	17	17.83	-0.8265
32	19	16.37	2.633
33	16	16.58	-0.5809
34	13	15.79	-2.792
35	16	16.65	-0.6536
36	12	18	-5.995
37	17	17.02	-0.02383
38	17	17.78	-0.7758
39	17	14.81	2.191
40	16	17.81	-1.806
41	16	16.52	-0.5247
42	14	16.07	-2.067
43	16	16.53	-0.5252
44	13	16.04	-3.039
45	16	16.65	-0.6459
46	14	16.14	-2.145
47	19	17.29	1.706
48	18	16.63	1.369
49	14	16.68	-2.677
50	18	16.55	1.451
51	15	16.08	-1.082
52	17	17.86	-0.8588
53	13	17.05	-4.048
54	19	18.22	0.7774
55	18	17.72	0.2844
56	15	15.94	-0.9368
57	20	17.02	2.983
58	19	16.88	2.117
59	18	17.05	0.9526
60	15	17.48	-2.476
61	20	18.23	1.77
62	17	17.76	-0.7624
63	19	17.04	1.96
64	20	17.33	2.675
65	18	16.6	1.396
66	17	16.55	0.4466
67	18	16.19	1.81
68	17	15.8	1.2
69	20	18.26	1.735
70	16	16.22	-0.2203
71	14	15.96	-1.955
72	15	15.98	-0.9777
73	20	17.25	2.751
74	17	16.14	0.8552
75	17	15.77	1.226
76	18	14.67	3.329
77	20	16.93	3.068
78	16	16.13	-0.1277
79	18	17.79	0.2147
80	15	16.58	-1.583
81	18	18.36	-0.3583
82	20	17.73	2.268
83	14	16.94	-2.941
84	15	16.54	-1.543
85	17	17.78	-0.7834
86	18	17.8	0.2018
87	20	16.94	3.061
88	17	16.56	0.4358
89	16	16.11	-0.109
90	11	15.93	-4.925
91	15	17.19	-2.191
92	18	16.73	1.271
93	16	17.79	-1.791
94	18	17.18	0.8242
95	15	17.1	-2.103
96	17	16.21	0.787
97	19	17.22	1.776
98	16	17.09	-1.085
99	14	17.16	-3.159

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 &  14 &  16.54 & -2.544 \tabularnewline
2 &  19 &  17.61 &  1.392 \tabularnewline
3 &  17 &  17.3 & -0.302 \tabularnewline
4 &  20 &  17.4 &  2.6 \tabularnewline
5 &  15 &  17.69 & -2.695 \tabularnewline
6 &  19 &  17.3 &  1.698 \tabularnewline
7 &  20 &  16.44 &  3.556 \tabularnewline
8 &  18 &  16.67 &  1.33 \tabularnewline
9 &  15 &  15.7 & -0.7039 \tabularnewline
10 &  14 &  16.64 & -2.641 \tabularnewline
11 &  16 &  15.96 &  0.04474 \tabularnewline
12 &  19 &  17.05 &  1.953 \tabularnewline
13 &  18 &  16.72 &  1.279 \tabularnewline
14 &  17 &  16.67 &  0.3285 \tabularnewline
15 &  19 &  16.87 &  2.127 \tabularnewline
16 &  17 &  17.02 & -0.01741 \tabularnewline
17 &  19 &  17.83 &  1.17 \tabularnewline
18 &  20 &  17.51 &  2.494 \tabularnewline
19 &  19 &  16.46 &  2.542 \tabularnewline
20 &  16 &  16.6 & -0.598 \tabularnewline
21 &  16 &  16.44 & -0.4425 \tabularnewline
22 &  18 &  16.68 &  1.323 \tabularnewline
23 &  16 &  16.69 & -0.6944 \tabularnewline
24 &  17 &  17.73 & -0.7323 \tabularnewline
25 &  20 &  17.42 &  2.578 \tabularnewline
26 &  19 &  16.41 &  2.593 \tabularnewline
27 &  7 &  16.32 & -9.324 \tabularnewline
28 &  16 &  14.84 &  1.162 \tabularnewline
29 &  16 &  17.23 & -1.234 \tabularnewline
30 &  18 &  16.67 &  1.327 \tabularnewline
31 &  17 &  17.83 & -0.8265 \tabularnewline
32 &  19 &  16.37 &  2.633 \tabularnewline
33 &  16 &  16.58 & -0.5809 \tabularnewline
34 &  13 &  15.79 & -2.792 \tabularnewline
35 &  16 &  16.65 & -0.6536 \tabularnewline
36 &  12 &  18 & -5.995 \tabularnewline
37 &  17 &  17.02 & -0.02383 \tabularnewline
38 &  17 &  17.78 & -0.7758 \tabularnewline
39 &  17 &  14.81 &  2.191 \tabularnewline
40 &  16 &  17.81 & -1.806 \tabularnewline
41 &  16 &  16.52 & -0.5247 \tabularnewline
42 &  14 &  16.07 & -2.067 \tabularnewline
43 &  16 &  16.53 & -0.5252 \tabularnewline
44 &  13 &  16.04 & -3.039 \tabularnewline
45 &  16 &  16.65 & -0.6459 \tabularnewline
46 &  14 &  16.14 & -2.145 \tabularnewline
47 &  19 &  17.29 &  1.706 \tabularnewline
48 &  18 &  16.63 &  1.369 \tabularnewline
49 &  14 &  16.68 & -2.677 \tabularnewline
50 &  18 &  16.55 &  1.451 \tabularnewline
51 &  15 &  16.08 & -1.082 \tabularnewline
52 &  17 &  17.86 & -0.8588 \tabularnewline
53 &  13 &  17.05 & -4.048 \tabularnewline
54 &  19 &  18.22 &  0.7774 \tabularnewline
55 &  18 &  17.72 &  0.2844 \tabularnewline
56 &  15 &  15.94 & -0.9368 \tabularnewline
57 &  20 &  17.02 &  2.983 \tabularnewline
58 &  19 &  16.88 &  2.117 \tabularnewline
59 &  18 &  17.05 &  0.9526 \tabularnewline
60 &  15 &  17.48 & -2.476 \tabularnewline
61 &  20 &  18.23 &  1.77 \tabularnewline
62 &  17 &  17.76 & -0.7624 \tabularnewline
63 &  19 &  17.04 &  1.96 \tabularnewline
64 &  20 &  17.33 &  2.675 \tabularnewline
65 &  18 &  16.6 &  1.396 \tabularnewline
66 &  17 &  16.55 &  0.4466 \tabularnewline
67 &  18 &  16.19 &  1.81 \tabularnewline
68 &  17 &  15.8 &  1.2 \tabularnewline
69 &  20 &  18.26 &  1.735 \tabularnewline
70 &  16 &  16.22 & -0.2203 \tabularnewline
71 &  14 &  15.96 & -1.955 \tabularnewline
72 &  15 &  15.98 & -0.9777 \tabularnewline
73 &  20 &  17.25 &  2.751 \tabularnewline
74 &  17 &  16.14 &  0.8552 \tabularnewline
75 &  17 &  15.77 &  1.226 \tabularnewline
76 &  18 &  14.67 &  3.329 \tabularnewline
77 &  20 &  16.93 &  3.068 \tabularnewline
78 &  16 &  16.13 & -0.1277 \tabularnewline
79 &  18 &  17.79 &  0.2147 \tabularnewline
80 &  15 &  16.58 & -1.583 \tabularnewline
81 &  18 &  18.36 & -0.3583 \tabularnewline
82 &  20 &  17.73 &  2.268 \tabularnewline
83 &  14 &  16.94 & -2.941 \tabularnewline
84 &  15 &  16.54 & -1.543 \tabularnewline
85 &  17 &  17.78 & -0.7834 \tabularnewline
86 &  18 &  17.8 &  0.2018 \tabularnewline
87 &  20 &  16.94 &  3.061 \tabularnewline
88 &  17 &  16.56 &  0.4358 \tabularnewline
89 &  16 &  16.11 & -0.109 \tabularnewline
90 &  11 &  15.93 & -4.925 \tabularnewline
91 &  15 &  17.19 & -2.191 \tabularnewline
92 &  18 &  16.73 &  1.271 \tabularnewline
93 &  16 &  17.79 & -1.791 \tabularnewline
94 &  18 &  17.18 &  0.8242 \tabularnewline
95 &  15 &  17.1 & -2.103 \tabularnewline
96 &  17 &  16.21 &  0.787 \tabularnewline
97 &  19 &  17.22 &  1.776 \tabularnewline
98 &  16 &  17.09 & -1.085 \tabularnewline
99 &  14 &  17.16 & -3.159 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=304819&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] 14[/C][C] 16.54[/C][C]-2.544[/C][/ROW]
[ROW][C]2[/C][C] 19[/C][C] 17.61[/C][C] 1.392[/C][/ROW]
[ROW][C]3[/C][C] 17[/C][C] 17.3[/C][C]-0.302[/C][/ROW]
[ROW][C]4[/C][C] 20[/C][C] 17.4[/C][C] 2.6[/C][/ROW]
[ROW][C]5[/C][C] 15[/C][C] 17.69[/C][C]-2.695[/C][/ROW]
[ROW][C]6[/C][C] 19[/C][C] 17.3[/C][C] 1.698[/C][/ROW]
[ROW][C]7[/C][C] 20[/C][C] 16.44[/C][C] 3.556[/C][/ROW]
[ROW][C]8[/C][C] 18[/C][C] 16.67[/C][C] 1.33[/C][/ROW]
[ROW][C]9[/C][C] 15[/C][C] 15.7[/C][C]-0.7039[/C][/ROW]
[ROW][C]10[/C][C] 14[/C][C] 16.64[/C][C]-2.641[/C][/ROW]
[ROW][C]11[/C][C] 16[/C][C] 15.96[/C][C] 0.04474[/C][/ROW]
[ROW][C]12[/C][C] 19[/C][C] 17.05[/C][C] 1.953[/C][/ROW]
[ROW][C]13[/C][C] 18[/C][C] 16.72[/C][C] 1.279[/C][/ROW]
[ROW][C]14[/C][C] 17[/C][C] 16.67[/C][C] 0.3285[/C][/ROW]
[ROW][C]15[/C][C] 19[/C][C] 16.87[/C][C] 2.127[/C][/ROW]
[ROW][C]16[/C][C] 17[/C][C] 17.02[/C][C]-0.01741[/C][/ROW]
[ROW][C]17[/C][C] 19[/C][C] 17.83[/C][C] 1.17[/C][/ROW]
[ROW][C]18[/C][C] 20[/C][C] 17.51[/C][C] 2.494[/C][/ROW]
[ROW][C]19[/C][C] 19[/C][C] 16.46[/C][C] 2.542[/C][/ROW]
[ROW][C]20[/C][C] 16[/C][C] 16.6[/C][C]-0.598[/C][/ROW]
[ROW][C]21[/C][C] 16[/C][C] 16.44[/C][C]-0.4425[/C][/ROW]
[ROW][C]22[/C][C] 18[/C][C] 16.68[/C][C] 1.323[/C][/ROW]
[ROW][C]23[/C][C] 16[/C][C] 16.69[/C][C]-0.6944[/C][/ROW]
[ROW][C]24[/C][C] 17[/C][C] 17.73[/C][C]-0.7323[/C][/ROW]
[ROW][C]25[/C][C] 20[/C][C] 17.42[/C][C] 2.578[/C][/ROW]
[ROW][C]26[/C][C] 19[/C][C] 16.41[/C][C] 2.593[/C][/ROW]
[ROW][C]27[/C][C] 7[/C][C] 16.32[/C][C]-9.324[/C][/ROW]
[ROW][C]28[/C][C] 16[/C][C] 14.84[/C][C] 1.162[/C][/ROW]
[ROW][C]29[/C][C] 16[/C][C] 17.23[/C][C]-1.234[/C][/ROW]
[ROW][C]30[/C][C] 18[/C][C] 16.67[/C][C] 1.327[/C][/ROW]
[ROW][C]31[/C][C] 17[/C][C] 17.83[/C][C]-0.8265[/C][/ROW]
[ROW][C]32[/C][C] 19[/C][C] 16.37[/C][C] 2.633[/C][/ROW]
[ROW][C]33[/C][C] 16[/C][C] 16.58[/C][C]-0.5809[/C][/ROW]
[ROW][C]34[/C][C] 13[/C][C] 15.79[/C][C]-2.792[/C][/ROW]
[ROW][C]35[/C][C] 16[/C][C] 16.65[/C][C]-0.6536[/C][/ROW]
[ROW][C]36[/C][C] 12[/C][C] 18[/C][C]-5.995[/C][/ROW]
[ROW][C]37[/C][C] 17[/C][C] 17.02[/C][C]-0.02383[/C][/ROW]
[ROW][C]38[/C][C] 17[/C][C] 17.78[/C][C]-0.7758[/C][/ROW]
[ROW][C]39[/C][C] 17[/C][C] 14.81[/C][C] 2.191[/C][/ROW]
[ROW][C]40[/C][C] 16[/C][C] 17.81[/C][C]-1.806[/C][/ROW]
[ROW][C]41[/C][C] 16[/C][C] 16.52[/C][C]-0.5247[/C][/ROW]
[ROW][C]42[/C][C] 14[/C][C] 16.07[/C][C]-2.067[/C][/ROW]
[ROW][C]43[/C][C] 16[/C][C] 16.53[/C][C]-0.5252[/C][/ROW]
[ROW][C]44[/C][C] 13[/C][C] 16.04[/C][C]-3.039[/C][/ROW]
[ROW][C]45[/C][C] 16[/C][C] 16.65[/C][C]-0.6459[/C][/ROW]
[ROW][C]46[/C][C] 14[/C][C] 16.14[/C][C]-2.145[/C][/ROW]
[ROW][C]47[/C][C] 19[/C][C] 17.29[/C][C] 1.706[/C][/ROW]
[ROW][C]48[/C][C] 18[/C][C] 16.63[/C][C] 1.369[/C][/ROW]
[ROW][C]49[/C][C] 14[/C][C] 16.68[/C][C]-2.677[/C][/ROW]
[ROW][C]50[/C][C] 18[/C][C] 16.55[/C][C] 1.451[/C][/ROW]
[ROW][C]51[/C][C] 15[/C][C] 16.08[/C][C]-1.082[/C][/ROW]
[ROW][C]52[/C][C] 17[/C][C] 17.86[/C][C]-0.8588[/C][/ROW]
[ROW][C]53[/C][C] 13[/C][C] 17.05[/C][C]-4.048[/C][/ROW]
[ROW][C]54[/C][C] 19[/C][C] 18.22[/C][C] 0.7774[/C][/ROW]
[ROW][C]55[/C][C] 18[/C][C] 17.72[/C][C] 0.2844[/C][/ROW]
[ROW][C]56[/C][C] 15[/C][C] 15.94[/C][C]-0.9368[/C][/ROW]
[ROW][C]57[/C][C] 20[/C][C] 17.02[/C][C] 2.983[/C][/ROW]
[ROW][C]58[/C][C] 19[/C][C] 16.88[/C][C] 2.117[/C][/ROW]
[ROW][C]59[/C][C] 18[/C][C] 17.05[/C][C] 0.9526[/C][/ROW]
[ROW][C]60[/C][C] 15[/C][C] 17.48[/C][C]-2.476[/C][/ROW]
[ROW][C]61[/C][C] 20[/C][C] 18.23[/C][C] 1.77[/C][/ROW]
[ROW][C]62[/C][C] 17[/C][C] 17.76[/C][C]-0.7624[/C][/ROW]
[ROW][C]63[/C][C] 19[/C][C] 17.04[/C][C] 1.96[/C][/ROW]
[ROW][C]64[/C][C] 20[/C][C] 17.33[/C][C] 2.675[/C][/ROW]
[ROW][C]65[/C][C] 18[/C][C] 16.6[/C][C] 1.396[/C][/ROW]
[ROW][C]66[/C][C] 17[/C][C] 16.55[/C][C] 0.4466[/C][/ROW]
[ROW][C]67[/C][C] 18[/C][C] 16.19[/C][C] 1.81[/C][/ROW]
[ROW][C]68[/C][C] 17[/C][C] 15.8[/C][C] 1.2[/C][/ROW]
[ROW][C]69[/C][C] 20[/C][C] 18.26[/C][C] 1.735[/C][/ROW]
[ROW][C]70[/C][C] 16[/C][C] 16.22[/C][C]-0.2203[/C][/ROW]
[ROW][C]71[/C][C] 14[/C][C] 15.96[/C][C]-1.955[/C][/ROW]
[ROW][C]72[/C][C] 15[/C][C] 15.98[/C][C]-0.9777[/C][/ROW]
[ROW][C]73[/C][C] 20[/C][C] 17.25[/C][C] 2.751[/C][/ROW]
[ROW][C]74[/C][C] 17[/C][C] 16.14[/C][C] 0.8552[/C][/ROW]
[ROW][C]75[/C][C] 17[/C][C] 15.77[/C][C] 1.226[/C][/ROW]
[ROW][C]76[/C][C] 18[/C][C] 14.67[/C][C] 3.329[/C][/ROW]
[ROW][C]77[/C][C] 20[/C][C] 16.93[/C][C] 3.068[/C][/ROW]
[ROW][C]78[/C][C] 16[/C][C] 16.13[/C][C]-0.1277[/C][/ROW]
[ROW][C]79[/C][C] 18[/C][C] 17.79[/C][C] 0.2147[/C][/ROW]
[ROW][C]80[/C][C] 15[/C][C] 16.58[/C][C]-1.583[/C][/ROW]
[ROW][C]81[/C][C] 18[/C][C] 18.36[/C][C]-0.3583[/C][/ROW]
[ROW][C]82[/C][C] 20[/C][C] 17.73[/C][C] 2.268[/C][/ROW]
[ROW][C]83[/C][C] 14[/C][C] 16.94[/C][C]-2.941[/C][/ROW]
[ROW][C]84[/C][C] 15[/C][C] 16.54[/C][C]-1.543[/C][/ROW]
[ROW][C]85[/C][C] 17[/C][C] 17.78[/C][C]-0.7834[/C][/ROW]
[ROW][C]86[/C][C] 18[/C][C] 17.8[/C][C] 0.2018[/C][/ROW]
[ROW][C]87[/C][C] 20[/C][C] 16.94[/C][C] 3.061[/C][/ROW]
[ROW][C]88[/C][C] 17[/C][C] 16.56[/C][C] 0.4358[/C][/ROW]
[ROW][C]89[/C][C] 16[/C][C] 16.11[/C][C]-0.109[/C][/ROW]
[ROW][C]90[/C][C] 11[/C][C] 15.93[/C][C]-4.925[/C][/ROW]
[ROW][C]91[/C][C] 15[/C][C] 17.19[/C][C]-2.191[/C][/ROW]
[ROW][C]92[/C][C] 18[/C][C] 16.73[/C][C] 1.271[/C][/ROW]
[ROW][C]93[/C][C] 16[/C][C] 17.79[/C][C]-1.791[/C][/ROW]
[ROW][C]94[/C][C] 18[/C][C] 17.18[/C][C] 0.8242[/C][/ROW]
[ROW][C]95[/C][C] 15[/C][C] 17.1[/C][C]-2.103[/C][/ROW]
[ROW][C]96[/C][C] 17[/C][C] 16.21[/C][C] 0.787[/C][/ROW]
[ROW][C]97[/C][C] 19[/C][C] 17.22[/C][C] 1.776[/C][/ROW]
[ROW][C]98[/C][C] 16[/C][C] 17.09[/C][C]-1.085[/C][/ROW]
[ROW][C]99[/C][C] 14[/C][C] 17.16[/C][C]-3.159[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=304819&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=304819&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	14	16.54	-2.544
2	19	17.61	1.392
3	17	17.3	-0.302
4	20	17.4	2.6
5	15	17.69	-2.695
6	19	17.3	1.698
7	20	16.44	3.556
8	18	16.67	1.33
9	15	15.7	-0.7039
10	14	16.64	-2.641
11	16	15.96	0.04474
12	19	17.05	1.953
13	18	16.72	1.279
14	17	16.67	0.3285
15	19	16.87	2.127
16	17	17.02	-0.01741
17	19	17.83	1.17
18	20	17.51	2.494
19	19	16.46	2.542
20	16	16.6	-0.598
21	16	16.44	-0.4425
22	18	16.68	1.323
23	16	16.69	-0.6944
24	17	17.73	-0.7323
25	20	17.42	2.578
26	19	16.41	2.593
27	7	16.32	-9.324
28	16	14.84	1.162
29	16	17.23	-1.234
30	18	16.67	1.327
31	17	17.83	-0.8265
32	19	16.37	2.633
33	16	16.58	-0.5809
34	13	15.79	-2.792
35	16	16.65	-0.6536
36	12	18	-5.995
37	17	17.02	-0.02383
38	17	17.78	-0.7758
39	17	14.81	2.191
40	16	17.81	-1.806
41	16	16.52	-0.5247
42	14	16.07	-2.067
43	16	16.53	-0.5252
44	13	16.04	-3.039
45	16	16.65	-0.6459
46	14	16.14	-2.145
47	19	17.29	1.706
48	18	16.63	1.369
49	14	16.68	-2.677
50	18	16.55	1.451
51	15	16.08	-1.082
52	17	17.86	-0.8588
53	13	17.05	-4.048
54	19	18.22	0.7774
55	18	17.72	0.2844
56	15	15.94	-0.9368
57	20	17.02	2.983
58	19	16.88	2.117
59	18	17.05	0.9526
60	15	17.48	-2.476
61	20	18.23	1.77
62	17	17.76	-0.7624
63	19	17.04	1.96
64	20	17.33	2.675
65	18	16.6	1.396
66	17	16.55	0.4466
67	18	16.19	1.81
68	17	15.8	1.2
69	20	18.26	1.735
70	16	16.22	-0.2203
71	14	15.96	-1.955
72	15	15.98	-0.9777
73	20	17.25	2.751
74	17	16.14	0.8552
75	17	15.77	1.226
76	18	14.67	3.329
77	20	16.93	3.068
78	16	16.13	-0.1277
79	18	17.79	0.2147
80	15	16.58	-1.583
81	18	18.36	-0.3583
82	20	17.73	2.268
83	14	16.94	-2.941
84	15	16.54	-1.543
85	17	17.78	-0.7834
86	18	17.8	0.2018
87	20	16.94	3.061
88	17	16.56	0.4358
89	16	16.11	-0.109
90	11	15.93	-4.925
91	15	17.19	-2.191
92	18	16.73	1.271
93	16	17.79	-1.791
94	18	17.18	0.8242
95	15	17.1	-2.103
96	17	16.21	0.787
97	19	17.22	1.776
98	16	17.09	-1.085
99	14	17.16	-3.159

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
11	0.583	0.8341	0.417
12	0.4751	0.9501	0.5249
13	0.3629	0.7258	0.6371
14	0.2419	0.4837	0.7581
15	0.5728	0.8544	0.4272
16	0.4622	0.9244	0.5378
17	0.3623	0.7246	0.6377
18	0.2815	0.5629	0.7185
19	0.3154	0.6308	0.6846
20	0.2424	0.4848	0.7576
21	0.2162	0.4323	0.7838
22	0.1615	0.3231	0.8385
23	0.1584	0.3168	0.8416
24	0.1228	0.2455	0.8772
25	0.1614	0.3228	0.8386
26	0.1515	0.3031	0.8485
27	0.9246	0.1508	0.07541
28	0.9243	0.1514	0.0757
29	0.9033	0.1933	0.09667
30	0.8773	0.2454	0.1227
31	0.8642	0.2716	0.1358
32	0.8918	0.2164	0.1082
33	0.858	0.2839	0.142
34	0.8901	0.2197	0.1099
35	0.8644	0.2713	0.1356
36	0.9722	0.05565	0.02782
37	0.9677	0.06452	0.03226
38	0.9582	0.08358	0.04179
39	0.97	0.05994	0.02997
40	0.9693	0.06134	0.03067
41	0.9578	0.08436	0.04218
42	0.9583	0.08336	0.04168
43	0.9512	0.09753	0.04876
44	0.9623	0.07544	0.03772
45	0.9487	0.1026	0.05131
46	0.9439	0.1121	0.05607
47	0.9357	0.1286	0.0643
48	0.9245	0.1509	0.07547
49	0.93	0.14	0.06999
50	0.9219	0.1561	0.07807
51	0.9106	0.1787	0.08935
52	0.8937	0.2126	0.1063
53	0.9497	0.1006	0.05029
54	0.9341	0.1318	0.06592
55	0.9165	0.1671	0.08353
56	0.8928	0.2144	0.1072
57	0.9174	0.1652	0.08261
58	0.922	0.156	0.07801
59	0.9005	0.199	0.09949
60	0.9327	0.1345	0.06726
61	0.921	0.1581	0.07904
62	0.9003	0.1994	0.09968
63	0.891	0.218	0.109
64	0.8885	0.223	0.1115
65	0.8713	0.2574	0.1287
66	0.8385	0.323	0.1615
67	0.8575	0.2849	0.1425
68	0.8252	0.3496	0.1748
69	0.8153	0.3693	0.1847
70	0.7945	0.4111	0.2055
71	0.7618	0.4764	0.2382
72	0.7075	0.5851	0.2925
73	0.7238	0.5523	0.2762
74	0.6715	0.657	0.3285
75	0.6288	0.7423	0.3712
76	0.6679	0.6643	0.3321
77	0.6586	0.6829	0.3414
78	0.6124	0.7752	0.3876
79	0.5241	0.9517	0.4759
80	0.4866	0.9732	0.5134
81	0.4611	0.9222	0.5389
82	0.3789	0.7579	0.6211
83	0.3402	0.6804	0.6598
84	0.2729	0.5458	0.7271
85	0.1987	0.3974	0.8013
86	0.1246	0.2492	0.8754
87	0.1826	0.3651	0.8174
88	0.2048	0.4097	0.7952

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
11 &  0.583 &  0.8341 &  0.417 \tabularnewline
12 &  0.4751 &  0.9501 &  0.5249 \tabularnewline
13 &  0.3629 &  0.7258 &  0.6371 \tabularnewline
14 &  0.2419 &  0.4837 &  0.7581 \tabularnewline
15 &  0.5728 &  0.8544 &  0.4272 \tabularnewline
16 &  0.4622 &  0.9244 &  0.5378 \tabularnewline
17 &  0.3623 &  0.7246 &  0.6377 \tabularnewline
18 &  0.2815 &  0.5629 &  0.7185 \tabularnewline
19 &  0.3154 &  0.6308 &  0.6846 \tabularnewline
20 &  0.2424 &  0.4848 &  0.7576 \tabularnewline
21 &  0.2162 &  0.4323 &  0.7838 \tabularnewline
22 &  0.1615 &  0.3231 &  0.8385 \tabularnewline
23 &  0.1584 &  0.3168 &  0.8416 \tabularnewline
24 &  0.1228 &  0.2455 &  0.8772 \tabularnewline
25 &  0.1614 &  0.3228 &  0.8386 \tabularnewline
26 &  0.1515 &  0.3031 &  0.8485 \tabularnewline
27 &  0.9246 &  0.1508 &  0.07541 \tabularnewline
28 &  0.9243 &  0.1514 &  0.0757 \tabularnewline
29 &  0.9033 &  0.1933 &  0.09667 \tabularnewline
30 &  0.8773 &  0.2454 &  0.1227 \tabularnewline
31 &  0.8642 &  0.2716 &  0.1358 \tabularnewline
32 &  0.8918 &  0.2164 &  0.1082 \tabularnewline
33 &  0.858 &  0.2839 &  0.142 \tabularnewline
34 &  0.8901 &  0.2197 &  0.1099 \tabularnewline
35 &  0.8644 &  0.2713 &  0.1356 \tabularnewline
36 &  0.9722 &  0.05565 &  0.02782 \tabularnewline
37 &  0.9677 &  0.06452 &  0.03226 \tabularnewline
38 &  0.9582 &  0.08358 &  0.04179 \tabularnewline
39 &  0.97 &  0.05994 &  0.02997 \tabularnewline
40 &  0.9693 &  0.06134 &  0.03067 \tabularnewline
41 &  0.9578 &  0.08436 &  0.04218 \tabularnewline
42 &  0.9583 &  0.08336 &  0.04168 \tabularnewline
43 &  0.9512 &  0.09753 &  0.04876 \tabularnewline
44 &  0.9623 &  0.07544 &  0.03772 \tabularnewline
45 &  0.9487 &  0.1026 &  0.05131 \tabularnewline
46 &  0.9439 &  0.1121 &  0.05607 \tabularnewline
47 &  0.9357 &  0.1286 &  0.0643 \tabularnewline
48 &  0.9245 &  0.1509 &  0.07547 \tabularnewline
49 &  0.93 &  0.14 &  0.06999 \tabularnewline
50 &  0.9219 &  0.1561 &  0.07807 \tabularnewline
51 &  0.9106 &  0.1787 &  0.08935 \tabularnewline
52 &  0.8937 &  0.2126 &  0.1063 \tabularnewline
53 &  0.9497 &  0.1006 &  0.05029 \tabularnewline
54 &  0.9341 &  0.1318 &  0.06592 \tabularnewline
55 &  0.9165 &  0.1671 &  0.08353 \tabularnewline
56 &  0.8928 &  0.2144 &  0.1072 \tabularnewline
57 &  0.9174 &  0.1652 &  0.08261 \tabularnewline
58 &  0.922 &  0.156 &  0.07801 \tabularnewline
59 &  0.9005 &  0.199 &  0.09949 \tabularnewline
60 &  0.9327 &  0.1345 &  0.06726 \tabularnewline
61 &  0.921 &  0.1581 &  0.07904 \tabularnewline
62 &  0.9003 &  0.1994 &  0.09968 \tabularnewline
63 &  0.891 &  0.218 &  0.109 \tabularnewline
64 &  0.8885 &  0.223 &  0.1115 \tabularnewline
65 &  0.8713 &  0.2574 &  0.1287 \tabularnewline
66 &  0.8385 &  0.323 &  0.1615 \tabularnewline
67 &  0.8575 &  0.2849 &  0.1425 \tabularnewline
68 &  0.8252 &  0.3496 &  0.1748 \tabularnewline
69 &  0.8153 &  0.3693 &  0.1847 \tabularnewline
70 &  0.7945 &  0.4111 &  0.2055 \tabularnewline
71 &  0.7618 &  0.4764 &  0.2382 \tabularnewline
72 &  0.7075 &  0.5851 &  0.2925 \tabularnewline
73 &  0.7238 &  0.5523 &  0.2762 \tabularnewline
74 &  0.6715 &  0.657 &  0.3285 \tabularnewline
75 &  0.6288 &  0.7423 &  0.3712 \tabularnewline
76 &  0.6679 &  0.6643 &  0.3321 \tabularnewline
77 &  0.6586 &  0.6829 &  0.3414 \tabularnewline
78 &  0.6124 &  0.7752 &  0.3876 \tabularnewline
79 &  0.5241 &  0.9517 &  0.4759 \tabularnewline
80 &  0.4866 &  0.9732 &  0.5134 \tabularnewline
81 &  0.4611 &  0.9222 &  0.5389 \tabularnewline
82 &  0.3789 &  0.7579 &  0.6211 \tabularnewline
83 &  0.3402 &  0.6804 &  0.6598 \tabularnewline
84 &  0.2729 &  0.5458 &  0.7271 \tabularnewline
85 &  0.1987 &  0.3974 &  0.8013 \tabularnewline
86 &  0.1246 &  0.2492 &  0.8754 \tabularnewline
87 &  0.1826 &  0.3651 &  0.8174 \tabularnewline
88 &  0.2048 &  0.4097 &  0.7952 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=304819&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] 0.583[/C][C] 0.8341[/C][C] 0.417[/C][/ROW]
[ROW][C]12[/C][C] 0.4751[/C][C] 0.9501[/C][C] 0.5249[/C][/ROW]
[ROW][C]13[/C][C] 0.3629[/C][C] 0.7258[/C][C] 0.6371[/C][/ROW]
[ROW][C]14[/C][C] 0.2419[/C][C] 0.4837[/C][C] 0.7581[/C][/ROW]
[ROW][C]15[/C][C] 0.5728[/C][C] 0.8544[/C][C] 0.4272[/C][/ROW]
[ROW][C]16[/C][C] 0.4622[/C][C] 0.9244[/C][C] 0.5378[/C][/ROW]
[ROW][C]17[/C][C] 0.3623[/C][C] 0.7246[/C][C] 0.6377[/C][/ROW]
[ROW][C]18[/C][C] 0.2815[/C][C] 0.5629[/C][C] 0.7185[/C][/ROW]
[ROW][C]19[/C][C] 0.3154[/C][C] 0.6308[/C][C] 0.6846[/C][/ROW]
[ROW][C]20[/C][C] 0.2424[/C][C] 0.4848[/C][C] 0.7576[/C][/ROW]
[ROW][C]21[/C][C] 0.2162[/C][C] 0.4323[/C][C] 0.7838[/C][/ROW]
[ROW][C]22[/C][C] 0.1615[/C][C] 0.3231[/C][C] 0.8385[/C][/ROW]
[ROW][C]23[/C][C] 0.1584[/C][C] 0.3168[/C][C] 0.8416[/C][/ROW]
[ROW][C]24[/C][C] 0.1228[/C][C] 0.2455[/C][C] 0.8772[/C][/ROW]
[ROW][C]25[/C][C] 0.1614[/C][C] 0.3228[/C][C] 0.8386[/C][/ROW]
[ROW][C]26[/C][C] 0.1515[/C][C] 0.3031[/C][C] 0.8485[/C][/ROW]
[ROW][C]27[/C][C] 0.9246[/C][C] 0.1508[/C][C] 0.07541[/C][/ROW]
[ROW][C]28[/C][C] 0.9243[/C][C] 0.1514[/C][C] 0.0757[/C][/ROW]
[ROW][C]29[/C][C] 0.9033[/C][C] 0.1933[/C][C] 0.09667[/C][/ROW]
[ROW][C]30[/C][C] 0.8773[/C][C] 0.2454[/C][C] 0.1227[/C][/ROW]
[ROW][C]31[/C][C] 0.8642[/C][C] 0.2716[/C][C] 0.1358[/C][/ROW]
[ROW][C]32[/C][C] 0.8918[/C][C] 0.2164[/C][C] 0.1082[/C][/ROW]
[ROW][C]33[/C][C] 0.858[/C][C] 0.2839[/C][C] 0.142[/C][/ROW]
[ROW][C]34[/C][C] 0.8901[/C][C] 0.2197[/C][C] 0.1099[/C][/ROW]
[ROW][C]35[/C][C] 0.8644[/C][C] 0.2713[/C][C] 0.1356[/C][/ROW]
[ROW][C]36[/C][C] 0.9722[/C][C] 0.05565[/C][C] 0.02782[/C][/ROW]
[ROW][C]37[/C][C] 0.9677[/C][C] 0.06452[/C][C] 0.03226[/C][/ROW]
[ROW][C]38[/C][C] 0.9582[/C][C] 0.08358[/C][C] 0.04179[/C][/ROW]
[ROW][C]39[/C][C] 0.97[/C][C] 0.05994[/C][C] 0.02997[/C][/ROW]
[ROW][C]40[/C][C] 0.9693[/C][C] 0.06134[/C][C] 0.03067[/C][/ROW]
[ROW][C]41[/C][C] 0.9578[/C][C] 0.08436[/C][C] 0.04218[/C][/ROW]
[ROW][C]42[/C][C] 0.9583[/C][C] 0.08336[/C][C] 0.04168[/C][/ROW]
[ROW][C]43[/C][C] 0.9512[/C][C] 0.09753[/C][C] 0.04876[/C][/ROW]
[ROW][C]44[/C][C] 0.9623[/C][C] 0.07544[/C][C] 0.03772[/C][/ROW]
[ROW][C]45[/C][C] 0.9487[/C][C] 0.1026[/C][C] 0.05131[/C][/ROW]
[ROW][C]46[/C][C] 0.9439[/C][C] 0.1121[/C][C] 0.05607[/C][/ROW]
[ROW][C]47[/C][C] 0.9357[/C][C] 0.1286[/C][C] 0.0643[/C][/ROW]
[ROW][C]48[/C][C] 0.9245[/C][C] 0.1509[/C][C] 0.07547[/C][/ROW]
[ROW][C]49[/C][C] 0.93[/C][C] 0.14[/C][C] 0.06999[/C][/ROW]
[ROW][C]50[/C][C] 0.9219[/C][C] 0.1561[/C][C] 0.07807[/C][/ROW]
[ROW][C]51[/C][C] 0.9106[/C][C] 0.1787[/C][C] 0.08935[/C][/ROW]
[ROW][C]52[/C][C] 0.8937[/C][C] 0.2126[/C][C] 0.1063[/C][/ROW]
[ROW][C]53[/C][C] 0.9497[/C][C] 0.1006[/C][C] 0.05029[/C][/ROW]
[ROW][C]54[/C][C] 0.9341[/C][C] 0.1318[/C][C] 0.06592[/C][/ROW]
[ROW][C]55[/C][C] 0.9165[/C][C] 0.1671[/C][C] 0.08353[/C][/ROW]
[ROW][C]56[/C][C] 0.8928[/C][C] 0.2144[/C][C] 0.1072[/C][/ROW]
[ROW][C]57[/C][C] 0.9174[/C][C] 0.1652[/C][C] 0.08261[/C][/ROW]
[ROW][C]58[/C][C] 0.922[/C][C] 0.156[/C][C] 0.07801[/C][/ROW]
[ROW][C]59[/C][C] 0.9005[/C][C] 0.199[/C][C] 0.09949[/C][/ROW]
[ROW][C]60[/C][C] 0.9327[/C][C] 0.1345[/C][C] 0.06726[/C][/ROW]
[ROW][C]61[/C][C] 0.921[/C][C] 0.1581[/C][C] 0.07904[/C][/ROW]
[ROW][C]62[/C][C] 0.9003[/C][C] 0.1994[/C][C] 0.09968[/C][/ROW]
[ROW][C]63[/C][C] 0.891[/C][C] 0.218[/C][C] 0.109[/C][/ROW]
[ROW][C]64[/C][C] 0.8885[/C][C] 0.223[/C][C] 0.1115[/C][/ROW]
[ROW][C]65[/C][C] 0.8713[/C][C] 0.2574[/C][C] 0.1287[/C][/ROW]
[ROW][C]66[/C][C] 0.8385[/C][C] 0.323[/C][C] 0.1615[/C][/ROW]
[ROW][C]67[/C][C] 0.8575[/C][C] 0.2849[/C][C] 0.1425[/C][/ROW]
[ROW][C]68[/C][C] 0.8252[/C][C] 0.3496[/C][C] 0.1748[/C][/ROW]
[ROW][C]69[/C][C] 0.8153[/C][C] 0.3693[/C][C] 0.1847[/C][/ROW]
[ROW][C]70[/C][C] 0.7945[/C][C] 0.4111[/C][C] 0.2055[/C][/ROW]
[ROW][C]71[/C][C] 0.7618[/C][C] 0.4764[/C][C] 0.2382[/C][/ROW]
[ROW][C]72[/C][C] 0.7075[/C][C] 0.5851[/C][C] 0.2925[/C][/ROW]
[ROW][C]73[/C][C] 0.7238[/C][C] 0.5523[/C][C] 0.2762[/C][/ROW]
[ROW][C]74[/C][C] 0.6715[/C][C] 0.657[/C][C] 0.3285[/C][/ROW]
[ROW][C]75[/C][C] 0.6288[/C][C] 0.7423[/C][C] 0.3712[/C][/ROW]
[ROW][C]76[/C][C] 0.6679[/C][C] 0.6643[/C][C] 0.3321[/C][/ROW]
[ROW][C]77[/C][C] 0.6586[/C][C] 0.6829[/C][C] 0.3414[/C][/ROW]
[ROW][C]78[/C][C] 0.6124[/C][C] 0.7752[/C][C] 0.3876[/C][/ROW]
[ROW][C]79[/C][C] 0.5241[/C][C] 0.9517[/C][C] 0.4759[/C][/ROW]
[ROW][C]80[/C][C] 0.4866[/C][C] 0.9732[/C][C] 0.5134[/C][/ROW]
[ROW][C]81[/C][C] 0.4611[/C][C] 0.9222[/C][C] 0.5389[/C][/ROW]
[ROW][C]82[/C][C] 0.3789[/C][C] 0.7579[/C][C] 0.6211[/C][/ROW]
[ROW][C]83[/C][C] 0.3402[/C][C] 0.6804[/C][C] 0.6598[/C][/ROW]
[ROW][C]84[/C][C] 0.2729[/C][C] 0.5458[/C][C] 0.7271[/C][/ROW]
[ROW][C]85[/C][C] 0.1987[/C][C] 0.3974[/C][C] 0.8013[/C][/ROW]
[ROW][C]86[/C][C] 0.1246[/C][C] 0.2492[/C][C] 0.8754[/C][/ROW]
[ROW][C]87[/C][C] 0.1826[/C][C] 0.3651[/C][C] 0.8174[/C][/ROW]
[ROW][C]88[/C][C] 0.2048[/C][C] 0.4097[/C][C] 0.7952[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=304819&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=304819&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	0.583	0.8341	0.417
12	0.4751	0.9501	0.5249
13	0.3629	0.7258	0.6371
14	0.2419	0.4837	0.7581
15	0.5728	0.8544	0.4272
16	0.4622	0.9244	0.5378
17	0.3623	0.7246	0.6377
18	0.2815	0.5629	0.7185
19	0.3154	0.6308	0.6846
20	0.2424	0.4848	0.7576
21	0.2162	0.4323	0.7838
22	0.1615	0.3231	0.8385
23	0.1584	0.3168	0.8416
24	0.1228	0.2455	0.8772
25	0.1614	0.3228	0.8386
26	0.1515	0.3031	0.8485
27	0.9246	0.1508	0.07541
28	0.9243	0.1514	0.0757
29	0.9033	0.1933	0.09667
30	0.8773	0.2454	0.1227
31	0.8642	0.2716	0.1358
32	0.8918	0.2164	0.1082
33	0.858	0.2839	0.142
34	0.8901	0.2197	0.1099
35	0.8644	0.2713	0.1356
36	0.9722	0.05565	0.02782
37	0.9677	0.06452	0.03226
38	0.9582	0.08358	0.04179
39	0.97	0.05994	0.02997
40	0.9693	0.06134	0.03067
41	0.9578	0.08436	0.04218
42	0.9583	0.08336	0.04168
43	0.9512	0.09753	0.04876
44	0.9623	0.07544	0.03772
45	0.9487	0.1026	0.05131
46	0.9439	0.1121	0.05607
47	0.9357	0.1286	0.0643
48	0.9245	0.1509	0.07547
49	0.93	0.14	0.06999
50	0.9219	0.1561	0.07807
51	0.9106	0.1787	0.08935
52	0.8937	0.2126	0.1063
53	0.9497	0.1006	0.05029
54	0.9341	0.1318	0.06592
55	0.9165	0.1671	0.08353
56	0.8928	0.2144	0.1072
57	0.9174	0.1652	0.08261
58	0.922	0.156	0.07801
59	0.9005	0.199	0.09949
60	0.9327	0.1345	0.06726
61	0.921	0.1581	0.07904
62	0.9003	0.1994	0.09968
63	0.891	0.218	0.109
64	0.8885	0.223	0.1115
65	0.8713	0.2574	0.1287
66	0.8385	0.323	0.1615
67	0.8575	0.2849	0.1425
68	0.8252	0.3496	0.1748
69	0.8153	0.3693	0.1847
70	0.7945	0.4111	0.2055
71	0.7618	0.4764	0.2382
72	0.7075	0.5851	0.2925
73	0.7238	0.5523	0.2762
74	0.6715	0.657	0.3285
75	0.6288	0.7423	0.3712
76	0.6679	0.6643	0.3321
77	0.6586	0.6829	0.3414
78	0.6124	0.7752	0.3876
79	0.5241	0.9517	0.4759
80	0.4866	0.9732	0.5134
81	0.4611	0.9222	0.5389
82	0.3789	0.7579	0.6211
83	0.3402	0.6804	0.6598
84	0.2729	0.5458	0.7271
85	0.1987	0.3974	0.8013
86	0.1246	0.2492	0.8754
87	0.1826	0.3651	0.8174
88	0.2048	0.4097	0.7952

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	9	0.115385	NOK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 &  0 & OK \tabularnewline
5% type I error level & 0 & 0 & OK \tabularnewline
10% type I error level & 9 & 0.115385 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=304819&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]9[/C][C]0.115385[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=304819&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=304819&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	9	0.115385	NOK

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted RESET test data: mylm RESET = 2.8313, df1 = 2, df2 = 89, p-value = 0.06425
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 0.84467, df1 = 14, df2 = 77, p-value = 0.6196
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 0.79247, df1 = 2, df2 = 89, p-value = 0.4559

\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 = 2.8313, df1 = 2, df2 = 89, p-value = 0.06425
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of regressors \tabularnewline
> reset_test_regressors
	RESET test
data:  mylm
RESET = 0.84467, df1 = 14, df2 = 77, p-value = 0.6196
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of principal components \tabularnewline
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 0.79247, df1 = 2, df2 = 89, p-value = 0.4559
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=304819&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 = 2.8313, df1 = 2, df2 = 89, p-value = 0.06425
[/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 = 0.84467, df1 = 14, df2 = 77, p-value = 0.6196
[/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 = 0.79247, df1 = 2, df2 = 89, p-value = 0.4559
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=304819&T=7

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=304819&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 = 2.8313, df1 = 2, df2 = 89, p-value = 0.06425
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 0.84467, df1 = 14, df2 = 77, p-value = 0.6196
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 0.79247, df1 = 2, df2 = 89, p-value = 0.4559

Variance Inflation Factors (Multicollinearity)
> vif Bevr_Leeftijd TVDC SKEOU1 SKEOU2 SKEOU4 1.046149 1.574638 1.189404 1.440390 1.123632 SKEOU5 SKEOU6 1.023528 1.022214

\begin{tabular}{lllllllll}
\hline
Variance Inflation Factors (Multicollinearity) \tabularnewline
> vif
Bevr_Leeftijd          TVDC        SKEOU1        SKEOU2        SKEOU4 
     1.046149      1.574638      1.189404      1.440390      1.123632 
       SKEOU5        SKEOU6 
     1.023528      1.022214 
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=304819&T=8

[TABLE]
[ROW][C]Variance Inflation Factors (Multicollinearity)[/C][/ROW]
[ROW][C]> vif
Bevr_Leeftijd          TVDC        SKEOU1        SKEOU2        SKEOU4 
     1.046149      1.574638      1.189404      1.440390      1.123632 
       SKEOU5        SKEOU6 
     1.023528      1.022214 
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=304819&T=8

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=304819&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 TVDC SKEOU1 SKEOU2 SKEOU4 1.046149 1.574638 1.189404 1.440390 1.123632 SKEOU5 SKEOU6 1.023528 1.022214

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 = ; 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