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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationSun, 11 Dec 2016 19:07:27 +0100
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2016/Dec/11/t1481479670e9hx9yp9u89xrxh.htm/, Retrieved Fri, 01 Nov 2024 03:48:19 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=298845, Retrieved Fri, 01 Nov 2024 03:48:19 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact113
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [] [2016-12-11 18:07:27] [c4ef4c70482680cab119953cba46aca4] [Current]
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Dataseries X:
13	3	4	3	4
16	5	5	5	4
17	5	4	4	4
16	5	5	5	5
17	5	4	3	3
17	5	5	5	4
16	5	5	5	5
14	5	5	4	4
16	4	4	3	4
17	3	4	4	3
16	4	4	4	4
16	5	5	4	5
16	4	5	5	4
15	4	5	4	4
16	5	5	4	5
16	5	5	4	3
15	5	5	4	5
17	5	5	5	5
13	5	5	4	5
17	4	5	4	3
14	4	4	4	4
14	5	5	4	4
18	4	4	5	3
17	5	4	4	4
16	5	5	5	5
15	5	5	5	4
15	2	2	1	2
15	4	4	3	5
13	4	5	3	4
17	5	5	4	4
11	5	5	3	4
14	5	5	5	4
13	4	4	4	4
17	4	5	3	1
16	4	4	4	4
17	4	4	3	1
16	4	5	4	4
16	5	4	4	4
16	4	5	4	4
15	4	5	4	3
12	4	4	4	4
17	4	3	3	4
14	4	4	4	4
14	2	4	4	3
16	4	5	4	3
15	4	3	3	4
16	5	5	5	4
14	4	5	4	5
15	4	3	3	4
17	5	5	3	5
10	5	4	3	3
17	5	4	4	4
20	2	5	4	2
17	5	4	5	5
18	5	5	4	4
17	5	4	4	2
14	4	4	4	3
17	5	5	5	4
16	5	4	5	4
18	4	4	4	3
18	5	5	5	5
16	5	5	5	2
15	5	5	5	4
13	5	5	5	5
16	5	5	4	4
12	4	4	5	4
16	5	5	4	4
16	5	4	4	4
16	5	5	5	5
14	5	4	4	3
15	4	4	3	3
14	4	4	3	4
15	5	5	5	5
15	5	5	3	4
16	4	5	4	4
11	5	4	5	4
18	5	5	5	5
11	4	4	4	4
18	4	4	5	5
15	4	4	4	3
19	5	4	5	4
17	5	5	5	5
14	4	4	4	2
13	5	4	4	2
17	5	4	4	4
14	5	4	5	4
19	5	5	5	5
14	5	3	5	4
16	5	4	4	3
16	3	3	3	2
15	3	4	4	4
12	4	5	4	5
17	3	5	3	5
18	5	5	4	4
15	5	4	4	2
18	5	4	4	4
15	5	5	5	4
16	4	4	4	4
16	2	4	5	3




Summary of computational transaction
Raw Input view raw input (R code)
Raw Outputview raw output of R engine
Computing time8 seconds
R ServerBig Analytics Cloud Computing Center

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input view raw input (R code)  \tabularnewline
Raw Outputview raw output of R engine  \tabularnewline
Computing time8 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=298845&T=0

[TABLE]
[ROW]
Summary of computational transaction[/C][/ROW] [ROW]Raw Input[/C] view raw input (R code) [/C][/ROW] [ROW]Raw Output[/C]view raw output of R engine [/C][/ROW] [ROW]Computing time[/C]8 seconds[/C][/ROW] [ROW]R Server[/C]Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=298845&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=298845&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 Outputview raw output of R engine
Computing time8 seconds
R ServerBig Analytics Cloud Computing Center







Multiple Linear Regression - Estimated Regression Equation
TVDC[t] = + 13.5644 -0.0918125ITH1[t] + 0.325818ITH2[t] + 0.414529ITH3[t] -0.204403ITH4[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
TVDC[t] =  +  13.5644 -0.0918125ITH1[t] +  0.325818ITH2[t] +  0.414529ITH3[t] -0.204403ITH4[t]  + e[t] \tabularnewline
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=298845&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]TVDC[t] =  +  13.5644 -0.0918125ITH1[t] +  0.325818ITH2[t] +  0.414529ITH3[t] -0.204403ITH4[t]  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=298845&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=298845&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] = + 13.5644 -0.0918125ITH1[t] + 0.325818ITH2[t] + 0.414529ITH3[t] -0.204403ITH4[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)+13.56 1.51+8.9850e+00 2.643e-14 1.321e-14
ITH1-0.09181 0.2829-3.2460e-01 0.7462 0.3731
ITH2+0.3258 0.3408+9.5610e-01 0.3415 0.1707
ITH3+0.4145 0.2901+1.4290e+00 0.1563 0.07814
ITH4-0.2044 0.2271-8.9990e-01 0.3705 0.1852

\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) & +13.56 &  1.51 & +8.9850e+00 &  2.643e-14 &  1.321e-14 \tabularnewline
ITH1 & -0.09181 &  0.2829 & -3.2460e-01 &  0.7462 &  0.3731 \tabularnewline
ITH2 & +0.3258 &  0.3408 & +9.5610e-01 &  0.3415 &  0.1707 \tabularnewline
ITH3 & +0.4145 &  0.2901 & +1.4290e+00 &  0.1563 &  0.07814 \tabularnewline
ITH4 & -0.2044 &  0.2271 & -8.9990e-01 &  0.3705 &  0.1852 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=298845&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]+13.56[/C][C] 1.51[/C][C]+8.9850e+00[/C][C] 2.643e-14[/C][C] 1.321e-14[/C][/ROW]
[ROW][C]ITH1[/C][C]-0.09181[/C][C] 0.2829[/C][C]-3.2460e-01[/C][C] 0.7462[/C][C] 0.3731[/C][/ROW]
[ROW][C]ITH2[/C][C]+0.3258[/C][C] 0.3408[/C][C]+9.5610e-01[/C][C] 0.3415[/C][C] 0.1707[/C][/ROW]
[ROW][C]ITH3[/C][C]+0.4145[/C][C] 0.2901[/C][C]+1.4290e+00[/C][C] 0.1563[/C][C] 0.07814[/C][/ROW]
[ROW][C]ITH4[/C][C]-0.2044[/C][C] 0.2271[/C][C]-8.9990e-01[/C][C] 0.3705[/C][C] 0.1852[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=298845&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=298845&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
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)+13.56 1.51+8.9850e+00 2.643e-14 1.321e-14
ITH1-0.09181 0.2829-3.2460e-01 0.7462 0.3731
ITH2+0.3258 0.3408+9.5610e-01 0.3415 0.1707
ITH3+0.4145 0.2901+1.4290e+00 0.1563 0.07814
ITH4-0.2044 0.2271-8.9990e-01 0.3705 0.1852







Multiple Linear Regression - Regression Statistics
Multiple R 0.206
R-squared 0.04245
Adjusted R-squared 0.001702
F-TEST (value) 1.042
F-TEST (DF numerator)4
F-TEST (DF denominator)94
p-value 0.39
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation 1.868
Sum Squared Residuals 328.2

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.206 \tabularnewline
R-squared &  0.04245 \tabularnewline
Adjusted R-squared &  0.001702 \tabularnewline
F-TEST (value) &  1.042 \tabularnewline
F-TEST (DF numerator) & 4 \tabularnewline
F-TEST (DF denominator) & 94 \tabularnewline
p-value &  0.39 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  1.868 \tabularnewline
Sum Squared Residuals &  328.2 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=298845&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.206[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.04245[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.001702[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 1.042[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]4[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]94[/C][/ROW]
[ROW][C]p-value[/C][C] 0.39[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C] 1.868[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 328.2[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=298845&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=298845&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 0.206
R-squared 0.04245
Adjusted R-squared 0.001702
F-TEST (value) 1.042
F-TEST (DF numerator)4
F-TEST (DF denominator)94
p-value 0.39
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation 1.868
Sum Squared Residuals 328.2







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1 13 15.02-2.018
2 16 15.99 0.0105
3 17 15.25 1.751
4 16 15.79 0.2149
5 17 15.04 1.961
6 17 15.99 1.01
7 16 15.79 0.2149
8 14 15.57-1.575
9 16 14.93 1.074
10 17 15.64 1.363
11 16 15.34 0.659
12 16 15.37 0.6294
13 16 16.08-0.08132
14 15 15.67-0.6668
15 16 15.37 0.6294
16 16 15.78 0.2206
17 15 15.37-0.3706
18 17 15.79 1.215
19 13 15.37-2.371
20 17 15.87 1.129
21 14 15.34-1.341
22 14 15.57-1.575
23 18 15.96 2.04
24 17 15.25 1.751
25 16 15.79 0.2149
26 15 15.99-0.9895
27 15 14.04 0.9618
28 15 14.72 0.278
29 13 15.25-2.252
30 17 15.57 1.425
31 11 15.16-4.16
32 14 15.99-1.99
33 13 15.34-2.341
34 17 15.87 1.135
35 16 15.34 0.659
36 17 15.54 1.46
37 16 15.67 0.3332
38 16 15.25 0.7508
39 16 15.67 0.3332
40 15 15.87-0.8712
41 12 15.34-3.341
42 17 14.6 2.399
43 14 15.34-1.341
44 14 15.73-1.729
45 16 15.87 0.1288
46 15 14.6 0.3994
47 16 15.99 0.0105
48 14 15.46-1.462
49 15 14.6 0.3994
50 17 14.96 2.044
51 10 15.04-5.039
52 17 15.25 1.751
53 20 16.26 3.741
54 17 15.46 1.541
55 18 15.57 2.425
56 17 15.66 1.342
57 14 15.55-1.545
58 17 15.99 1.01
59 16 15.66 0.3363
60 18 15.55 2.455
61 18 15.79 2.215
62 16 16.4-0.3983
63 15 15.99-0.9895
64 13 15.79-2.785
65 16 15.57 0.425
66 12 15.76-3.756
67 16 15.57 0.425
68 16 15.25 0.7508
69 16 15.79 0.2149
70 14 15.45-1.454
71 15 15.13-0.1308
72 14 14.93-0.9264
73 15 15.79-0.7851
74 15 15.16-0.1604
75 16 15.67 0.3332
76 11 15.66-4.664
77 18 15.79 2.215
78 11 15.34-4.341
79 18 15.55 2.449
80 15 15.55-0.5454
81 19 15.66 3.336
82 17 15.79 1.215
83 14 15.75-1.75
84 13 15.66-2.658
85 17 15.25 1.751
86 14 15.66-1.664
87 19 15.79 3.215
88 14 15.34-1.338
89 16 15.45 0.5464
90 16 15.1 0.8988
91 15 15.43-0.4328
92 12 15.46-3.462
93 17 15.14 1.86
94 18 15.57 2.425
95 15 15.66-0.658
96 18 15.25 2.751
97 15 15.99-0.9895
98 16 15.34 0.659
99 16 16.14-0.1435

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 &  13 &  15.02 & -2.018 \tabularnewline
2 &  16 &  15.99 &  0.0105 \tabularnewline
3 &  17 &  15.25 &  1.751 \tabularnewline
4 &  16 &  15.79 &  0.2149 \tabularnewline
5 &  17 &  15.04 &  1.961 \tabularnewline
6 &  17 &  15.99 &  1.01 \tabularnewline
7 &  16 &  15.79 &  0.2149 \tabularnewline
8 &  14 &  15.57 & -1.575 \tabularnewline
9 &  16 &  14.93 &  1.074 \tabularnewline
10 &  17 &  15.64 &  1.363 \tabularnewline
11 &  16 &  15.34 &  0.659 \tabularnewline
12 &  16 &  15.37 &  0.6294 \tabularnewline
13 &  16 &  16.08 & -0.08132 \tabularnewline
14 &  15 &  15.67 & -0.6668 \tabularnewline
15 &  16 &  15.37 &  0.6294 \tabularnewline
16 &  16 &  15.78 &  0.2206 \tabularnewline
17 &  15 &  15.37 & -0.3706 \tabularnewline
18 &  17 &  15.79 &  1.215 \tabularnewline
19 &  13 &  15.37 & -2.371 \tabularnewline
20 &  17 &  15.87 &  1.129 \tabularnewline
21 &  14 &  15.34 & -1.341 \tabularnewline
22 &  14 &  15.57 & -1.575 \tabularnewline
23 &  18 &  15.96 &  2.04 \tabularnewline
24 &  17 &  15.25 &  1.751 \tabularnewline
25 &  16 &  15.79 &  0.2149 \tabularnewline
26 &  15 &  15.99 & -0.9895 \tabularnewline
27 &  15 &  14.04 &  0.9618 \tabularnewline
28 &  15 &  14.72 &  0.278 \tabularnewline
29 &  13 &  15.25 & -2.252 \tabularnewline
30 &  17 &  15.57 &  1.425 \tabularnewline
31 &  11 &  15.16 & -4.16 \tabularnewline
32 &  14 &  15.99 & -1.99 \tabularnewline
33 &  13 &  15.34 & -2.341 \tabularnewline
34 &  17 &  15.87 &  1.135 \tabularnewline
35 &  16 &  15.34 &  0.659 \tabularnewline
36 &  17 &  15.54 &  1.46 \tabularnewline
37 &  16 &  15.67 &  0.3332 \tabularnewline
38 &  16 &  15.25 &  0.7508 \tabularnewline
39 &  16 &  15.67 &  0.3332 \tabularnewline
40 &  15 &  15.87 & -0.8712 \tabularnewline
41 &  12 &  15.34 & -3.341 \tabularnewline
42 &  17 &  14.6 &  2.399 \tabularnewline
43 &  14 &  15.34 & -1.341 \tabularnewline
44 &  14 &  15.73 & -1.729 \tabularnewline
45 &  16 &  15.87 &  0.1288 \tabularnewline
46 &  15 &  14.6 &  0.3994 \tabularnewline
47 &  16 &  15.99 &  0.0105 \tabularnewline
48 &  14 &  15.46 & -1.462 \tabularnewline
49 &  15 &  14.6 &  0.3994 \tabularnewline
50 &  17 &  14.96 &  2.044 \tabularnewline
51 &  10 &  15.04 & -5.039 \tabularnewline
52 &  17 &  15.25 &  1.751 \tabularnewline
53 &  20 &  16.26 &  3.741 \tabularnewline
54 &  17 &  15.46 &  1.541 \tabularnewline
55 &  18 &  15.57 &  2.425 \tabularnewline
56 &  17 &  15.66 &  1.342 \tabularnewline
57 &  14 &  15.55 & -1.545 \tabularnewline
58 &  17 &  15.99 &  1.01 \tabularnewline
59 &  16 &  15.66 &  0.3363 \tabularnewline
60 &  18 &  15.55 &  2.455 \tabularnewline
61 &  18 &  15.79 &  2.215 \tabularnewline
62 &  16 &  16.4 & -0.3983 \tabularnewline
63 &  15 &  15.99 & -0.9895 \tabularnewline
64 &  13 &  15.79 & -2.785 \tabularnewline
65 &  16 &  15.57 &  0.425 \tabularnewline
66 &  12 &  15.76 & -3.756 \tabularnewline
67 &  16 &  15.57 &  0.425 \tabularnewline
68 &  16 &  15.25 &  0.7508 \tabularnewline
69 &  16 &  15.79 &  0.2149 \tabularnewline
70 &  14 &  15.45 & -1.454 \tabularnewline
71 &  15 &  15.13 & -0.1308 \tabularnewline
72 &  14 &  14.93 & -0.9264 \tabularnewline
73 &  15 &  15.79 & -0.7851 \tabularnewline
74 &  15 &  15.16 & -0.1604 \tabularnewline
75 &  16 &  15.67 &  0.3332 \tabularnewline
76 &  11 &  15.66 & -4.664 \tabularnewline
77 &  18 &  15.79 &  2.215 \tabularnewline
78 &  11 &  15.34 & -4.341 \tabularnewline
79 &  18 &  15.55 &  2.449 \tabularnewline
80 &  15 &  15.55 & -0.5454 \tabularnewline
81 &  19 &  15.66 &  3.336 \tabularnewline
82 &  17 &  15.79 &  1.215 \tabularnewline
83 &  14 &  15.75 & -1.75 \tabularnewline
84 &  13 &  15.66 & -2.658 \tabularnewline
85 &  17 &  15.25 &  1.751 \tabularnewline
86 &  14 &  15.66 & -1.664 \tabularnewline
87 &  19 &  15.79 &  3.215 \tabularnewline
88 &  14 &  15.34 & -1.338 \tabularnewline
89 &  16 &  15.45 &  0.5464 \tabularnewline
90 &  16 &  15.1 &  0.8988 \tabularnewline
91 &  15 &  15.43 & -0.4328 \tabularnewline
92 &  12 &  15.46 & -3.462 \tabularnewline
93 &  17 &  15.14 &  1.86 \tabularnewline
94 &  18 &  15.57 &  2.425 \tabularnewline
95 &  15 &  15.66 & -0.658 \tabularnewline
96 &  18 &  15.25 &  2.751 \tabularnewline
97 &  15 &  15.99 & -0.9895 \tabularnewline
98 &  16 &  15.34 &  0.659 \tabularnewline
99 &  16 &  16.14 & -0.1435 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=298845&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] 15.02[/C][C]-2.018[/C][/ROW]
[ROW][C]2[/C][C] 16[/C][C] 15.99[/C][C] 0.0105[/C][/ROW]
[ROW][C]3[/C][C] 17[/C][C] 15.25[/C][C] 1.751[/C][/ROW]
[ROW][C]4[/C][C] 16[/C][C] 15.79[/C][C] 0.2149[/C][/ROW]
[ROW][C]5[/C][C] 17[/C][C] 15.04[/C][C] 1.961[/C][/ROW]
[ROW][C]6[/C][C] 17[/C][C] 15.99[/C][C] 1.01[/C][/ROW]
[ROW][C]7[/C][C] 16[/C][C] 15.79[/C][C] 0.2149[/C][/ROW]
[ROW][C]8[/C][C] 14[/C][C] 15.57[/C][C]-1.575[/C][/ROW]
[ROW][C]9[/C][C] 16[/C][C] 14.93[/C][C] 1.074[/C][/ROW]
[ROW][C]10[/C][C] 17[/C][C] 15.64[/C][C] 1.363[/C][/ROW]
[ROW][C]11[/C][C] 16[/C][C] 15.34[/C][C] 0.659[/C][/ROW]
[ROW][C]12[/C][C] 16[/C][C] 15.37[/C][C] 0.6294[/C][/ROW]
[ROW][C]13[/C][C] 16[/C][C] 16.08[/C][C]-0.08132[/C][/ROW]
[ROW][C]14[/C][C] 15[/C][C] 15.67[/C][C]-0.6668[/C][/ROW]
[ROW][C]15[/C][C] 16[/C][C] 15.37[/C][C] 0.6294[/C][/ROW]
[ROW][C]16[/C][C] 16[/C][C] 15.78[/C][C] 0.2206[/C][/ROW]
[ROW][C]17[/C][C] 15[/C][C] 15.37[/C][C]-0.3706[/C][/ROW]
[ROW][C]18[/C][C] 17[/C][C] 15.79[/C][C] 1.215[/C][/ROW]
[ROW][C]19[/C][C] 13[/C][C] 15.37[/C][C]-2.371[/C][/ROW]
[ROW][C]20[/C][C] 17[/C][C] 15.87[/C][C] 1.129[/C][/ROW]
[ROW][C]21[/C][C] 14[/C][C] 15.34[/C][C]-1.341[/C][/ROW]
[ROW][C]22[/C][C] 14[/C][C] 15.57[/C][C]-1.575[/C][/ROW]
[ROW][C]23[/C][C] 18[/C][C] 15.96[/C][C] 2.04[/C][/ROW]
[ROW][C]24[/C][C] 17[/C][C] 15.25[/C][C] 1.751[/C][/ROW]
[ROW][C]25[/C][C] 16[/C][C] 15.79[/C][C] 0.2149[/C][/ROW]
[ROW][C]26[/C][C] 15[/C][C] 15.99[/C][C]-0.9895[/C][/ROW]
[ROW][C]27[/C][C] 15[/C][C] 14.04[/C][C] 0.9618[/C][/ROW]
[ROW][C]28[/C][C] 15[/C][C] 14.72[/C][C] 0.278[/C][/ROW]
[ROW][C]29[/C][C] 13[/C][C] 15.25[/C][C]-2.252[/C][/ROW]
[ROW][C]30[/C][C] 17[/C][C] 15.57[/C][C] 1.425[/C][/ROW]
[ROW][C]31[/C][C] 11[/C][C] 15.16[/C][C]-4.16[/C][/ROW]
[ROW][C]32[/C][C] 14[/C][C] 15.99[/C][C]-1.99[/C][/ROW]
[ROW][C]33[/C][C] 13[/C][C] 15.34[/C][C]-2.341[/C][/ROW]
[ROW][C]34[/C][C] 17[/C][C] 15.87[/C][C] 1.135[/C][/ROW]
[ROW][C]35[/C][C] 16[/C][C] 15.34[/C][C] 0.659[/C][/ROW]
[ROW][C]36[/C][C] 17[/C][C] 15.54[/C][C] 1.46[/C][/ROW]
[ROW][C]37[/C][C] 16[/C][C] 15.67[/C][C] 0.3332[/C][/ROW]
[ROW][C]38[/C][C] 16[/C][C] 15.25[/C][C] 0.7508[/C][/ROW]
[ROW][C]39[/C][C] 16[/C][C] 15.67[/C][C] 0.3332[/C][/ROW]
[ROW][C]40[/C][C] 15[/C][C] 15.87[/C][C]-0.8712[/C][/ROW]
[ROW][C]41[/C][C] 12[/C][C] 15.34[/C][C]-3.341[/C][/ROW]
[ROW][C]42[/C][C] 17[/C][C] 14.6[/C][C] 2.399[/C][/ROW]
[ROW][C]43[/C][C] 14[/C][C] 15.34[/C][C]-1.341[/C][/ROW]
[ROW][C]44[/C][C] 14[/C][C] 15.73[/C][C]-1.729[/C][/ROW]
[ROW][C]45[/C][C] 16[/C][C] 15.87[/C][C] 0.1288[/C][/ROW]
[ROW][C]46[/C][C] 15[/C][C] 14.6[/C][C] 0.3994[/C][/ROW]
[ROW][C]47[/C][C] 16[/C][C] 15.99[/C][C] 0.0105[/C][/ROW]
[ROW][C]48[/C][C] 14[/C][C] 15.46[/C][C]-1.462[/C][/ROW]
[ROW][C]49[/C][C] 15[/C][C] 14.6[/C][C] 0.3994[/C][/ROW]
[ROW][C]50[/C][C] 17[/C][C] 14.96[/C][C] 2.044[/C][/ROW]
[ROW][C]51[/C][C] 10[/C][C] 15.04[/C][C]-5.039[/C][/ROW]
[ROW][C]52[/C][C] 17[/C][C] 15.25[/C][C] 1.751[/C][/ROW]
[ROW][C]53[/C][C] 20[/C][C] 16.26[/C][C] 3.741[/C][/ROW]
[ROW][C]54[/C][C] 17[/C][C] 15.46[/C][C] 1.541[/C][/ROW]
[ROW][C]55[/C][C] 18[/C][C] 15.57[/C][C] 2.425[/C][/ROW]
[ROW][C]56[/C][C] 17[/C][C] 15.66[/C][C] 1.342[/C][/ROW]
[ROW][C]57[/C][C] 14[/C][C] 15.55[/C][C]-1.545[/C][/ROW]
[ROW][C]58[/C][C] 17[/C][C] 15.99[/C][C] 1.01[/C][/ROW]
[ROW][C]59[/C][C] 16[/C][C] 15.66[/C][C] 0.3363[/C][/ROW]
[ROW][C]60[/C][C] 18[/C][C] 15.55[/C][C] 2.455[/C][/ROW]
[ROW][C]61[/C][C] 18[/C][C] 15.79[/C][C] 2.215[/C][/ROW]
[ROW][C]62[/C][C] 16[/C][C] 16.4[/C][C]-0.3983[/C][/ROW]
[ROW][C]63[/C][C] 15[/C][C] 15.99[/C][C]-0.9895[/C][/ROW]
[ROW][C]64[/C][C] 13[/C][C] 15.79[/C][C]-2.785[/C][/ROW]
[ROW][C]65[/C][C] 16[/C][C] 15.57[/C][C] 0.425[/C][/ROW]
[ROW][C]66[/C][C] 12[/C][C] 15.76[/C][C]-3.756[/C][/ROW]
[ROW][C]67[/C][C] 16[/C][C] 15.57[/C][C] 0.425[/C][/ROW]
[ROW][C]68[/C][C] 16[/C][C] 15.25[/C][C] 0.7508[/C][/ROW]
[ROW][C]69[/C][C] 16[/C][C] 15.79[/C][C] 0.2149[/C][/ROW]
[ROW][C]70[/C][C] 14[/C][C] 15.45[/C][C]-1.454[/C][/ROW]
[ROW][C]71[/C][C] 15[/C][C] 15.13[/C][C]-0.1308[/C][/ROW]
[ROW][C]72[/C][C] 14[/C][C] 14.93[/C][C]-0.9264[/C][/ROW]
[ROW][C]73[/C][C] 15[/C][C] 15.79[/C][C]-0.7851[/C][/ROW]
[ROW][C]74[/C][C] 15[/C][C] 15.16[/C][C]-0.1604[/C][/ROW]
[ROW][C]75[/C][C] 16[/C][C] 15.67[/C][C] 0.3332[/C][/ROW]
[ROW][C]76[/C][C] 11[/C][C] 15.66[/C][C]-4.664[/C][/ROW]
[ROW][C]77[/C][C] 18[/C][C] 15.79[/C][C] 2.215[/C][/ROW]
[ROW][C]78[/C][C] 11[/C][C] 15.34[/C][C]-4.341[/C][/ROW]
[ROW][C]79[/C][C] 18[/C][C] 15.55[/C][C] 2.449[/C][/ROW]
[ROW][C]80[/C][C] 15[/C][C] 15.55[/C][C]-0.5454[/C][/ROW]
[ROW][C]81[/C][C] 19[/C][C] 15.66[/C][C] 3.336[/C][/ROW]
[ROW][C]82[/C][C] 17[/C][C] 15.79[/C][C] 1.215[/C][/ROW]
[ROW][C]83[/C][C] 14[/C][C] 15.75[/C][C]-1.75[/C][/ROW]
[ROW][C]84[/C][C] 13[/C][C] 15.66[/C][C]-2.658[/C][/ROW]
[ROW][C]85[/C][C] 17[/C][C] 15.25[/C][C] 1.751[/C][/ROW]
[ROW][C]86[/C][C] 14[/C][C] 15.66[/C][C]-1.664[/C][/ROW]
[ROW][C]87[/C][C] 19[/C][C] 15.79[/C][C] 3.215[/C][/ROW]
[ROW][C]88[/C][C] 14[/C][C] 15.34[/C][C]-1.338[/C][/ROW]
[ROW][C]89[/C][C] 16[/C][C] 15.45[/C][C] 0.5464[/C][/ROW]
[ROW][C]90[/C][C] 16[/C][C] 15.1[/C][C] 0.8988[/C][/ROW]
[ROW][C]91[/C][C] 15[/C][C] 15.43[/C][C]-0.4328[/C][/ROW]
[ROW][C]92[/C][C] 12[/C][C] 15.46[/C][C]-3.462[/C][/ROW]
[ROW][C]93[/C][C] 17[/C][C] 15.14[/C][C] 1.86[/C][/ROW]
[ROW][C]94[/C][C] 18[/C][C] 15.57[/C][C] 2.425[/C][/ROW]
[ROW][C]95[/C][C] 15[/C][C] 15.66[/C][C]-0.658[/C][/ROW]
[ROW][C]96[/C][C] 18[/C][C] 15.25[/C][C] 2.751[/C][/ROW]
[ROW][C]97[/C][C] 15[/C][C] 15.99[/C][C]-0.9895[/C][/ROW]
[ROW][C]98[/C][C] 16[/C][C] 15.34[/C][C] 0.659[/C][/ROW]
[ROW][C]99[/C][C] 16[/C][C] 16.14[/C][C]-0.1435[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=298845&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=298845&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 IndexActualsInterpolationForecastResidualsPrediction Error
1 13 15.02-2.018
2 16 15.99 0.0105
3 17 15.25 1.751
4 16 15.79 0.2149
5 17 15.04 1.961
6 17 15.99 1.01
7 16 15.79 0.2149
8 14 15.57-1.575
9 16 14.93 1.074
10 17 15.64 1.363
11 16 15.34 0.659
12 16 15.37 0.6294
13 16 16.08-0.08132
14 15 15.67-0.6668
15 16 15.37 0.6294
16 16 15.78 0.2206
17 15 15.37-0.3706
18 17 15.79 1.215
19 13 15.37-2.371
20 17 15.87 1.129
21 14 15.34-1.341
22 14 15.57-1.575
23 18 15.96 2.04
24 17 15.25 1.751
25 16 15.79 0.2149
26 15 15.99-0.9895
27 15 14.04 0.9618
28 15 14.72 0.278
29 13 15.25-2.252
30 17 15.57 1.425
31 11 15.16-4.16
32 14 15.99-1.99
33 13 15.34-2.341
34 17 15.87 1.135
35 16 15.34 0.659
36 17 15.54 1.46
37 16 15.67 0.3332
38 16 15.25 0.7508
39 16 15.67 0.3332
40 15 15.87-0.8712
41 12 15.34-3.341
42 17 14.6 2.399
43 14 15.34-1.341
44 14 15.73-1.729
45 16 15.87 0.1288
46 15 14.6 0.3994
47 16 15.99 0.0105
48 14 15.46-1.462
49 15 14.6 0.3994
50 17 14.96 2.044
51 10 15.04-5.039
52 17 15.25 1.751
53 20 16.26 3.741
54 17 15.46 1.541
55 18 15.57 2.425
56 17 15.66 1.342
57 14 15.55-1.545
58 17 15.99 1.01
59 16 15.66 0.3363
60 18 15.55 2.455
61 18 15.79 2.215
62 16 16.4-0.3983
63 15 15.99-0.9895
64 13 15.79-2.785
65 16 15.57 0.425
66 12 15.76-3.756
67 16 15.57 0.425
68 16 15.25 0.7508
69 16 15.79 0.2149
70 14 15.45-1.454
71 15 15.13-0.1308
72 14 14.93-0.9264
73 15 15.79-0.7851
74 15 15.16-0.1604
75 16 15.67 0.3332
76 11 15.66-4.664
77 18 15.79 2.215
78 11 15.34-4.341
79 18 15.55 2.449
80 15 15.55-0.5454
81 19 15.66 3.336
82 17 15.79 1.215
83 14 15.75-1.75
84 13 15.66-2.658
85 17 15.25 1.751
86 14 15.66-1.664
87 19 15.79 3.215
88 14 15.34-1.338
89 16 15.45 0.5464
90 16 15.1 0.8988
91 15 15.43-0.4328
92 12 15.46-3.462
93 17 15.14 1.86
94 18 15.57 2.425
95 15 15.66-0.658
96 18 15.25 2.751
97 15 15.99-0.9895
98 16 15.34 0.659
99 16 16.14-0.1435







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
8 0.07092 0.1418 0.9291
9 0.08084 0.1617 0.9192
10 0.04394 0.08789 0.9561
11 0.02373 0.04746 0.9763
12 0.03854 0.07708 0.9615
13 0.01889 0.03777 0.9811
14 0.009458 0.01892 0.9905
15 0.006754 0.01351 0.9932
16 0.002906 0.005812 0.9971
17 0.001203 0.002407 0.9988
18 0.0006805 0.001361 0.9993
19 0.002129 0.004259 0.9979
20 0.002536 0.005073 0.9975
21 0.005448 0.0109 0.9946
22 0.006096 0.01219 0.9939
23 0.003598 0.007195 0.9964
24 0.002076 0.004151 0.9979
25 0.001055 0.00211 0.9989
26 0.001204 0.002409 0.9988
27 0.0006476 0.001295 0.9994
28 0.0003833 0.0007666 0.9996
29 0.0002567 0.0005133 0.9997
30 0.000278 0.0005561 0.9997
31 0.00287 0.00574 0.9971
32 0.006104 0.01221 0.9939
33 0.01699 0.03398 0.983
34 0.01445 0.0289 0.9856
35 0.00945 0.0189 0.9906
36 0.006703 0.01341 0.9933
37 0.005179 0.01036 0.9948
38 0.003279 0.006557 0.9967
39 0.002373 0.004746 0.9976
40 0.001579 0.003157 0.9984
41 0.00936 0.01872 0.9906
42 0.01015 0.0203 0.9898
43 0.009533 0.01907 0.9905
44 0.007974 0.01595 0.992
45 0.005319 0.01064 0.9947
46 0.003554 0.007108 0.9964
47 0.002235 0.004471 0.9978
48 0.00177 0.00354 0.9982
49 0.001143 0.002287 0.9989
50 0.002335 0.00467 0.9977
51 0.06043 0.1209 0.9396
52 0.05444 0.1089 0.9456
53 0.1387 0.2773 0.8613
54 0.1215 0.243 0.8785
55 0.1403 0.2806 0.8597
56 0.1254 0.2508 0.8746
57 0.1204 0.2409 0.8796
58 0.09881 0.1976 0.9012
59 0.07683 0.1537 0.9232
60 0.09535 0.1907 0.9047
61 0.1022 0.2044 0.8978
62 0.08716 0.1743 0.9128
63 0.07018 0.1404 0.9298
64 0.1036 0.2072 0.8964
65 0.07916 0.1583 0.9208
66 0.1671 0.3343 0.8329
67 0.1313 0.2626 0.8687
68 0.1027 0.2054 0.8973
69 0.07749 0.155 0.9225
70 0.06683 0.1337 0.9332
71 0.0481 0.0962 0.9519
72 0.0379 0.07581 0.9621
73 0.02984 0.05969 0.9702
74 0.0214 0.0428 0.9786
75 0.01413 0.02827 0.9859
76 0.09084 0.1817 0.9092
77 0.08203 0.1641 0.918
78 0.313 0.626 0.687
79 0.2993 0.5986 0.7007
80 0.2364 0.4728 0.7636
81 0.3562 0.7124 0.6438
82 0.2948 0.5897 0.7052
83 0.2476 0.4952 0.7524
84 0.3094 0.6189 0.6906
85 0.2563 0.5127 0.7437
86 0.2233 0.4465 0.7767
87 0.3918 0.7836 0.6082
88 0.3146 0.6293 0.6854
89 0.2141 0.4283 0.7859
90 0.1416 0.2831 0.8584
91 0.08476 0.1695 0.9152

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
8 &  0.07092 &  0.1418 &  0.9291 \tabularnewline
9 &  0.08084 &  0.1617 &  0.9192 \tabularnewline
10 &  0.04394 &  0.08789 &  0.9561 \tabularnewline
11 &  0.02373 &  0.04746 &  0.9763 \tabularnewline
12 &  0.03854 &  0.07708 &  0.9615 \tabularnewline
13 &  0.01889 &  0.03777 &  0.9811 \tabularnewline
14 &  0.009458 &  0.01892 &  0.9905 \tabularnewline
15 &  0.006754 &  0.01351 &  0.9932 \tabularnewline
16 &  0.002906 &  0.005812 &  0.9971 \tabularnewline
17 &  0.001203 &  0.002407 &  0.9988 \tabularnewline
18 &  0.0006805 &  0.001361 &  0.9993 \tabularnewline
19 &  0.002129 &  0.004259 &  0.9979 \tabularnewline
20 &  0.002536 &  0.005073 &  0.9975 \tabularnewline
21 &  0.005448 &  0.0109 &  0.9946 \tabularnewline
22 &  0.006096 &  0.01219 &  0.9939 \tabularnewline
23 &  0.003598 &  0.007195 &  0.9964 \tabularnewline
24 &  0.002076 &  0.004151 &  0.9979 \tabularnewline
25 &  0.001055 &  0.00211 &  0.9989 \tabularnewline
26 &  0.001204 &  0.002409 &  0.9988 \tabularnewline
27 &  0.0006476 &  0.001295 &  0.9994 \tabularnewline
28 &  0.0003833 &  0.0007666 &  0.9996 \tabularnewline
29 &  0.0002567 &  0.0005133 &  0.9997 \tabularnewline
30 &  0.000278 &  0.0005561 &  0.9997 \tabularnewline
31 &  0.00287 &  0.00574 &  0.9971 \tabularnewline
32 &  0.006104 &  0.01221 &  0.9939 \tabularnewline
33 &  0.01699 &  0.03398 &  0.983 \tabularnewline
34 &  0.01445 &  0.0289 &  0.9856 \tabularnewline
35 &  0.00945 &  0.0189 &  0.9906 \tabularnewline
36 &  0.006703 &  0.01341 &  0.9933 \tabularnewline
37 &  0.005179 &  0.01036 &  0.9948 \tabularnewline
38 &  0.003279 &  0.006557 &  0.9967 \tabularnewline
39 &  0.002373 &  0.004746 &  0.9976 \tabularnewline
40 &  0.001579 &  0.003157 &  0.9984 \tabularnewline
41 &  0.00936 &  0.01872 &  0.9906 \tabularnewline
42 &  0.01015 &  0.0203 &  0.9898 \tabularnewline
43 &  0.009533 &  0.01907 &  0.9905 \tabularnewline
44 &  0.007974 &  0.01595 &  0.992 \tabularnewline
45 &  0.005319 &  0.01064 &  0.9947 \tabularnewline
46 &  0.003554 &  0.007108 &  0.9964 \tabularnewline
47 &  0.002235 &  0.004471 &  0.9978 \tabularnewline
48 &  0.00177 &  0.00354 &  0.9982 \tabularnewline
49 &  0.001143 &  0.002287 &  0.9989 \tabularnewline
50 &  0.002335 &  0.00467 &  0.9977 \tabularnewline
51 &  0.06043 &  0.1209 &  0.9396 \tabularnewline
52 &  0.05444 &  0.1089 &  0.9456 \tabularnewline
53 &  0.1387 &  0.2773 &  0.8613 \tabularnewline
54 &  0.1215 &  0.243 &  0.8785 \tabularnewline
55 &  0.1403 &  0.2806 &  0.8597 \tabularnewline
56 &  0.1254 &  0.2508 &  0.8746 \tabularnewline
57 &  0.1204 &  0.2409 &  0.8796 \tabularnewline
58 &  0.09881 &  0.1976 &  0.9012 \tabularnewline
59 &  0.07683 &  0.1537 &  0.9232 \tabularnewline
60 &  0.09535 &  0.1907 &  0.9047 \tabularnewline
61 &  0.1022 &  0.2044 &  0.8978 \tabularnewline
62 &  0.08716 &  0.1743 &  0.9128 \tabularnewline
63 &  0.07018 &  0.1404 &  0.9298 \tabularnewline
64 &  0.1036 &  0.2072 &  0.8964 \tabularnewline
65 &  0.07916 &  0.1583 &  0.9208 \tabularnewline
66 &  0.1671 &  0.3343 &  0.8329 \tabularnewline
67 &  0.1313 &  0.2626 &  0.8687 \tabularnewline
68 &  0.1027 &  0.2054 &  0.8973 \tabularnewline
69 &  0.07749 &  0.155 &  0.9225 \tabularnewline
70 &  0.06683 &  0.1337 &  0.9332 \tabularnewline
71 &  0.0481 &  0.0962 &  0.9519 \tabularnewline
72 &  0.0379 &  0.07581 &  0.9621 \tabularnewline
73 &  0.02984 &  0.05969 &  0.9702 \tabularnewline
74 &  0.0214 &  0.0428 &  0.9786 \tabularnewline
75 &  0.01413 &  0.02827 &  0.9859 \tabularnewline
76 &  0.09084 &  0.1817 &  0.9092 \tabularnewline
77 &  0.08203 &  0.1641 &  0.918 \tabularnewline
78 &  0.313 &  0.626 &  0.687 \tabularnewline
79 &  0.2993 &  0.5986 &  0.7007 \tabularnewline
80 &  0.2364 &  0.4728 &  0.7636 \tabularnewline
81 &  0.3562 &  0.7124 &  0.6438 \tabularnewline
82 &  0.2948 &  0.5897 &  0.7052 \tabularnewline
83 &  0.2476 &  0.4952 &  0.7524 \tabularnewline
84 &  0.3094 &  0.6189 &  0.6906 \tabularnewline
85 &  0.2563 &  0.5127 &  0.7437 \tabularnewline
86 &  0.2233 &  0.4465 &  0.7767 \tabularnewline
87 &  0.3918 &  0.7836 &  0.6082 \tabularnewline
88 &  0.3146 &  0.6293 &  0.6854 \tabularnewline
89 &  0.2141 &  0.4283 &  0.7859 \tabularnewline
90 &  0.1416 &  0.2831 &  0.8584 \tabularnewline
91 &  0.08476 &  0.1695 &  0.9152 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=298845&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]8[/C][C] 0.07092[/C][C] 0.1418[/C][C] 0.9291[/C][/ROW]
[ROW][C]9[/C][C] 0.08084[/C][C] 0.1617[/C][C] 0.9192[/C][/ROW]
[ROW][C]10[/C][C] 0.04394[/C][C] 0.08789[/C][C] 0.9561[/C][/ROW]
[ROW][C]11[/C][C] 0.02373[/C][C] 0.04746[/C][C] 0.9763[/C][/ROW]
[ROW][C]12[/C][C] 0.03854[/C][C] 0.07708[/C][C] 0.9615[/C][/ROW]
[ROW][C]13[/C][C] 0.01889[/C][C] 0.03777[/C][C] 0.9811[/C][/ROW]
[ROW][C]14[/C][C] 0.009458[/C][C] 0.01892[/C][C] 0.9905[/C][/ROW]
[ROW][C]15[/C][C] 0.006754[/C][C] 0.01351[/C][C] 0.9932[/C][/ROW]
[ROW][C]16[/C][C] 0.002906[/C][C] 0.005812[/C][C] 0.9971[/C][/ROW]
[ROW][C]17[/C][C] 0.001203[/C][C] 0.002407[/C][C] 0.9988[/C][/ROW]
[ROW][C]18[/C][C] 0.0006805[/C][C] 0.001361[/C][C] 0.9993[/C][/ROW]
[ROW][C]19[/C][C] 0.002129[/C][C] 0.004259[/C][C] 0.9979[/C][/ROW]
[ROW][C]20[/C][C] 0.002536[/C][C] 0.005073[/C][C] 0.9975[/C][/ROW]
[ROW][C]21[/C][C] 0.005448[/C][C] 0.0109[/C][C] 0.9946[/C][/ROW]
[ROW][C]22[/C][C] 0.006096[/C][C] 0.01219[/C][C] 0.9939[/C][/ROW]
[ROW][C]23[/C][C] 0.003598[/C][C] 0.007195[/C][C] 0.9964[/C][/ROW]
[ROW][C]24[/C][C] 0.002076[/C][C] 0.004151[/C][C] 0.9979[/C][/ROW]
[ROW][C]25[/C][C] 0.001055[/C][C] 0.00211[/C][C] 0.9989[/C][/ROW]
[ROW][C]26[/C][C] 0.001204[/C][C] 0.002409[/C][C] 0.9988[/C][/ROW]
[ROW][C]27[/C][C] 0.0006476[/C][C] 0.001295[/C][C] 0.9994[/C][/ROW]
[ROW][C]28[/C][C] 0.0003833[/C][C] 0.0007666[/C][C] 0.9996[/C][/ROW]
[ROW][C]29[/C][C] 0.0002567[/C][C] 0.0005133[/C][C] 0.9997[/C][/ROW]
[ROW][C]30[/C][C] 0.000278[/C][C] 0.0005561[/C][C] 0.9997[/C][/ROW]
[ROW][C]31[/C][C] 0.00287[/C][C] 0.00574[/C][C] 0.9971[/C][/ROW]
[ROW][C]32[/C][C] 0.006104[/C][C] 0.01221[/C][C] 0.9939[/C][/ROW]
[ROW][C]33[/C][C] 0.01699[/C][C] 0.03398[/C][C] 0.983[/C][/ROW]
[ROW][C]34[/C][C] 0.01445[/C][C] 0.0289[/C][C] 0.9856[/C][/ROW]
[ROW][C]35[/C][C] 0.00945[/C][C] 0.0189[/C][C] 0.9906[/C][/ROW]
[ROW][C]36[/C][C] 0.006703[/C][C] 0.01341[/C][C] 0.9933[/C][/ROW]
[ROW][C]37[/C][C] 0.005179[/C][C] 0.01036[/C][C] 0.9948[/C][/ROW]
[ROW][C]38[/C][C] 0.003279[/C][C] 0.006557[/C][C] 0.9967[/C][/ROW]
[ROW][C]39[/C][C] 0.002373[/C][C] 0.004746[/C][C] 0.9976[/C][/ROW]
[ROW][C]40[/C][C] 0.001579[/C][C] 0.003157[/C][C] 0.9984[/C][/ROW]
[ROW][C]41[/C][C] 0.00936[/C][C] 0.01872[/C][C] 0.9906[/C][/ROW]
[ROW][C]42[/C][C] 0.01015[/C][C] 0.0203[/C][C] 0.9898[/C][/ROW]
[ROW][C]43[/C][C] 0.009533[/C][C] 0.01907[/C][C] 0.9905[/C][/ROW]
[ROW][C]44[/C][C] 0.007974[/C][C] 0.01595[/C][C] 0.992[/C][/ROW]
[ROW][C]45[/C][C] 0.005319[/C][C] 0.01064[/C][C] 0.9947[/C][/ROW]
[ROW][C]46[/C][C] 0.003554[/C][C] 0.007108[/C][C] 0.9964[/C][/ROW]
[ROW][C]47[/C][C] 0.002235[/C][C] 0.004471[/C][C] 0.9978[/C][/ROW]
[ROW][C]48[/C][C] 0.00177[/C][C] 0.00354[/C][C] 0.9982[/C][/ROW]
[ROW][C]49[/C][C] 0.001143[/C][C] 0.002287[/C][C] 0.9989[/C][/ROW]
[ROW][C]50[/C][C] 0.002335[/C][C] 0.00467[/C][C] 0.9977[/C][/ROW]
[ROW][C]51[/C][C] 0.06043[/C][C] 0.1209[/C][C] 0.9396[/C][/ROW]
[ROW][C]52[/C][C] 0.05444[/C][C] 0.1089[/C][C] 0.9456[/C][/ROW]
[ROW][C]53[/C][C] 0.1387[/C][C] 0.2773[/C][C] 0.8613[/C][/ROW]
[ROW][C]54[/C][C] 0.1215[/C][C] 0.243[/C][C] 0.8785[/C][/ROW]
[ROW][C]55[/C][C] 0.1403[/C][C] 0.2806[/C][C] 0.8597[/C][/ROW]
[ROW][C]56[/C][C] 0.1254[/C][C] 0.2508[/C][C] 0.8746[/C][/ROW]
[ROW][C]57[/C][C] 0.1204[/C][C] 0.2409[/C][C] 0.8796[/C][/ROW]
[ROW][C]58[/C][C] 0.09881[/C][C] 0.1976[/C][C] 0.9012[/C][/ROW]
[ROW][C]59[/C][C] 0.07683[/C][C] 0.1537[/C][C] 0.9232[/C][/ROW]
[ROW][C]60[/C][C] 0.09535[/C][C] 0.1907[/C][C] 0.9047[/C][/ROW]
[ROW][C]61[/C][C] 0.1022[/C][C] 0.2044[/C][C] 0.8978[/C][/ROW]
[ROW][C]62[/C][C] 0.08716[/C][C] 0.1743[/C][C] 0.9128[/C][/ROW]
[ROW][C]63[/C][C] 0.07018[/C][C] 0.1404[/C][C] 0.9298[/C][/ROW]
[ROW][C]64[/C][C] 0.1036[/C][C] 0.2072[/C][C] 0.8964[/C][/ROW]
[ROW][C]65[/C][C] 0.07916[/C][C] 0.1583[/C][C] 0.9208[/C][/ROW]
[ROW][C]66[/C][C] 0.1671[/C][C] 0.3343[/C][C] 0.8329[/C][/ROW]
[ROW][C]67[/C][C] 0.1313[/C][C] 0.2626[/C][C] 0.8687[/C][/ROW]
[ROW][C]68[/C][C] 0.1027[/C][C] 0.2054[/C][C] 0.8973[/C][/ROW]
[ROW][C]69[/C][C] 0.07749[/C][C] 0.155[/C][C] 0.9225[/C][/ROW]
[ROW][C]70[/C][C] 0.06683[/C][C] 0.1337[/C][C] 0.9332[/C][/ROW]
[ROW][C]71[/C][C] 0.0481[/C][C] 0.0962[/C][C] 0.9519[/C][/ROW]
[ROW][C]72[/C][C] 0.0379[/C][C] 0.07581[/C][C] 0.9621[/C][/ROW]
[ROW][C]73[/C][C] 0.02984[/C][C] 0.05969[/C][C] 0.9702[/C][/ROW]
[ROW][C]74[/C][C] 0.0214[/C][C] 0.0428[/C][C] 0.9786[/C][/ROW]
[ROW][C]75[/C][C] 0.01413[/C][C] 0.02827[/C][C] 0.9859[/C][/ROW]
[ROW][C]76[/C][C] 0.09084[/C][C] 0.1817[/C][C] 0.9092[/C][/ROW]
[ROW][C]77[/C][C] 0.08203[/C][C] 0.1641[/C][C] 0.918[/C][/ROW]
[ROW][C]78[/C][C] 0.313[/C][C] 0.626[/C][C] 0.687[/C][/ROW]
[ROW][C]79[/C][C] 0.2993[/C][C] 0.5986[/C][C] 0.7007[/C][/ROW]
[ROW][C]80[/C][C] 0.2364[/C][C] 0.4728[/C][C] 0.7636[/C][/ROW]
[ROW][C]81[/C][C] 0.3562[/C][C] 0.7124[/C][C] 0.6438[/C][/ROW]
[ROW][C]82[/C][C] 0.2948[/C][C] 0.5897[/C][C] 0.7052[/C][/ROW]
[ROW][C]83[/C][C] 0.2476[/C][C] 0.4952[/C][C] 0.7524[/C][/ROW]
[ROW][C]84[/C][C] 0.3094[/C][C] 0.6189[/C][C] 0.6906[/C][/ROW]
[ROW][C]85[/C][C] 0.2563[/C][C] 0.5127[/C][C] 0.7437[/C][/ROW]
[ROW][C]86[/C][C] 0.2233[/C][C] 0.4465[/C][C] 0.7767[/C][/ROW]
[ROW][C]87[/C][C] 0.3918[/C][C] 0.7836[/C][C] 0.6082[/C][/ROW]
[ROW][C]88[/C][C] 0.3146[/C][C] 0.6293[/C][C] 0.6854[/C][/ROW]
[ROW][C]89[/C][C] 0.2141[/C][C] 0.4283[/C][C] 0.7859[/C][/ROW]
[ROW][C]90[/C][C] 0.1416[/C][C] 0.2831[/C][C] 0.8584[/C][/ROW]
[ROW][C]91[/C][C] 0.08476[/C][C] 0.1695[/C][C] 0.9152[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=298845&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=298845&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-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
8 0.07092 0.1418 0.9291
9 0.08084 0.1617 0.9192
10 0.04394 0.08789 0.9561
11 0.02373 0.04746 0.9763
12 0.03854 0.07708 0.9615
13 0.01889 0.03777 0.9811
14 0.009458 0.01892 0.9905
15 0.006754 0.01351 0.9932
16 0.002906 0.005812 0.9971
17 0.001203 0.002407 0.9988
18 0.0006805 0.001361 0.9993
19 0.002129 0.004259 0.9979
20 0.002536 0.005073 0.9975
21 0.005448 0.0109 0.9946
22 0.006096 0.01219 0.9939
23 0.003598 0.007195 0.9964
24 0.002076 0.004151 0.9979
25 0.001055 0.00211 0.9989
26 0.001204 0.002409 0.9988
27 0.0006476 0.001295 0.9994
28 0.0003833 0.0007666 0.9996
29 0.0002567 0.0005133 0.9997
30 0.000278 0.0005561 0.9997
31 0.00287 0.00574 0.9971
32 0.006104 0.01221 0.9939
33 0.01699 0.03398 0.983
34 0.01445 0.0289 0.9856
35 0.00945 0.0189 0.9906
36 0.006703 0.01341 0.9933
37 0.005179 0.01036 0.9948
38 0.003279 0.006557 0.9967
39 0.002373 0.004746 0.9976
40 0.001579 0.003157 0.9984
41 0.00936 0.01872 0.9906
42 0.01015 0.0203 0.9898
43 0.009533 0.01907 0.9905
44 0.007974 0.01595 0.992
45 0.005319 0.01064 0.9947
46 0.003554 0.007108 0.9964
47 0.002235 0.004471 0.9978
48 0.00177 0.00354 0.9982
49 0.001143 0.002287 0.9989
50 0.002335 0.00467 0.9977
51 0.06043 0.1209 0.9396
52 0.05444 0.1089 0.9456
53 0.1387 0.2773 0.8613
54 0.1215 0.243 0.8785
55 0.1403 0.2806 0.8597
56 0.1254 0.2508 0.8746
57 0.1204 0.2409 0.8796
58 0.09881 0.1976 0.9012
59 0.07683 0.1537 0.9232
60 0.09535 0.1907 0.9047
61 0.1022 0.2044 0.8978
62 0.08716 0.1743 0.9128
63 0.07018 0.1404 0.9298
64 0.1036 0.2072 0.8964
65 0.07916 0.1583 0.9208
66 0.1671 0.3343 0.8329
67 0.1313 0.2626 0.8687
68 0.1027 0.2054 0.8973
69 0.07749 0.155 0.9225
70 0.06683 0.1337 0.9332
71 0.0481 0.0962 0.9519
72 0.0379 0.07581 0.9621
73 0.02984 0.05969 0.9702
74 0.0214 0.0428 0.9786
75 0.01413 0.02827 0.9859
76 0.09084 0.1817 0.9092
77 0.08203 0.1641 0.918
78 0.313 0.626 0.687
79 0.2993 0.5986 0.7007
80 0.2364 0.4728 0.7636
81 0.3562 0.7124 0.6438
82 0.2948 0.5897 0.7052
83 0.2476 0.4952 0.7524
84 0.3094 0.6189 0.6906
85 0.2563 0.5127 0.7437
86 0.2233 0.4465 0.7767
87 0.3918 0.7836 0.6082
88 0.3146 0.6293 0.6854
89 0.2141 0.4283 0.7859
90 0.1416 0.2831 0.8584
91 0.08476 0.1695 0.9152







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level22 0.2619NOK
5% type I error level410.488095NOK
10% type I error level460.547619NOK

\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 & 22 &  0.2619 & NOK \tabularnewline
5% type I error level & 41 & 0.488095 & NOK \tabularnewline
10% type I error level & 46 & 0.547619 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=298845&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]22[/C][C] 0.2619[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]41[/C][C]0.488095[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]46[/C][C]0.547619[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=298845&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=298845&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 testsOK/NOK
1% type I error level22 0.2619NOK
5% type I error level410.488095NOK
10% type I error level460.547619NOK







Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted
	RESET test
data:  mylm
RESET = 1.0644, df1 = 2, df2 = 92, p-value = 0.3492
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors
	RESET test
data:  mylm
RESET = 0.74163, df1 = 8, df2 = 86, p-value = 0.6546
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 1.8157, df1 = 2, df2 = 92, p-value = 0.1685

\begin{tabular}{lllllllll}
\hline
Ramsey RESET F-Test for powers (2 and 3) of fitted values \tabularnewline
> reset_test_fitted
	RESET test
data:  mylm
RESET = 1.0644, df1 = 2, df2 = 92, p-value = 0.3492
\tabularnewline Ramsey RESET F-Test for powers (2 and 3) of regressors \tabularnewline
> reset_test_regressors
	RESET test
data:  mylm
RESET = 0.74163, df1 = 8, df2 = 86, p-value = 0.6546
\tabularnewline Ramsey RESET F-Test for powers (2 and 3) of principal components \tabularnewline
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 1.8157, df1 = 2, df2 = 92, p-value = 0.1685
\tabularnewline \hline \end{tabular} %Source: https://freestatistics.org/blog/index.php?pk=298845&T=7

[TABLE]
[ROW][C]Ramsey RESET F-Test for powers (2 and 3) of fitted values[/C][/ROW]
[ROW][C]
> reset_test_fitted
	RESET test
data:  mylm
RESET = 1.0644, df1 = 2, df2 = 92, p-value = 0.3492
[/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 = 0.74163, df1 = 8, df2 = 86, p-value = 0.6546
[/C][/ROW] [ROW][C]Ramsey RESET F-Test for powers (2 and 3) of principal components[/C][/ROW] [ROW][C]
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 1.8157, df1 = 2, df2 = 92, p-value = 0.1685
[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=298845&T=7

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=298845&T=7

As an alternative you can also use a QR Code:  

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

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted
	RESET test
data:  mylm
RESET = 1.0644, df1 = 2, df2 = 92, p-value = 0.3492
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors
	RESET test
data:  mylm
RESET = 0.74163, df1 = 8, df2 = 86, p-value = 0.6546
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 1.8157, df1 = 2, df2 = 92, p-value = 0.1685







Variance Inflation Factors (Multicollinearity)
> vif
    ITH1     ITH2     ITH3     ITH4 
1.333552 1.336490 1.373339 1.262235 

\begin{tabular}{lllllllll}
\hline
Variance Inflation Factors (Multicollinearity) \tabularnewline
> vif
    ITH1     ITH2     ITH3     ITH4 
1.333552 1.336490 1.373339 1.262235 
\tabularnewline \hline \end{tabular} %Source: https://freestatistics.org/blog/index.php?pk=298845&T=8

[TABLE]
[ROW][C]Variance Inflation Factors (Multicollinearity)[/C][/ROW]
[ROW][C]
> vif
    ITH1     ITH2     ITH3     ITH4 
1.333552 1.336490 1.373339 1.262235 
[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=298845&T=8

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=298845&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
    ITH1     ITH2     ITH3     ITH4 
1.333552 1.336490 1.373339 1.262235 



Parameters (Session):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ; par4 = 0 ; par5 = 0 ;
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')