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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Tue, 05 Dec 2017 11:30:49 +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/Dec/05/t1512470129j3mqa3ifaccutdz.htm/, Retrieved Tue, 14 May 2024 16:58:52 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=308535, Retrieved Tue, 14 May 2024 16:58:52 +0000

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-       [Multiple Regression] [Dataset 2 Positio...] [2017-12-05 10:30:49] [e158d092b4ed09fb9d2052d6342a716b] [Current]

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Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	8 seconds
R Server	Big Analytics Cloud Computing Center
R Framework error message	The field 'Names of X columns' contains a hard return which cannot be interpreted. Please, resubmit your request without hard returns in the 'Names of X columns'.

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input view raw input (R code)  \tabularnewline
Raw Outputview raw output of R engine  \tabularnewline
Computing time8 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
R Framework error message & The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'. \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=308535&T=0

[TABLE]
[ROW]

Summary of computational transaction[/C][/ROW] [ROW]

Raw Input[/C]

view raw input (R code) [/C][/ROW] [ROW]

Raw Output[/C]

view raw output of R engine [/C][/ROW] [ROW]

Computing time[/C]

8 seconds[/C][/ROW] [ROW]

R Server[/C]

Big Analytics Cloud Computing Center[/C][/ROW] [ROW]

R Framework error message[/C][C]

The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.

[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=308535&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=308535&T=0

As an alternative you can also use a QR Code:

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

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	8 seconds
R Server	Big Analytics Cloud Computing Center
R Framework error message	The field 'Names of X columns' contains a hard return which cannot be interpreted. Please, resubmit your request without hard returns in the 'Names of X columns'.

Multiple Linear Regression - Estimated Regression Equation
Position[t] = + 7.18038 + 0.318263Last[t] -7.83001`Ratio\r`[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Position[t] =  +  7.18038 +  0.318263Last[t] -7.83001`Ratio\r`[t]  + e[t] \tabularnewline
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=308535&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Position[t] =  +  7.18038 +  0.318263Last[t] -7.83001`Ratio\r`[t]  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=308535&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=308535&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
Position[t] = + 7.18038 + 0.318263Last[t] -7.83001`Ratio\r`[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)	+7.18	1.023	+7.0210e+00	2.8e-10	1.4e-10
Last	+0.3183	0.1038	+3.0670e+00	0.002786	0.001393
`Ratio\r`	-7.83	4.043	-1.9370e+00	0.05564	0.02782

\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) & +7.18 &  1.023 & +7.0210e+00 &  2.8e-10 &  1.4e-10 \tabularnewline
Last & +0.3183 &  0.1038 & +3.0670e+00 &  0.002786 &  0.001393 \tabularnewline
`Ratio\r` & -7.83 &  4.043 & -1.9370e+00 &  0.05564 &  0.02782 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=308535&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]+7.18[/C][C] 1.023[/C][C]+7.0210e+00[/C][C] 2.8e-10[/C][C] 1.4e-10[/C][/ROW]
[ROW][C]Last[/C][C]+0.3183[/C][C] 0.1038[/C][C]+3.0670e+00[/C][C] 0.002786[/C][C] 0.001393[/C][/ROW]
[ROW][C]`Ratio\r`[/C][C]-7.83[/C][C] 4.043[/C][C]-1.9370e+00[/C][C] 0.05564[/C][C] 0.02782[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=308535&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=308535&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)	+7.18	1.023	+7.0210e+00	2.8e-10	1.4e-10
Last	+0.3183	0.1038	+3.0670e+00	0.002786	0.001393
`Ratio\r`	-7.83	4.043	-1.9370e+00	0.05564	0.02782

Multiple Linear Regression - Regression Statistics
Multiple R	0.3448
R-squared	0.1189
Adjusted R-squared	0.1011
F-TEST (value)	6.678
F-TEST (DF numerator)	2
F-TEST (DF denominator)	99
p-value	0.001903
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	4.106
Sum Squared Residuals	1669

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.3448 \tabularnewline
R-squared &  0.1189 \tabularnewline
Adjusted R-squared &  0.1011 \tabularnewline
F-TEST (value) &  6.678 \tabularnewline
F-TEST (DF numerator) & 2 \tabularnewline
F-TEST (DF denominator) & 99 \tabularnewline
p-value &  0.001903 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  4.106 \tabularnewline
Sum Squared Residuals &  1669 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=308535&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.3448[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.1189[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.1011[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 6.678[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]2[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]99[/C][/ROW]
[ROW][C]p-value[/C][C] 0.001903[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C] 4.106[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 1669[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=308535&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=308535&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	0.3448
R-squared	0.1189
Adjusted R-squared	0.1011
F-TEST (value)	6.678
F-TEST (DF numerator)	2
F-TEST (DF denominator)	99
p-value	0.001903
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	4.106
Sum Squared Residuals	1669

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

\begin{tabular}{lllllllll}
\hline
Menu of Residual Diagnostics \tabularnewline
Description & Link \tabularnewline
Histogram & Compute \tabularnewline
Central Tendency & Compute \tabularnewline
QQ Plot & Compute \tabularnewline
Kernel Density Plot & Compute \tabularnewline
Skewness/Kurtosis Test & Compute \tabularnewline
Skewness-Kurtosis Plot & Compute \tabularnewline
Harrell-Davis Plot & Compute \tabularnewline
Bootstrap Plot -- Central Tendency & Compute \tabularnewline
Blocked Bootstrap Plot -- Central Tendency & Compute \tabularnewline
(Partial) Autocorrelation Plot & Compute \tabularnewline
Spectral Analysis & Compute \tabularnewline
Tukey lambda PPCC Plot & Compute \tabularnewline
Box-Cox Normality Plot & Compute \tabularnewline
Summary Statistics & Compute \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=308535&T=4

[TABLE]
[ROW][C]Menu of Residual Diagnostics[/C][/ROW]
[ROW][C]Description[/C][C]Link[/C][/ROW]
[ROW][C]Histogram[/C][C]Compute[/C][/ROW]
[ROW][C]Central Tendency[/C][C]Compute[/C][/ROW]
[ROW][C]QQ Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Kernel Density Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Skewness/Kurtosis Test[/C][C]Compute[/C][/ROW]
[ROW][C]Skewness-Kurtosis Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Harrell-Davis Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Bootstrap Plot -- Central Tendency[/C][C]Compute[/C][/ROW]
[ROW][C]Blocked Bootstrap Plot -- Central Tendency[/C][C]Compute[/C][/ROW]
[ROW][C](Partial) Autocorrelation Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Spectral Analysis[/C][C]Compute[/C][/ROW]
[ROW][C]Tukey lambda PPCC Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Box-Cox Normality Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Summary Statistics[/C][C]Compute[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=308535&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=308535&T=4

As an alternative you can also use a QR Code:

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

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	1	6.246	-5.246
2	4	6.961	-2.961
3	8	10.07	-2.07
4	4	7.597	-3.597
5	1	5.859	-4.859
6	7	7.43	-0.4305
7	12	6.402	5.598
8	9	7.602	1.398
9	2	5.698	-3.698
10	1	4.304	-3.304
11	8	9.58	-1.58
12	7	7.524	-0.524
13	7	5.071	1.929
14	2	7.274	-5.274
15	3	6.016	-3.016
16	10	7.029	2.971
17	5	7.274	-2.274
18	4	6.637	-2.637
19	3	6.877	-3.877
20	13	7.612	5.388
21	11	6.721	4.279
22	3	7.357	-4.357
23	18	9.105	8.895
24	14	8.704	5.296
25	5	6.653	-1.653
26	12	5.541	6.459
27	11	7.769	3.231
28	9	8.709	0.2914
29	7	8.704	-1.704
30	3	7.509	-4.509
31	6	7.592	-1.592
32	8	6.726	1.274
33	6	6.887	-0.8874
34	1	5.938	-4.938
35	12	9.032	2.968
36	17	9.272	7.728
37	2	5.875	-3.875
38	13	7.43	5.57
39	3	4.685	-1.685
40	13	8.307	4.693
41	7	5.698	1.302
42	12	5.781	6.219
43	8	11.18	-3.181
44	5	7.842	-2.842
45	15	4.758	10.24
46	5	6.564	-1.564
47	12	10.95	1.048
48	8	7.441	0.5594
49	11	8.15	2.85
50	11	8.698	2.302
51	12	7.122	4.878
52	3	5.463	-2.463
53	1	10.61	-9.608
54	11	6.496	4.504
55	1	6.642	-5.642
56	3	8.16	-5.16
57	6	7.436	-1.436
58	13	9.189	3.811
59	5	5.933	-0.9326
60	3	7.377	-4.377
61	9	6.486	2.514
62	6	7.143	-1.143
63	7	8.135	-1.135
64	2	7.989	-5.989
65	13	9.027	3.973
66	10	12.29	-2.293
67	9	8.072	0.9279
68	1	6.104	-5.104
69	2	6.569	-4.569
70	4	6.814	-2.814
71	4	7.754	-3.754
72	9	7.915	1.085
73	4	5.723	-1.723
74	8	6.726	1.274
75	10	6.746	3.254
76	6	7.039	-1.039
77	10	5.776	4.224
78	2	3.897	-1.897
79	4	6.716	-2.716
80	2	6.976	-4.976
81	2	4.758	-2.758
82	5	6.246	-1.246
83	4	5.933	-1.933
84	5	8.772	-3.772
85	11	5.854	5.146
86	7	8.494	-1.494
87	19	7.279	11.72
88	8	5.811	2.189
89	14	6.089	7.911
90	7	9.105	-2.105
91	6	8.949	-2.949
92	16	9.752	6.248
93	10	7.299	2.701
94	1	4.915	-3.915
95	8	6.168	1.832
96	9	7.504	1.496
97	6	7.817	-1.817
98	14	7.91	6.09
99	5	7.524	-2.524
100	6	8.39	-2.39
101	9	6.173	2.827
102	10	7.289	2.711

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 &  1 &  6.246 & -5.246 \tabularnewline
2 &  4 &  6.961 & -2.961 \tabularnewline
3 &  8 &  10.07 & -2.07 \tabularnewline
4 &  4 &  7.597 & -3.597 \tabularnewline
5 &  1 &  5.859 & -4.859 \tabularnewline
6 &  7 &  7.43 & -0.4305 \tabularnewline
7 &  12 &  6.402 &  5.598 \tabularnewline
8 &  9 &  7.602 &  1.398 \tabularnewline
9 &  2 &  5.698 & -3.698 \tabularnewline
10 &  1 &  4.304 & -3.304 \tabularnewline
11 &  8 &  9.58 & -1.58 \tabularnewline
12 &  7 &  7.524 & -0.524 \tabularnewline
13 &  7 &  5.071 &  1.929 \tabularnewline
14 &  2 &  7.274 & -5.274 \tabularnewline
15 &  3 &  6.016 & -3.016 \tabularnewline
16 &  10 &  7.029 &  2.971 \tabularnewline
17 &  5 &  7.274 & -2.274 \tabularnewline
18 &  4 &  6.637 & -2.637 \tabularnewline
19 &  3 &  6.877 & -3.877 \tabularnewline
20 &  13 &  7.612 &  5.388 \tabularnewline
21 &  11 &  6.721 &  4.279 \tabularnewline
22 &  3 &  7.357 & -4.357 \tabularnewline
23 &  18 &  9.105 &  8.895 \tabularnewline
24 &  14 &  8.704 &  5.296 \tabularnewline
25 &  5 &  6.653 & -1.653 \tabularnewline
26 &  12 &  5.541 &  6.459 \tabularnewline
27 &  11 &  7.769 &  3.231 \tabularnewline
28 &  9 &  8.709 &  0.2914 \tabularnewline
29 &  7 &  8.704 & -1.704 \tabularnewline
30 &  3 &  7.509 & -4.509 \tabularnewline
31 &  6 &  7.592 & -1.592 \tabularnewline
32 &  8 &  6.726 &  1.274 \tabularnewline
33 &  6 &  6.887 & -0.8874 \tabularnewline
34 &  1 &  5.938 & -4.938 \tabularnewline
35 &  12 &  9.032 &  2.968 \tabularnewline
36 &  17 &  9.272 &  7.728 \tabularnewline
37 &  2 &  5.875 & -3.875 \tabularnewline
38 &  13 &  7.43 &  5.57 \tabularnewline
39 &  3 &  4.685 & -1.685 \tabularnewline
40 &  13 &  8.307 &  4.693 \tabularnewline
41 &  7 &  5.698 &  1.302 \tabularnewline
42 &  12 &  5.781 &  6.219 \tabularnewline
43 &  8 &  11.18 & -3.181 \tabularnewline
44 &  5 &  7.842 & -2.842 \tabularnewline
45 &  15 &  4.758 &  10.24 \tabularnewline
46 &  5 &  6.564 & -1.564 \tabularnewline
47 &  12 &  10.95 &  1.048 \tabularnewline
48 &  8 &  7.441 &  0.5594 \tabularnewline
49 &  11 &  8.15 &  2.85 \tabularnewline
50 &  11 &  8.698 &  2.302 \tabularnewline
51 &  12 &  7.122 &  4.878 \tabularnewline
52 &  3 &  5.463 & -2.463 \tabularnewline
53 &  1 &  10.61 & -9.608 \tabularnewline
54 &  11 &  6.496 &  4.504 \tabularnewline
55 &  1 &  6.642 & -5.642 \tabularnewline
56 &  3 &  8.16 & -5.16 \tabularnewline
57 &  6 &  7.436 & -1.436 \tabularnewline
58 &  13 &  9.189 &  3.811 \tabularnewline
59 &  5 &  5.933 & -0.9326 \tabularnewline
60 &  3 &  7.377 & -4.377 \tabularnewline
61 &  9 &  6.486 &  2.514 \tabularnewline
62 &  6 &  7.143 & -1.143 \tabularnewline
63 &  7 &  8.135 & -1.135 \tabularnewline
64 &  2 &  7.989 & -5.989 \tabularnewline
65 &  13 &  9.027 &  3.973 \tabularnewline
66 &  10 &  12.29 & -2.293 \tabularnewline
67 &  9 &  8.072 &  0.9279 \tabularnewline
68 &  1 &  6.104 & -5.104 \tabularnewline
69 &  2 &  6.569 & -4.569 \tabularnewline
70 &  4 &  6.814 & -2.814 \tabularnewline
71 &  4 &  7.754 & -3.754 \tabularnewline
72 &  9 &  7.915 &  1.085 \tabularnewline
73 &  4 &  5.723 & -1.723 \tabularnewline
74 &  8 &  6.726 &  1.274 \tabularnewline
75 &  10 &  6.746 &  3.254 \tabularnewline
76 &  6 &  7.039 & -1.039 \tabularnewline
77 &  10 &  5.776 &  4.224 \tabularnewline
78 &  2 &  3.897 & -1.897 \tabularnewline
79 &  4 &  6.716 & -2.716 \tabularnewline
80 &  2 &  6.976 & -4.976 \tabularnewline
81 &  2 &  4.758 & -2.758 \tabularnewline
82 &  5 &  6.246 & -1.246 \tabularnewline
83 &  4 &  5.933 & -1.933 \tabularnewline
84 &  5 &  8.772 & -3.772 \tabularnewline
85 &  11 &  5.854 &  5.146 \tabularnewline
86 &  7 &  8.494 & -1.494 \tabularnewline
87 &  19 &  7.279 &  11.72 \tabularnewline
88 &  8 &  5.811 &  2.189 \tabularnewline
89 &  14 &  6.089 &  7.911 \tabularnewline
90 &  7 &  9.105 & -2.105 \tabularnewline
91 &  6 &  8.949 & -2.949 \tabularnewline
92 &  16 &  9.752 &  6.248 \tabularnewline
93 &  10 &  7.299 &  2.701 \tabularnewline
94 &  1 &  4.915 & -3.915 \tabularnewline
95 &  8 &  6.168 &  1.832 \tabularnewline
96 &  9 &  7.504 &  1.496 \tabularnewline
97 &  6 &  7.817 & -1.817 \tabularnewline
98 &  14 &  7.91 &  6.09 \tabularnewline
99 &  5 &  7.524 & -2.524 \tabularnewline
100 &  6 &  8.39 & -2.39 \tabularnewline
101 &  9 &  6.173 &  2.827 \tabularnewline
102 &  10 &  7.289 &  2.711 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=308535&T=5

[TABLE]
[ROW][C]Multiple Linear Regression - Actuals, Interpolation, and Residuals[/C][/ROW]
[ROW][C]Time or Index[/C][C]Actuals[/C][C]InterpolationForecast[/C][C]ResidualsPrediction Error[/C][/ROW]
[ROW][C]1[/C][C] 1[/C][C] 6.246[/C][C]-5.246[/C][/ROW]
[ROW][C]2[/C][C] 4[/C][C] 6.961[/C][C]-2.961[/C][/ROW]
[ROW][C]3[/C][C] 8[/C][C] 10.07[/C][C]-2.07[/C][/ROW]
[ROW][C]4[/C][C] 4[/C][C] 7.597[/C][C]-3.597[/C][/ROW]
[ROW][C]5[/C][C] 1[/C][C] 5.859[/C][C]-4.859[/C][/ROW]
[ROW][C]6[/C][C] 7[/C][C] 7.43[/C][C]-0.4305[/C][/ROW]
[ROW][C]7[/C][C] 12[/C][C] 6.402[/C][C] 5.598[/C][/ROW]
[ROW][C]8[/C][C] 9[/C][C] 7.602[/C][C] 1.398[/C][/ROW]
[ROW][C]9[/C][C] 2[/C][C] 5.698[/C][C]-3.698[/C][/ROW]
[ROW][C]10[/C][C] 1[/C][C] 4.304[/C][C]-3.304[/C][/ROW]
[ROW][C]11[/C][C] 8[/C][C] 9.58[/C][C]-1.58[/C][/ROW]
[ROW][C]12[/C][C] 7[/C][C] 7.524[/C][C]-0.524[/C][/ROW]
[ROW][C]13[/C][C] 7[/C][C] 5.071[/C][C] 1.929[/C][/ROW]
[ROW][C]14[/C][C] 2[/C][C] 7.274[/C][C]-5.274[/C][/ROW]
[ROW][C]15[/C][C] 3[/C][C] 6.016[/C][C]-3.016[/C][/ROW]
[ROW][C]16[/C][C] 10[/C][C] 7.029[/C][C] 2.971[/C][/ROW]
[ROW][C]17[/C][C] 5[/C][C] 7.274[/C][C]-2.274[/C][/ROW]
[ROW][C]18[/C][C] 4[/C][C] 6.637[/C][C]-2.637[/C][/ROW]
[ROW][C]19[/C][C] 3[/C][C] 6.877[/C][C]-3.877[/C][/ROW]
[ROW][C]20[/C][C] 13[/C][C] 7.612[/C][C] 5.388[/C][/ROW]
[ROW][C]21[/C][C] 11[/C][C] 6.721[/C][C] 4.279[/C][/ROW]
[ROW][C]22[/C][C] 3[/C][C] 7.357[/C][C]-4.357[/C][/ROW]
[ROW][C]23[/C][C] 18[/C][C] 9.105[/C][C] 8.895[/C][/ROW]
[ROW][C]24[/C][C] 14[/C][C] 8.704[/C][C] 5.296[/C][/ROW]
[ROW][C]25[/C][C] 5[/C][C] 6.653[/C][C]-1.653[/C][/ROW]
[ROW][C]26[/C][C] 12[/C][C] 5.541[/C][C] 6.459[/C][/ROW]
[ROW][C]27[/C][C] 11[/C][C] 7.769[/C][C] 3.231[/C][/ROW]
[ROW][C]28[/C][C] 9[/C][C] 8.709[/C][C] 0.2914[/C][/ROW]
[ROW][C]29[/C][C] 7[/C][C] 8.704[/C][C]-1.704[/C][/ROW]
[ROW][C]30[/C][C] 3[/C][C] 7.509[/C][C]-4.509[/C][/ROW]
[ROW][C]31[/C][C] 6[/C][C] 7.592[/C][C]-1.592[/C][/ROW]
[ROW][C]32[/C][C] 8[/C][C] 6.726[/C][C] 1.274[/C][/ROW]
[ROW][C]33[/C][C] 6[/C][C] 6.887[/C][C]-0.8874[/C][/ROW]
[ROW][C]34[/C][C] 1[/C][C] 5.938[/C][C]-4.938[/C][/ROW]
[ROW][C]35[/C][C] 12[/C][C] 9.032[/C][C] 2.968[/C][/ROW]
[ROW][C]36[/C][C] 17[/C][C] 9.272[/C][C] 7.728[/C][/ROW]
[ROW][C]37[/C][C] 2[/C][C] 5.875[/C][C]-3.875[/C][/ROW]
[ROW][C]38[/C][C] 13[/C][C] 7.43[/C][C] 5.57[/C][/ROW]
[ROW][C]39[/C][C] 3[/C][C] 4.685[/C][C]-1.685[/C][/ROW]
[ROW][C]40[/C][C] 13[/C][C] 8.307[/C][C] 4.693[/C][/ROW]
[ROW][C]41[/C][C] 7[/C][C] 5.698[/C][C] 1.302[/C][/ROW]
[ROW][C]42[/C][C] 12[/C][C] 5.781[/C][C] 6.219[/C][/ROW]
[ROW][C]43[/C][C] 8[/C][C] 11.18[/C][C]-3.181[/C][/ROW]
[ROW][C]44[/C][C] 5[/C][C] 7.842[/C][C]-2.842[/C][/ROW]
[ROW][C]45[/C][C] 15[/C][C] 4.758[/C][C] 10.24[/C][/ROW]
[ROW][C]46[/C][C] 5[/C][C] 6.564[/C][C]-1.564[/C][/ROW]
[ROW][C]47[/C][C] 12[/C][C] 10.95[/C][C] 1.048[/C][/ROW]
[ROW][C]48[/C][C] 8[/C][C] 7.441[/C][C] 0.5594[/C][/ROW]
[ROW][C]49[/C][C] 11[/C][C] 8.15[/C][C] 2.85[/C][/ROW]
[ROW][C]50[/C][C] 11[/C][C] 8.698[/C][C] 2.302[/C][/ROW]
[ROW][C]51[/C][C] 12[/C][C] 7.122[/C][C] 4.878[/C][/ROW]
[ROW][C]52[/C][C] 3[/C][C] 5.463[/C][C]-2.463[/C][/ROW]
[ROW][C]53[/C][C] 1[/C][C] 10.61[/C][C]-9.608[/C][/ROW]
[ROW][C]54[/C][C] 11[/C][C] 6.496[/C][C] 4.504[/C][/ROW]
[ROW][C]55[/C][C] 1[/C][C] 6.642[/C][C]-5.642[/C][/ROW]
[ROW][C]56[/C][C] 3[/C][C] 8.16[/C][C]-5.16[/C][/ROW]
[ROW][C]57[/C][C] 6[/C][C] 7.436[/C][C]-1.436[/C][/ROW]
[ROW][C]58[/C][C] 13[/C][C] 9.189[/C][C] 3.811[/C][/ROW]
[ROW][C]59[/C][C] 5[/C][C] 5.933[/C][C]-0.9326[/C][/ROW]
[ROW][C]60[/C][C] 3[/C][C] 7.377[/C][C]-4.377[/C][/ROW]
[ROW][C]61[/C][C] 9[/C][C] 6.486[/C][C] 2.514[/C][/ROW]
[ROW][C]62[/C][C] 6[/C][C] 7.143[/C][C]-1.143[/C][/ROW]
[ROW][C]63[/C][C] 7[/C][C] 8.135[/C][C]-1.135[/C][/ROW]
[ROW][C]64[/C][C] 2[/C][C] 7.989[/C][C]-5.989[/C][/ROW]
[ROW][C]65[/C][C] 13[/C][C] 9.027[/C][C] 3.973[/C][/ROW]
[ROW][C]66[/C][C] 10[/C][C] 12.29[/C][C]-2.293[/C][/ROW]
[ROW][C]67[/C][C] 9[/C][C] 8.072[/C][C] 0.9279[/C][/ROW]
[ROW][C]68[/C][C] 1[/C][C] 6.104[/C][C]-5.104[/C][/ROW]
[ROW][C]69[/C][C] 2[/C][C] 6.569[/C][C]-4.569[/C][/ROW]
[ROW][C]70[/C][C] 4[/C][C] 6.814[/C][C]-2.814[/C][/ROW]
[ROW][C]71[/C][C] 4[/C][C] 7.754[/C][C]-3.754[/C][/ROW]
[ROW][C]72[/C][C] 9[/C][C] 7.915[/C][C] 1.085[/C][/ROW]
[ROW][C]73[/C][C] 4[/C][C] 5.723[/C][C]-1.723[/C][/ROW]
[ROW][C]74[/C][C] 8[/C][C] 6.726[/C][C] 1.274[/C][/ROW]
[ROW][C]75[/C][C] 10[/C][C] 6.746[/C][C] 3.254[/C][/ROW]
[ROW][C]76[/C][C] 6[/C][C] 7.039[/C][C]-1.039[/C][/ROW]
[ROW][C]77[/C][C] 10[/C][C] 5.776[/C][C] 4.224[/C][/ROW]
[ROW][C]78[/C][C] 2[/C][C] 3.897[/C][C]-1.897[/C][/ROW]
[ROW][C]79[/C][C] 4[/C][C] 6.716[/C][C]-2.716[/C][/ROW]
[ROW][C]80[/C][C] 2[/C][C] 6.976[/C][C]-4.976[/C][/ROW]
[ROW][C]81[/C][C] 2[/C][C] 4.758[/C][C]-2.758[/C][/ROW]
[ROW][C]82[/C][C] 5[/C][C] 6.246[/C][C]-1.246[/C][/ROW]
[ROW][C]83[/C][C] 4[/C][C] 5.933[/C][C]-1.933[/C][/ROW]
[ROW][C]84[/C][C] 5[/C][C] 8.772[/C][C]-3.772[/C][/ROW]
[ROW][C]85[/C][C] 11[/C][C] 5.854[/C][C] 5.146[/C][/ROW]
[ROW][C]86[/C][C] 7[/C][C] 8.494[/C][C]-1.494[/C][/ROW]
[ROW][C]87[/C][C] 19[/C][C] 7.279[/C][C] 11.72[/C][/ROW]
[ROW][C]88[/C][C] 8[/C][C] 5.811[/C][C] 2.189[/C][/ROW]
[ROW][C]89[/C][C] 14[/C][C] 6.089[/C][C] 7.911[/C][/ROW]
[ROW][C]90[/C][C] 7[/C][C] 9.105[/C][C]-2.105[/C][/ROW]
[ROW][C]91[/C][C] 6[/C][C] 8.949[/C][C]-2.949[/C][/ROW]
[ROW][C]92[/C][C] 16[/C][C] 9.752[/C][C] 6.248[/C][/ROW]
[ROW][C]93[/C][C] 10[/C][C] 7.299[/C][C] 2.701[/C][/ROW]
[ROW][C]94[/C][C] 1[/C][C] 4.915[/C][C]-3.915[/C][/ROW]
[ROW][C]95[/C][C] 8[/C][C] 6.168[/C][C] 1.832[/C][/ROW]
[ROW][C]96[/C][C] 9[/C][C] 7.504[/C][C] 1.496[/C][/ROW]
[ROW][C]97[/C][C] 6[/C][C] 7.817[/C][C]-1.817[/C][/ROW]
[ROW][C]98[/C][C] 14[/C][C] 7.91[/C][C] 6.09[/C][/ROW]
[ROW][C]99[/C][C] 5[/C][C] 7.524[/C][C]-2.524[/C][/ROW]
[ROW][C]100[/C][C] 6[/C][C] 8.39[/C][C]-2.39[/C][/ROW]
[ROW][C]101[/C][C] 9[/C][C] 6.173[/C][C] 2.827[/C][/ROW]
[ROW][C]102[/C][C] 10[/C][C] 7.289[/C][C] 2.711[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=308535&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=308535&T=5

As an alternative you can also use a QR Code:

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	1	6.246	-5.246
2	4	6.961	-2.961
3	8	10.07	-2.07
4	4	7.597	-3.597
5	1	5.859	-4.859
6	7	7.43	-0.4305
7	12	6.402	5.598
8	9	7.602	1.398
9	2	5.698	-3.698
10	1	4.304	-3.304
11	8	9.58	-1.58
12	7	7.524	-0.524
13	7	5.071	1.929
14	2	7.274	-5.274
15	3	6.016	-3.016
16	10	7.029	2.971
17	5	7.274	-2.274
18	4	6.637	-2.637
19	3	6.877	-3.877
20	13	7.612	5.388
21	11	6.721	4.279
22	3	7.357	-4.357
23	18	9.105	8.895
24	14	8.704	5.296
25	5	6.653	-1.653
26	12	5.541	6.459
27	11	7.769	3.231
28	9	8.709	0.2914
29	7	8.704	-1.704
30	3	7.509	-4.509
31	6	7.592	-1.592
32	8	6.726	1.274
33	6	6.887	-0.8874
34	1	5.938	-4.938
35	12	9.032	2.968
36	17	9.272	7.728
37	2	5.875	-3.875
38	13	7.43	5.57
39	3	4.685	-1.685
40	13	8.307	4.693
41	7	5.698	1.302
42	12	5.781	6.219
43	8	11.18	-3.181
44	5	7.842	-2.842
45	15	4.758	10.24
46	5	6.564	-1.564
47	12	10.95	1.048
48	8	7.441	0.5594
49	11	8.15	2.85
50	11	8.698	2.302
51	12	7.122	4.878
52	3	5.463	-2.463
53	1	10.61	-9.608
54	11	6.496	4.504
55	1	6.642	-5.642
56	3	8.16	-5.16
57	6	7.436	-1.436
58	13	9.189	3.811
59	5	5.933	-0.9326
60	3	7.377	-4.377
61	9	6.486	2.514
62	6	7.143	-1.143
63	7	8.135	-1.135
64	2	7.989	-5.989
65	13	9.027	3.973
66	10	12.29	-2.293
67	9	8.072	0.9279
68	1	6.104	-5.104
69	2	6.569	-4.569
70	4	6.814	-2.814
71	4	7.754	-3.754
72	9	7.915	1.085
73	4	5.723	-1.723
74	8	6.726	1.274
75	10	6.746	3.254
76	6	7.039	-1.039
77	10	5.776	4.224
78	2	3.897	-1.897
79	4	6.716	-2.716
80	2	6.976	-4.976
81	2	4.758	-2.758
82	5	6.246	-1.246
83	4	5.933	-1.933
84	5	8.772	-3.772
85	11	5.854	5.146
86	7	8.494	-1.494
87	19	7.279	11.72
88	8	5.811	2.189
89	14	6.089	7.911
90	7	9.105	-2.105
91	6	8.949	-2.949
92	16	9.752	6.248
93	10	7.299	2.701
94	1	4.915	-3.915
95	8	6.168	1.832
96	9	7.504	1.496
97	6	7.817	-1.817
98	14	7.91	6.09
99	5	7.524	-2.524
100	6	8.39	-2.39
101	9	6.173	2.827
102	10	7.289	2.711

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
6	0.04216	0.08432	0.9578
7	0.5743	0.8514	0.4257
8	0.5435	0.9131	0.4565
9	0.4169	0.8339	0.5831
10	0.3714	0.7428	0.6286
11	0.2697	0.5395	0.7303
12	0.1944	0.3888	0.8056
13	0.2048	0.4095	0.7952
14	0.2187	0.4375	0.7813
15	0.1633	0.3265	0.8367
16	0.1673	0.3346	0.8327
17	0.123	0.2461	0.877
18	0.09157	0.1831	0.9084
19	0.07689	0.1538	0.9231
20	0.1716	0.3432	0.8284
21	0.2227	0.4455	0.7773
22	0.2109	0.4218	0.7891
23	0.4716	0.9432	0.5284
24	0.4995	0.999	0.5005
25	0.4345	0.8689	0.5655
26	0.5975	0.805	0.4025
27	0.564	0.872	0.436
28	0.4986	0.9972	0.5014
29	0.4504	0.9008	0.5496
30	0.4525	0.9051	0.5475
31	0.396	0.792	0.604
32	0.3464	0.6929	0.6536
33	0.2912	0.5823	0.7088
34	0.2991	0.5983	0.7009
35	0.2632	0.5265	0.7368
36	0.355	0.7101	0.645
37	0.3448	0.6895	0.6552
38	0.4047	0.8094	0.5953
39	0.3562	0.7125	0.6438
40	0.3633	0.7267	0.6367
41	0.3261	0.6523	0.6739
42	0.4236	0.8472	0.5764
43	0.448	0.896	0.552
44	0.4195	0.839	0.5805
45	0.7142	0.5716	0.2858
46	0.6697	0.6605	0.3303
47	0.6222	0.7556	0.3778
48	0.5667	0.8666	0.4333
49	0.5358	0.9284	0.4642
50	0.4961	0.9921	0.5039
51	0.5178	0.9643	0.4822
52	0.4804	0.9608	0.5196
53	0.7148	0.5704	0.2852
54	0.7235	0.553	0.2765
55	0.7621	0.4758	0.2379
56	0.786	0.4281	0.214
57	0.7465	0.507	0.2535
58	0.7393	0.5214	0.2607
59	0.6919	0.6162	0.3081
60	0.6969	0.6061	0.3031
61	0.6624	0.6751	0.3376
62	0.6103	0.7794	0.3897
63	0.5565	0.8871	0.4435
64	0.6238	0.7524	0.3762
65	0.6163	0.7673	0.3837
66	0.5764	0.8472	0.4236
67	0.5173	0.9653	0.4827
68	0.5495	0.901	0.4505
69	0.5687	0.8626	0.4313
70	0.5406	0.9188	0.4594
71	0.5418	0.9164	0.4582
72	0.4796	0.9591	0.5204
73	0.431	0.8619	0.569
74	0.3707	0.7414	0.6293
75	0.3375	0.6751	0.6625
76	0.2863	0.5726	0.7137
77	0.2763	0.5525	0.7237
78	0.2349	0.4699	0.7651
79	0.2129	0.4259	0.7871
80	0.2518	0.5036	0.7482
81	0.2484	0.4968	0.7516
82	0.2134	0.4268	0.7866
83	0.2019	0.4038	0.7981
84	0.2128	0.4256	0.7872
85	0.1913	0.3826	0.8087
86	0.1561	0.3121	0.8439
87	0.5257	0.9486	0.4743
88	0.4386	0.8771	0.5614
89	0.6494	0.7012	0.3506
90	0.6292	0.7415	0.3708
91	0.7123	0.5755	0.2877
92	0.6623	0.6754	0.3377
93	0.5806	0.8388	0.4194
94	0.6578	0.6844	0.3422
95	0.5194	0.9612	0.4806
96	0.3594	0.7188	0.6406

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
6 &  0.04216 &  0.08432 &  0.9578 \tabularnewline
7 &  0.5743 &  0.8514 &  0.4257 \tabularnewline
8 &  0.5435 &  0.9131 &  0.4565 \tabularnewline
9 &  0.4169 &  0.8339 &  0.5831 \tabularnewline
10 &  0.3714 &  0.7428 &  0.6286 \tabularnewline
11 &  0.2697 &  0.5395 &  0.7303 \tabularnewline
12 &  0.1944 &  0.3888 &  0.8056 \tabularnewline
13 &  0.2048 &  0.4095 &  0.7952 \tabularnewline
14 &  0.2187 &  0.4375 &  0.7813 \tabularnewline
15 &  0.1633 &  0.3265 &  0.8367 \tabularnewline
16 &  0.1673 &  0.3346 &  0.8327 \tabularnewline
17 &  0.123 &  0.2461 &  0.877 \tabularnewline
18 &  0.09157 &  0.1831 &  0.9084 \tabularnewline
19 &  0.07689 &  0.1538 &  0.9231 \tabularnewline
20 &  0.1716 &  0.3432 &  0.8284 \tabularnewline
21 &  0.2227 &  0.4455 &  0.7773 \tabularnewline
22 &  0.2109 &  0.4218 &  0.7891 \tabularnewline
23 &  0.4716 &  0.9432 &  0.5284 \tabularnewline
24 &  0.4995 &  0.999 &  0.5005 \tabularnewline
25 &  0.4345 &  0.8689 &  0.5655 \tabularnewline
26 &  0.5975 &  0.805 &  0.4025 \tabularnewline
27 &  0.564 &  0.872 &  0.436 \tabularnewline
28 &  0.4986 &  0.9972 &  0.5014 \tabularnewline
29 &  0.4504 &  0.9008 &  0.5496 \tabularnewline
30 &  0.4525 &  0.9051 &  0.5475 \tabularnewline
31 &  0.396 &  0.792 &  0.604 \tabularnewline
32 &  0.3464 &  0.6929 &  0.6536 \tabularnewline
33 &  0.2912 &  0.5823 &  0.7088 \tabularnewline
34 &  0.2991 &  0.5983 &  0.7009 \tabularnewline
35 &  0.2632 &  0.5265 &  0.7368 \tabularnewline
36 &  0.355 &  0.7101 &  0.645 \tabularnewline
37 &  0.3448 &  0.6895 &  0.6552 \tabularnewline
38 &  0.4047 &  0.8094 &  0.5953 \tabularnewline
39 &  0.3562 &  0.7125 &  0.6438 \tabularnewline
40 &  0.3633 &  0.7267 &  0.6367 \tabularnewline
41 &  0.3261 &  0.6523 &  0.6739 \tabularnewline
42 &  0.4236 &  0.8472 &  0.5764 \tabularnewline
43 &  0.448 &  0.896 &  0.552 \tabularnewline
44 &  0.4195 &  0.839 &  0.5805 \tabularnewline
45 &  0.7142 &  0.5716 &  0.2858 \tabularnewline
46 &  0.6697 &  0.6605 &  0.3303 \tabularnewline
47 &  0.6222 &  0.7556 &  0.3778 \tabularnewline
48 &  0.5667 &  0.8666 &  0.4333 \tabularnewline
49 &  0.5358 &  0.9284 &  0.4642 \tabularnewline
50 &  0.4961 &  0.9921 &  0.5039 \tabularnewline
51 &  0.5178 &  0.9643 &  0.4822 \tabularnewline
52 &  0.4804 &  0.9608 &  0.5196 \tabularnewline
53 &  0.7148 &  0.5704 &  0.2852 \tabularnewline
54 &  0.7235 &  0.553 &  0.2765 \tabularnewline
55 &  0.7621 &  0.4758 &  0.2379 \tabularnewline
56 &  0.786 &  0.4281 &  0.214 \tabularnewline
57 &  0.7465 &  0.507 &  0.2535 \tabularnewline
58 &  0.7393 &  0.5214 &  0.2607 \tabularnewline
59 &  0.6919 &  0.6162 &  0.3081 \tabularnewline
60 &  0.6969 &  0.6061 &  0.3031 \tabularnewline
61 &  0.6624 &  0.6751 &  0.3376 \tabularnewline
62 &  0.6103 &  0.7794 &  0.3897 \tabularnewline
63 &  0.5565 &  0.8871 &  0.4435 \tabularnewline
64 &  0.6238 &  0.7524 &  0.3762 \tabularnewline
65 &  0.6163 &  0.7673 &  0.3837 \tabularnewline
66 &  0.5764 &  0.8472 &  0.4236 \tabularnewline
67 &  0.5173 &  0.9653 &  0.4827 \tabularnewline
68 &  0.5495 &  0.901 &  0.4505 \tabularnewline
69 &  0.5687 &  0.8626 &  0.4313 \tabularnewline
70 &  0.5406 &  0.9188 &  0.4594 \tabularnewline
71 &  0.5418 &  0.9164 &  0.4582 \tabularnewline
72 &  0.4796 &  0.9591 &  0.5204 \tabularnewline
73 &  0.431 &  0.8619 &  0.569 \tabularnewline
74 &  0.3707 &  0.7414 &  0.6293 \tabularnewline
75 &  0.3375 &  0.6751 &  0.6625 \tabularnewline
76 &  0.2863 &  0.5726 &  0.7137 \tabularnewline
77 &  0.2763 &  0.5525 &  0.7237 \tabularnewline
78 &  0.2349 &  0.4699 &  0.7651 \tabularnewline
79 &  0.2129 &  0.4259 &  0.7871 \tabularnewline
80 &  0.2518 &  0.5036 &  0.7482 \tabularnewline
81 &  0.2484 &  0.4968 &  0.7516 \tabularnewline
82 &  0.2134 &  0.4268 &  0.7866 \tabularnewline
83 &  0.2019 &  0.4038 &  0.7981 \tabularnewline
84 &  0.2128 &  0.4256 &  0.7872 \tabularnewline
85 &  0.1913 &  0.3826 &  0.8087 \tabularnewline
86 &  0.1561 &  0.3121 &  0.8439 \tabularnewline
87 &  0.5257 &  0.9486 &  0.4743 \tabularnewline
88 &  0.4386 &  0.8771 &  0.5614 \tabularnewline
89 &  0.6494 &  0.7012 &  0.3506 \tabularnewline
90 &  0.6292 &  0.7415 &  0.3708 \tabularnewline
91 &  0.7123 &  0.5755 &  0.2877 \tabularnewline
92 &  0.6623 &  0.6754 &  0.3377 \tabularnewline
93 &  0.5806 &  0.8388 &  0.4194 \tabularnewline
94 &  0.6578 &  0.6844 &  0.3422 \tabularnewline
95 &  0.5194 &  0.9612 &  0.4806 \tabularnewline
96 &  0.3594 &  0.7188 &  0.6406 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=308535&T=6

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]6[/C][C] 0.04216[/C][C] 0.08432[/C][C] 0.9578[/C][/ROW]
[ROW][C]7[/C][C] 0.5743[/C][C] 0.8514[/C][C] 0.4257[/C][/ROW]
[ROW][C]8[/C][C] 0.5435[/C][C] 0.9131[/C][C] 0.4565[/C][/ROW]
[ROW][C]9[/C][C] 0.4169[/C][C] 0.8339[/C][C] 0.5831[/C][/ROW]
[ROW][C]10[/C][C] 0.3714[/C][C] 0.7428[/C][C] 0.6286[/C][/ROW]
[ROW][C]11[/C][C] 0.2697[/C][C] 0.5395[/C][C] 0.7303[/C][/ROW]
[ROW][C]12[/C][C] 0.1944[/C][C] 0.3888[/C][C] 0.8056[/C][/ROW]
[ROW][C]13[/C][C] 0.2048[/C][C] 0.4095[/C][C] 0.7952[/C][/ROW]
[ROW][C]14[/C][C] 0.2187[/C][C] 0.4375[/C][C] 0.7813[/C][/ROW]
[ROW][C]15[/C][C] 0.1633[/C][C] 0.3265[/C][C] 0.8367[/C][/ROW]
[ROW][C]16[/C][C] 0.1673[/C][C] 0.3346[/C][C] 0.8327[/C][/ROW]
[ROW][C]17[/C][C] 0.123[/C][C] 0.2461[/C][C] 0.877[/C][/ROW]
[ROW][C]18[/C][C] 0.09157[/C][C] 0.1831[/C][C] 0.9084[/C][/ROW]
[ROW][C]19[/C][C] 0.07689[/C][C] 0.1538[/C][C] 0.9231[/C][/ROW]
[ROW][C]20[/C][C] 0.1716[/C][C] 0.3432[/C][C] 0.8284[/C][/ROW]
[ROW][C]21[/C][C] 0.2227[/C][C] 0.4455[/C][C] 0.7773[/C][/ROW]
[ROW][C]22[/C][C] 0.2109[/C][C] 0.4218[/C][C] 0.7891[/C][/ROW]
[ROW][C]23[/C][C] 0.4716[/C][C] 0.9432[/C][C] 0.5284[/C][/ROW]
[ROW][C]24[/C][C] 0.4995[/C][C] 0.999[/C][C] 0.5005[/C][/ROW]
[ROW][C]25[/C][C] 0.4345[/C][C] 0.8689[/C][C] 0.5655[/C][/ROW]
[ROW][C]26[/C][C] 0.5975[/C][C] 0.805[/C][C] 0.4025[/C][/ROW]
[ROW][C]27[/C][C] 0.564[/C][C] 0.872[/C][C] 0.436[/C][/ROW]
[ROW][C]28[/C][C] 0.4986[/C][C] 0.9972[/C][C] 0.5014[/C][/ROW]
[ROW][C]29[/C][C] 0.4504[/C][C] 0.9008[/C][C] 0.5496[/C][/ROW]
[ROW][C]30[/C][C] 0.4525[/C][C] 0.9051[/C][C] 0.5475[/C][/ROW]
[ROW][C]31[/C][C] 0.396[/C][C] 0.792[/C][C] 0.604[/C][/ROW]
[ROW][C]32[/C][C] 0.3464[/C][C] 0.6929[/C][C] 0.6536[/C][/ROW]
[ROW][C]33[/C][C] 0.2912[/C][C] 0.5823[/C][C] 0.7088[/C][/ROW]
[ROW][C]34[/C][C] 0.2991[/C][C] 0.5983[/C][C] 0.7009[/C][/ROW]
[ROW][C]35[/C][C] 0.2632[/C][C] 0.5265[/C][C] 0.7368[/C][/ROW]
[ROW][C]36[/C][C] 0.355[/C][C] 0.7101[/C][C] 0.645[/C][/ROW]
[ROW][C]37[/C][C] 0.3448[/C][C] 0.6895[/C][C] 0.6552[/C][/ROW]
[ROW][C]38[/C][C] 0.4047[/C][C] 0.8094[/C][C] 0.5953[/C][/ROW]
[ROW][C]39[/C][C] 0.3562[/C][C] 0.7125[/C][C] 0.6438[/C][/ROW]
[ROW][C]40[/C][C] 0.3633[/C][C] 0.7267[/C][C] 0.6367[/C][/ROW]
[ROW][C]41[/C][C] 0.3261[/C][C] 0.6523[/C][C] 0.6739[/C][/ROW]
[ROW][C]42[/C][C] 0.4236[/C][C] 0.8472[/C][C] 0.5764[/C][/ROW]
[ROW][C]43[/C][C] 0.448[/C][C] 0.896[/C][C] 0.552[/C][/ROW]
[ROW][C]44[/C][C] 0.4195[/C][C] 0.839[/C][C] 0.5805[/C][/ROW]
[ROW][C]45[/C][C] 0.7142[/C][C] 0.5716[/C][C] 0.2858[/C][/ROW]
[ROW][C]46[/C][C] 0.6697[/C][C] 0.6605[/C][C] 0.3303[/C][/ROW]
[ROW][C]47[/C][C] 0.6222[/C][C] 0.7556[/C][C] 0.3778[/C][/ROW]
[ROW][C]48[/C][C] 0.5667[/C][C] 0.8666[/C][C] 0.4333[/C][/ROW]
[ROW][C]49[/C][C] 0.5358[/C][C] 0.9284[/C][C] 0.4642[/C][/ROW]
[ROW][C]50[/C][C] 0.4961[/C][C] 0.9921[/C][C] 0.5039[/C][/ROW]
[ROW][C]51[/C][C] 0.5178[/C][C] 0.9643[/C][C] 0.4822[/C][/ROW]
[ROW][C]52[/C][C] 0.4804[/C][C] 0.9608[/C][C] 0.5196[/C][/ROW]
[ROW][C]53[/C][C] 0.7148[/C][C] 0.5704[/C][C] 0.2852[/C][/ROW]
[ROW][C]54[/C][C] 0.7235[/C][C] 0.553[/C][C] 0.2765[/C][/ROW]
[ROW][C]55[/C][C] 0.7621[/C][C] 0.4758[/C][C] 0.2379[/C][/ROW]
[ROW][C]56[/C][C] 0.786[/C][C] 0.4281[/C][C] 0.214[/C][/ROW]
[ROW][C]57[/C][C] 0.7465[/C][C] 0.507[/C][C] 0.2535[/C][/ROW]
[ROW][C]58[/C][C] 0.7393[/C][C] 0.5214[/C][C] 0.2607[/C][/ROW]
[ROW][C]59[/C][C] 0.6919[/C][C] 0.6162[/C][C] 0.3081[/C][/ROW]
[ROW][C]60[/C][C] 0.6969[/C][C] 0.6061[/C][C] 0.3031[/C][/ROW]
[ROW][C]61[/C][C] 0.6624[/C][C] 0.6751[/C][C] 0.3376[/C][/ROW]
[ROW][C]62[/C][C] 0.6103[/C][C] 0.7794[/C][C] 0.3897[/C][/ROW]
[ROW][C]63[/C][C] 0.5565[/C][C] 0.8871[/C][C] 0.4435[/C][/ROW]
[ROW][C]64[/C][C] 0.6238[/C][C] 0.7524[/C][C] 0.3762[/C][/ROW]
[ROW][C]65[/C][C] 0.6163[/C][C] 0.7673[/C][C] 0.3837[/C][/ROW]
[ROW][C]66[/C][C] 0.5764[/C][C] 0.8472[/C][C] 0.4236[/C][/ROW]
[ROW][C]67[/C][C] 0.5173[/C][C] 0.9653[/C][C] 0.4827[/C][/ROW]
[ROW][C]68[/C][C] 0.5495[/C][C] 0.901[/C][C] 0.4505[/C][/ROW]
[ROW][C]69[/C][C] 0.5687[/C][C] 0.8626[/C][C] 0.4313[/C][/ROW]
[ROW][C]70[/C][C] 0.5406[/C][C] 0.9188[/C][C] 0.4594[/C][/ROW]
[ROW][C]71[/C][C] 0.5418[/C][C] 0.9164[/C][C] 0.4582[/C][/ROW]
[ROW][C]72[/C][C] 0.4796[/C][C] 0.9591[/C][C] 0.5204[/C][/ROW]
[ROW][C]73[/C][C] 0.431[/C][C] 0.8619[/C][C] 0.569[/C][/ROW]
[ROW][C]74[/C][C] 0.3707[/C][C] 0.7414[/C][C] 0.6293[/C][/ROW]
[ROW][C]75[/C][C] 0.3375[/C][C] 0.6751[/C][C] 0.6625[/C][/ROW]
[ROW][C]76[/C][C] 0.2863[/C][C] 0.5726[/C][C] 0.7137[/C][/ROW]
[ROW][C]77[/C][C] 0.2763[/C][C] 0.5525[/C][C] 0.7237[/C][/ROW]
[ROW][C]78[/C][C] 0.2349[/C][C] 0.4699[/C][C] 0.7651[/C][/ROW]
[ROW][C]79[/C][C] 0.2129[/C][C] 0.4259[/C][C] 0.7871[/C][/ROW]
[ROW][C]80[/C][C] 0.2518[/C][C] 0.5036[/C][C] 0.7482[/C][/ROW]
[ROW][C]81[/C][C] 0.2484[/C][C] 0.4968[/C][C] 0.7516[/C][/ROW]
[ROW][C]82[/C][C] 0.2134[/C][C] 0.4268[/C][C] 0.7866[/C][/ROW]
[ROW][C]83[/C][C] 0.2019[/C][C] 0.4038[/C][C] 0.7981[/C][/ROW]
[ROW][C]84[/C][C] 0.2128[/C][C] 0.4256[/C][C] 0.7872[/C][/ROW]
[ROW][C]85[/C][C] 0.1913[/C][C] 0.3826[/C][C] 0.8087[/C][/ROW]
[ROW][C]86[/C][C] 0.1561[/C][C] 0.3121[/C][C] 0.8439[/C][/ROW]
[ROW][C]87[/C][C] 0.5257[/C][C] 0.9486[/C][C] 0.4743[/C][/ROW]
[ROW][C]88[/C][C] 0.4386[/C][C] 0.8771[/C][C] 0.5614[/C][/ROW]
[ROW][C]89[/C][C] 0.6494[/C][C] 0.7012[/C][C] 0.3506[/C][/ROW]
[ROW][C]90[/C][C] 0.6292[/C][C] 0.7415[/C][C] 0.3708[/C][/ROW]
[ROW][C]91[/C][C] 0.7123[/C][C] 0.5755[/C][C] 0.2877[/C][/ROW]
[ROW][C]92[/C][C] 0.6623[/C][C] 0.6754[/C][C] 0.3377[/C][/ROW]
[ROW][C]93[/C][C] 0.5806[/C][C] 0.8388[/C][C] 0.4194[/C][/ROW]
[ROW][C]94[/C][C] 0.6578[/C][C] 0.6844[/C][C] 0.3422[/C][/ROW]
[ROW][C]95[/C][C] 0.5194[/C][C] 0.9612[/C][C] 0.4806[/C][/ROW]
[ROW][C]96[/C][C] 0.3594[/C][C] 0.7188[/C][C] 0.6406[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=308535&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=308535&T=6

As an alternative you can also use a QR Code:

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

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
6	0.04216	0.08432	0.9578
7	0.5743	0.8514	0.4257
8	0.5435	0.9131	0.4565
9	0.4169	0.8339	0.5831
10	0.3714	0.7428	0.6286
11	0.2697	0.5395	0.7303
12	0.1944	0.3888	0.8056
13	0.2048	0.4095	0.7952
14	0.2187	0.4375	0.7813
15	0.1633	0.3265	0.8367
16	0.1673	0.3346	0.8327
17	0.123	0.2461	0.877
18	0.09157	0.1831	0.9084
19	0.07689	0.1538	0.9231
20	0.1716	0.3432	0.8284
21	0.2227	0.4455	0.7773
22	0.2109	0.4218	0.7891
23	0.4716	0.9432	0.5284
24	0.4995	0.999	0.5005
25	0.4345	0.8689	0.5655
26	0.5975	0.805	0.4025
27	0.564	0.872	0.436
28	0.4986	0.9972	0.5014
29	0.4504	0.9008	0.5496
30	0.4525	0.9051	0.5475
31	0.396	0.792	0.604
32	0.3464	0.6929	0.6536
33	0.2912	0.5823	0.7088
34	0.2991	0.5983	0.7009
35	0.2632	0.5265	0.7368
36	0.355	0.7101	0.645
37	0.3448	0.6895	0.6552
38	0.4047	0.8094	0.5953
39	0.3562	0.7125	0.6438
40	0.3633	0.7267	0.6367
41	0.3261	0.6523	0.6739
42	0.4236	0.8472	0.5764
43	0.448	0.896	0.552
44	0.4195	0.839	0.5805
45	0.7142	0.5716	0.2858
46	0.6697	0.6605	0.3303
47	0.6222	0.7556	0.3778
48	0.5667	0.8666	0.4333
49	0.5358	0.9284	0.4642
50	0.4961	0.9921	0.5039
51	0.5178	0.9643	0.4822
52	0.4804	0.9608	0.5196
53	0.7148	0.5704	0.2852
54	0.7235	0.553	0.2765
55	0.7621	0.4758	0.2379
56	0.786	0.4281	0.214
57	0.7465	0.507	0.2535
58	0.7393	0.5214	0.2607
59	0.6919	0.6162	0.3081
60	0.6969	0.6061	0.3031
61	0.6624	0.6751	0.3376
62	0.6103	0.7794	0.3897
63	0.5565	0.8871	0.4435
64	0.6238	0.7524	0.3762
65	0.6163	0.7673	0.3837
66	0.5764	0.8472	0.4236
67	0.5173	0.9653	0.4827
68	0.5495	0.901	0.4505
69	0.5687	0.8626	0.4313
70	0.5406	0.9188	0.4594
71	0.5418	0.9164	0.4582
72	0.4796	0.9591	0.5204
73	0.431	0.8619	0.569
74	0.3707	0.7414	0.6293
75	0.3375	0.6751	0.6625
76	0.2863	0.5726	0.7137
77	0.2763	0.5525	0.7237
78	0.2349	0.4699	0.7651
79	0.2129	0.4259	0.7871
80	0.2518	0.5036	0.7482
81	0.2484	0.4968	0.7516
82	0.2134	0.4268	0.7866
83	0.2019	0.4038	0.7981
84	0.2128	0.4256	0.7872
85	0.1913	0.3826	0.8087
86	0.1561	0.3121	0.8439
87	0.5257	0.9486	0.4743
88	0.4386	0.8771	0.5614
89	0.6494	0.7012	0.3506
90	0.6292	0.7415	0.3708
91	0.7123	0.5755	0.2877
92	0.6623	0.6754	0.3377
93	0.5806	0.8388	0.4194
94	0.6578	0.6844	0.3422
95	0.5194	0.9612	0.4806
96	0.3594	0.7188	0.6406

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	0	0	OK
5% type I error level	0	0	OK
10% type I error level	1	0.010989	OK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 &  0 & OK \tabularnewline
5% type I error level & 0 & 0 & OK \tabularnewline
10% type I error level & 1 & 0.010989 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=308535&T=7

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C] 0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]1[/C][C]0.010989[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=308535&T=7

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=308535&T=7

As an alternative you can also use a QR Code:

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	0	0	OK
5% type I error level	0	0	OK
10% type I error level	1	0.010989	OK

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted RESET test data: mylm RESET = 0.76019, df1 = 2, df2 = 97, p-value = 0.4703
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 0.96752, df1 = 4, df2 = 95, p-value = 0.4291
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 1.0101, df1 = 2, df2 = 97, p-value = 0.368

\begin{tabular}{lllllllll}
\hline
Ramsey RESET F-Test for powers (2 and 3) of fitted values \tabularnewline
> reset_test_fitted
	RESET test
data:  mylm
RESET = 0.76019, df1 = 2, df2 = 97, p-value = 0.4703
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of regressors \tabularnewline
> reset_test_regressors
	RESET test
data:  mylm
RESET = 0.96752, df1 = 4, df2 = 95, p-value = 0.4291
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of principal components \tabularnewline
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 1.0101, df1 = 2, df2 = 97, p-value = 0.368
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=308535&T=8

[TABLE]
[ROW][C]Ramsey RESET F-Test for powers (2 and 3) of fitted values[/C][/ROW]
[ROW][C]> reset_test_fitted
	RESET test
data:  mylm
RESET = 0.76019, df1 = 2, df2 = 97, p-value = 0.4703
[/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 = 0.96752, df1 = 4, df2 = 95, p-value = 0.4291
[/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 = 1.0101, df1 = 2, df2 = 97, p-value = 0.368
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=308535&T=8

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=308535&T=8

As an alternative you can also use a QR Code:

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

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted RESET test data: mylm RESET = 0.76019, df1 = 2, df2 = 97, p-value = 0.4703
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 0.96752, df1 = 4, df2 = 95, p-value = 0.4291
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 1.0101, df1 = 2, df2 = 97, p-value = 0.368

Variance Inflation Factors (Multicollinearity)
> vif Last `Ratio\\r` 1.000267 1.000267

\begin{tabular}{lllllllll}
\hline
Variance Inflation Factors (Multicollinearity) \tabularnewline
> vif
      Last `Ratio\\r` 
  1.000267   1.000267 
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=308535&T=9

[TABLE]
[ROW][C]Variance Inflation Factors (Multicollinearity)[/C][/ROW]
[ROW][C]> vif
      Last `Ratio\\r` 
  1.000267   1.000267 
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=308535&T=9

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=308535&T=9

As an alternative you can also use a QR Code:

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

Variance Inflation Factors (Multicollinearity)
> vif Last `Ratio\\r` 1.000267 1.000267

Figure 1

PNG link

Postscript link

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Figure 2

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Figure 3

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Figure 4

PNG link

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Figure 5

PNG link

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Figure 6

PNG link

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Figure 7

PNG link

Postscript link

PDF link

Figure 8

PNG link

Postscript link

PDF link

Figure 9

PNG link

Postscript link

PDF link

Figure 10

PNG link

Postscript link

PDF link

Parameters (Session):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ; par4 = 0 ; par5 = 0 ; par6 = 12 ;

Parameters (R input):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ; par4 = 0 ; par5 = 0 ; par6 = 12 ;

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

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

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code