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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Tue, 16 Dec 2014 11:10:20 +0000

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2014/Dec/16/t1418728230lzcrye7h5jt2tem.htm/, Retrieved Thu, 16 May 2024 15:51:01 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=269322, Retrieved Thu, 16 May 2024 15:51:01 +0000

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Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)

-       [Multiple Regression] [] [2014-12-16 11:10:20] [d6d52749fb51c32aec4577a0cf80c32e] [Current]

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1.8 12.9
2.1 12.2
2.2 12.8
2.3 7.4
2.1 6.7
2.7 12.6
2.1 14.8
2.4 13.3
2.9 11.1
2.2 8.2
2.1 11.4
2.2 6.4
2.2 10.6
2.7 12
1.9 6.3
2 11.3
2.5 11.9
2.2 9.3
2.3 9.6
1.9 10
2.1 6.4
3.5 13.8
2.1 10.8
2.3 13.8
2.3 11.7
2.2 10.9
3.5 16.1
1.9 13.4
1.9 9.9
1.9 11.5
1.9 8.3
2.1 11.7
2 9
3.2 9.7
2.3 10.8
2.5 10.3
1.8 10.4
2.4 12.7
2.8 9.3
2.3 11.8
2 5.9
2.5 11.4
2.3 13
1.8 10.8
1.9 12.3
2.6 11.3
2 11.8
2.6 7.9
1.6 12.7
2.2 12.3
2.1 11.6
1.8 6.7
1.8 10.9
1.9 12.1
2.4 13.3
1.9 10.1
2 5.7
2.1 14.3
1.7 8
1.9 13.3
2.1 9.3
2.4 12.5
1.8 7.6
2.3 15.9
2.1 9.2
2 9.1
2.8 11.1
2 13
2.7 14.5
2.1 12.2
2.9 12.3
2 11.4
1.8 8.8
2.6 14.6
2.1 12.6
2.3 NA
2.3 13
2.2 12.6
2 13.2
2.2 9.9
2.1 7.7
2.1 10.5
1.9 13.4
2 10.9
1.7 4.3
2.2 10.3
2.2 11.8
2.3 11.2
2.4 11.4
2.1 8.6
1.9 13.2
1.7 12.6
1.8 5.6
1.5 9.9
1.9 8.8
1.9 7.7
1.7 9
1.9 7.3
1.9 11.4
1.8 13.6
2.4 7.9
1.8 10.7
1.9 10.3
1.8 8.3
2.1 9.6
1.9 14.2
2.2 8.5
2 13.5
1.7 4.9
1.7 6.4
1.8 9.6
1.9 11.6

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	6 seconds
R Server	'Sir Ronald Aylmer Fisher' @ fisher.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 6 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ fisher.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=269322&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]6 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Ronald Aylmer Fisher' @ fisher.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=269322&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=269322&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	6 seconds
R Server	'Sir Ronald Aylmer Fisher' @ fisher.wessa.net

Multiple Linear Regression - Estimated Regression Equation
TOT[t] = + 5.82957 + 2.26913PR[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
TOT[t] =  +  5.82957 +  2.26913PR[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=269322&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]TOT[t] =  +  5.82957 +  2.26913PR[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=269322&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=269322&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
TOT[t] = + 5.82957 + 2.26913PR[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)	5.82957	1.36083	4.284	3.97369e-05	1.98684e-05
PR	2.26913	0.627361	3.617	0.000453236	0.000226618

\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) & 5.82957 & 1.36083 & 4.284 & 3.97369e-05 & 1.98684e-05 \tabularnewline
PR & 2.26913 & 0.627361 & 3.617 & 0.000453236 & 0.000226618 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=269322&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]5.82957[/C][C]1.36083[/C][C]4.284[/C][C]3.97369e-05[/C][C]1.98684e-05[/C][/ROW]
[ROW][C]PR[/C][C]2.26913[/C][C]0.627361[/C][C]3.617[/C][C]0.000453236[/C][C]0.000226618[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=269322&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=269322&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)	5.82957	1.36083	4.284	3.97369e-05	1.98684e-05
PR	2.26913	0.627361	3.617	0.000453236	0.000226618

Multiple Linear Regression - Regression Statistics
Multiple R	0.327352
R-squared	0.10716
Adjusted R-squared	0.0989683
F-TEST (value)	13.0823
F-TEST (DF numerator)	1
F-TEST (DF denominator)	109
p-value	0.000453236
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	2.3562
Sum Squared Residuals	605.135

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.327352 \tabularnewline
R-squared & 0.10716 \tabularnewline
Adjusted R-squared & 0.0989683 \tabularnewline
F-TEST (value) & 13.0823 \tabularnewline
F-TEST (DF numerator) & 1 \tabularnewline
F-TEST (DF denominator) & 109 \tabularnewline
p-value & 0.000453236 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.3562 \tabularnewline
Sum Squared Residuals & 605.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=269322&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.327352[/C][/ROW]
[ROW][C]R-squared[/C][C]0.10716[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.0989683[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]13.0823[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]1[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]109[/C][/ROW]
[ROW][C]p-value[/C][C]0.000453236[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]2.3562[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]605.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=269322&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=269322&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	0.327352
R-squared	0.10716
Adjusted R-squared	0.0989683
F-TEST (value)	13.0823
F-TEST (DF numerator)	1
F-TEST (DF denominator)	109
p-value	0.000453236
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	2.3562
Sum Squared Residuals	605.135

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	12.9	9.914	2.986
2	12.2	10.5947	1.60526
3	12.8	10.8217	1.97835
4	7.4	11.0486	-3.64856
5	6.7	10.5947	-3.89474
6	12.6	11.9562	0.643786
7	14.8	10.5947	4.20526
8	13.3	11.2755	2.02452
9	11.1	12.41	-1.31004
10	8.2	10.8217	-2.62165
11	11.4	10.5947	0.805263
12	6.4	10.8217	-4.42165
13	10.6	10.8217	-0.22165
14	12	11.9562	0.0437856
15	6.3	10.1409	-3.84091
16	11.3	10.3678	0.932176
17	11.9	11.5024	0.397611
18	9.3	10.8217	-1.52165
19	9.6	11.0486	-1.44856
20	10	10.1409	-0.140912
21	6.4	10.5947	-4.19474
22	13.8	13.7715	0.0284827
23	10.8	10.5947	0.205263
24	13.8	11.0486	2.75144
25	11.7	11.0486	0.651437
26	10.9	10.8217	0.0783499
27	16.1	13.7715	2.32848
28	13.4	10.1409	3.25909
29	9.9	10.1409	-0.240912
30	11.5	10.1409	1.35909
31	8.3	10.1409	-1.84091
32	11.7	10.5947	1.10526
33	9	10.3678	-1.36782
34	9.7	13.0908	-3.39078
35	10.8	11.0486	-0.248563
36	10.3	11.5024	-1.20239
37	10.4	9.914	0.486001
38	12.7	11.2755	1.42452
39	9.3	12.1831	-2.88313
40	11.8	11.0486	0.751437
41	5.9	10.3678	-4.46782
42	11.4	11.5024	-0.102389
43	13	11.0486	1.95144
44	10.8	9.914	0.886001
45	12.3	10.1409	2.15909
46	11.3	11.7293	-0.429302
47	11.8	10.3678	1.43218
48	7.9	11.7293	-3.8293
49	12.7	9.46017	3.23983
50	12.3	10.8217	1.47835
51	11.6	10.5947	1.00526
52	6.7	9.914	-3.214
53	10.9	9.914	0.986001
54	12.1	10.1409	1.95909
55	13.3	11.2755	2.02452
56	10.1	10.1409	-0.0409115
57	5.7	10.3678	-4.66782
58	14.3	10.5947	3.70526
59	8	9.68709	-1.68709
60	13.3	10.1409	3.15909
61	9.3	10.5947	-1.29474
62	12.5	11.2755	1.22452
63	7.6	9.914	-2.314
64	15.9	11.0486	4.85144
65	9.2	10.5947	-1.39474
66	9.1	10.3678	-1.26782
67	11.1	12.1831	-1.08313
68	13	10.3678	2.63218
69	14.5	11.9562	2.54379
70	12.2	10.5947	1.60526
71	12.3	12.41	-0.11004
72	11.4	10.3678	1.03218
73	8.8	9.914	-1.114
74	14.6	11.7293	2.8707
75	12.6	10.5947	2.00526
76	NA	NA	1.95144
77	13	11.2217	1.77835
78	12.6	9.76782	2.83218
79	13.2	14.1217	-0.92165
80	9.9	12.7947	-2.89474
81	7.7	7.79474	-0.0947372
82	10.5	7.24091	3.25909
83	13.4	12.8678	0.532176
84	10.9	16.2871	-5.38709
85	4.3	4.82165	-0.52165
86	10.3	9.32165	0.97835
87	11.8	11.6486	0.151437
88	11.2	11.0755	0.124524
89	11.4	13.3947	-1.99474
90	8.6	5.54091	3.05909
91	13.2	10.2871	2.91291
92	12.6	16.914	-4.314
93	5.6	4.93326	0.66674
94	9.9	11.2409	-1.34091
95	8.8	11.2409	-2.44091
96	7.7	8.38709	-0.687086
97	9	11.8409	-2.84091
98	7.3	6.04091	1.25909
99	11.4	7.714	3.686
100	13.6	16.9755	-3.37548
101	7.9	7.114	0.786001
102	10.7	10.5409	0.159088
103	10.3	11.914	-1.614
104	8.3	9.29474	-0.994737
105	9.6	5.54091	4.05909
106	14.2	16.5217	-2.32165
107	8.5	5.36782	3.13218
108	13.5	18.2871	-4.78709
109	4.9	8.18709	-3.28709
110	6.4	6.714	-0.313999
111	9.6	8.14091	1.45909
112	11.6	NA	NA

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 12.9 & 9.914 & 2.986 \tabularnewline
2 & 12.2 & 10.5947 & 1.60526 \tabularnewline
3 & 12.8 & 10.8217 & 1.97835 \tabularnewline
4 & 7.4 & 11.0486 & -3.64856 \tabularnewline
5 & 6.7 & 10.5947 & -3.89474 \tabularnewline
6 & 12.6 & 11.9562 & 0.643786 \tabularnewline
7 & 14.8 & 10.5947 & 4.20526 \tabularnewline
8 & 13.3 & 11.2755 & 2.02452 \tabularnewline
9 & 11.1 & 12.41 & -1.31004 \tabularnewline
10 & 8.2 & 10.8217 & -2.62165 \tabularnewline
11 & 11.4 & 10.5947 & 0.805263 \tabularnewline
12 & 6.4 & 10.8217 & -4.42165 \tabularnewline
13 & 10.6 & 10.8217 & -0.22165 \tabularnewline
14 & 12 & 11.9562 & 0.0437856 \tabularnewline
15 & 6.3 & 10.1409 & -3.84091 \tabularnewline
16 & 11.3 & 10.3678 & 0.932176 \tabularnewline
17 & 11.9 & 11.5024 & 0.397611 \tabularnewline
18 & 9.3 & 10.8217 & -1.52165 \tabularnewline
19 & 9.6 & 11.0486 & -1.44856 \tabularnewline
20 & 10 & 10.1409 & -0.140912 \tabularnewline
21 & 6.4 & 10.5947 & -4.19474 \tabularnewline
22 & 13.8 & 13.7715 & 0.0284827 \tabularnewline
23 & 10.8 & 10.5947 & 0.205263 \tabularnewline
24 & 13.8 & 11.0486 & 2.75144 \tabularnewline
25 & 11.7 & 11.0486 & 0.651437 \tabularnewline
26 & 10.9 & 10.8217 & 0.0783499 \tabularnewline
27 & 16.1 & 13.7715 & 2.32848 \tabularnewline
28 & 13.4 & 10.1409 & 3.25909 \tabularnewline
29 & 9.9 & 10.1409 & -0.240912 \tabularnewline
30 & 11.5 & 10.1409 & 1.35909 \tabularnewline
31 & 8.3 & 10.1409 & -1.84091 \tabularnewline
32 & 11.7 & 10.5947 & 1.10526 \tabularnewline
33 & 9 & 10.3678 & -1.36782 \tabularnewline
34 & 9.7 & 13.0908 & -3.39078 \tabularnewline
35 & 10.8 & 11.0486 & -0.248563 \tabularnewline
36 & 10.3 & 11.5024 & -1.20239 \tabularnewline
37 & 10.4 & 9.914 & 0.486001 \tabularnewline
38 & 12.7 & 11.2755 & 1.42452 \tabularnewline
39 & 9.3 & 12.1831 & -2.88313 \tabularnewline
40 & 11.8 & 11.0486 & 0.751437 \tabularnewline
41 & 5.9 & 10.3678 & -4.46782 \tabularnewline
42 & 11.4 & 11.5024 & -0.102389 \tabularnewline
43 & 13 & 11.0486 & 1.95144 \tabularnewline
44 & 10.8 & 9.914 & 0.886001 \tabularnewline
45 & 12.3 & 10.1409 & 2.15909 \tabularnewline
46 & 11.3 & 11.7293 & -0.429302 \tabularnewline
47 & 11.8 & 10.3678 & 1.43218 \tabularnewline
48 & 7.9 & 11.7293 & -3.8293 \tabularnewline
49 & 12.7 & 9.46017 & 3.23983 \tabularnewline
50 & 12.3 & 10.8217 & 1.47835 \tabularnewline
51 & 11.6 & 10.5947 & 1.00526 \tabularnewline
52 & 6.7 & 9.914 & -3.214 \tabularnewline
53 & 10.9 & 9.914 & 0.986001 \tabularnewline
54 & 12.1 & 10.1409 & 1.95909 \tabularnewline
55 & 13.3 & 11.2755 & 2.02452 \tabularnewline
56 & 10.1 & 10.1409 & -0.0409115 \tabularnewline
57 & 5.7 & 10.3678 & -4.66782 \tabularnewline
58 & 14.3 & 10.5947 & 3.70526 \tabularnewline
59 & 8 & 9.68709 & -1.68709 \tabularnewline
60 & 13.3 & 10.1409 & 3.15909 \tabularnewline
61 & 9.3 & 10.5947 & -1.29474 \tabularnewline
62 & 12.5 & 11.2755 & 1.22452 \tabularnewline
63 & 7.6 & 9.914 & -2.314 \tabularnewline
64 & 15.9 & 11.0486 & 4.85144 \tabularnewline
65 & 9.2 & 10.5947 & -1.39474 \tabularnewline
66 & 9.1 & 10.3678 & -1.26782 \tabularnewline
67 & 11.1 & 12.1831 & -1.08313 \tabularnewline
68 & 13 & 10.3678 & 2.63218 \tabularnewline
69 & 14.5 & 11.9562 & 2.54379 \tabularnewline
70 & 12.2 & 10.5947 & 1.60526 \tabularnewline
71 & 12.3 & 12.41 & -0.11004 \tabularnewline
72 & 11.4 & 10.3678 & 1.03218 \tabularnewline
73 & 8.8 & 9.914 & -1.114 \tabularnewline
74 & 14.6 & 11.7293 & 2.8707 \tabularnewline
75 & 12.6 & 10.5947 & 2.00526 \tabularnewline
76 & NA & NA & 1.95144 \tabularnewline
77 & 13 & 11.2217 & 1.77835 \tabularnewline
78 & 12.6 & 9.76782 & 2.83218 \tabularnewline
79 & 13.2 & 14.1217 & -0.92165 \tabularnewline
80 & 9.9 & 12.7947 & -2.89474 \tabularnewline
81 & 7.7 & 7.79474 & -0.0947372 \tabularnewline
82 & 10.5 & 7.24091 & 3.25909 \tabularnewline
83 & 13.4 & 12.8678 & 0.532176 \tabularnewline
84 & 10.9 & 16.2871 & -5.38709 \tabularnewline
85 & 4.3 & 4.82165 & -0.52165 \tabularnewline
86 & 10.3 & 9.32165 & 0.97835 \tabularnewline
87 & 11.8 & 11.6486 & 0.151437 \tabularnewline
88 & 11.2 & 11.0755 & 0.124524 \tabularnewline
89 & 11.4 & 13.3947 & -1.99474 \tabularnewline
90 & 8.6 & 5.54091 & 3.05909 \tabularnewline
91 & 13.2 & 10.2871 & 2.91291 \tabularnewline
92 & 12.6 & 16.914 & -4.314 \tabularnewline
93 & 5.6 & 4.93326 & 0.66674 \tabularnewline
94 & 9.9 & 11.2409 & -1.34091 \tabularnewline
95 & 8.8 & 11.2409 & -2.44091 \tabularnewline
96 & 7.7 & 8.38709 & -0.687086 \tabularnewline
97 & 9 & 11.8409 & -2.84091 \tabularnewline
98 & 7.3 & 6.04091 & 1.25909 \tabularnewline
99 & 11.4 & 7.714 & 3.686 \tabularnewline
100 & 13.6 & 16.9755 & -3.37548 \tabularnewline
101 & 7.9 & 7.114 & 0.786001 \tabularnewline
102 & 10.7 & 10.5409 & 0.159088 \tabularnewline
103 & 10.3 & 11.914 & -1.614 \tabularnewline
104 & 8.3 & 9.29474 & -0.994737 \tabularnewline
105 & 9.6 & 5.54091 & 4.05909 \tabularnewline
106 & 14.2 & 16.5217 & -2.32165 \tabularnewline
107 & 8.5 & 5.36782 & 3.13218 \tabularnewline
108 & 13.5 & 18.2871 & -4.78709 \tabularnewline
109 & 4.9 & 8.18709 & -3.28709 \tabularnewline
110 & 6.4 & 6.714 & -0.313999 \tabularnewline
111 & 9.6 & 8.14091 & 1.45909 \tabularnewline
112 & 11.6 & NA & NA \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=269322&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]12.9[/C][C]9.914[/C][C]2.986[/C][/ROW]
[ROW][C]2[/C][C]12.2[/C][C]10.5947[/C][C]1.60526[/C][/ROW]
[ROW][C]3[/C][C]12.8[/C][C]10.8217[/C][C]1.97835[/C][/ROW]
[ROW][C]4[/C][C]7.4[/C][C]11.0486[/C][C]-3.64856[/C][/ROW]
[ROW][C]5[/C][C]6.7[/C][C]10.5947[/C][C]-3.89474[/C][/ROW]
[ROW][C]6[/C][C]12.6[/C][C]11.9562[/C][C]0.643786[/C][/ROW]
[ROW][C]7[/C][C]14.8[/C][C]10.5947[/C][C]4.20526[/C][/ROW]
[ROW][C]8[/C][C]13.3[/C][C]11.2755[/C][C]2.02452[/C][/ROW]
[ROW][C]9[/C][C]11.1[/C][C]12.41[/C][C]-1.31004[/C][/ROW]
[ROW][C]10[/C][C]8.2[/C][C]10.8217[/C][C]-2.62165[/C][/ROW]
[ROW][C]11[/C][C]11.4[/C][C]10.5947[/C][C]0.805263[/C][/ROW]
[ROW][C]12[/C][C]6.4[/C][C]10.8217[/C][C]-4.42165[/C][/ROW]
[ROW][C]13[/C][C]10.6[/C][C]10.8217[/C][C]-0.22165[/C][/ROW]
[ROW][C]14[/C][C]12[/C][C]11.9562[/C][C]0.0437856[/C][/ROW]
[ROW][C]15[/C][C]6.3[/C][C]10.1409[/C][C]-3.84091[/C][/ROW]
[ROW][C]16[/C][C]11.3[/C][C]10.3678[/C][C]0.932176[/C][/ROW]
[ROW][C]17[/C][C]11.9[/C][C]11.5024[/C][C]0.397611[/C][/ROW]
[ROW][C]18[/C][C]9.3[/C][C]10.8217[/C][C]-1.52165[/C][/ROW]
[ROW][C]19[/C][C]9.6[/C][C]11.0486[/C][C]-1.44856[/C][/ROW]
[ROW][C]20[/C][C]10[/C][C]10.1409[/C][C]-0.140912[/C][/ROW]
[ROW][C]21[/C][C]6.4[/C][C]10.5947[/C][C]-4.19474[/C][/ROW]
[ROW][C]22[/C][C]13.8[/C][C]13.7715[/C][C]0.0284827[/C][/ROW]
[ROW][C]23[/C][C]10.8[/C][C]10.5947[/C][C]0.205263[/C][/ROW]
[ROW][C]24[/C][C]13.8[/C][C]11.0486[/C][C]2.75144[/C][/ROW]
[ROW][C]25[/C][C]11.7[/C][C]11.0486[/C][C]0.651437[/C][/ROW]
[ROW][C]26[/C][C]10.9[/C][C]10.8217[/C][C]0.0783499[/C][/ROW]
[ROW][C]27[/C][C]16.1[/C][C]13.7715[/C][C]2.32848[/C][/ROW]
[ROW][C]28[/C][C]13.4[/C][C]10.1409[/C][C]3.25909[/C][/ROW]
[ROW][C]29[/C][C]9.9[/C][C]10.1409[/C][C]-0.240912[/C][/ROW]
[ROW][C]30[/C][C]11.5[/C][C]10.1409[/C][C]1.35909[/C][/ROW]
[ROW][C]31[/C][C]8.3[/C][C]10.1409[/C][C]-1.84091[/C][/ROW]
[ROW][C]32[/C][C]11.7[/C][C]10.5947[/C][C]1.10526[/C][/ROW]
[ROW][C]33[/C][C]9[/C][C]10.3678[/C][C]-1.36782[/C][/ROW]
[ROW][C]34[/C][C]9.7[/C][C]13.0908[/C][C]-3.39078[/C][/ROW]
[ROW][C]35[/C][C]10.8[/C][C]11.0486[/C][C]-0.248563[/C][/ROW]
[ROW][C]36[/C][C]10.3[/C][C]11.5024[/C][C]-1.20239[/C][/ROW]
[ROW][C]37[/C][C]10.4[/C][C]9.914[/C][C]0.486001[/C][/ROW]
[ROW][C]38[/C][C]12.7[/C][C]11.2755[/C][C]1.42452[/C][/ROW]
[ROW][C]39[/C][C]9.3[/C][C]12.1831[/C][C]-2.88313[/C][/ROW]
[ROW][C]40[/C][C]11.8[/C][C]11.0486[/C][C]0.751437[/C][/ROW]
[ROW][C]41[/C][C]5.9[/C][C]10.3678[/C][C]-4.46782[/C][/ROW]
[ROW][C]42[/C][C]11.4[/C][C]11.5024[/C][C]-0.102389[/C][/ROW]
[ROW][C]43[/C][C]13[/C][C]11.0486[/C][C]1.95144[/C][/ROW]
[ROW][C]44[/C][C]10.8[/C][C]9.914[/C][C]0.886001[/C][/ROW]
[ROW][C]45[/C][C]12.3[/C][C]10.1409[/C][C]2.15909[/C][/ROW]
[ROW][C]46[/C][C]11.3[/C][C]11.7293[/C][C]-0.429302[/C][/ROW]
[ROW][C]47[/C][C]11.8[/C][C]10.3678[/C][C]1.43218[/C][/ROW]
[ROW][C]48[/C][C]7.9[/C][C]11.7293[/C][C]-3.8293[/C][/ROW]
[ROW][C]49[/C][C]12.7[/C][C]9.46017[/C][C]3.23983[/C][/ROW]
[ROW][C]50[/C][C]12.3[/C][C]10.8217[/C][C]1.47835[/C][/ROW]
[ROW][C]51[/C][C]11.6[/C][C]10.5947[/C][C]1.00526[/C][/ROW]
[ROW][C]52[/C][C]6.7[/C][C]9.914[/C][C]-3.214[/C][/ROW]
[ROW][C]53[/C][C]10.9[/C][C]9.914[/C][C]0.986001[/C][/ROW]
[ROW][C]54[/C][C]12.1[/C][C]10.1409[/C][C]1.95909[/C][/ROW]
[ROW][C]55[/C][C]13.3[/C][C]11.2755[/C][C]2.02452[/C][/ROW]
[ROW][C]56[/C][C]10.1[/C][C]10.1409[/C][C]-0.0409115[/C][/ROW]
[ROW][C]57[/C][C]5.7[/C][C]10.3678[/C][C]-4.66782[/C][/ROW]
[ROW][C]58[/C][C]14.3[/C][C]10.5947[/C][C]3.70526[/C][/ROW]
[ROW][C]59[/C][C]8[/C][C]9.68709[/C][C]-1.68709[/C][/ROW]
[ROW][C]60[/C][C]13.3[/C][C]10.1409[/C][C]3.15909[/C][/ROW]
[ROW][C]61[/C][C]9.3[/C][C]10.5947[/C][C]-1.29474[/C][/ROW]
[ROW][C]62[/C][C]12.5[/C][C]11.2755[/C][C]1.22452[/C][/ROW]
[ROW][C]63[/C][C]7.6[/C][C]9.914[/C][C]-2.314[/C][/ROW]
[ROW][C]64[/C][C]15.9[/C][C]11.0486[/C][C]4.85144[/C][/ROW]
[ROW][C]65[/C][C]9.2[/C][C]10.5947[/C][C]-1.39474[/C][/ROW]
[ROW][C]66[/C][C]9.1[/C][C]10.3678[/C][C]-1.26782[/C][/ROW]
[ROW][C]67[/C][C]11.1[/C][C]12.1831[/C][C]-1.08313[/C][/ROW]
[ROW][C]68[/C][C]13[/C][C]10.3678[/C][C]2.63218[/C][/ROW]
[ROW][C]69[/C][C]14.5[/C][C]11.9562[/C][C]2.54379[/C][/ROW]
[ROW][C]70[/C][C]12.2[/C][C]10.5947[/C][C]1.60526[/C][/ROW]
[ROW][C]71[/C][C]12.3[/C][C]12.41[/C][C]-0.11004[/C][/ROW]
[ROW][C]72[/C][C]11.4[/C][C]10.3678[/C][C]1.03218[/C][/ROW]
[ROW][C]73[/C][C]8.8[/C][C]9.914[/C][C]-1.114[/C][/ROW]
[ROW][C]74[/C][C]14.6[/C][C]11.7293[/C][C]2.8707[/C][/ROW]
[ROW][C]75[/C][C]12.6[/C][C]10.5947[/C][C]2.00526[/C][/ROW]
[ROW][C]76[/C][C]NA[/C][C]NA[/C][C]1.95144[/C][/ROW]
[ROW][C]77[/C][C]13[/C][C]11.2217[/C][C]1.77835[/C][/ROW]
[ROW][C]78[/C][C]12.6[/C][C]9.76782[/C][C]2.83218[/C][/ROW]
[ROW][C]79[/C][C]13.2[/C][C]14.1217[/C][C]-0.92165[/C][/ROW]
[ROW][C]80[/C][C]9.9[/C][C]12.7947[/C][C]-2.89474[/C][/ROW]
[ROW][C]81[/C][C]7.7[/C][C]7.79474[/C][C]-0.0947372[/C][/ROW]
[ROW][C]82[/C][C]10.5[/C][C]7.24091[/C][C]3.25909[/C][/ROW]
[ROW][C]83[/C][C]13.4[/C][C]12.8678[/C][C]0.532176[/C][/ROW]
[ROW][C]84[/C][C]10.9[/C][C]16.2871[/C][C]-5.38709[/C][/ROW]
[ROW][C]85[/C][C]4.3[/C][C]4.82165[/C][C]-0.52165[/C][/ROW]
[ROW][C]86[/C][C]10.3[/C][C]9.32165[/C][C]0.97835[/C][/ROW]
[ROW][C]87[/C][C]11.8[/C][C]11.6486[/C][C]0.151437[/C][/ROW]
[ROW][C]88[/C][C]11.2[/C][C]11.0755[/C][C]0.124524[/C][/ROW]
[ROW][C]89[/C][C]11.4[/C][C]13.3947[/C][C]-1.99474[/C][/ROW]
[ROW][C]90[/C][C]8.6[/C][C]5.54091[/C][C]3.05909[/C][/ROW]
[ROW][C]91[/C][C]13.2[/C][C]10.2871[/C][C]2.91291[/C][/ROW]
[ROW][C]92[/C][C]12.6[/C][C]16.914[/C][C]-4.314[/C][/ROW]
[ROW][C]93[/C][C]5.6[/C][C]4.93326[/C][C]0.66674[/C][/ROW]
[ROW][C]94[/C][C]9.9[/C][C]11.2409[/C][C]-1.34091[/C][/ROW]
[ROW][C]95[/C][C]8.8[/C][C]11.2409[/C][C]-2.44091[/C][/ROW]
[ROW][C]96[/C][C]7.7[/C][C]8.38709[/C][C]-0.687086[/C][/ROW]
[ROW][C]97[/C][C]9[/C][C]11.8409[/C][C]-2.84091[/C][/ROW]
[ROW][C]98[/C][C]7.3[/C][C]6.04091[/C][C]1.25909[/C][/ROW]
[ROW][C]99[/C][C]11.4[/C][C]7.714[/C][C]3.686[/C][/ROW]
[ROW][C]100[/C][C]13.6[/C][C]16.9755[/C][C]-3.37548[/C][/ROW]
[ROW][C]101[/C][C]7.9[/C][C]7.114[/C][C]0.786001[/C][/ROW]
[ROW][C]102[/C][C]10.7[/C][C]10.5409[/C][C]0.159088[/C][/ROW]
[ROW][C]103[/C][C]10.3[/C][C]11.914[/C][C]-1.614[/C][/ROW]
[ROW][C]104[/C][C]8.3[/C][C]9.29474[/C][C]-0.994737[/C][/ROW]
[ROW][C]105[/C][C]9.6[/C][C]5.54091[/C][C]4.05909[/C][/ROW]
[ROW][C]106[/C][C]14.2[/C][C]16.5217[/C][C]-2.32165[/C][/ROW]
[ROW][C]107[/C][C]8.5[/C][C]5.36782[/C][C]3.13218[/C][/ROW]
[ROW][C]108[/C][C]13.5[/C][C]18.2871[/C][C]-4.78709[/C][/ROW]
[ROW][C]109[/C][C]4.9[/C][C]8.18709[/C][C]-3.28709[/C][/ROW]
[ROW][C]110[/C][C]6.4[/C][C]6.714[/C][C]-0.313999[/C][/ROW]
[ROW][C]111[/C][C]9.6[/C][C]8.14091[/C][C]1.45909[/C][/ROW]
[ROW][C]112[/C][C]11.6[/C][C]NA[/C][C]NA[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=269322&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=269322&T=4

As an alternative you can also use a QR Code:

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	12.9	9.914	2.986
2	12.2	10.5947	1.60526
3	12.8	10.8217	1.97835
4	7.4	11.0486	-3.64856
5	6.7	10.5947	-3.89474
6	12.6	11.9562	0.643786
7	14.8	10.5947	4.20526
8	13.3	11.2755	2.02452
9	11.1	12.41	-1.31004
10	8.2	10.8217	-2.62165
11	11.4	10.5947	0.805263
12	6.4	10.8217	-4.42165
13	10.6	10.8217	-0.22165
14	12	11.9562	0.0437856
15	6.3	10.1409	-3.84091
16	11.3	10.3678	0.932176
17	11.9	11.5024	0.397611
18	9.3	10.8217	-1.52165
19	9.6	11.0486	-1.44856
20	10	10.1409	-0.140912
21	6.4	10.5947	-4.19474
22	13.8	13.7715	0.0284827
23	10.8	10.5947	0.205263
24	13.8	11.0486	2.75144
25	11.7	11.0486	0.651437
26	10.9	10.8217	0.0783499
27	16.1	13.7715	2.32848
28	13.4	10.1409	3.25909
29	9.9	10.1409	-0.240912
30	11.5	10.1409	1.35909
31	8.3	10.1409	-1.84091
32	11.7	10.5947	1.10526
33	9	10.3678	-1.36782
34	9.7	13.0908	-3.39078
35	10.8	11.0486	-0.248563
36	10.3	11.5024	-1.20239
37	10.4	9.914	0.486001
38	12.7	11.2755	1.42452
39	9.3	12.1831	-2.88313
40	11.8	11.0486	0.751437
41	5.9	10.3678	-4.46782
42	11.4	11.5024	-0.102389
43	13	11.0486	1.95144
44	10.8	9.914	0.886001
45	12.3	10.1409	2.15909
46	11.3	11.7293	-0.429302
47	11.8	10.3678	1.43218
48	7.9	11.7293	-3.8293
49	12.7	9.46017	3.23983
50	12.3	10.8217	1.47835
51	11.6	10.5947	1.00526
52	6.7	9.914	-3.214
53	10.9	9.914	0.986001
54	12.1	10.1409	1.95909
55	13.3	11.2755	2.02452
56	10.1	10.1409	-0.0409115
57	5.7	10.3678	-4.66782
58	14.3	10.5947	3.70526
59	8	9.68709	-1.68709
60	13.3	10.1409	3.15909
61	9.3	10.5947	-1.29474
62	12.5	11.2755	1.22452
63	7.6	9.914	-2.314
64	15.9	11.0486	4.85144
65	9.2	10.5947	-1.39474
66	9.1	10.3678	-1.26782
67	11.1	12.1831	-1.08313
68	13	10.3678	2.63218
69	14.5	11.9562	2.54379
70	12.2	10.5947	1.60526
71	12.3	12.41	-0.11004
72	11.4	10.3678	1.03218
73	8.8	9.914	-1.114
74	14.6	11.7293	2.8707
75	12.6	10.5947	2.00526
76	NA	NA	1.95144
77	13	11.2217	1.77835
78	12.6	9.76782	2.83218
79	13.2	14.1217	-0.92165
80	9.9	12.7947	-2.89474
81	7.7	7.79474	-0.0947372
82	10.5	7.24091	3.25909
83	13.4	12.8678	0.532176
84	10.9	16.2871	-5.38709
85	4.3	4.82165	-0.52165
86	10.3	9.32165	0.97835
87	11.8	11.6486	0.151437
88	11.2	11.0755	0.124524
89	11.4	13.3947	-1.99474
90	8.6	5.54091	3.05909
91	13.2	10.2871	2.91291
92	12.6	16.914	-4.314
93	5.6	4.93326	0.66674
94	9.9	11.2409	-1.34091
95	8.8	11.2409	-2.44091
96	7.7	8.38709	-0.687086
97	9	11.8409	-2.84091
98	7.3	6.04091	1.25909
99	11.4	7.714	3.686
100	13.6	16.9755	-3.37548
101	7.9	7.114	0.786001
102	10.7	10.5409	0.159088
103	10.3	11.914	-1.614
104	8.3	9.29474	-0.994737
105	9.6	5.54091	4.05909
106	14.2	16.5217	-2.32165
107	8.5	5.36782	3.13218
108	13.5	18.2871	-4.78709
109	4.9	8.18709	-3.28709
110	6.4	6.714	-0.313999
111	9.6	8.14091	1.45909
112	11.6	NA	NA

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
5	0.84828	0.303439	0.15172
6	0.888563	0.222875	0.111437
7	0.927424	0.145153	0.0725764
8	0.903524	0.192953	0.0964763
9	0.850051	0.299899	0.149949
10	0.871862	0.256275	0.128138
11	0.814051	0.371898	0.185949
12	0.914502	0.170996	0.0854979
13	0.874808	0.250385	0.125192
14	0.83141	0.33718	0.16859
15	0.891508	0.216984	0.108492
16	0.857958	0.284083	0.142042
17	0.813906	0.372188	0.186094
18	0.774526	0.450948	0.225474
19	0.728395	0.543211	0.271605
20	0.664113	0.671774	0.335887
21	0.756124	0.487752	0.243876
22	0.700035	0.59993	0.299965
23	0.641855	0.71629	0.358145
24	0.678584	0.642832	0.321416
25	0.625107	0.749787	0.374893
26	0.562048	0.875904	0.437952
27	0.54658	0.90684	0.45342
28	0.619887	0.760227	0.380113
29	0.558199	0.883603	0.441801
30	0.520153	0.959693	0.479847
31	0.48952	0.979041	0.51048
32	0.443783	0.887566	0.556217
33	0.400245	0.80049	0.599755
34	0.464323	0.928646	0.535677
35	0.405579	0.811158	0.594421
36	0.361475	0.722949	0.638525
37	0.310626	0.621253	0.689374
38	0.280804	0.561608	0.719196
39	0.304097	0.608195	0.695903
40	0.261238	0.522476	0.738762
41	0.389131	0.778261	0.610869
42	0.336956	0.673913	0.663044
43	0.32383	0.64766	0.67617
44	0.282908	0.565816	0.717092
45	0.277293	0.554587	0.722707
46	0.235041	0.470081	0.764959
47	0.208937	0.417873	0.791063
48	0.290297	0.580595	0.709703
49	0.339954	0.679907	0.660046
50	0.307524	0.615049	0.692476
51	0.267389	0.534777	0.732611
52	0.310008	0.620016	0.689992
53	0.271653	0.543305	0.728347
54	0.257456	0.514912	0.742544
55	0.242071	0.484141	0.757929
56	0.201383	0.402765	0.798617
57	0.336139	0.672278	0.663861
58	0.406081	0.812161	0.593919
59	0.37809	0.75618	0.62191
60	0.420381	0.840762	0.579619
61	0.385037	0.770075	0.614963
62	0.343427	0.686855	0.656573
63	0.338525	0.677049	0.661475
64	0.493949	0.987897	0.506051
65	0.459136	0.918273	0.540864
66	0.42014	0.840281	0.57986
67	0.393832	0.787663	0.606168
68	0.403934	0.807869	0.596066
69	0.393187	0.786375	0.606813
70	0.361489	0.722979	0.638511
71	0.317982	0.635965	0.682018
72	0.277238	0.554476	0.722762
73	0.238753	0.477506	0.761247
74	0.243147	0.486294	0.756853
75	0.229366	0.458733	0.770634
76	0.214867	0.429734	0.785133
77	0.199148	0.398297	0.800852
78	0.222754	0.445508	0.777246
79	0.184177	0.368354	0.815823
80	0.194082	0.388164	0.805918
81	0.155099	0.310198	0.844901
82	0.195218	0.390436	0.804782
83	0.159447	0.318894	0.840553
84	0.334939	0.669879	0.665061
85	0.278868	0.557737	0.721132
86	0.243277	0.486554	0.756723
87	0.200002	0.400004	0.799998
88	0.166349	0.332698	0.833651
89	0.139913	0.279826	0.860087
90	0.17493	0.34986	0.82507
91	0.201318	0.402635	0.798682
92	0.295506	0.591012	0.704494
93	0.236927	0.473854	0.763073
94	0.18999	0.37998	0.81001
95	0.174919	0.349837	0.825081
96	0.130981	0.261962	0.869019
97	0.134088	0.268175	0.865912
98	0.103974	0.207949	0.896026
99	0.163247	0.326494	0.836753
100	0.225801	0.451602	0.774199
101	0.181248	0.362495	0.818752
102	0.123562	0.247125	0.876438
103	0.0797905	0.159581	0.920209
104	0.0560524	0.112105	0.943948
105	0.143087	0.286174	0.856913
106	0.923657	0.152686	0.076343
107	0.845648	0.308705	0.154352

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
5 & 0.84828 & 0.303439 & 0.15172 \tabularnewline
6 & 0.888563 & 0.222875 & 0.111437 \tabularnewline
7 & 0.927424 & 0.145153 & 0.0725764 \tabularnewline
8 & 0.903524 & 0.192953 & 0.0964763 \tabularnewline
9 & 0.850051 & 0.299899 & 0.149949 \tabularnewline
10 & 0.871862 & 0.256275 & 0.128138 \tabularnewline
11 & 0.814051 & 0.371898 & 0.185949 \tabularnewline
12 & 0.914502 & 0.170996 & 0.0854979 \tabularnewline
13 & 0.874808 & 0.250385 & 0.125192 \tabularnewline
14 & 0.83141 & 0.33718 & 0.16859 \tabularnewline
15 & 0.891508 & 0.216984 & 0.108492 \tabularnewline
16 & 0.857958 & 0.284083 & 0.142042 \tabularnewline
17 & 0.813906 & 0.372188 & 0.186094 \tabularnewline
18 & 0.774526 & 0.450948 & 0.225474 \tabularnewline
19 & 0.728395 & 0.543211 & 0.271605 \tabularnewline
20 & 0.664113 & 0.671774 & 0.335887 \tabularnewline
21 & 0.756124 & 0.487752 & 0.243876 \tabularnewline
22 & 0.700035 & 0.59993 & 0.299965 \tabularnewline
23 & 0.641855 & 0.71629 & 0.358145 \tabularnewline
24 & 0.678584 & 0.642832 & 0.321416 \tabularnewline
25 & 0.625107 & 0.749787 & 0.374893 \tabularnewline
26 & 0.562048 & 0.875904 & 0.437952 \tabularnewline
27 & 0.54658 & 0.90684 & 0.45342 \tabularnewline
28 & 0.619887 & 0.760227 & 0.380113 \tabularnewline
29 & 0.558199 & 0.883603 & 0.441801 \tabularnewline
30 & 0.520153 & 0.959693 & 0.479847 \tabularnewline
31 & 0.48952 & 0.979041 & 0.51048 \tabularnewline
32 & 0.443783 & 0.887566 & 0.556217 \tabularnewline
33 & 0.400245 & 0.80049 & 0.599755 \tabularnewline
34 & 0.464323 & 0.928646 & 0.535677 \tabularnewline
35 & 0.405579 & 0.811158 & 0.594421 \tabularnewline
36 & 0.361475 & 0.722949 & 0.638525 \tabularnewline
37 & 0.310626 & 0.621253 & 0.689374 \tabularnewline
38 & 0.280804 & 0.561608 & 0.719196 \tabularnewline
39 & 0.304097 & 0.608195 & 0.695903 \tabularnewline
40 & 0.261238 & 0.522476 & 0.738762 \tabularnewline
41 & 0.389131 & 0.778261 & 0.610869 \tabularnewline
42 & 0.336956 & 0.673913 & 0.663044 \tabularnewline
43 & 0.32383 & 0.64766 & 0.67617 \tabularnewline
44 & 0.282908 & 0.565816 & 0.717092 \tabularnewline
45 & 0.277293 & 0.554587 & 0.722707 \tabularnewline
46 & 0.235041 & 0.470081 & 0.764959 \tabularnewline
47 & 0.208937 & 0.417873 & 0.791063 \tabularnewline
48 & 0.290297 & 0.580595 & 0.709703 \tabularnewline
49 & 0.339954 & 0.679907 & 0.660046 \tabularnewline
50 & 0.307524 & 0.615049 & 0.692476 \tabularnewline
51 & 0.267389 & 0.534777 & 0.732611 \tabularnewline
52 & 0.310008 & 0.620016 & 0.689992 \tabularnewline
53 & 0.271653 & 0.543305 & 0.728347 \tabularnewline
54 & 0.257456 & 0.514912 & 0.742544 \tabularnewline
55 & 0.242071 & 0.484141 & 0.757929 \tabularnewline
56 & 0.201383 & 0.402765 & 0.798617 \tabularnewline
57 & 0.336139 & 0.672278 & 0.663861 \tabularnewline
58 & 0.406081 & 0.812161 & 0.593919 \tabularnewline
59 & 0.37809 & 0.75618 & 0.62191 \tabularnewline
60 & 0.420381 & 0.840762 & 0.579619 \tabularnewline
61 & 0.385037 & 0.770075 & 0.614963 \tabularnewline
62 & 0.343427 & 0.686855 & 0.656573 \tabularnewline
63 & 0.338525 & 0.677049 & 0.661475 \tabularnewline
64 & 0.493949 & 0.987897 & 0.506051 \tabularnewline
65 & 0.459136 & 0.918273 & 0.540864 \tabularnewline
66 & 0.42014 & 0.840281 & 0.57986 \tabularnewline
67 & 0.393832 & 0.787663 & 0.606168 \tabularnewline
68 & 0.403934 & 0.807869 & 0.596066 \tabularnewline
69 & 0.393187 & 0.786375 & 0.606813 \tabularnewline
70 & 0.361489 & 0.722979 & 0.638511 \tabularnewline
71 & 0.317982 & 0.635965 & 0.682018 \tabularnewline
72 & 0.277238 & 0.554476 & 0.722762 \tabularnewline
73 & 0.238753 & 0.477506 & 0.761247 \tabularnewline
74 & 0.243147 & 0.486294 & 0.756853 \tabularnewline
75 & 0.229366 & 0.458733 & 0.770634 \tabularnewline
76 & 0.214867 & 0.429734 & 0.785133 \tabularnewline
77 & 0.199148 & 0.398297 & 0.800852 \tabularnewline
78 & 0.222754 & 0.445508 & 0.777246 \tabularnewline
79 & 0.184177 & 0.368354 & 0.815823 \tabularnewline
80 & 0.194082 & 0.388164 & 0.805918 \tabularnewline
81 & 0.155099 & 0.310198 & 0.844901 \tabularnewline
82 & 0.195218 & 0.390436 & 0.804782 \tabularnewline
83 & 0.159447 & 0.318894 & 0.840553 \tabularnewline
84 & 0.334939 & 0.669879 & 0.665061 \tabularnewline
85 & 0.278868 & 0.557737 & 0.721132 \tabularnewline
86 & 0.243277 & 0.486554 & 0.756723 \tabularnewline
87 & 0.200002 & 0.400004 & 0.799998 \tabularnewline
88 & 0.166349 & 0.332698 & 0.833651 \tabularnewline
89 & 0.139913 & 0.279826 & 0.860087 \tabularnewline
90 & 0.17493 & 0.34986 & 0.82507 \tabularnewline
91 & 0.201318 & 0.402635 & 0.798682 \tabularnewline
92 & 0.295506 & 0.591012 & 0.704494 \tabularnewline
93 & 0.236927 & 0.473854 & 0.763073 \tabularnewline
94 & 0.18999 & 0.37998 & 0.81001 \tabularnewline
95 & 0.174919 & 0.349837 & 0.825081 \tabularnewline
96 & 0.130981 & 0.261962 & 0.869019 \tabularnewline
97 & 0.134088 & 0.268175 & 0.865912 \tabularnewline
98 & 0.103974 & 0.207949 & 0.896026 \tabularnewline
99 & 0.163247 & 0.326494 & 0.836753 \tabularnewline
100 & 0.225801 & 0.451602 & 0.774199 \tabularnewline
101 & 0.181248 & 0.362495 & 0.818752 \tabularnewline
102 & 0.123562 & 0.247125 & 0.876438 \tabularnewline
103 & 0.0797905 & 0.159581 & 0.920209 \tabularnewline
104 & 0.0560524 & 0.112105 & 0.943948 \tabularnewline
105 & 0.143087 & 0.286174 & 0.856913 \tabularnewline
106 & 0.923657 & 0.152686 & 0.076343 \tabularnewline
107 & 0.845648 & 0.308705 & 0.154352 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=269322&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]5[/C][C]0.84828[/C][C]0.303439[/C][C]0.15172[/C][/ROW]
[ROW][C]6[/C][C]0.888563[/C][C]0.222875[/C][C]0.111437[/C][/ROW]
[ROW][C]7[/C][C]0.927424[/C][C]0.145153[/C][C]0.0725764[/C][/ROW]
[ROW][C]8[/C][C]0.903524[/C][C]0.192953[/C][C]0.0964763[/C][/ROW]
[ROW][C]9[/C][C]0.850051[/C][C]0.299899[/C][C]0.149949[/C][/ROW]
[ROW][C]10[/C][C]0.871862[/C][C]0.256275[/C][C]0.128138[/C][/ROW]
[ROW][C]11[/C][C]0.814051[/C][C]0.371898[/C][C]0.185949[/C][/ROW]
[ROW][C]12[/C][C]0.914502[/C][C]0.170996[/C][C]0.0854979[/C][/ROW]
[ROW][C]13[/C][C]0.874808[/C][C]0.250385[/C][C]0.125192[/C][/ROW]
[ROW][C]14[/C][C]0.83141[/C][C]0.33718[/C][C]0.16859[/C][/ROW]
[ROW][C]15[/C][C]0.891508[/C][C]0.216984[/C][C]0.108492[/C][/ROW]
[ROW][C]16[/C][C]0.857958[/C][C]0.284083[/C][C]0.142042[/C][/ROW]
[ROW][C]17[/C][C]0.813906[/C][C]0.372188[/C][C]0.186094[/C][/ROW]
[ROW][C]18[/C][C]0.774526[/C][C]0.450948[/C][C]0.225474[/C][/ROW]
[ROW][C]19[/C][C]0.728395[/C][C]0.543211[/C][C]0.271605[/C][/ROW]
[ROW][C]20[/C][C]0.664113[/C][C]0.671774[/C][C]0.335887[/C][/ROW]
[ROW][C]21[/C][C]0.756124[/C][C]0.487752[/C][C]0.243876[/C][/ROW]
[ROW][C]22[/C][C]0.700035[/C][C]0.59993[/C][C]0.299965[/C][/ROW]
[ROW][C]23[/C][C]0.641855[/C][C]0.71629[/C][C]0.358145[/C][/ROW]
[ROW][C]24[/C][C]0.678584[/C][C]0.642832[/C][C]0.321416[/C][/ROW]
[ROW][C]25[/C][C]0.625107[/C][C]0.749787[/C][C]0.374893[/C][/ROW]
[ROW][C]26[/C][C]0.562048[/C][C]0.875904[/C][C]0.437952[/C][/ROW]
[ROW][C]27[/C][C]0.54658[/C][C]0.90684[/C][C]0.45342[/C][/ROW]
[ROW][C]28[/C][C]0.619887[/C][C]0.760227[/C][C]0.380113[/C][/ROW]
[ROW][C]29[/C][C]0.558199[/C][C]0.883603[/C][C]0.441801[/C][/ROW]
[ROW][C]30[/C][C]0.520153[/C][C]0.959693[/C][C]0.479847[/C][/ROW]
[ROW][C]31[/C][C]0.48952[/C][C]0.979041[/C][C]0.51048[/C][/ROW]
[ROW][C]32[/C][C]0.443783[/C][C]0.887566[/C][C]0.556217[/C][/ROW]
[ROW][C]33[/C][C]0.400245[/C][C]0.80049[/C][C]0.599755[/C][/ROW]
[ROW][C]34[/C][C]0.464323[/C][C]0.928646[/C][C]0.535677[/C][/ROW]
[ROW][C]35[/C][C]0.405579[/C][C]0.811158[/C][C]0.594421[/C][/ROW]
[ROW][C]36[/C][C]0.361475[/C][C]0.722949[/C][C]0.638525[/C][/ROW]
[ROW][C]37[/C][C]0.310626[/C][C]0.621253[/C][C]0.689374[/C][/ROW]
[ROW][C]38[/C][C]0.280804[/C][C]0.561608[/C][C]0.719196[/C][/ROW]
[ROW][C]39[/C][C]0.304097[/C][C]0.608195[/C][C]0.695903[/C][/ROW]
[ROW][C]40[/C][C]0.261238[/C][C]0.522476[/C][C]0.738762[/C][/ROW]
[ROW][C]41[/C][C]0.389131[/C][C]0.778261[/C][C]0.610869[/C][/ROW]
[ROW][C]42[/C][C]0.336956[/C][C]0.673913[/C][C]0.663044[/C][/ROW]
[ROW][C]43[/C][C]0.32383[/C][C]0.64766[/C][C]0.67617[/C][/ROW]
[ROW][C]44[/C][C]0.282908[/C][C]0.565816[/C][C]0.717092[/C][/ROW]
[ROW][C]45[/C][C]0.277293[/C][C]0.554587[/C][C]0.722707[/C][/ROW]
[ROW][C]46[/C][C]0.235041[/C][C]0.470081[/C][C]0.764959[/C][/ROW]
[ROW][C]47[/C][C]0.208937[/C][C]0.417873[/C][C]0.791063[/C][/ROW]
[ROW][C]48[/C][C]0.290297[/C][C]0.580595[/C][C]0.709703[/C][/ROW]
[ROW][C]49[/C][C]0.339954[/C][C]0.679907[/C][C]0.660046[/C][/ROW]
[ROW][C]50[/C][C]0.307524[/C][C]0.615049[/C][C]0.692476[/C][/ROW]
[ROW][C]51[/C][C]0.267389[/C][C]0.534777[/C][C]0.732611[/C][/ROW]
[ROW][C]52[/C][C]0.310008[/C][C]0.620016[/C][C]0.689992[/C][/ROW]
[ROW][C]53[/C][C]0.271653[/C][C]0.543305[/C][C]0.728347[/C][/ROW]
[ROW][C]54[/C][C]0.257456[/C][C]0.514912[/C][C]0.742544[/C][/ROW]
[ROW][C]55[/C][C]0.242071[/C][C]0.484141[/C][C]0.757929[/C][/ROW]
[ROW][C]56[/C][C]0.201383[/C][C]0.402765[/C][C]0.798617[/C][/ROW]
[ROW][C]57[/C][C]0.336139[/C][C]0.672278[/C][C]0.663861[/C][/ROW]
[ROW][C]58[/C][C]0.406081[/C][C]0.812161[/C][C]0.593919[/C][/ROW]
[ROW][C]59[/C][C]0.37809[/C][C]0.75618[/C][C]0.62191[/C][/ROW]
[ROW][C]60[/C][C]0.420381[/C][C]0.840762[/C][C]0.579619[/C][/ROW]
[ROW][C]61[/C][C]0.385037[/C][C]0.770075[/C][C]0.614963[/C][/ROW]
[ROW][C]62[/C][C]0.343427[/C][C]0.686855[/C][C]0.656573[/C][/ROW]
[ROW][C]63[/C][C]0.338525[/C][C]0.677049[/C][C]0.661475[/C][/ROW]
[ROW][C]64[/C][C]0.493949[/C][C]0.987897[/C][C]0.506051[/C][/ROW]
[ROW][C]65[/C][C]0.459136[/C][C]0.918273[/C][C]0.540864[/C][/ROW]
[ROW][C]66[/C][C]0.42014[/C][C]0.840281[/C][C]0.57986[/C][/ROW]
[ROW][C]67[/C][C]0.393832[/C][C]0.787663[/C][C]0.606168[/C][/ROW]
[ROW][C]68[/C][C]0.403934[/C][C]0.807869[/C][C]0.596066[/C][/ROW]
[ROW][C]69[/C][C]0.393187[/C][C]0.786375[/C][C]0.606813[/C][/ROW]
[ROW][C]70[/C][C]0.361489[/C][C]0.722979[/C][C]0.638511[/C][/ROW]
[ROW][C]71[/C][C]0.317982[/C][C]0.635965[/C][C]0.682018[/C][/ROW]
[ROW][C]72[/C][C]0.277238[/C][C]0.554476[/C][C]0.722762[/C][/ROW]
[ROW][C]73[/C][C]0.238753[/C][C]0.477506[/C][C]0.761247[/C][/ROW]
[ROW][C]74[/C][C]0.243147[/C][C]0.486294[/C][C]0.756853[/C][/ROW]
[ROW][C]75[/C][C]0.229366[/C][C]0.458733[/C][C]0.770634[/C][/ROW]
[ROW][C]76[/C][C]0.214867[/C][C]0.429734[/C][C]0.785133[/C][/ROW]
[ROW][C]77[/C][C]0.199148[/C][C]0.398297[/C][C]0.800852[/C][/ROW]
[ROW][C]78[/C][C]0.222754[/C][C]0.445508[/C][C]0.777246[/C][/ROW]
[ROW][C]79[/C][C]0.184177[/C][C]0.368354[/C][C]0.815823[/C][/ROW]
[ROW][C]80[/C][C]0.194082[/C][C]0.388164[/C][C]0.805918[/C][/ROW]
[ROW][C]81[/C][C]0.155099[/C][C]0.310198[/C][C]0.844901[/C][/ROW]
[ROW][C]82[/C][C]0.195218[/C][C]0.390436[/C][C]0.804782[/C][/ROW]
[ROW][C]83[/C][C]0.159447[/C][C]0.318894[/C][C]0.840553[/C][/ROW]
[ROW][C]84[/C][C]0.334939[/C][C]0.669879[/C][C]0.665061[/C][/ROW]
[ROW][C]85[/C][C]0.278868[/C][C]0.557737[/C][C]0.721132[/C][/ROW]
[ROW][C]86[/C][C]0.243277[/C][C]0.486554[/C][C]0.756723[/C][/ROW]
[ROW][C]87[/C][C]0.200002[/C][C]0.400004[/C][C]0.799998[/C][/ROW]
[ROW][C]88[/C][C]0.166349[/C][C]0.332698[/C][C]0.833651[/C][/ROW]
[ROW][C]89[/C][C]0.139913[/C][C]0.279826[/C][C]0.860087[/C][/ROW]
[ROW][C]90[/C][C]0.17493[/C][C]0.34986[/C][C]0.82507[/C][/ROW]
[ROW][C]91[/C][C]0.201318[/C][C]0.402635[/C][C]0.798682[/C][/ROW]
[ROW][C]92[/C][C]0.295506[/C][C]0.591012[/C][C]0.704494[/C][/ROW]
[ROW][C]93[/C][C]0.236927[/C][C]0.473854[/C][C]0.763073[/C][/ROW]
[ROW][C]94[/C][C]0.18999[/C][C]0.37998[/C][C]0.81001[/C][/ROW]
[ROW][C]95[/C][C]0.174919[/C][C]0.349837[/C][C]0.825081[/C][/ROW]
[ROW][C]96[/C][C]0.130981[/C][C]0.261962[/C][C]0.869019[/C][/ROW]
[ROW][C]97[/C][C]0.134088[/C][C]0.268175[/C][C]0.865912[/C][/ROW]
[ROW][C]98[/C][C]0.103974[/C][C]0.207949[/C][C]0.896026[/C][/ROW]
[ROW][C]99[/C][C]0.163247[/C][C]0.326494[/C][C]0.836753[/C][/ROW]
[ROW][C]100[/C][C]0.225801[/C][C]0.451602[/C][C]0.774199[/C][/ROW]
[ROW][C]101[/C][C]0.181248[/C][C]0.362495[/C][C]0.818752[/C][/ROW]
[ROW][C]102[/C][C]0.123562[/C][C]0.247125[/C][C]0.876438[/C][/ROW]
[ROW][C]103[/C][C]0.0797905[/C][C]0.159581[/C][C]0.920209[/C][/ROW]
[ROW][C]104[/C][C]0.0560524[/C][C]0.112105[/C][C]0.943948[/C][/ROW]
[ROW][C]105[/C][C]0.143087[/C][C]0.286174[/C][C]0.856913[/C][/ROW]
[ROW][C]106[/C][C]0.923657[/C][C]0.152686[/C][C]0.076343[/C][/ROW]
[ROW][C]107[/C][C]0.845648[/C][C]0.308705[/C][C]0.154352[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=269322&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=269322&T=5

As an alternative you can also use a QR Code:

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

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
5	0.84828	0.303439	0.15172
6	0.888563	0.222875	0.111437
7	0.927424	0.145153	0.0725764
8	0.903524	0.192953	0.0964763
9	0.850051	0.299899	0.149949
10	0.871862	0.256275	0.128138
11	0.814051	0.371898	0.185949
12	0.914502	0.170996	0.0854979
13	0.874808	0.250385	0.125192
14	0.83141	0.33718	0.16859
15	0.891508	0.216984	0.108492
16	0.857958	0.284083	0.142042
17	0.813906	0.372188	0.186094
18	0.774526	0.450948	0.225474
19	0.728395	0.543211	0.271605
20	0.664113	0.671774	0.335887
21	0.756124	0.487752	0.243876
22	0.700035	0.59993	0.299965
23	0.641855	0.71629	0.358145
24	0.678584	0.642832	0.321416
25	0.625107	0.749787	0.374893
26	0.562048	0.875904	0.437952
27	0.54658	0.90684	0.45342
28	0.619887	0.760227	0.380113
29	0.558199	0.883603	0.441801
30	0.520153	0.959693	0.479847
31	0.48952	0.979041	0.51048
32	0.443783	0.887566	0.556217
33	0.400245	0.80049	0.599755
34	0.464323	0.928646	0.535677
35	0.405579	0.811158	0.594421
36	0.361475	0.722949	0.638525
37	0.310626	0.621253	0.689374
38	0.280804	0.561608	0.719196
39	0.304097	0.608195	0.695903
40	0.261238	0.522476	0.738762
41	0.389131	0.778261	0.610869
42	0.336956	0.673913	0.663044
43	0.32383	0.64766	0.67617
44	0.282908	0.565816	0.717092
45	0.277293	0.554587	0.722707
46	0.235041	0.470081	0.764959
47	0.208937	0.417873	0.791063
48	0.290297	0.580595	0.709703
49	0.339954	0.679907	0.660046
50	0.307524	0.615049	0.692476
51	0.267389	0.534777	0.732611
52	0.310008	0.620016	0.689992
53	0.271653	0.543305	0.728347
54	0.257456	0.514912	0.742544
55	0.242071	0.484141	0.757929
56	0.201383	0.402765	0.798617
57	0.336139	0.672278	0.663861
58	0.406081	0.812161	0.593919
59	0.37809	0.75618	0.62191
60	0.420381	0.840762	0.579619
61	0.385037	0.770075	0.614963
62	0.343427	0.686855	0.656573
63	0.338525	0.677049	0.661475
64	0.493949	0.987897	0.506051
65	0.459136	0.918273	0.540864
66	0.42014	0.840281	0.57986
67	0.393832	0.787663	0.606168
68	0.403934	0.807869	0.596066
69	0.393187	0.786375	0.606813
70	0.361489	0.722979	0.638511
71	0.317982	0.635965	0.682018
72	0.277238	0.554476	0.722762
73	0.238753	0.477506	0.761247
74	0.243147	0.486294	0.756853
75	0.229366	0.458733	0.770634
76	0.214867	0.429734	0.785133
77	0.199148	0.398297	0.800852
78	0.222754	0.445508	0.777246
79	0.184177	0.368354	0.815823
80	0.194082	0.388164	0.805918
81	0.155099	0.310198	0.844901
82	0.195218	0.390436	0.804782
83	0.159447	0.318894	0.840553
84	0.334939	0.669879	0.665061
85	0.278868	0.557737	0.721132
86	0.243277	0.486554	0.756723
87	0.200002	0.400004	0.799998
88	0.166349	0.332698	0.833651
89	0.139913	0.279826	0.860087
90	0.17493	0.34986	0.82507
91	0.201318	0.402635	0.798682
92	0.295506	0.591012	0.704494
93	0.236927	0.473854	0.763073
94	0.18999	0.37998	0.81001
95	0.174919	0.349837	0.825081
96	0.130981	0.261962	0.869019
97	0.134088	0.268175	0.865912
98	0.103974	0.207949	0.896026
99	0.163247	0.326494	0.836753
100	0.225801	0.451602	0.774199
101	0.181248	0.362495	0.818752
102	0.123562	0.247125	0.876438
103	0.0797905	0.159581	0.920209
104	0.0560524	0.112105	0.943948
105	0.143087	0.286174	0.856913
106	0.923657	0.152686	0.076343
107	0.845648	0.308705	0.154352

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

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 0 & 0 & OK \tabularnewline
10% type I error level & 0 & 0 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=269322&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=269322&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=269322&T=6

As an alternative you can also use a QR Code:

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

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

Figure 1

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Figure 3

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Figure 4

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Figure 5

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Figure 6

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Figure 7

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Figure 8

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Figure 9

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Figure 10

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Parameters (Session):

par1 = 2 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 2 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

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

library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- 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'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
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[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
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$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,signif(mysum$coefficients[i,1],6))
a<-table.element(a, signif(mysum$coefficients[i,2],6))
a<-table.element(a, signif(mysum$coefficients[i,3],4))
a<-table.element(a, signif(mysum$coefficients[i,4],6))
a<-table.element(a, signif(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, signif(sqrt(mysum$r.squared),6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, signif(mysum$r.squared,6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, signif(mysum$adj.r.squared,6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[1],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, signif(mysum$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$fstatistic[3],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, signif(1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]),6))
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, signif(mysum$sigma,6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, signif(sum(myerror*myerror),6))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,signif(x[i],6))
a<-table.element(a,signif(x[i]-mysum$resid[i],6))
a<-table.element(a,signif(mysum$resid[i],6))
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,signif(gqarr[mypoint-kp3+1,1],6))
a<-table.element(a,signif(gqarr[mypoint-kp3+1,2],6))
a<-table.element(a,signif(gqarr[mypoint-kp3+1,3],6))
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,signif(numsignificant1/numgqtests,6))
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')
}

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code