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rwasp_multipleregression.wasp

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Multiple Regression

Date of computation

Wed, 17 Dec 2014 21:14:50 +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/17/t1418850931l116646oiuu75cd.htm/, Retrieved Thu, 16 May 2024 20:01:50 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=270697, Retrieved Thu, 16 May 2024 20:01:50 +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] [] [2014-12-17 21:14:50] [92b9176a7d614ba60c8f41dcecd4e71d] [Current]

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13 149 18 68
13 139 31 39
11 148 39 32
14 158 46 62
15 128 31 33
14 224 67 52
11 159 35 62
13 105 52 77
16 159 77 76
14 167 37 41
14 165 32 48
15 159 36 63
15 119 38 30
13 176 69 78
14 54 21 19
11 91 26 31
12 163 54 66
14 124 36 35
13 137 42 42
12 121 23 45
15 153 34 21
15 148 112 25
14 221 35 44
14 188 47 69
12 149 47 54
12 244 37 74
12 148 109 80
15 92 24 42
14 150 20 61
16 153 22 41
12 94 23 46
12 156 32 39
14 132 30 34
16 161 92 51
15 105 43 42
12 97 55 31
14 151 16 39
13 131 49 20
14 166 71 49
16 157 43 53
12 111 29 31
14 145 56 39
15 162 46 54
13 163 19 49
16 59 23 34
16 187 59 46
12 109 30 55
12 90 61 42
16 105 7 50
12 83 38 13
15 116 32 37
12 42 16 25
13 148 19 30
12 155 22 28
14 125 48 45
14 116 23 35
11 128 26 28
10 138 33 41
12 49 9 6
11 96 24 45
16 164 34 73
14 162 48 17
14 99 18 40
15 202 43 64
15 186 33 37
14 66 28 25
13 183 71 65
11 214 26 100
16 188 67 28
12 104 34 35
15 177 80 56
14 126 29 29
15 76 16 43
14 99 59 59
13 139 32 50
6 78 47 3
12 162 43 59
12 108 38 27
14 159 29 61
14 74 36 28
15 110 32 51
11 96 35 35
13 116 21 29
14 87 29 48
16 97 12 25
13 127 37 44
14 106 37 64
16 80 47 32
11 74 51 20
13 91 32 28
13 133 21 34
15 74 13 31
12 114 14 26
13 140 -2 58
12 95 20 23
14 98 24 21
14 121 11 21
16 126 23 33
15 98 24 16
14 95 14 20
13 110 52 37
14 70 15 35
15 102 23 33
14 86 19 27
12 130 35 41
7 96 24 40
12 102 39 35
15 100 29 28
12 94 13 32
13 52 8 22
11 98 18 44
14 118 24 27

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	5 seconds
R Server	'Gertrude Mary Cox' @ cox.wessa.net
R Framework error message	Warning: there are blank lines in the 'Data X' field. Please, use NA for missing data - blank lines are simply deleted and are NOT treated as missing values.

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 5 seconds \tabularnewline
R Server & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
R Framework error message & Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values. \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=270697&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]5 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[ROW][C]R Framework error message[/C][C]Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=270697&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=270697&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	5 seconds
R Server	'Gertrude Mary Cox' @ cox.wessa.net
R Framework error message	Warning: there are blank lines in the 'Data X' field. Please, use NA for missing data - blank lines are simply deleted and are NOT treated as missing values.

Multiple Linear Regression - Estimated Regression Equation
STRESSTOT[t] = + 12.5083 + 0.00654663LFM[t] + 0.00307172PRH[t] -0.00106607CH[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
STRESSTOT[t] =  +  12.5083 +  0.00654663LFM[t] +  0.00307172PRH[t] -0.00106607CH[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=270697&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]STRESSTOT[t] =  +  12.5083 +  0.00654663LFM[t] +  0.00307172PRH[t] -0.00106607CH[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=270697&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=270697&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
STRESSTOT[t] = + 12.5083 + 0.00654663LFM[t] + 0.00307172PRH[t] -0.00106607CH[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)	12.5083	0.574505	21.77	2.66406e-41	1.33203e-41
LFM	0.00654663	0.00538171	1.216	0.226462	0.113231
PRH	0.00307172	0.00936333	0.3281	0.743502	0.371751
CH	-0.00106607	0.0120774	-0.08827	0.929826	0.464913

\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) & 12.5083 & 0.574505 & 21.77 & 2.66406e-41 & 1.33203e-41 \tabularnewline
LFM & 0.00654663 & 0.00538171 & 1.216 & 0.226462 & 0.113231 \tabularnewline
PRH & 0.00307172 & 0.00936333 & 0.3281 & 0.743502 & 0.371751 \tabularnewline
CH & -0.00106607 & 0.0120774 & -0.08827 & 0.929826 & 0.464913 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=270697&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]12.5083[/C][C]0.574505[/C][C]21.77[/C][C]2.66406e-41[/C][C]1.33203e-41[/C][/ROW]
[ROW][C]LFM[/C][C]0.00654663[/C][C]0.00538171[/C][C]1.216[/C][C]0.226462[/C][C]0.113231[/C][/ROW]
[ROW][C]PRH[/C][C]0.00307172[/C][C]0.00936333[/C][C]0.3281[/C][C]0.743502[/C][C]0.371751[/C][/ROW]
[ROW][C]CH[/C][C]-0.00106607[/C][C]0.0120774[/C][C]-0.08827[/C][C]0.929826[/C][C]0.464913[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=270697&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=270697&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)	12.5083	0.574505	21.77	2.66406e-41	1.33203e-41
LFM	0.00654663	0.00538171	1.216	0.226462	0.113231
PRH	0.00307172	0.00936333	0.3281	0.743502	0.371751
CH	-0.00106607	0.0120774	-0.08827	0.929826	0.464913

Multiple Linear Regression - Regression Statistics
Multiple R	0.159113
R-squared	0.025317
Adjusted R-squared	-0.0017575
F-TEST (value)	0.935086
F-TEST (DF numerator)	3
F-TEST (DF denominator)	108
p-value	0.42644
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.76433
Sum Squared Residuals	336.187

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.159113 \tabularnewline
R-squared & 0.025317 \tabularnewline
Adjusted R-squared & -0.0017575 \tabularnewline
F-TEST (value) & 0.935086 \tabularnewline
F-TEST (DF numerator) & 3 \tabularnewline
F-TEST (DF denominator) & 108 \tabularnewline
p-value & 0.42644 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 1.76433 \tabularnewline
Sum Squared Residuals & 336.187 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=270697&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.159113[/C][/ROW]
[ROW][C]R-squared[/C][C]0.025317[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]-0.0017575[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]0.935086[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]3[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]108[/C][/ROW]
[ROW][C]p-value[/C][C]0.42644[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]1.76433[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]336.187[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=270697&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=270697&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	0.159113
R-squared	0.025317
Adjusted R-squared	-0.0017575
F-TEST (value)	0.935086
F-TEST (DF numerator)	3
F-TEST (DF denominator)	108
p-value	0.42644
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.76433
Sum Squared Residuals	336.187

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	13	13.4666	-0.466562
2	13	13.4719	-0.471944
3	11	13.5629	-2.5629
4	14	13.6179	0.382113
5	15	13.4063	1.59367
6	14	14.1251	-0.125131
7	11	13.5906	-2.59064
8	13	13.2734	-0.273355
9	16	13.7047	2.29527
10	14	13.6715	0.328452
11	14	13.6356	0.364366
12	15	13.5926	1.40735
13	15	13.3721	1.62789
14	13	13.7893	-0.789318
15	14	12.9061	1.09391
16	11	13.1509	-2.15088
17	12	13.6709	-1.67093
18	14	13.3934	0.606632
19	13	13.4894	-0.489442
20	12	13.3231	-1.32313
21	15	13.592	1.408
22	15	13.7946	1.2054
23	14	14.0157	-0.0157244
24	14	13.8099	0.190105
25	12	13.5706	-1.57057
26	12	14.1405	-2.14046
27	12	13.7267	-1.72675
28	15	13.1396	1.86045
29	14	13.4867	0.513285
30	16	13.5338	2.46618
31	12	13.1453	-1.14531
32	12	13.5863	-1.58631
33	14	13.4284	0.571623
34	16	13.7906	2.20945
35	15	13.283	1.71698
36	12	13.2792	-1.27924
37	14	13.5044	0.495572
38	13	13.4951	-0.495118
39	14	13.7609	0.239089
40	16	13.6117	2.38828
41	12	13.291	-1.29102
42	14	13.588	0.411983
43	15	13.6526	1.3474
44	13	13.5815	-0.581542
45	16	12.929	3.07103
46	16	13.8647	2.13527
47	12	13.2554	-1.25542
48	12	13.2401	-1.24011
49	16	13.1639	2.83609
50	12	13.1546	-1.15455
51	15	13.3266	1.67342
52	12	12.8058	-0.80577
53	13	13.5036	-0.503598
54	12	13.5608	-1.56077
55	14	13.4261	0.573886
56	14	13.3011	0.698938
57	11	13.3963	-2.3963
58	10	13.4694	-3.46941
59	12	12.8504	-0.85035
60	11	13.1625	-2.16254
61	16	13.6086	2.39142
62	14	13.6982	0.30181
63	14	13.1691	0.830919
64	15	13.8946	1.10541
65	15	13.7879	1.21209
66	14	12.9998	1.00025
67	13	13.8551	-0.855147
68	11	13.8826	-2.88255
69	16	13.915	2.08496
70	12	13.2563	-1.25629
71	15	13.8531	1.14689
72	14	13.3914	0.608645
73	15	13.0092	1.99083
74	14	13.2748	0.725234
75	13	13.4633	-0.463289
76	6	13.1601	-7.16013
77	12	13.6381	-1.63806
78	12	13.3033	-1.30329
79	14	13.5733	0.42672
80	14	13.0735	0.926501
81	15	13.2724	1.72763
82	11	13.207	-2.20699
83	13	13.3013	-0.301315
84	14	13.1158	0.884218
85	16	13.1535	2.84645
86	13	13.4065	-0.406485
87	14	13.2477	0.752316
88	16	13.1423	2.8577
89	11	13.1281	-2.1281
90	13	13.1725	-0.172505
91	13	13.4073	-0.407278
92	15	12.9997	2.00035
93	12	13.2699	-1.26992
94	13	13.3569	-0.356869
95	12	13.1672	-1.16716
96	14	13.2012	0.79878
97	14	13.3119	0.68814
98	16	13.3687	2.63134
99	15	13.2066	1.79345
100	14	13.1519	0.848071
101	13	13.3487	-0.34873
102	14	12.9753	1.02466
103	15	13.2115	1.78846
104	14	13.1009	0.899095
105	12	13.4232	-1.42318
106	7	13.1679	-6.16787
107	12	13.2586	-1.25856
108	15	13.2222	1.77779
109	12	13.1295	-1.12952
110	13	12.8499	0.150139
111	11	13.1583	-2.15827
112	14	13.3258	0.674244

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 13 & 13.4666 & -0.466562 \tabularnewline
2 & 13 & 13.4719 & -0.471944 \tabularnewline
3 & 11 & 13.5629 & -2.5629 \tabularnewline
4 & 14 & 13.6179 & 0.382113 \tabularnewline
5 & 15 & 13.4063 & 1.59367 \tabularnewline
6 & 14 & 14.1251 & -0.125131 \tabularnewline
7 & 11 & 13.5906 & -2.59064 \tabularnewline
8 & 13 & 13.2734 & -0.273355 \tabularnewline
9 & 16 & 13.7047 & 2.29527 \tabularnewline
10 & 14 & 13.6715 & 0.328452 \tabularnewline
11 & 14 & 13.6356 & 0.364366 \tabularnewline
12 & 15 & 13.5926 & 1.40735 \tabularnewline
13 & 15 & 13.3721 & 1.62789 \tabularnewline
14 & 13 & 13.7893 & -0.789318 \tabularnewline
15 & 14 & 12.9061 & 1.09391 \tabularnewline
16 & 11 & 13.1509 & -2.15088 \tabularnewline
17 & 12 & 13.6709 & -1.67093 \tabularnewline
18 & 14 & 13.3934 & 0.606632 \tabularnewline
19 & 13 & 13.4894 & -0.489442 \tabularnewline
20 & 12 & 13.3231 & -1.32313 \tabularnewline
21 & 15 & 13.592 & 1.408 \tabularnewline
22 & 15 & 13.7946 & 1.2054 \tabularnewline
23 & 14 & 14.0157 & -0.0157244 \tabularnewline
24 & 14 & 13.8099 & 0.190105 \tabularnewline
25 & 12 & 13.5706 & -1.57057 \tabularnewline
26 & 12 & 14.1405 & -2.14046 \tabularnewline
27 & 12 & 13.7267 & -1.72675 \tabularnewline
28 & 15 & 13.1396 & 1.86045 \tabularnewline
29 & 14 & 13.4867 & 0.513285 \tabularnewline
30 & 16 & 13.5338 & 2.46618 \tabularnewline
31 & 12 & 13.1453 & -1.14531 \tabularnewline
32 & 12 & 13.5863 & -1.58631 \tabularnewline
33 & 14 & 13.4284 & 0.571623 \tabularnewline
34 & 16 & 13.7906 & 2.20945 \tabularnewline
35 & 15 & 13.283 & 1.71698 \tabularnewline
36 & 12 & 13.2792 & -1.27924 \tabularnewline
37 & 14 & 13.5044 & 0.495572 \tabularnewline
38 & 13 & 13.4951 & -0.495118 \tabularnewline
39 & 14 & 13.7609 & 0.239089 \tabularnewline
40 & 16 & 13.6117 & 2.38828 \tabularnewline
41 & 12 & 13.291 & -1.29102 \tabularnewline
42 & 14 & 13.588 & 0.411983 \tabularnewline
43 & 15 & 13.6526 & 1.3474 \tabularnewline
44 & 13 & 13.5815 & -0.581542 \tabularnewline
45 & 16 & 12.929 & 3.07103 \tabularnewline
46 & 16 & 13.8647 & 2.13527 \tabularnewline
47 & 12 & 13.2554 & -1.25542 \tabularnewline
48 & 12 & 13.2401 & -1.24011 \tabularnewline
49 & 16 & 13.1639 & 2.83609 \tabularnewline
50 & 12 & 13.1546 & -1.15455 \tabularnewline
51 & 15 & 13.3266 & 1.67342 \tabularnewline
52 & 12 & 12.8058 & -0.80577 \tabularnewline
53 & 13 & 13.5036 & -0.503598 \tabularnewline
54 & 12 & 13.5608 & -1.56077 \tabularnewline
55 & 14 & 13.4261 & 0.573886 \tabularnewline
56 & 14 & 13.3011 & 0.698938 \tabularnewline
57 & 11 & 13.3963 & -2.3963 \tabularnewline
58 & 10 & 13.4694 & -3.46941 \tabularnewline
59 & 12 & 12.8504 & -0.85035 \tabularnewline
60 & 11 & 13.1625 & -2.16254 \tabularnewline
61 & 16 & 13.6086 & 2.39142 \tabularnewline
62 & 14 & 13.6982 & 0.30181 \tabularnewline
63 & 14 & 13.1691 & 0.830919 \tabularnewline
64 & 15 & 13.8946 & 1.10541 \tabularnewline
65 & 15 & 13.7879 & 1.21209 \tabularnewline
66 & 14 & 12.9998 & 1.00025 \tabularnewline
67 & 13 & 13.8551 & -0.855147 \tabularnewline
68 & 11 & 13.8826 & -2.88255 \tabularnewline
69 & 16 & 13.915 & 2.08496 \tabularnewline
70 & 12 & 13.2563 & -1.25629 \tabularnewline
71 & 15 & 13.8531 & 1.14689 \tabularnewline
72 & 14 & 13.3914 & 0.608645 \tabularnewline
73 & 15 & 13.0092 & 1.99083 \tabularnewline
74 & 14 & 13.2748 & 0.725234 \tabularnewline
75 & 13 & 13.4633 & -0.463289 \tabularnewline
76 & 6 & 13.1601 & -7.16013 \tabularnewline
77 & 12 & 13.6381 & -1.63806 \tabularnewline
78 & 12 & 13.3033 & -1.30329 \tabularnewline
79 & 14 & 13.5733 & 0.42672 \tabularnewline
80 & 14 & 13.0735 & 0.926501 \tabularnewline
81 & 15 & 13.2724 & 1.72763 \tabularnewline
82 & 11 & 13.207 & -2.20699 \tabularnewline
83 & 13 & 13.3013 & -0.301315 \tabularnewline
84 & 14 & 13.1158 & 0.884218 \tabularnewline
85 & 16 & 13.1535 & 2.84645 \tabularnewline
86 & 13 & 13.4065 & -0.406485 \tabularnewline
87 & 14 & 13.2477 & 0.752316 \tabularnewline
88 & 16 & 13.1423 & 2.8577 \tabularnewline
89 & 11 & 13.1281 & -2.1281 \tabularnewline
90 & 13 & 13.1725 & -0.172505 \tabularnewline
91 & 13 & 13.4073 & -0.407278 \tabularnewline
92 & 15 & 12.9997 & 2.00035 \tabularnewline
93 & 12 & 13.2699 & -1.26992 \tabularnewline
94 & 13 & 13.3569 & -0.356869 \tabularnewline
95 & 12 & 13.1672 & -1.16716 \tabularnewline
96 & 14 & 13.2012 & 0.79878 \tabularnewline
97 & 14 & 13.3119 & 0.68814 \tabularnewline
98 & 16 & 13.3687 & 2.63134 \tabularnewline
99 & 15 & 13.2066 & 1.79345 \tabularnewline
100 & 14 & 13.1519 & 0.848071 \tabularnewline
101 & 13 & 13.3487 & -0.34873 \tabularnewline
102 & 14 & 12.9753 & 1.02466 \tabularnewline
103 & 15 & 13.2115 & 1.78846 \tabularnewline
104 & 14 & 13.1009 & 0.899095 \tabularnewline
105 & 12 & 13.4232 & -1.42318 \tabularnewline
106 & 7 & 13.1679 & -6.16787 \tabularnewline
107 & 12 & 13.2586 & -1.25856 \tabularnewline
108 & 15 & 13.2222 & 1.77779 \tabularnewline
109 & 12 & 13.1295 & -1.12952 \tabularnewline
110 & 13 & 12.8499 & 0.150139 \tabularnewline
111 & 11 & 13.1583 & -2.15827 \tabularnewline
112 & 14 & 13.3258 & 0.674244 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=270697&T=4

[TABLE]
[ROW][C]Multiple Linear Regression - Actuals, Interpolation, and Residuals[/C][/ROW]
[ROW][C]Time or Index[/C][C]Actuals[/C][C]InterpolationForecast[/C][C]ResidualsPrediction Error[/C][/ROW]
[ROW][C]1[/C][C]13[/C][C]13.4666[/C][C]-0.466562[/C][/ROW]
[ROW][C]2[/C][C]13[/C][C]13.4719[/C][C]-0.471944[/C][/ROW]
[ROW][C]3[/C][C]11[/C][C]13.5629[/C][C]-2.5629[/C][/ROW]
[ROW][C]4[/C][C]14[/C][C]13.6179[/C][C]0.382113[/C][/ROW]
[ROW][C]5[/C][C]15[/C][C]13.4063[/C][C]1.59367[/C][/ROW]
[ROW][C]6[/C][C]14[/C][C]14.1251[/C][C]-0.125131[/C][/ROW]
[ROW][C]7[/C][C]11[/C][C]13.5906[/C][C]-2.59064[/C][/ROW]
[ROW][C]8[/C][C]13[/C][C]13.2734[/C][C]-0.273355[/C][/ROW]
[ROW][C]9[/C][C]16[/C][C]13.7047[/C][C]2.29527[/C][/ROW]
[ROW][C]10[/C][C]14[/C][C]13.6715[/C][C]0.328452[/C][/ROW]
[ROW][C]11[/C][C]14[/C][C]13.6356[/C][C]0.364366[/C][/ROW]
[ROW][C]12[/C][C]15[/C][C]13.5926[/C][C]1.40735[/C][/ROW]
[ROW][C]13[/C][C]15[/C][C]13.3721[/C][C]1.62789[/C][/ROW]
[ROW][C]14[/C][C]13[/C][C]13.7893[/C][C]-0.789318[/C][/ROW]
[ROW][C]15[/C][C]14[/C][C]12.9061[/C][C]1.09391[/C][/ROW]
[ROW][C]16[/C][C]11[/C][C]13.1509[/C][C]-2.15088[/C][/ROW]
[ROW][C]17[/C][C]12[/C][C]13.6709[/C][C]-1.67093[/C][/ROW]
[ROW][C]18[/C][C]14[/C][C]13.3934[/C][C]0.606632[/C][/ROW]
[ROW][C]19[/C][C]13[/C][C]13.4894[/C][C]-0.489442[/C][/ROW]
[ROW][C]20[/C][C]12[/C][C]13.3231[/C][C]-1.32313[/C][/ROW]
[ROW][C]21[/C][C]15[/C][C]13.592[/C][C]1.408[/C][/ROW]
[ROW][C]22[/C][C]15[/C][C]13.7946[/C][C]1.2054[/C][/ROW]
[ROW][C]23[/C][C]14[/C][C]14.0157[/C][C]-0.0157244[/C][/ROW]
[ROW][C]24[/C][C]14[/C][C]13.8099[/C][C]0.190105[/C][/ROW]
[ROW][C]25[/C][C]12[/C][C]13.5706[/C][C]-1.57057[/C][/ROW]
[ROW][C]26[/C][C]12[/C][C]14.1405[/C][C]-2.14046[/C][/ROW]
[ROW][C]27[/C][C]12[/C][C]13.7267[/C][C]-1.72675[/C][/ROW]
[ROW][C]28[/C][C]15[/C][C]13.1396[/C][C]1.86045[/C][/ROW]
[ROW][C]29[/C][C]14[/C][C]13.4867[/C][C]0.513285[/C][/ROW]
[ROW][C]30[/C][C]16[/C][C]13.5338[/C][C]2.46618[/C][/ROW]
[ROW][C]31[/C][C]12[/C][C]13.1453[/C][C]-1.14531[/C][/ROW]
[ROW][C]32[/C][C]12[/C][C]13.5863[/C][C]-1.58631[/C][/ROW]
[ROW][C]33[/C][C]14[/C][C]13.4284[/C][C]0.571623[/C][/ROW]
[ROW][C]34[/C][C]16[/C][C]13.7906[/C][C]2.20945[/C][/ROW]
[ROW][C]35[/C][C]15[/C][C]13.283[/C][C]1.71698[/C][/ROW]
[ROW][C]36[/C][C]12[/C][C]13.2792[/C][C]-1.27924[/C][/ROW]
[ROW][C]37[/C][C]14[/C][C]13.5044[/C][C]0.495572[/C][/ROW]
[ROW][C]38[/C][C]13[/C][C]13.4951[/C][C]-0.495118[/C][/ROW]
[ROW][C]39[/C][C]14[/C][C]13.7609[/C][C]0.239089[/C][/ROW]
[ROW][C]40[/C][C]16[/C][C]13.6117[/C][C]2.38828[/C][/ROW]
[ROW][C]41[/C][C]12[/C][C]13.291[/C][C]-1.29102[/C][/ROW]
[ROW][C]42[/C][C]14[/C][C]13.588[/C][C]0.411983[/C][/ROW]
[ROW][C]43[/C][C]15[/C][C]13.6526[/C][C]1.3474[/C][/ROW]
[ROW][C]44[/C][C]13[/C][C]13.5815[/C][C]-0.581542[/C][/ROW]
[ROW][C]45[/C][C]16[/C][C]12.929[/C][C]3.07103[/C][/ROW]
[ROW][C]46[/C][C]16[/C][C]13.8647[/C][C]2.13527[/C][/ROW]
[ROW][C]47[/C][C]12[/C][C]13.2554[/C][C]-1.25542[/C][/ROW]
[ROW][C]48[/C][C]12[/C][C]13.2401[/C][C]-1.24011[/C][/ROW]
[ROW][C]49[/C][C]16[/C][C]13.1639[/C][C]2.83609[/C][/ROW]
[ROW][C]50[/C][C]12[/C][C]13.1546[/C][C]-1.15455[/C][/ROW]
[ROW][C]51[/C][C]15[/C][C]13.3266[/C][C]1.67342[/C][/ROW]
[ROW][C]52[/C][C]12[/C][C]12.8058[/C][C]-0.80577[/C][/ROW]
[ROW][C]53[/C][C]13[/C][C]13.5036[/C][C]-0.503598[/C][/ROW]
[ROW][C]54[/C][C]12[/C][C]13.5608[/C][C]-1.56077[/C][/ROW]
[ROW][C]55[/C][C]14[/C][C]13.4261[/C][C]0.573886[/C][/ROW]
[ROW][C]56[/C][C]14[/C][C]13.3011[/C][C]0.698938[/C][/ROW]
[ROW][C]57[/C][C]11[/C][C]13.3963[/C][C]-2.3963[/C][/ROW]
[ROW][C]58[/C][C]10[/C][C]13.4694[/C][C]-3.46941[/C][/ROW]
[ROW][C]59[/C][C]12[/C][C]12.8504[/C][C]-0.85035[/C][/ROW]
[ROW][C]60[/C][C]11[/C][C]13.1625[/C][C]-2.16254[/C][/ROW]
[ROW][C]61[/C][C]16[/C][C]13.6086[/C][C]2.39142[/C][/ROW]
[ROW][C]62[/C][C]14[/C][C]13.6982[/C][C]0.30181[/C][/ROW]
[ROW][C]63[/C][C]14[/C][C]13.1691[/C][C]0.830919[/C][/ROW]
[ROW][C]64[/C][C]15[/C][C]13.8946[/C][C]1.10541[/C][/ROW]
[ROW][C]65[/C][C]15[/C][C]13.7879[/C][C]1.21209[/C][/ROW]
[ROW][C]66[/C][C]14[/C][C]12.9998[/C][C]1.00025[/C][/ROW]
[ROW][C]67[/C][C]13[/C][C]13.8551[/C][C]-0.855147[/C][/ROW]
[ROW][C]68[/C][C]11[/C][C]13.8826[/C][C]-2.88255[/C][/ROW]
[ROW][C]69[/C][C]16[/C][C]13.915[/C][C]2.08496[/C][/ROW]
[ROW][C]70[/C][C]12[/C][C]13.2563[/C][C]-1.25629[/C][/ROW]
[ROW][C]71[/C][C]15[/C][C]13.8531[/C][C]1.14689[/C][/ROW]
[ROW][C]72[/C][C]14[/C][C]13.3914[/C][C]0.608645[/C][/ROW]
[ROW][C]73[/C][C]15[/C][C]13.0092[/C][C]1.99083[/C][/ROW]
[ROW][C]74[/C][C]14[/C][C]13.2748[/C][C]0.725234[/C][/ROW]
[ROW][C]75[/C][C]13[/C][C]13.4633[/C][C]-0.463289[/C][/ROW]
[ROW][C]76[/C][C]6[/C][C]13.1601[/C][C]-7.16013[/C][/ROW]
[ROW][C]77[/C][C]12[/C][C]13.6381[/C][C]-1.63806[/C][/ROW]
[ROW][C]78[/C][C]12[/C][C]13.3033[/C][C]-1.30329[/C][/ROW]
[ROW][C]79[/C][C]14[/C][C]13.5733[/C][C]0.42672[/C][/ROW]
[ROW][C]80[/C][C]14[/C][C]13.0735[/C][C]0.926501[/C][/ROW]
[ROW][C]81[/C][C]15[/C][C]13.2724[/C][C]1.72763[/C][/ROW]
[ROW][C]82[/C][C]11[/C][C]13.207[/C][C]-2.20699[/C][/ROW]
[ROW][C]83[/C][C]13[/C][C]13.3013[/C][C]-0.301315[/C][/ROW]
[ROW][C]84[/C][C]14[/C][C]13.1158[/C][C]0.884218[/C][/ROW]
[ROW][C]85[/C][C]16[/C][C]13.1535[/C][C]2.84645[/C][/ROW]
[ROW][C]86[/C][C]13[/C][C]13.4065[/C][C]-0.406485[/C][/ROW]
[ROW][C]87[/C][C]14[/C][C]13.2477[/C][C]0.752316[/C][/ROW]
[ROW][C]88[/C][C]16[/C][C]13.1423[/C][C]2.8577[/C][/ROW]
[ROW][C]89[/C][C]11[/C][C]13.1281[/C][C]-2.1281[/C][/ROW]
[ROW][C]90[/C][C]13[/C][C]13.1725[/C][C]-0.172505[/C][/ROW]
[ROW][C]91[/C][C]13[/C][C]13.4073[/C][C]-0.407278[/C][/ROW]
[ROW][C]92[/C][C]15[/C][C]12.9997[/C][C]2.00035[/C][/ROW]
[ROW][C]93[/C][C]12[/C][C]13.2699[/C][C]-1.26992[/C][/ROW]
[ROW][C]94[/C][C]13[/C][C]13.3569[/C][C]-0.356869[/C][/ROW]
[ROW][C]95[/C][C]12[/C][C]13.1672[/C][C]-1.16716[/C][/ROW]
[ROW][C]96[/C][C]14[/C][C]13.2012[/C][C]0.79878[/C][/ROW]
[ROW][C]97[/C][C]14[/C][C]13.3119[/C][C]0.68814[/C][/ROW]
[ROW][C]98[/C][C]16[/C][C]13.3687[/C][C]2.63134[/C][/ROW]
[ROW][C]99[/C][C]15[/C][C]13.2066[/C][C]1.79345[/C][/ROW]
[ROW][C]100[/C][C]14[/C][C]13.1519[/C][C]0.848071[/C][/ROW]
[ROW][C]101[/C][C]13[/C][C]13.3487[/C][C]-0.34873[/C][/ROW]
[ROW][C]102[/C][C]14[/C][C]12.9753[/C][C]1.02466[/C][/ROW]
[ROW][C]103[/C][C]15[/C][C]13.2115[/C][C]1.78846[/C][/ROW]
[ROW][C]104[/C][C]14[/C][C]13.1009[/C][C]0.899095[/C][/ROW]
[ROW][C]105[/C][C]12[/C][C]13.4232[/C][C]-1.42318[/C][/ROW]
[ROW][C]106[/C][C]7[/C][C]13.1679[/C][C]-6.16787[/C][/ROW]
[ROW][C]107[/C][C]12[/C][C]13.2586[/C][C]-1.25856[/C][/ROW]
[ROW][C]108[/C][C]15[/C][C]13.2222[/C][C]1.77779[/C][/ROW]
[ROW][C]109[/C][C]12[/C][C]13.1295[/C][C]-1.12952[/C][/ROW]
[ROW][C]110[/C][C]13[/C][C]12.8499[/C][C]0.150139[/C][/ROW]
[ROW][C]111[/C][C]11[/C][C]13.1583[/C][C]-2.15827[/C][/ROW]
[ROW][C]112[/C][C]14[/C][C]13.3258[/C][C]0.674244[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=270697&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=270697&T=4

As an alternative you can also use a QR Code:

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	13	13.4666	-0.466562
2	13	13.4719	-0.471944
3	11	13.5629	-2.5629
4	14	13.6179	0.382113
5	15	13.4063	1.59367
6	14	14.1251	-0.125131
7	11	13.5906	-2.59064
8	13	13.2734	-0.273355
9	16	13.7047	2.29527
10	14	13.6715	0.328452
11	14	13.6356	0.364366
12	15	13.5926	1.40735
13	15	13.3721	1.62789
14	13	13.7893	-0.789318
15	14	12.9061	1.09391
16	11	13.1509	-2.15088
17	12	13.6709	-1.67093
18	14	13.3934	0.606632
19	13	13.4894	-0.489442
20	12	13.3231	-1.32313
21	15	13.592	1.408
22	15	13.7946	1.2054
23	14	14.0157	-0.0157244
24	14	13.8099	0.190105
25	12	13.5706	-1.57057
26	12	14.1405	-2.14046
27	12	13.7267	-1.72675
28	15	13.1396	1.86045
29	14	13.4867	0.513285
30	16	13.5338	2.46618
31	12	13.1453	-1.14531
32	12	13.5863	-1.58631
33	14	13.4284	0.571623
34	16	13.7906	2.20945
35	15	13.283	1.71698
36	12	13.2792	-1.27924
37	14	13.5044	0.495572
38	13	13.4951	-0.495118
39	14	13.7609	0.239089
40	16	13.6117	2.38828
41	12	13.291	-1.29102
42	14	13.588	0.411983
43	15	13.6526	1.3474
44	13	13.5815	-0.581542
45	16	12.929	3.07103
46	16	13.8647	2.13527
47	12	13.2554	-1.25542
48	12	13.2401	-1.24011
49	16	13.1639	2.83609
50	12	13.1546	-1.15455
51	15	13.3266	1.67342
52	12	12.8058	-0.80577
53	13	13.5036	-0.503598
54	12	13.5608	-1.56077
55	14	13.4261	0.573886
56	14	13.3011	0.698938
57	11	13.3963	-2.3963
58	10	13.4694	-3.46941
59	12	12.8504	-0.85035
60	11	13.1625	-2.16254
61	16	13.6086	2.39142
62	14	13.6982	0.30181
63	14	13.1691	0.830919
64	15	13.8946	1.10541
65	15	13.7879	1.21209
66	14	12.9998	1.00025
67	13	13.8551	-0.855147
68	11	13.8826	-2.88255
69	16	13.915	2.08496
70	12	13.2563	-1.25629
71	15	13.8531	1.14689
72	14	13.3914	0.608645
73	15	13.0092	1.99083
74	14	13.2748	0.725234
75	13	13.4633	-0.463289
76	6	13.1601	-7.16013
77	12	13.6381	-1.63806
78	12	13.3033	-1.30329
79	14	13.5733	0.42672
80	14	13.0735	0.926501
81	15	13.2724	1.72763
82	11	13.207	-2.20699
83	13	13.3013	-0.301315
84	14	13.1158	0.884218
85	16	13.1535	2.84645
86	13	13.4065	-0.406485
87	14	13.2477	0.752316
88	16	13.1423	2.8577
89	11	13.1281	-2.1281
90	13	13.1725	-0.172505
91	13	13.4073	-0.407278
92	15	12.9997	2.00035
93	12	13.2699	-1.26992
94	13	13.3569	-0.356869
95	12	13.1672	-1.16716
96	14	13.2012	0.79878
97	14	13.3119	0.68814
98	16	13.3687	2.63134
99	15	13.2066	1.79345
100	14	13.1519	0.848071
101	13	13.3487	-0.34873
102	14	12.9753	1.02466
103	15	13.2115	1.78846
104	14	13.1009	0.899095
105	12	13.4232	-1.42318
106	7	13.1679	-6.16787
107	12	13.2586	-1.25856
108	15	13.2222	1.77779
109	12	13.1295	-1.12952
110	13	12.8499	0.150139
111	11	13.1583	-2.15827
112	14	13.3258	0.674244

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
7	0.747651	0.504698	0.252349
8	0.615665	0.768671	0.384335
9	0.584153	0.831694	0.415847
10	0.492142	0.984283	0.507858
11	0.412956	0.825913	0.587044
12	0.403824	0.807649	0.596176
13	0.367227	0.734454	0.632773
14	0.319892	0.639784	0.680108
15	0.239714	0.479427	0.760286
16	0.326617	0.653235	0.673383
17	0.323231	0.646461	0.676769
18	0.255954	0.511908	0.744046
19	0.198742	0.397483	0.801258
20	0.162095	0.32419	0.837905
21	0.145531	0.291062	0.854469
22	0.116363	0.232726	0.883637
23	0.0836763	0.167353	0.916324
24	0.0600564	0.120113	0.939944
25	0.0576087	0.115217	0.942391
26	0.053553	0.107106	0.946447
27	0.0624897	0.124979	0.93751
28	0.069181	0.138362	0.930819
29	0.0551928	0.110386	0.944807
30	0.0824827	0.164965	0.917517
31	0.0723344	0.144669	0.927666
32	0.0723331	0.144666	0.927667
33	0.0534479	0.106896	0.946552
34	0.0619918	0.123984	0.938008
35	0.0581576	0.116315	0.941842
36	0.0610112	0.122022	0.938989
37	0.0456908	0.0913816	0.954309
38	0.0366929	0.0733858	0.963307
39	0.0260041	0.0520083	0.973996
40	0.0383786	0.0767572	0.961621
41	0.0354731	0.0709461	0.964527
42	0.0255482	0.0510965	0.974452
43	0.0228529	0.0457057	0.977147
44	0.0164835	0.032967	0.983516
45	0.0307595	0.061519	0.96924
46	0.0371131	0.0742262	0.962887
47	0.0322914	0.0645827	0.967709
48	0.0297899	0.0595799	0.97021
49	0.0492476	0.0984952	0.950752
50	0.0465011	0.0930022	0.953499
51	0.0436598	0.0873195	0.95634
52	0.036297	0.0725939	0.963703
53	0.0275715	0.0551429	0.972429
54	0.0266982	0.0533964	0.973302
55	0.0198093	0.0396187	0.980191
56	0.0145934	0.0291867	0.985407
57	0.0206999	0.0413999	0.9793
58	0.0512359	0.102472	0.948764
59	0.0413442	0.0826883	0.958656
60	0.0471637	0.0943274	0.952836
61	0.0597037	0.119407	0.940296
62	0.0452	0.0904	0.9548
63	0.0356578	0.0713155	0.964342
64	0.0295712	0.0591424	0.970429
65	0.0250308	0.0500616	0.974969
66	0.0198143	0.0396286	0.980186
67	0.0148869	0.0297738	0.985113
68	0.0282407	0.0564813	0.971759
69	0.0367704	0.0735409	0.96323
70	0.0309584	0.0619168	0.969042
71	0.0338654	0.0677308	0.966135
72	0.0268604	0.0537209	0.97314
73	0.0252736	0.0505473	0.974726
74	0.0203256	0.0406513	0.979674
75	0.0143466	0.0286931	0.985653
76	0.390887	0.781774	0.609113
77	0.358413	0.716826	0.641587
78	0.330397	0.660794	0.669603
79	0.287599	0.575197	0.712401
80	0.247874	0.495748	0.752126
81	0.262452	0.524903	0.737548
82	0.279434	0.558868	0.720566
83	0.231026	0.462052	0.768974
84	0.20419	0.408379	0.79581
85	0.252158	0.504316	0.747842
86	0.202497	0.404995	0.797503
87	0.217676	0.435352	0.782324
88	0.395872	0.791743	0.604128
89	0.399362	0.798723	0.600638
90	0.331091	0.662181	0.668909
91	0.270919	0.541838	0.729081
92	0.310524	0.621048	0.689476
93	0.328865	0.65773	0.671135
94	0.31624	0.632481	0.68376
95	0.332834	0.665667	0.667166
96	0.271316	0.542632	0.728684
97	0.238063	0.476127	0.761937
98	0.323773	0.647545	0.676227
99	0.266712	0.533423	0.733288
100	0.220036	0.440072	0.779964
101	0.154528	0.309056	0.845472
102	0.256961	0.513923	0.743039
103	0.331812	0.663625	0.668188
104	0.232711	0.465423	0.767289
105	0.141257	0.282514	0.858743

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
7 & 0.747651 & 0.504698 & 0.252349 \tabularnewline
8 & 0.615665 & 0.768671 & 0.384335 \tabularnewline
9 & 0.584153 & 0.831694 & 0.415847 \tabularnewline
10 & 0.492142 & 0.984283 & 0.507858 \tabularnewline
11 & 0.412956 & 0.825913 & 0.587044 \tabularnewline
12 & 0.403824 & 0.807649 & 0.596176 \tabularnewline
13 & 0.367227 & 0.734454 & 0.632773 \tabularnewline
14 & 0.319892 & 0.639784 & 0.680108 \tabularnewline
15 & 0.239714 & 0.479427 & 0.760286 \tabularnewline
16 & 0.326617 & 0.653235 & 0.673383 \tabularnewline
17 & 0.323231 & 0.646461 & 0.676769 \tabularnewline
18 & 0.255954 & 0.511908 & 0.744046 \tabularnewline
19 & 0.198742 & 0.397483 & 0.801258 \tabularnewline
20 & 0.162095 & 0.32419 & 0.837905 \tabularnewline
21 & 0.145531 & 0.291062 & 0.854469 \tabularnewline
22 & 0.116363 & 0.232726 & 0.883637 \tabularnewline
23 & 0.0836763 & 0.167353 & 0.916324 \tabularnewline
24 & 0.0600564 & 0.120113 & 0.939944 \tabularnewline
25 & 0.0576087 & 0.115217 & 0.942391 \tabularnewline
26 & 0.053553 & 0.107106 & 0.946447 \tabularnewline
27 & 0.0624897 & 0.124979 & 0.93751 \tabularnewline
28 & 0.069181 & 0.138362 & 0.930819 \tabularnewline
29 & 0.0551928 & 0.110386 & 0.944807 \tabularnewline
30 & 0.0824827 & 0.164965 & 0.917517 \tabularnewline
31 & 0.0723344 & 0.144669 & 0.927666 \tabularnewline
32 & 0.0723331 & 0.144666 & 0.927667 \tabularnewline
33 & 0.0534479 & 0.106896 & 0.946552 \tabularnewline
34 & 0.0619918 & 0.123984 & 0.938008 \tabularnewline
35 & 0.0581576 & 0.116315 & 0.941842 \tabularnewline
36 & 0.0610112 & 0.122022 & 0.938989 \tabularnewline
37 & 0.0456908 & 0.0913816 & 0.954309 \tabularnewline
38 & 0.0366929 & 0.0733858 & 0.963307 \tabularnewline
39 & 0.0260041 & 0.0520083 & 0.973996 \tabularnewline
40 & 0.0383786 & 0.0767572 & 0.961621 \tabularnewline
41 & 0.0354731 & 0.0709461 & 0.964527 \tabularnewline
42 & 0.0255482 & 0.0510965 & 0.974452 \tabularnewline
43 & 0.0228529 & 0.0457057 & 0.977147 \tabularnewline
44 & 0.0164835 & 0.032967 & 0.983516 \tabularnewline
45 & 0.0307595 & 0.061519 & 0.96924 \tabularnewline
46 & 0.0371131 & 0.0742262 & 0.962887 \tabularnewline
47 & 0.0322914 & 0.0645827 & 0.967709 \tabularnewline
48 & 0.0297899 & 0.0595799 & 0.97021 \tabularnewline
49 & 0.0492476 & 0.0984952 & 0.950752 \tabularnewline
50 & 0.0465011 & 0.0930022 & 0.953499 \tabularnewline
51 & 0.0436598 & 0.0873195 & 0.95634 \tabularnewline
52 & 0.036297 & 0.0725939 & 0.963703 \tabularnewline
53 & 0.0275715 & 0.0551429 & 0.972429 \tabularnewline
54 & 0.0266982 & 0.0533964 & 0.973302 \tabularnewline
55 & 0.0198093 & 0.0396187 & 0.980191 \tabularnewline
56 & 0.0145934 & 0.0291867 & 0.985407 \tabularnewline
57 & 0.0206999 & 0.0413999 & 0.9793 \tabularnewline
58 & 0.0512359 & 0.102472 & 0.948764 \tabularnewline
59 & 0.0413442 & 0.0826883 & 0.958656 \tabularnewline
60 & 0.0471637 & 0.0943274 & 0.952836 \tabularnewline
61 & 0.0597037 & 0.119407 & 0.940296 \tabularnewline
62 & 0.0452 & 0.0904 & 0.9548 \tabularnewline
63 & 0.0356578 & 0.0713155 & 0.964342 \tabularnewline
64 & 0.0295712 & 0.0591424 & 0.970429 \tabularnewline
65 & 0.0250308 & 0.0500616 & 0.974969 \tabularnewline
66 & 0.0198143 & 0.0396286 & 0.980186 \tabularnewline
67 & 0.0148869 & 0.0297738 & 0.985113 \tabularnewline
68 & 0.0282407 & 0.0564813 & 0.971759 \tabularnewline
69 & 0.0367704 & 0.0735409 & 0.96323 \tabularnewline
70 & 0.0309584 & 0.0619168 & 0.969042 \tabularnewline
71 & 0.0338654 & 0.0677308 & 0.966135 \tabularnewline
72 & 0.0268604 & 0.0537209 & 0.97314 \tabularnewline
73 & 0.0252736 & 0.0505473 & 0.974726 \tabularnewline
74 & 0.0203256 & 0.0406513 & 0.979674 \tabularnewline
75 & 0.0143466 & 0.0286931 & 0.985653 \tabularnewline
76 & 0.390887 & 0.781774 & 0.609113 \tabularnewline
77 & 0.358413 & 0.716826 & 0.641587 \tabularnewline
78 & 0.330397 & 0.660794 & 0.669603 \tabularnewline
79 & 0.287599 & 0.575197 & 0.712401 \tabularnewline
80 & 0.247874 & 0.495748 & 0.752126 \tabularnewline
81 & 0.262452 & 0.524903 & 0.737548 \tabularnewline
82 & 0.279434 & 0.558868 & 0.720566 \tabularnewline
83 & 0.231026 & 0.462052 & 0.768974 \tabularnewline
84 & 0.20419 & 0.408379 & 0.79581 \tabularnewline
85 & 0.252158 & 0.504316 & 0.747842 \tabularnewline
86 & 0.202497 & 0.404995 & 0.797503 \tabularnewline
87 & 0.217676 & 0.435352 & 0.782324 \tabularnewline
88 & 0.395872 & 0.791743 & 0.604128 \tabularnewline
89 & 0.399362 & 0.798723 & 0.600638 \tabularnewline
90 & 0.331091 & 0.662181 & 0.668909 \tabularnewline
91 & 0.270919 & 0.541838 & 0.729081 \tabularnewline
92 & 0.310524 & 0.621048 & 0.689476 \tabularnewline
93 & 0.328865 & 0.65773 & 0.671135 \tabularnewline
94 & 0.31624 & 0.632481 & 0.68376 \tabularnewline
95 & 0.332834 & 0.665667 & 0.667166 \tabularnewline
96 & 0.271316 & 0.542632 & 0.728684 \tabularnewline
97 & 0.238063 & 0.476127 & 0.761937 \tabularnewline
98 & 0.323773 & 0.647545 & 0.676227 \tabularnewline
99 & 0.266712 & 0.533423 & 0.733288 \tabularnewline
100 & 0.220036 & 0.440072 & 0.779964 \tabularnewline
101 & 0.154528 & 0.309056 & 0.845472 \tabularnewline
102 & 0.256961 & 0.513923 & 0.743039 \tabularnewline
103 & 0.331812 & 0.663625 & 0.668188 \tabularnewline
104 & 0.232711 & 0.465423 & 0.767289 \tabularnewline
105 & 0.141257 & 0.282514 & 0.858743 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=270697&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]7[/C][C]0.747651[/C][C]0.504698[/C][C]0.252349[/C][/ROW]
[ROW][C]8[/C][C]0.615665[/C][C]0.768671[/C][C]0.384335[/C][/ROW]
[ROW][C]9[/C][C]0.584153[/C][C]0.831694[/C][C]0.415847[/C][/ROW]
[ROW][C]10[/C][C]0.492142[/C][C]0.984283[/C][C]0.507858[/C][/ROW]
[ROW][C]11[/C][C]0.412956[/C][C]0.825913[/C][C]0.587044[/C][/ROW]
[ROW][C]12[/C][C]0.403824[/C][C]0.807649[/C][C]0.596176[/C][/ROW]
[ROW][C]13[/C][C]0.367227[/C][C]0.734454[/C][C]0.632773[/C][/ROW]
[ROW][C]14[/C][C]0.319892[/C][C]0.639784[/C][C]0.680108[/C][/ROW]
[ROW][C]15[/C][C]0.239714[/C][C]0.479427[/C][C]0.760286[/C][/ROW]
[ROW][C]16[/C][C]0.326617[/C][C]0.653235[/C][C]0.673383[/C][/ROW]
[ROW][C]17[/C][C]0.323231[/C][C]0.646461[/C][C]0.676769[/C][/ROW]
[ROW][C]18[/C][C]0.255954[/C][C]0.511908[/C][C]0.744046[/C][/ROW]
[ROW][C]19[/C][C]0.198742[/C][C]0.397483[/C][C]0.801258[/C][/ROW]
[ROW][C]20[/C][C]0.162095[/C][C]0.32419[/C][C]0.837905[/C][/ROW]
[ROW][C]21[/C][C]0.145531[/C][C]0.291062[/C][C]0.854469[/C][/ROW]
[ROW][C]22[/C][C]0.116363[/C][C]0.232726[/C][C]0.883637[/C][/ROW]
[ROW][C]23[/C][C]0.0836763[/C][C]0.167353[/C][C]0.916324[/C][/ROW]
[ROW][C]24[/C][C]0.0600564[/C][C]0.120113[/C][C]0.939944[/C][/ROW]
[ROW][C]25[/C][C]0.0576087[/C][C]0.115217[/C][C]0.942391[/C][/ROW]
[ROW][C]26[/C][C]0.053553[/C][C]0.107106[/C][C]0.946447[/C][/ROW]
[ROW][C]27[/C][C]0.0624897[/C][C]0.124979[/C][C]0.93751[/C][/ROW]
[ROW][C]28[/C][C]0.069181[/C][C]0.138362[/C][C]0.930819[/C][/ROW]
[ROW][C]29[/C][C]0.0551928[/C][C]0.110386[/C][C]0.944807[/C][/ROW]
[ROW][C]30[/C][C]0.0824827[/C][C]0.164965[/C][C]0.917517[/C][/ROW]
[ROW][C]31[/C][C]0.0723344[/C][C]0.144669[/C][C]0.927666[/C][/ROW]
[ROW][C]32[/C][C]0.0723331[/C][C]0.144666[/C][C]0.927667[/C][/ROW]
[ROW][C]33[/C][C]0.0534479[/C][C]0.106896[/C][C]0.946552[/C][/ROW]
[ROW][C]34[/C][C]0.0619918[/C][C]0.123984[/C][C]0.938008[/C][/ROW]
[ROW][C]35[/C][C]0.0581576[/C][C]0.116315[/C][C]0.941842[/C][/ROW]
[ROW][C]36[/C][C]0.0610112[/C][C]0.122022[/C][C]0.938989[/C][/ROW]
[ROW][C]37[/C][C]0.0456908[/C][C]0.0913816[/C][C]0.954309[/C][/ROW]
[ROW][C]38[/C][C]0.0366929[/C][C]0.0733858[/C][C]0.963307[/C][/ROW]
[ROW][C]39[/C][C]0.0260041[/C][C]0.0520083[/C][C]0.973996[/C][/ROW]
[ROW][C]40[/C][C]0.0383786[/C][C]0.0767572[/C][C]0.961621[/C][/ROW]
[ROW][C]41[/C][C]0.0354731[/C][C]0.0709461[/C][C]0.964527[/C][/ROW]
[ROW][C]42[/C][C]0.0255482[/C][C]0.0510965[/C][C]0.974452[/C][/ROW]
[ROW][C]43[/C][C]0.0228529[/C][C]0.0457057[/C][C]0.977147[/C][/ROW]
[ROW][C]44[/C][C]0.0164835[/C][C]0.032967[/C][C]0.983516[/C][/ROW]
[ROW][C]45[/C][C]0.0307595[/C][C]0.061519[/C][C]0.96924[/C][/ROW]
[ROW][C]46[/C][C]0.0371131[/C][C]0.0742262[/C][C]0.962887[/C][/ROW]
[ROW][C]47[/C][C]0.0322914[/C][C]0.0645827[/C][C]0.967709[/C][/ROW]
[ROW][C]48[/C][C]0.0297899[/C][C]0.0595799[/C][C]0.97021[/C][/ROW]
[ROW][C]49[/C][C]0.0492476[/C][C]0.0984952[/C][C]0.950752[/C][/ROW]
[ROW][C]50[/C][C]0.0465011[/C][C]0.0930022[/C][C]0.953499[/C][/ROW]
[ROW][C]51[/C][C]0.0436598[/C][C]0.0873195[/C][C]0.95634[/C][/ROW]
[ROW][C]52[/C][C]0.036297[/C][C]0.0725939[/C][C]0.963703[/C][/ROW]
[ROW][C]53[/C][C]0.0275715[/C][C]0.0551429[/C][C]0.972429[/C][/ROW]
[ROW][C]54[/C][C]0.0266982[/C][C]0.0533964[/C][C]0.973302[/C][/ROW]
[ROW][C]55[/C][C]0.0198093[/C][C]0.0396187[/C][C]0.980191[/C][/ROW]
[ROW][C]56[/C][C]0.0145934[/C][C]0.0291867[/C][C]0.985407[/C][/ROW]
[ROW][C]57[/C][C]0.0206999[/C][C]0.0413999[/C][C]0.9793[/C][/ROW]
[ROW][C]58[/C][C]0.0512359[/C][C]0.102472[/C][C]0.948764[/C][/ROW]
[ROW][C]59[/C][C]0.0413442[/C][C]0.0826883[/C][C]0.958656[/C][/ROW]
[ROW][C]60[/C][C]0.0471637[/C][C]0.0943274[/C][C]0.952836[/C][/ROW]
[ROW][C]61[/C][C]0.0597037[/C][C]0.119407[/C][C]0.940296[/C][/ROW]
[ROW][C]62[/C][C]0.0452[/C][C]0.0904[/C][C]0.9548[/C][/ROW]
[ROW][C]63[/C][C]0.0356578[/C][C]0.0713155[/C][C]0.964342[/C][/ROW]
[ROW][C]64[/C][C]0.0295712[/C][C]0.0591424[/C][C]0.970429[/C][/ROW]
[ROW][C]65[/C][C]0.0250308[/C][C]0.0500616[/C][C]0.974969[/C][/ROW]
[ROW][C]66[/C][C]0.0198143[/C][C]0.0396286[/C][C]0.980186[/C][/ROW]
[ROW][C]67[/C][C]0.0148869[/C][C]0.0297738[/C][C]0.985113[/C][/ROW]
[ROW][C]68[/C][C]0.0282407[/C][C]0.0564813[/C][C]0.971759[/C][/ROW]
[ROW][C]69[/C][C]0.0367704[/C][C]0.0735409[/C][C]0.96323[/C][/ROW]
[ROW][C]70[/C][C]0.0309584[/C][C]0.0619168[/C][C]0.969042[/C][/ROW]
[ROW][C]71[/C][C]0.0338654[/C][C]0.0677308[/C][C]0.966135[/C][/ROW]
[ROW][C]72[/C][C]0.0268604[/C][C]0.0537209[/C][C]0.97314[/C][/ROW]
[ROW][C]73[/C][C]0.0252736[/C][C]0.0505473[/C][C]0.974726[/C][/ROW]
[ROW][C]74[/C][C]0.0203256[/C][C]0.0406513[/C][C]0.979674[/C][/ROW]
[ROW][C]75[/C][C]0.0143466[/C][C]0.0286931[/C][C]0.985653[/C][/ROW]
[ROW][C]76[/C][C]0.390887[/C][C]0.781774[/C][C]0.609113[/C][/ROW]
[ROW][C]77[/C][C]0.358413[/C][C]0.716826[/C][C]0.641587[/C][/ROW]
[ROW][C]78[/C][C]0.330397[/C][C]0.660794[/C][C]0.669603[/C][/ROW]
[ROW][C]79[/C][C]0.287599[/C][C]0.575197[/C][C]0.712401[/C][/ROW]
[ROW][C]80[/C][C]0.247874[/C][C]0.495748[/C][C]0.752126[/C][/ROW]
[ROW][C]81[/C][C]0.262452[/C][C]0.524903[/C][C]0.737548[/C][/ROW]
[ROW][C]82[/C][C]0.279434[/C][C]0.558868[/C][C]0.720566[/C][/ROW]
[ROW][C]83[/C][C]0.231026[/C][C]0.462052[/C][C]0.768974[/C][/ROW]
[ROW][C]84[/C][C]0.20419[/C][C]0.408379[/C][C]0.79581[/C][/ROW]
[ROW][C]85[/C][C]0.252158[/C][C]0.504316[/C][C]0.747842[/C][/ROW]
[ROW][C]86[/C][C]0.202497[/C][C]0.404995[/C][C]0.797503[/C][/ROW]
[ROW][C]87[/C][C]0.217676[/C][C]0.435352[/C][C]0.782324[/C][/ROW]
[ROW][C]88[/C][C]0.395872[/C][C]0.791743[/C][C]0.604128[/C][/ROW]
[ROW][C]89[/C][C]0.399362[/C][C]0.798723[/C][C]0.600638[/C][/ROW]
[ROW][C]90[/C][C]0.331091[/C][C]0.662181[/C][C]0.668909[/C][/ROW]
[ROW][C]91[/C][C]0.270919[/C][C]0.541838[/C][C]0.729081[/C][/ROW]
[ROW][C]92[/C][C]0.310524[/C][C]0.621048[/C][C]0.689476[/C][/ROW]
[ROW][C]93[/C][C]0.328865[/C][C]0.65773[/C][C]0.671135[/C][/ROW]
[ROW][C]94[/C][C]0.31624[/C][C]0.632481[/C][C]0.68376[/C][/ROW]
[ROW][C]95[/C][C]0.332834[/C][C]0.665667[/C][C]0.667166[/C][/ROW]
[ROW][C]96[/C][C]0.271316[/C][C]0.542632[/C][C]0.728684[/C][/ROW]
[ROW][C]97[/C][C]0.238063[/C][C]0.476127[/C][C]0.761937[/C][/ROW]
[ROW][C]98[/C][C]0.323773[/C][C]0.647545[/C][C]0.676227[/C][/ROW]
[ROW][C]99[/C][C]0.266712[/C][C]0.533423[/C][C]0.733288[/C][/ROW]
[ROW][C]100[/C][C]0.220036[/C][C]0.440072[/C][C]0.779964[/C][/ROW]
[ROW][C]101[/C][C]0.154528[/C][C]0.309056[/C][C]0.845472[/C][/ROW]
[ROW][C]102[/C][C]0.256961[/C][C]0.513923[/C][C]0.743039[/C][/ROW]
[ROW][C]103[/C][C]0.331812[/C][C]0.663625[/C][C]0.668188[/C][/ROW]
[ROW][C]104[/C][C]0.232711[/C][C]0.465423[/C][C]0.767289[/C][/ROW]
[ROW][C]105[/C][C]0.141257[/C][C]0.282514[/C][C]0.858743[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=270697&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=270697&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
7	0.747651	0.504698	0.252349
8	0.615665	0.768671	0.384335
9	0.584153	0.831694	0.415847
10	0.492142	0.984283	0.507858
11	0.412956	0.825913	0.587044
12	0.403824	0.807649	0.596176
13	0.367227	0.734454	0.632773
14	0.319892	0.639784	0.680108
15	0.239714	0.479427	0.760286
16	0.326617	0.653235	0.673383
17	0.323231	0.646461	0.676769
18	0.255954	0.511908	0.744046
19	0.198742	0.397483	0.801258
20	0.162095	0.32419	0.837905
21	0.145531	0.291062	0.854469
22	0.116363	0.232726	0.883637
23	0.0836763	0.167353	0.916324
24	0.0600564	0.120113	0.939944
25	0.0576087	0.115217	0.942391
26	0.053553	0.107106	0.946447
27	0.0624897	0.124979	0.93751
28	0.069181	0.138362	0.930819
29	0.0551928	0.110386	0.944807
30	0.0824827	0.164965	0.917517
31	0.0723344	0.144669	0.927666
32	0.0723331	0.144666	0.927667
33	0.0534479	0.106896	0.946552
34	0.0619918	0.123984	0.938008
35	0.0581576	0.116315	0.941842
36	0.0610112	0.122022	0.938989
37	0.0456908	0.0913816	0.954309
38	0.0366929	0.0733858	0.963307
39	0.0260041	0.0520083	0.973996
40	0.0383786	0.0767572	0.961621
41	0.0354731	0.0709461	0.964527
42	0.0255482	0.0510965	0.974452
43	0.0228529	0.0457057	0.977147
44	0.0164835	0.032967	0.983516
45	0.0307595	0.061519	0.96924
46	0.0371131	0.0742262	0.962887
47	0.0322914	0.0645827	0.967709
48	0.0297899	0.0595799	0.97021
49	0.0492476	0.0984952	0.950752
50	0.0465011	0.0930022	0.953499
51	0.0436598	0.0873195	0.95634
52	0.036297	0.0725939	0.963703
53	0.0275715	0.0551429	0.972429
54	0.0266982	0.0533964	0.973302
55	0.0198093	0.0396187	0.980191
56	0.0145934	0.0291867	0.985407
57	0.0206999	0.0413999	0.9793
58	0.0512359	0.102472	0.948764
59	0.0413442	0.0826883	0.958656
60	0.0471637	0.0943274	0.952836
61	0.0597037	0.119407	0.940296
62	0.0452	0.0904	0.9548
63	0.0356578	0.0713155	0.964342
64	0.0295712	0.0591424	0.970429
65	0.0250308	0.0500616	0.974969
66	0.0198143	0.0396286	0.980186
67	0.0148869	0.0297738	0.985113
68	0.0282407	0.0564813	0.971759
69	0.0367704	0.0735409	0.96323
70	0.0309584	0.0619168	0.969042
71	0.0338654	0.0677308	0.966135
72	0.0268604	0.0537209	0.97314
73	0.0252736	0.0505473	0.974726
74	0.0203256	0.0406513	0.979674
75	0.0143466	0.0286931	0.985653
76	0.390887	0.781774	0.609113
77	0.358413	0.716826	0.641587
78	0.330397	0.660794	0.669603
79	0.287599	0.575197	0.712401
80	0.247874	0.495748	0.752126
81	0.262452	0.524903	0.737548
82	0.279434	0.558868	0.720566
83	0.231026	0.462052	0.768974
84	0.20419	0.408379	0.79581
85	0.252158	0.504316	0.747842
86	0.202497	0.404995	0.797503
87	0.217676	0.435352	0.782324
88	0.395872	0.791743	0.604128
89	0.399362	0.798723	0.600638
90	0.331091	0.662181	0.668909
91	0.270919	0.541838	0.729081
92	0.310524	0.621048	0.689476
93	0.328865	0.65773	0.671135
94	0.31624	0.632481	0.68376
95	0.332834	0.665667	0.667166
96	0.271316	0.542632	0.728684
97	0.238063	0.476127	0.761937
98	0.323773	0.647545	0.676227
99	0.266712	0.533423	0.733288
100	0.220036	0.440072	0.779964
101	0.154528	0.309056	0.845472
102	0.256961	0.513923	0.743039
103	0.331812	0.663625	0.668188
104	0.232711	0.465423	0.767289
105	0.141257	0.282514	0.858743

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	9	0.0909091	NOK
10% type I error level	37	0.373737	NOK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 9 & 0.0909091 & NOK \tabularnewline
10% type I error level & 37 & 0.373737 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=270697&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]9[/C][C]0.0909091[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]37[/C][C]0.373737[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=270697&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=270697&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	9	0.0909091	NOK
10% type I error level	37	0.373737	NOK

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 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

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

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

par3 <- 'No Linear Trend'
par2 <- 'Do not include Seasonal Dummies'
par1 <- '1'
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