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rwasp_multipleregression.wasp

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Multiple Regression

Date of computation

Mon, 15 Dec 2014 12:34:50 +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/15/t14186469394ns9try8t4lm04j.htm/, Retrieved Thu, 16 May 2024 18:03:48 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=268254, Retrieved Thu, 16 May 2024 18:03:48 +0000

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-       [Multiple Regression] [all] [2014-12-15 12:34:50] [4475d2f35de7f19e7f9792645feacf86] [Current]

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11 8 7 18 12 20 4 12.9
19 18 20 23 20 19 4 12.2
16 12 9 22 14 18 5 12.8
24 24 19 22 25 24 4 7.4
15 16 12 19 15 20 4 6.7
17 19 16 25 20 20 9 12.6
19 16 17 28 21 24 8 14.8
19 15 9 16 15 21 11 13.3
28 28 28 28 28 28 4 11.1
26 21 20 21 11 10 4 8.2
15 18 16 22 22 22 6 11.4
26 22 22 24 22 19 4 6.4
16 19 17 24 27 27 8 10.6
24 22 12 26 24 23 4 12
25 25 18 28 23 24 4 6.3
22 20 20 24 24 24 11 11.3
15 16 12 20 21 25 4 11.9
21 19 16 26 20 24 4 9.3
22 18 16 21 19 21 6 9.6
27 26 21 28 25 28 6 10
26 24 15 27 16 28 4 6.4
26 20 17 23 24 22 8 13.8
22 19 17 24 21 26 5 10.8
21 19 17 24 22 26 4 13.8
22 23 18 22 25 21 9 11.7
20 18 15 21 23 26 4 10.9
21 16 20 25 20 23 7 16.1
20 18 13 20 21 20 10 13.4
22 21 21 21 22 24 4 9.9
21 20 12 26 25 25 4 11.5
8 15 6 23 23 24 7 8.3
22 19 13 21 19 20 12 11.7
20 19 19 27 21 24 7 9
24 7 12 25 19 25 5 9.7
17 20 14 23 25 23 8 10.8
20 20 13 25 16 21 5 10.3
23 19 12 23 24 23 4 10.4
20 19 17 19 24 21 9 12.7
22 20 19 22 18 18 7 9.3
19 18 10 24 28 24 4 11.8
15 14 10 19 15 18 4 5.9
20 17 11 21 17 21 4 11.4
22 17 11 27 18 23 4 13
17 8 10 25 26 25 4 10.8
14 9 7 25 18 22 7 12.3
24 22 22 23 22 22 4 11.3
17 20 12 17 19 23 7 11.8
23 20 18 28 17 24 4 7.9
25 22 20 25 26 25 4 12.7
16 22 9 20 21 22 4 12.3
18 22 16 25 26 24 4 11.6
20 16 14 21 21 21 8 6.7
18 14 11 24 12 24 4 10.9
23 24 20 28 20 25 4 12.1
24 21 17 20 20 23 4 13.3
23 20 14 19 24 27 4 10.1
13 20 8 24 24 27 7 5.7
20 18 16 21 22 23 12 14.3
20 14 11 24 21 18 4 8
19 19 10 23 20 20 4 13.3
22 24 15 18 23 23 4 9.3
22 19 15 27 19 24 5 12.5
15 16 10 25 24 26 15 7.6
17 16 10 20 21 20 5 15.9
19 16 18 21 16 23 10 9.2
20 14 10 23 17 22 9 9.1
22 22 22 27 23 23 8 11.1
21 21 16 24 20 17 4 13
21 15 10 27 19 20 5 14.5
16 14 7 24 18 22 4 12.2
20 15 16 23 18 18 9 12.3
21 14 16 24 21 19 4 11.4
20 20 16 21 20 19 10 8.8
23 21 22 23 17 16 4 14.6
18 14 5 27 25 26 4 12.6
16 16 10 25 17 25 7 13
17 13 8 19 17 23 5 12.6
24 26 16 24 24 18 4 13.2
13 13 8 25 21 22 4 9.9
19 18 16 23 22 26 4 7.7
20 15 14 23 18 25 4 10.5
22 18 15 25 22 26 4 13.4
19 21 9 26 20 26 4 10.9
21 17 21 26 21 24 6 4.3
15 18 7 16 21 22 10 10.3
21 20 17 23 20 21 7 11.8
24 18 18 26 18 22 4 11.2
22 25 16 25 25 28 4 11.4
20 20 16 23 23 22 7 8.6
21 19 14 26 21 26 4 13.2
19 18 15 22 20 20 8 12.6
14 12 8 20 21 24 11 5.6
25 22 22 27 20 21 6 9.9
11 16 5 20 22 23 14 8.8
17 18 13 22 15 23 5 7.7
22 23 22 24 24 23 4 9
20 20 18 21 22 22 8 7.3
22 20 15 24 21 23 9 11.4
15 16 11 26 17 21 4 13.6
23 22 19 24 23 27 4 7.9
20 19 19 24 22 23 5 10.7
22 23 21 27 23 26 4 10.3
16 6 4 25 16 27 5 8.3
25 19 17 27 18 27 4 9.6
18 24 10 19 25 23 4 14.2
19 19 13 22 18 23 7 8.5
25 15 15 22 14 23 10 13.5
21 18 11 25 20 28 4 4.9
22 18 20 23 19 24 5 6.4
21 22 13 24 18 20 4 9.6
22 23 18 24 22 23 4 11.6
23 18 20 23 21 22 4 11.1

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	'Gwilym Jenkins' @ jenkins.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 & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=268254&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]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=268254&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=268254&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	'Gwilym Jenkins' @ jenkins.wessa.net

Multiple Linear Regression - Estimated Regression Equation
TOT[t] = + 12.1361 + 0.108558AMS.I1[t] -0.053327AMS.I2[t] -0.090622AMS.I3[t] + 0.0396221AMS.E1[t] + 0.136146AMS.E2[t] -0.218592AMS.E3[t] -0.0111095AMS.A[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
TOT[t] =  +  12.1361 +  0.108558AMS.I1[t] -0.053327AMS.I2[t] -0.090622AMS.I3[t] +  0.0396221AMS.E1[t] +  0.136146AMS.E2[t] -0.218592AMS.E3[t] -0.0111095AMS.A[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=268254&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]TOT[t] =  +  12.1361 +  0.108558AMS.I1[t] -0.053327AMS.I2[t] -0.090622AMS.I3[t] +  0.0396221AMS.E1[t] +  0.136146AMS.E2[t] -0.218592AMS.E3[t] -0.0111095AMS.A[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=268254&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=268254&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] = + 12.1361 + 0.108558AMS.I1[t] -0.053327AMS.I2[t] -0.090622AMS.I3[t] + 0.0396221AMS.E1[t] + 0.136146AMS.E2[t] -0.218592AMS.E3[t] -0.0111095AMS.A[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)	12.1361	2.85678	4.248	4.70642e-05	2.35321e-05
AMS.I1	0.108558	0.100375	1.082	0.281966	0.140983
AMS.I2	-0.053327	0.0863789	-0.6174	0.538346	0.269173
AMS.I3	-0.090622	0.0798712	-1.135	0.259151	0.129575
AMS.E1	0.0396221	0.101251	0.3913	0.696356	0.348178
AMS.E2	0.136146	0.086462	1.575	0.118378	0.059189
AMS.E3	-0.218592	0.0949488	-2.302	0.0233157	0.0116579
AMS.A	-0.0111095	0.0995849	-0.1116	0.911389	0.455694

\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) & 12.1361 & 2.85678 & 4.248 & 4.70642e-05 & 2.35321e-05 \tabularnewline
AMS.I1 & 0.108558 & 0.100375 & 1.082 & 0.281966 & 0.140983 \tabularnewline
AMS.I2 & -0.053327 & 0.0863789 & -0.6174 & 0.538346 & 0.269173 \tabularnewline
AMS.I3 & -0.090622 & 0.0798712 & -1.135 & 0.259151 & 0.129575 \tabularnewline
AMS.E1 & 0.0396221 & 0.101251 & 0.3913 & 0.696356 & 0.348178 \tabularnewline
AMS.E2 & 0.136146 & 0.086462 & 1.575 & 0.118378 & 0.059189 \tabularnewline
AMS.E3 & -0.218592 & 0.0949488 & -2.302 & 0.0233157 & 0.0116579 \tabularnewline
AMS.A & -0.0111095 & 0.0995849 & -0.1116 & 0.911389 & 0.455694 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=268254&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]12.1361[/C][C]2.85678[/C][C]4.248[/C][C]4.70642e-05[/C][C]2.35321e-05[/C][/ROW]
[ROW][C]AMS.I1[/C][C]0.108558[/C][C]0.100375[/C][C]1.082[/C][C]0.281966[/C][C]0.140983[/C][/ROW]
[ROW][C]AMS.I2[/C][C]-0.053327[/C][C]0.0863789[/C][C]-0.6174[/C][C]0.538346[/C][C]0.269173[/C][/ROW]
[ROW][C]AMS.I3[/C][C]-0.090622[/C][C]0.0798712[/C][C]-1.135[/C][C]0.259151[/C][C]0.129575[/C][/ROW]
[ROW][C]AMS.E1[/C][C]0.0396221[/C][C]0.101251[/C][C]0.3913[/C][C]0.696356[/C][C]0.348178[/C][/ROW]
[ROW][C]AMS.E2[/C][C]0.136146[/C][C]0.086462[/C][C]1.575[/C][C]0.118378[/C][C]0.059189[/C][/ROW]
[ROW][C]AMS.E3[/C][C]-0.218592[/C][C]0.0949488[/C][C]-2.302[/C][C]0.0233157[/C][C]0.0116579[/C][/ROW]
[ROW][C]AMS.A[/C][C]-0.0111095[/C][C]0.0995849[/C][C]-0.1116[/C][C]0.911389[/C][C]0.455694[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=268254&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=268254&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)	12.1361	2.85678	4.248	4.70642e-05	2.35321e-05
AMS.I1	0.108558	0.100375	1.082	0.281966	0.140983
AMS.I2	-0.053327	0.0863789	-0.6174	0.538346	0.269173
AMS.I3	-0.090622	0.0798712	-1.135	0.259151	0.129575
AMS.E1	0.0396221	0.101251	0.3913	0.696356	0.348178
AMS.E2	0.136146	0.086462	1.575	0.118378	0.059189
AMS.E3	-0.218592	0.0949488	-2.302	0.0233157	0.0116579
AMS.A	-0.0111095	0.0995849	-0.1116	0.911389	0.455694

Multiple Linear Regression - Regression Statistics
Multiple R	0.249633
R-squared	0.0623164
Adjusted R-squared	-0.000796893
F-TEST (value)	0.987374
F-TEST (DF numerator)	7
F-TEST (DF denominator)	104
p-value	0.44461
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	2.47232
Sum Squared Residuals	635.688

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.249633 \tabularnewline
R-squared & 0.0623164 \tabularnewline
Adjusted R-squared & -0.000796893 \tabularnewline
F-TEST (value) & 0.987374 \tabularnewline
F-TEST (DF numerator) & 7 \tabularnewline
F-TEST (DF denominator) & 104 \tabularnewline
p-value & 0.44461 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.47232 \tabularnewline
Sum Squared Residuals & 635.688 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=268254&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.249633[/C][/ROW]
[ROW][C]R-squared[/C][C]0.0623164[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]-0.000796893[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]0.987374[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]7[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]104[/C][/ROW]
[ROW][C]p-value[/C][C]0.44461[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]2.47232[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]635.688[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=268254&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=268254&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	0.249633
R-squared	0.0623164
Adjusted R-squared	-0.000796893
F-TEST (value)	0.987374
F-TEST (DF numerator)	7
F-TEST (DF denominator)	104
p-value	0.44461
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	2.47232
Sum Squared Residuals	635.688

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	12.9	10.2	2.70003
2	12.2	10.863	1.33705
3	12.8	11.2051	1.59494
4	7.4	10.7246	-3.32455
5	6.7	10.2025	-3.50254
6	12.6	10.7601	1.8399
7	14.8	10.4383	4.36167
8	13.3	10.5467	2.75326
9	11.1	9.90168	1.19832
10	8.2	12.1256	-3.92564
11	11.4	10.3459	1.05412
12	6.4	11.5402	-5.14022
13	10.6	9.95529	0.644713
14	12	11.7065	0.293507
15	6.3	10.8358	-4.53584
16	11.3	10.4955	0.804548
17	11.9	9.96608	1.93392
18	9.3	10.4151	-1.11514
19	9.6	10.8763	-1.27632
20	10	10.1035	-0.103474
21	6.4	9.40258	-3.00258
22	13.8	11.6324	2.16756
23	10.8	10.0417	0.75832
24	13.8	10.0804	3.71962
25	11.7	11.2516	0.448389
26	10.9	10.2237	0.67633
27	16.1	10.3583	5.74173
28	13.4	11.3379	2.06211
29	9.9	10.0381	-0.138111
30	11.5	11.1864	0.313566
31	8.3	10.3796	-2.07965
32	11.7	11.2468	0.453206
33	9	10.1772	-1.17715
34	9.7	11.3378	-1.63775
35	10.8	10.8448	-0.044837
36	10.3	10.5856	-0.285575
37	10.4	11.639	-1.23905
38	12.7	11.0834	1.61659
39	9.3	11.0459	-1.74594
40	11.8	11.805	-0.00500179
41	5.9	10.9276	-5.02762
42	11.4	10.9156	0.484432
43	13	11.0694	1.93062
44	10.8	11.6699	-0.869894
45	12.3	11.096	1.20396
46	11.3	10.6277	0.672294
47	11.8	9.98258	1.81742
48	7.9	10.0685	-2.16849
49	12.7	10.8856	1.81444
50	12.3	10.6823	1.61769
51	11.6	10.7067	0.893266
52	6.7	11.1972	-4.49717
53	10.9	9.64079	1.25921
54	12.1	9.86378	2.23622
55	13.3	10.5244	2.77561
56	10.1	10.3716	-0.271623
57	5.7	9.99455	-4.29455
58	14.3	10.5638	3.7362
59	8	12.3948	-4.39477
60	13.3	11.4973	1.80275
61	9.3	10.6577	-1.35773
62	12.5	10.5067	1.99332
63	7.6	10.4131	-2.81307
64	15.9	11.4463	4.45371
65	9.2	9.586	-0.385996
66	9.1	10.9713	-1.87128
67	11.1	10.4422	0.657806
68	13	11.7594	1.24062
69	14.5	11.9389	2.56109
70	12.2	11.0402	1.15978
71	12.3	11.3847	0.91527
72	11.4	11.8316	-0.431631
73	8.8	11.0814	-2.28144
74	14.6	11.2033	3.3967
75	12.6	11.6361	0.963894
76	13	9.87608	3.12392
77	12.6	10.5475	2.05247
78	13.2	12.1444	1.05559
79	9.9	11.1253	-1.22531
80	7.7	9.96759	-2.26759
81	10.5	10.0914	0.408621
82	13.4	10.4631	2.93687
83	10.9	10.2885	0.611465
84	4.3	10.1826	-5.88261
85	10.3	10.7432	-0.443162
86	11.8	10.7748	1.02523
87	11.2	10.7778	0.422216
88	11.4	9.97047	1.42953
89	8.6	10.9467	-2.34668
90	13.2	10.2953	2.90466
91	12.6	11.0134	1.58659
92	5.6	10.5741	-4.97414
93	9.9	10.8188	-0.918834
94	8.8	10.6284	-1.82843
95	7.7	9.67436	-1.97436
96	9	10.4506	-1.45059
97	7.3	10.5389	-3.23893
98	11.4	10.7809	0.619066
99	13.6	10.6242	2.97579
100	7.9	9.87382	-1.97382
101	10.7	10.4352	0.264759
102	10.3	9.86815	0.431848
103	8.3	10.402	-2.10197
104	9.6	9.8703	-0.270301
105	14.2	10.9885	3.21148
106	8.5	10.2244	-1.72437
107	13.5	10.3299	3.17013
108	4.9	10.0076	-5.10758
109	6.4	9.94841	-3.54841
110	9.6	11.0499	-1.44985
111	11.6	10.5408	1.05922
112	11.1	10.7776	0.322446

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 12.9 & 10.2 & 2.70003 \tabularnewline
2 & 12.2 & 10.863 & 1.33705 \tabularnewline
3 & 12.8 & 11.2051 & 1.59494 \tabularnewline
4 & 7.4 & 10.7246 & -3.32455 \tabularnewline
5 & 6.7 & 10.2025 & -3.50254 \tabularnewline
6 & 12.6 & 10.7601 & 1.8399 \tabularnewline
7 & 14.8 & 10.4383 & 4.36167 \tabularnewline
8 & 13.3 & 10.5467 & 2.75326 \tabularnewline
9 & 11.1 & 9.90168 & 1.19832 \tabularnewline
10 & 8.2 & 12.1256 & -3.92564 \tabularnewline
11 & 11.4 & 10.3459 & 1.05412 \tabularnewline
12 & 6.4 & 11.5402 & -5.14022 \tabularnewline
13 & 10.6 & 9.95529 & 0.644713 \tabularnewline
14 & 12 & 11.7065 & 0.293507 \tabularnewline
15 & 6.3 & 10.8358 & -4.53584 \tabularnewline
16 & 11.3 & 10.4955 & 0.804548 \tabularnewline
17 & 11.9 & 9.96608 & 1.93392 \tabularnewline
18 & 9.3 & 10.4151 & -1.11514 \tabularnewline
19 & 9.6 & 10.8763 & -1.27632 \tabularnewline
20 & 10 & 10.1035 & -0.103474 \tabularnewline
21 & 6.4 & 9.40258 & -3.00258 \tabularnewline
22 & 13.8 & 11.6324 & 2.16756 \tabularnewline
23 & 10.8 & 10.0417 & 0.75832 \tabularnewline
24 & 13.8 & 10.0804 & 3.71962 \tabularnewline
25 & 11.7 & 11.2516 & 0.448389 \tabularnewline
26 & 10.9 & 10.2237 & 0.67633 \tabularnewline
27 & 16.1 & 10.3583 & 5.74173 \tabularnewline
28 & 13.4 & 11.3379 & 2.06211 \tabularnewline
29 & 9.9 & 10.0381 & -0.138111 \tabularnewline
30 & 11.5 & 11.1864 & 0.313566 \tabularnewline
31 & 8.3 & 10.3796 & -2.07965 \tabularnewline
32 & 11.7 & 11.2468 & 0.453206 \tabularnewline
33 & 9 & 10.1772 & -1.17715 \tabularnewline
34 & 9.7 & 11.3378 & -1.63775 \tabularnewline
35 & 10.8 & 10.8448 & -0.044837 \tabularnewline
36 & 10.3 & 10.5856 & -0.285575 \tabularnewline
37 & 10.4 & 11.639 & -1.23905 \tabularnewline
38 & 12.7 & 11.0834 & 1.61659 \tabularnewline
39 & 9.3 & 11.0459 & -1.74594 \tabularnewline
40 & 11.8 & 11.805 & -0.00500179 \tabularnewline
41 & 5.9 & 10.9276 & -5.02762 \tabularnewline
42 & 11.4 & 10.9156 & 0.484432 \tabularnewline
43 & 13 & 11.0694 & 1.93062 \tabularnewline
44 & 10.8 & 11.6699 & -0.869894 \tabularnewline
45 & 12.3 & 11.096 & 1.20396 \tabularnewline
46 & 11.3 & 10.6277 & 0.672294 \tabularnewline
47 & 11.8 & 9.98258 & 1.81742 \tabularnewline
48 & 7.9 & 10.0685 & -2.16849 \tabularnewline
49 & 12.7 & 10.8856 & 1.81444 \tabularnewline
50 & 12.3 & 10.6823 & 1.61769 \tabularnewline
51 & 11.6 & 10.7067 & 0.893266 \tabularnewline
52 & 6.7 & 11.1972 & -4.49717 \tabularnewline
53 & 10.9 & 9.64079 & 1.25921 \tabularnewline
54 & 12.1 & 9.86378 & 2.23622 \tabularnewline
55 & 13.3 & 10.5244 & 2.77561 \tabularnewline
56 & 10.1 & 10.3716 & -0.271623 \tabularnewline
57 & 5.7 & 9.99455 & -4.29455 \tabularnewline
58 & 14.3 & 10.5638 & 3.7362 \tabularnewline
59 & 8 & 12.3948 & -4.39477 \tabularnewline
60 & 13.3 & 11.4973 & 1.80275 \tabularnewline
61 & 9.3 & 10.6577 & -1.35773 \tabularnewline
62 & 12.5 & 10.5067 & 1.99332 \tabularnewline
63 & 7.6 & 10.4131 & -2.81307 \tabularnewline
64 & 15.9 & 11.4463 & 4.45371 \tabularnewline
65 & 9.2 & 9.586 & -0.385996 \tabularnewline
66 & 9.1 & 10.9713 & -1.87128 \tabularnewline
67 & 11.1 & 10.4422 & 0.657806 \tabularnewline
68 & 13 & 11.7594 & 1.24062 \tabularnewline
69 & 14.5 & 11.9389 & 2.56109 \tabularnewline
70 & 12.2 & 11.0402 & 1.15978 \tabularnewline
71 & 12.3 & 11.3847 & 0.91527 \tabularnewline
72 & 11.4 & 11.8316 & -0.431631 \tabularnewline
73 & 8.8 & 11.0814 & -2.28144 \tabularnewline
74 & 14.6 & 11.2033 & 3.3967 \tabularnewline
75 & 12.6 & 11.6361 & 0.963894 \tabularnewline
76 & 13 & 9.87608 & 3.12392 \tabularnewline
77 & 12.6 & 10.5475 & 2.05247 \tabularnewline
78 & 13.2 & 12.1444 & 1.05559 \tabularnewline
79 & 9.9 & 11.1253 & -1.22531 \tabularnewline
80 & 7.7 & 9.96759 & -2.26759 \tabularnewline
81 & 10.5 & 10.0914 & 0.408621 \tabularnewline
82 & 13.4 & 10.4631 & 2.93687 \tabularnewline
83 & 10.9 & 10.2885 & 0.611465 \tabularnewline
84 & 4.3 & 10.1826 & -5.88261 \tabularnewline
85 & 10.3 & 10.7432 & -0.443162 \tabularnewline
86 & 11.8 & 10.7748 & 1.02523 \tabularnewline
87 & 11.2 & 10.7778 & 0.422216 \tabularnewline
88 & 11.4 & 9.97047 & 1.42953 \tabularnewline
89 & 8.6 & 10.9467 & -2.34668 \tabularnewline
90 & 13.2 & 10.2953 & 2.90466 \tabularnewline
91 & 12.6 & 11.0134 & 1.58659 \tabularnewline
92 & 5.6 & 10.5741 & -4.97414 \tabularnewline
93 & 9.9 & 10.8188 & -0.918834 \tabularnewline
94 & 8.8 & 10.6284 & -1.82843 \tabularnewline
95 & 7.7 & 9.67436 & -1.97436 \tabularnewline
96 & 9 & 10.4506 & -1.45059 \tabularnewline
97 & 7.3 & 10.5389 & -3.23893 \tabularnewline
98 & 11.4 & 10.7809 & 0.619066 \tabularnewline
99 & 13.6 & 10.6242 & 2.97579 \tabularnewline
100 & 7.9 & 9.87382 & -1.97382 \tabularnewline
101 & 10.7 & 10.4352 & 0.264759 \tabularnewline
102 & 10.3 & 9.86815 & 0.431848 \tabularnewline
103 & 8.3 & 10.402 & -2.10197 \tabularnewline
104 & 9.6 & 9.8703 & -0.270301 \tabularnewline
105 & 14.2 & 10.9885 & 3.21148 \tabularnewline
106 & 8.5 & 10.2244 & -1.72437 \tabularnewline
107 & 13.5 & 10.3299 & 3.17013 \tabularnewline
108 & 4.9 & 10.0076 & -5.10758 \tabularnewline
109 & 6.4 & 9.94841 & -3.54841 \tabularnewline
110 & 9.6 & 11.0499 & -1.44985 \tabularnewline
111 & 11.6 & 10.5408 & 1.05922 \tabularnewline
112 & 11.1 & 10.7776 & 0.322446 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=268254&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]10.2[/C][C]2.70003[/C][/ROW]
[ROW][C]2[/C][C]12.2[/C][C]10.863[/C][C]1.33705[/C][/ROW]
[ROW][C]3[/C][C]12.8[/C][C]11.2051[/C][C]1.59494[/C][/ROW]
[ROW][C]4[/C][C]7.4[/C][C]10.7246[/C][C]-3.32455[/C][/ROW]
[ROW][C]5[/C][C]6.7[/C][C]10.2025[/C][C]-3.50254[/C][/ROW]
[ROW][C]6[/C][C]12.6[/C][C]10.7601[/C][C]1.8399[/C][/ROW]
[ROW][C]7[/C][C]14.8[/C][C]10.4383[/C][C]4.36167[/C][/ROW]
[ROW][C]8[/C][C]13.3[/C][C]10.5467[/C][C]2.75326[/C][/ROW]
[ROW][C]9[/C][C]11.1[/C][C]9.90168[/C][C]1.19832[/C][/ROW]
[ROW][C]10[/C][C]8.2[/C][C]12.1256[/C][C]-3.92564[/C][/ROW]
[ROW][C]11[/C][C]11.4[/C][C]10.3459[/C][C]1.05412[/C][/ROW]
[ROW][C]12[/C][C]6.4[/C][C]11.5402[/C][C]-5.14022[/C][/ROW]
[ROW][C]13[/C][C]10.6[/C][C]9.95529[/C][C]0.644713[/C][/ROW]
[ROW][C]14[/C][C]12[/C][C]11.7065[/C][C]0.293507[/C][/ROW]
[ROW][C]15[/C][C]6.3[/C][C]10.8358[/C][C]-4.53584[/C][/ROW]
[ROW][C]16[/C][C]11.3[/C][C]10.4955[/C][C]0.804548[/C][/ROW]
[ROW][C]17[/C][C]11.9[/C][C]9.96608[/C][C]1.93392[/C][/ROW]
[ROW][C]18[/C][C]9.3[/C][C]10.4151[/C][C]-1.11514[/C][/ROW]
[ROW][C]19[/C][C]9.6[/C][C]10.8763[/C][C]-1.27632[/C][/ROW]
[ROW][C]20[/C][C]10[/C][C]10.1035[/C][C]-0.103474[/C][/ROW]
[ROW][C]21[/C][C]6.4[/C][C]9.40258[/C][C]-3.00258[/C][/ROW]
[ROW][C]22[/C][C]13.8[/C][C]11.6324[/C][C]2.16756[/C][/ROW]
[ROW][C]23[/C][C]10.8[/C][C]10.0417[/C][C]0.75832[/C][/ROW]
[ROW][C]24[/C][C]13.8[/C][C]10.0804[/C][C]3.71962[/C][/ROW]
[ROW][C]25[/C][C]11.7[/C][C]11.2516[/C][C]0.448389[/C][/ROW]
[ROW][C]26[/C][C]10.9[/C][C]10.2237[/C][C]0.67633[/C][/ROW]
[ROW][C]27[/C][C]16.1[/C][C]10.3583[/C][C]5.74173[/C][/ROW]
[ROW][C]28[/C][C]13.4[/C][C]11.3379[/C][C]2.06211[/C][/ROW]
[ROW][C]29[/C][C]9.9[/C][C]10.0381[/C][C]-0.138111[/C][/ROW]
[ROW][C]30[/C][C]11.5[/C][C]11.1864[/C][C]0.313566[/C][/ROW]
[ROW][C]31[/C][C]8.3[/C][C]10.3796[/C][C]-2.07965[/C][/ROW]
[ROW][C]32[/C][C]11.7[/C][C]11.2468[/C][C]0.453206[/C][/ROW]
[ROW][C]33[/C][C]9[/C][C]10.1772[/C][C]-1.17715[/C][/ROW]
[ROW][C]34[/C][C]9.7[/C][C]11.3378[/C][C]-1.63775[/C][/ROW]
[ROW][C]35[/C][C]10.8[/C][C]10.8448[/C][C]-0.044837[/C][/ROW]
[ROW][C]36[/C][C]10.3[/C][C]10.5856[/C][C]-0.285575[/C][/ROW]
[ROW][C]37[/C][C]10.4[/C][C]11.639[/C][C]-1.23905[/C][/ROW]
[ROW][C]38[/C][C]12.7[/C][C]11.0834[/C][C]1.61659[/C][/ROW]
[ROW][C]39[/C][C]9.3[/C][C]11.0459[/C][C]-1.74594[/C][/ROW]
[ROW][C]40[/C][C]11.8[/C][C]11.805[/C][C]-0.00500179[/C][/ROW]
[ROW][C]41[/C][C]5.9[/C][C]10.9276[/C][C]-5.02762[/C][/ROW]
[ROW][C]42[/C][C]11.4[/C][C]10.9156[/C][C]0.484432[/C][/ROW]
[ROW][C]43[/C][C]13[/C][C]11.0694[/C][C]1.93062[/C][/ROW]
[ROW][C]44[/C][C]10.8[/C][C]11.6699[/C][C]-0.869894[/C][/ROW]
[ROW][C]45[/C][C]12.3[/C][C]11.096[/C][C]1.20396[/C][/ROW]
[ROW][C]46[/C][C]11.3[/C][C]10.6277[/C][C]0.672294[/C][/ROW]
[ROW][C]47[/C][C]11.8[/C][C]9.98258[/C][C]1.81742[/C][/ROW]
[ROW][C]48[/C][C]7.9[/C][C]10.0685[/C][C]-2.16849[/C][/ROW]
[ROW][C]49[/C][C]12.7[/C][C]10.8856[/C][C]1.81444[/C][/ROW]
[ROW][C]50[/C][C]12.3[/C][C]10.6823[/C][C]1.61769[/C][/ROW]
[ROW][C]51[/C][C]11.6[/C][C]10.7067[/C][C]0.893266[/C][/ROW]
[ROW][C]52[/C][C]6.7[/C][C]11.1972[/C][C]-4.49717[/C][/ROW]
[ROW][C]53[/C][C]10.9[/C][C]9.64079[/C][C]1.25921[/C][/ROW]
[ROW][C]54[/C][C]12.1[/C][C]9.86378[/C][C]2.23622[/C][/ROW]
[ROW][C]55[/C][C]13.3[/C][C]10.5244[/C][C]2.77561[/C][/ROW]
[ROW][C]56[/C][C]10.1[/C][C]10.3716[/C][C]-0.271623[/C][/ROW]
[ROW][C]57[/C][C]5.7[/C][C]9.99455[/C][C]-4.29455[/C][/ROW]
[ROW][C]58[/C][C]14.3[/C][C]10.5638[/C][C]3.7362[/C][/ROW]
[ROW][C]59[/C][C]8[/C][C]12.3948[/C][C]-4.39477[/C][/ROW]
[ROW][C]60[/C][C]13.3[/C][C]11.4973[/C][C]1.80275[/C][/ROW]
[ROW][C]61[/C][C]9.3[/C][C]10.6577[/C][C]-1.35773[/C][/ROW]
[ROW][C]62[/C][C]12.5[/C][C]10.5067[/C][C]1.99332[/C][/ROW]
[ROW][C]63[/C][C]7.6[/C][C]10.4131[/C][C]-2.81307[/C][/ROW]
[ROW][C]64[/C][C]15.9[/C][C]11.4463[/C][C]4.45371[/C][/ROW]
[ROW][C]65[/C][C]9.2[/C][C]9.586[/C][C]-0.385996[/C][/ROW]
[ROW][C]66[/C][C]9.1[/C][C]10.9713[/C][C]-1.87128[/C][/ROW]
[ROW][C]67[/C][C]11.1[/C][C]10.4422[/C][C]0.657806[/C][/ROW]
[ROW][C]68[/C][C]13[/C][C]11.7594[/C][C]1.24062[/C][/ROW]
[ROW][C]69[/C][C]14.5[/C][C]11.9389[/C][C]2.56109[/C][/ROW]
[ROW][C]70[/C][C]12.2[/C][C]11.0402[/C][C]1.15978[/C][/ROW]
[ROW][C]71[/C][C]12.3[/C][C]11.3847[/C][C]0.91527[/C][/ROW]
[ROW][C]72[/C][C]11.4[/C][C]11.8316[/C][C]-0.431631[/C][/ROW]
[ROW][C]73[/C][C]8.8[/C][C]11.0814[/C][C]-2.28144[/C][/ROW]
[ROW][C]74[/C][C]14.6[/C][C]11.2033[/C][C]3.3967[/C][/ROW]
[ROW][C]75[/C][C]12.6[/C][C]11.6361[/C][C]0.963894[/C][/ROW]
[ROW][C]76[/C][C]13[/C][C]9.87608[/C][C]3.12392[/C][/ROW]
[ROW][C]77[/C][C]12.6[/C][C]10.5475[/C][C]2.05247[/C][/ROW]
[ROW][C]78[/C][C]13.2[/C][C]12.1444[/C][C]1.05559[/C][/ROW]
[ROW][C]79[/C][C]9.9[/C][C]11.1253[/C][C]-1.22531[/C][/ROW]
[ROW][C]80[/C][C]7.7[/C][C]9.96759[/C][C]-2.26759[/C][/ROW]
[ROW][C]81[/C][C]10.5[/C][C]10.0914[/C][C]0.408621[/C][/ROW]
[ROW][C]82[/C][C]13.4[/C][C]10.4631[/C][C]2.93687[/C][/ROW]
[ROW][C]83[/C][C]10.9[/C][C]10.2885[/C][C]0.611465[/C][/ROW]
[ROW][C]84[/C][C]4.3[/C][C]10.1826[/C][C]-5.88261[/C][/ROW]
[ROW][C]85[/C][C]10.3[/C][C]10.7432[/C][C]-0.443162[/C][/ROW]
[ROW][C]86[/C][C]11.8[/C][C]10.7748[/C][C]1.02523[/C][/ROW]
[ROW][C]87[/C][C]11.2[/C][C]10.7778[/C][C]0.422216[/C][/ROW]
[ROW][C]88[/C][C]11.4[/C][C]9.97047[/C][C]1.42953[/C][/ROW]
[ROW][C]89[/C][C]8.6[/C][C]10.9467[/C][C]-2.34668[/C][/ROW]
[ROW][C]90[/C][C]13.2[/C][C]10.2953[/C][C]2.90466[/C][/ROW]
[ROW][C]91[/C][C]12.6[/C][C]11.0134[/C][C]1.58659[/C][/ROW]
[ROW][C]92[/C][C]5.6[/C][C]10.5741[/C][C]-4.97414[/C][/ROW]
[ROW][C]93[/C][C]9.9[/C][C]10.8188[/C][C]-0.918834[/C][/ROW]
[ROW][C]94[/C][C]8.8[/C][C]10.6284[/C][C]-1.82843[/C][/ROW]
[ROW][C]95[/C][C]7.7[/C][C]9.67436[/C][C]-1.97436[/C][/ROW]
[ROW][C]96[/C][C]9[/C][C]10.4506[/C][C]-1.45059[/C][/ROW]
[ROW][C]97[/C][C]7.3[/C][C]10.5389[/C][C]-3.23893[/C][/ROW]
[ROW][C]98[/C][C]11.4[/C][C]10.7809[/C][C]0.619066[/C][/ROW]
[ROW][C]99[/C][C]13.6[/C][C]10.6242[/C][C]2.97579[/C][/ROW]
[ROW][C]100[/C][C]7.9[/C][C]9.87382[/C][C]-1.97382[/C][/ROW]
[ROW][C]101[/C][C]10.7[/C][C]10.4352[/C][C]0.264759[/C][/ROW]
[ROW][C]102[/C][C]10.3[/C][C]9.86815[/C][C]0.431848[/C][/ROW]
[ROW][C]103[/C][C]8.3[/C][C]10.402[/C][C]-2.10197[/C][/ROW]
[ROW][C]104[/C][C]9.6[/C][C]9.8703[/C][C]-0.270301[/C][/ROW]
[ROW][C]105[/C][C]14.2[/C][C]10.9885[/C][C]3.21148[/C][/ROW]
[ROW][C]106[/C][C]8.5[/C][C]10.2244[/C][C]-1.72437[/C][/ROW]
[ROW][C]107[/C][C]13.5[/C][C]10.3299[/C][C]3.17013[/C][/ROW]
[ROW][C]108[/C][C]4.9[/C][C]10.0076[/C][C]-5.10758[/C][/ROW]
[ROW][C]109[/C][C]6.4[/C][C]9.94841[/C][C]-3.54841[/C][/ROW]
[ROW][C]110[/C][C]9.6[/C][C]11.0499[/C][C]-1.44985[/C][/ROW]
[ROW][C]111[/C][C]11.6[/C][C]10.5408[/C][C]1.05922[/C][/ROW]
[ROW][C]112[/C][C]11.1[/C][C]10.7776[/C][C]0.322446[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=268254&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=268254&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	10.2	2.70003
2	12.2	10.863	1.33705
3	12.8	11.2051	1.59494
4	7.4	10.7246	-3.32455
5	6.7	10.2025	-3.50254
6	12.6	10.7601	1.8399
7	14.8	10.4383	4.36167
8	13.3	10.5467	2.75326
9	11.1	9.90168	1.19832
10	8.2	12.1256	-3.92564
11	11.4	10.3459	1.05412
12	6.4	11.5402	-5.14022
13	10.6	9.95529	0.644713
14	12	11.7065	0.293507
15	6.3	10.8358	-4.53584
16	11.3	10.4955	0.804548
17	11.9	9.96608	1.93392
18	9.3	10.4151	-1.11514
19	9.6	10.8763	-1.27632
20	10	10.1035	-0.103474
21	6.4	9.40258	-3.00258
22	13.8	11.6324	2.16756
23	10.8	10.0417	0.75832
24	13.8	10.0804	3.71962
25	11.7	11.2516	0.448389
26	10.9	10.2237	0.67633
27	16.1	10.3583	5.74173
28	13.4	11.3379	2.06211
29	9.9	10.0381	-0.138111
30	11.5	11.1864	0.313566
31	8.3	10.3796	-2.07965
32	11.7	11.2468	0.453206
33	9	10.1772	-1.17715
34	9.7	11.3378	-1.63775
35	10.8	10.8448	-0.044837
36	10.3	10.5856	-0.285575
37	10.4	11.639	-1.23905
38	12.7	11.0834	1.61659
39	9.3	11.0459	-1.74594
40	11.8	11.805	-0.00500179
41	5.9	10.9276	-5.02762
42	11.4	10.9156	0.484432
43	13	11.0694	1.93062
44	10.8	11.6699	-0.869894
45	12.3	11.096	1.20396
46	11.3	10.6277	0.672294
47	11.8	9.98258	1.81742
48	7.9	10.0685	-2.16849
49	12.7	10.8856	1.81444
50	12.3	10.6823	1.61769
51	11.6	10.7067	0.893266
52	6.7	11.1972	-4.49717
53	10.9	9.64079	1.25921
54	12.1	9.86378	2.23622
55	13.3	10.5244	2.77561
56	10.1	10.3716	-0.271623
57	5.7	9.99455	-4.29455
58	14.3	10.5638	3.7362
59	8	12.3948	-4.39477
60	13.3	11.4973	1.80275
61	9.3	10.6577	-1.35773
62	12.5	10.5067	1.99332
63	7.6	10.4131	-2.81307
64	15.9	11.4463	4.45371
65	9.2	9.586	-0.385996
66	9.1	10.9713	-1.87128
67	11.1	10.4422	0.657806
68	13	11.7594	1.24062
69	14.5	11.9389	2.56109
70	12.2	11.0402	1.15978
71	12.3	11.3847	0.91527
72	11.4	11.8316	-0.431631
73	8.8	11.0814	-2.28144
74	14.6	11.2033	3.3967
75	12.6	11.6361	0.963894
76	13	9.87608	3.12392
77	12.6	10.5475	2.05247
78	13.2	12.1444	1.05559
79	9.9	11.1253	-1.22531
80	7.7	9.96759	-2.26759
81	10.5	10.0914	0.408621
82	13.4	10.4631	2.93687
83	10.9	10.2885	0.611465
84	4.3	10.1826	-5.88261
85	10.3	10.7432	-0.443162
86	11.8	10.7748	1.02523
87	11.2	10.7778	0.422216
88	11.4	9.97047	1.42953
89	8.6	10.9467	-2.34668
90	13.2	10.2953	2.90466
91	12.6	11.0134	1.58659
92	5.6	10.5741	-4.97414
93	9.9	10.8188	-0.918834
94	8.8	10.6284	-1.82843
95	7.7	9.67436	-1.97436
96	9	10.4506	-1.45059
97	7.3	10.5389	-3.23893
98	11.4	10.7809	0.619066
99	13.6	10.6242	2.97579
100	7.9	9.87382	-1.97382
101	10.7	10.4352	0.264759
102	10.3	9.86815	0.431848
103	8.3	10.402	-2.10197
104	9.6	9.8703	-0.270301
105	14.2	10.9885	3.21148
106	8.5	10.2244	-1.72437
107	13.5	10.3299	3.17013
108	4.9	10.0076	-5.10758
109	6.4	9.94841	-3.54841
110	9.6	11.0499	-1.44985
111	11.6	10.5408	1.05922
112	11.1	10.7776	0.322446

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
11	0.23004	0.460081	0.76996
12	0.538049	0.923903	0.461951
13	0.476117	0.952234	0.523883
14	0.499085	0.998171	0.500915
15	0.621066	0.757868	0.378934
16	0.597396	0.805209	0.402604
17	0.542375	0.91525	0.457625
18	0.488761	0.977522	0.511239
19	0.41933	0.838661	0.58067
20	0.336419	0.672838	0.663581
21	0.305037	0.610073	0.694963
22	0.253785	0.50757	0.746215
23	0.19098	0.38196	0.80902
24	0.258165	0.516329	0.741835
25	0.21659	0.433181	0.78341
26	0.166135	0.33227	0.833865
27	0.231212	0.462424	0.768788
28	0.192766	0.385532	0.807234
29	0.148975	0.297951	0.851025
30	0.110905	0.22181	0.889095
31	0.119267	0.238535	0.880733
32	0.0902957	0.180591	0.909704
33	0.108983	0.217966	0.891017
34	0.47635	0.9527	0.52365
35	0.417551	0.835102	0.582449
36	0.373107	0.746214	0.626893
37	0.329564	0.659128	0.670436
38	0.293766	0.587531	0.706234
39	0.268626	0.537251	0.731374
40	0.22423	0.44846	0.77577
41	0.377636	0.755273	0.622364
42	0.351658	0.703316	0.648342
43	0.354995	0.709989	0.645005
44	0.35775	0.7155	0.64225
45	0.325827	0.651653	0.674173
46	0.289162	0.578323	0.710838
47	0.263056	0.526112	0.736944
48	0.264985	0.529969	0.735015
49	0.266236	0.532473	0.733764
50	0.273721	0.547441	0.726279
51	0.249377	0.498755	0.750623
52	0.416812	0.833625	0.583188
53	0.366735	0.733471	0.633265
54	0.359407	0.718815	0.640593
55	0.378002	0.756004	0.621998
56	0.328305	0.656609	0.671695
57	0.453468	0.906935	0.546532
58	0.569008	0.861984	0.430992
59	0.709644	0.580712	0.290356
60	0.716051	0.567899	0.283949
61	0.687409	0.625181	0.312591
62	0.665214	0.669571	0.334786
63	0.713678	0.572645	0.286322
64	0.824608	0.350785	0.175392
65	0.811936	0.376129	0.188064
66	0.802825	0.39435	0.197175
67	0.786877	0.426246	0.213123
68	0.762482	0.475037	0.237518
69	0.749176	0.501648	0.250824
70	0.704037	0.591925	0.295963
71	0.663058	0.673884	0.336942
72	0.607095	0.785811	0.392905
73	0.592313	0.815374	0.407687
74	0.608357	0.783286	0.391643
75	0.553227	0.893545	0.446773
76	0.639167	0.721666	0.360833
77	0.617988	0.764024	0.382012
78	0.637206	0.725587	0.362794
79	0.582813	0.834373	0.417187
80	0.540173	0.919653	0.459827
81	0.501666	0.996668	0.498334
82	0.556909	0.886181	0.443091
83	0.492025	0.98405	0.507975
84	0.627547	0.744905	0.372453
85	0.563954	0.872092	0.436046
86	0.500904	0.998193	0.499096
87	0.427005	0.854009	0.572995
88	0.397406	0.794812	0.602594
89	0.386114	0.772228	0.613886
90	0.459318	0.918635	0.540682
91	0.399018	0.798036	0.600982
92	0.451889	0.903779	0.548111
93	0.425596	0.851191	0.574404
94	0.350366	0.700733	0.649634
95	0.272286	0.544571	0.727714
96	0.208695	0.417391	0.791305
97	0.295477	0.590955	0.704523
98	0.380648	0.761295	0.619352
99	0.664034	0.671932	0.335966
100	0.553625	0.89275	0.446375
101	0.406725	0.813451	0.593275

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
11 & 0.23004 & 0.460081 & 0.76996 \tabularnewline
12 & 0.538049 & 0.923903 & 0.461951 \tabularnewline
13 & 0.476117 & 0.952234 & 0.523883 \tabularnewline
14 & 0.499085 & 0.998171 & 0.500915 \tabularnewline
15 & 0.621066 & 0.757868 & 0.378934 \tabularnewline
16 & 0.597396 & 0.805209 & 0.402604 \tabularnewline
17 & 0.542375 & 0.91525 & 0.457625 \tabularnewline
18 & 0.488761 & 0.977522 & 0.511239 \tabularnewline
19 & 0.41933 & 0.838661 & 0.58067 \tabularnewline
20 & 0.336419 & 0.672838 & 0.663581 \tabularnewline
21 & 0.305037 & 0.610073 & 0.694963 \tabularnewline
22 & 0.253785 & 0.50757 & 0.746215 \tabularnewline
23 & 0.19098 & 0.38196 & 0.80902 \tabularnewline
24 & 0.258165 & 0.516329 & 0.741835 \tabularnewline
25 & 0.21659 & 0.433181 & 0.78341 \tabularnewline
26 & 0.166135 & 0.33227 & 0.833865 \tabularnewline
27 & 0.231212 & 0.462424 & 0.768788 \tabularnewline
28 & 0.192766 & 0.385532 & 0.807234 \tabularnewline
29 & 0.148975 & 0.297951 & 0.851025 \tabularnewline
30 & 0.110905 & 0.22181 & 0.889095 \tabularnewline
31 & 0.119267 & 0.238535 & 0.880733 \tabularnewline
32 & 0.0902957 & 0.180591 & 0.909704 \tabularnewline
33 & 0.108983 & 0.217966 & 0.891017 \tabularnewline
34 & 0.47635 & 0.9527 & 0.52365 \tabularnewline
35 & 0.417551 & 0.835102 & 0.582449 \tabularnewline
36 & 0.373107 & 0.746214 & 0.626893 \tabularnewline
37 & 0.329564 & 0.659128 & 0.670436 \tabularnewline
38 & 0.293766 & 0.587531 & 0.706234 \tabularnewline
39 & 0.268626 & 0.537251 & 0.731374 \tabularnewline
40 & 0.22423 & 0.44846 & 0.77577 \tabularnewline
41 & 0.377636 & 0.755273 & 0.622364 \tabularnewline
42 & 0.351658 & 0.703316 & 0.648342 \tabularnewline
43 & 0.354995 & 0.709989 & 0.645005 \tabularnewline
44 & 0.35775 & 0.7155 & 0.64225 \tabularnewline
45 & 0.325827 & 0.651653 & 0.674173 \tabularnewline
46 & 0.289162 & 0.578323 & 0.710838 \tabularnewline
47 & 0.263056 & 0.526112 & 0.736944 \tabularnewline
48 & 0.264985 & 0.529969 & 0.735015 \tabularnewline
49 & 0.266236 & 0.532473 & 0.733764 \tabularnewline
50 & 0.273721 & 0.547441 & 0.726279 \tabularnewline
51 & 0.249377 & 0.498755 & 0.750623 \tabularnewline
52 & 0.416812 & 0.833625 & 0.583188 \tabularnewline
53 & 0.366735 & 0.733471 & 0.633265 \tabularnewline
54 & 0.359407 & 0.718815 & 0.640593 \tabularnewline
55 & 0.378002 & 0.756004 & 0.621998 \tabularnewline
56 & 0.328305 & 0.656609 & 0.671695 \tabularnewline
57 & 0.453468 & 0.906935 & 0.546532 \tabularnewline
58 & 0.569008 & 0.861984 & 0.430992 \tabularnewline
59 & 0.709644 & 0.580712 & 0.290356 \tabularnewline
60 & 0.716051 & 0.567899 & 0.283949 \tabularnewline
61 & 0.687409 & 0.625181 & 0.312591 \tabularnewline
62 & 0.665214 & 0.669571 & 0.334786 \tabularnewline
63 & 0.713678 & 0.572645 & 0.286322 \tabularnewline
64 & 0.824608 & 0.350785 & 0.175392 \tabularnewline
65 & 0.811936 & 0.376129 & 0.188064 \tabularnewline
66 & 0.802825 & 0.39435 & 0.197175 \tabularnewline
67 & 0.786877 & 0.426246 & 0.213123 \tabularnewline
68 & 0.762482 & 0.475037 & 0.237518 \tabularnewline
69 & 0.749176 & 0.501648 & 0.250824 \tabularnewline
70 & 0.704037 & 0.591925 & 0.295963 \tabularnewline
71 & 0.663058 & 0.673884 & 0.336942 \tabularnewline
72 & 0.607095 & 0.785811 & 0.392905 \tabularnewline
73 & 0.592313 & 0.815374 & 0.407687 \tabularnewline
74 & 0.608357 & 0.783286 & 0.391643 \tabularnewline
75 & 0.553227 & 0.893545 & 0.446773 \tabularnewline
76 & 0.639167 & 0.721666 & 0.360833 \tabularnewline
77 & 0.617988 & 0.764024 & 0.382012 \tabularnewline
78 & 0.637206 & 0.725587 & 0.362794 \tabularnewline
79 & 0.582813 & 0.834373 & 0.417187 \tabularnewline
80 & 0.540173 & 0.919653 & 0.459827 \tabularnewline
81 & 0.501666 & 0.996668 & 0.498334 \tabularnewline
82 & 0.556909 & 0.886181 & 0.443091 \tabularnewline
83 & 0.492025 & 0.98405 & 0.507975 \tabularnewline
84 & 0.627547 & 0.744905 & 0.372453 \tabularnewline
85 & 0.563954 & 0.872092 & 0.436046 \tabularnewline
86 & 0.500904 & 0.998193 & 0.499096 \tabularnewline
87 & 0.427005 & 0.854009 & 0.572995 \tabularnewline
88 & 0.397406 & 0.794812 & 0.602594 \tabularnewline
89 & 0.386114 & 0.772228 & 0.613886 \tabularnewline
90 & 0.459318 & 0.918635 & 0.540682 \tabularnewline
91 & 0.399018 & 0.798036 & 0.600982 \tabularnewline
92 & 0.451889 & 0.903779 & 0.548111 \tabularnewline
93 & 0.425596 & 0.851191 & 0.574404 \tabularnewline
94 & 0.350366 & 0.700733 & 0.649634 \tabularnewline
95 & 0.272286 & 0.544571 & 0.727714 \tabularnewline
96 & 0.208695 & 0.417391 & 0.791305 \tabularnewline
97 & 0.295477 & 0.590955 & 0.704523 \tabularnewline
98 & 0.380648 & 0.761295 & 0.619352 \tabularnewline
99 & 0.664034 & 0.671932 & 0.335966 \tabularnewline
100 & 0.553625 & 0.89275 & 0.446375 \tabularnewline
101 & 0.406725 & 0.813451 & 0.593275 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=268254&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]11[/C][C]0.23004[/C][C]0.460081[/C][C]0.76996[/C][/ROW]
[ROW][C]12[/C][C]0.538049[/C][C]0.923903[/C][C]0.461951[/C][/ROW]
[ROW][C]13[/C][C]0.476117[/C][C]0.952234[/C][C]0.523883[/C][/ROW]
[ROW][C]14[/C][C]0.499085[/C][C]0.998171[/C][C]0.500915[/C][/ROW]
[ROW][C]15[/C][C]0.621066[/C][C]0.757868[/C][C]0.378934[/C][/ROW]
[ROW][C]16[/C][C]0.597396[/C][C]0.805209[/C][C]0.402604[/C][/ROW]
[ROW][C]17[/C][C]0.542375[/C][C]0.91525[/C][C]0.457625[/C][/ROW]
[ROW][C]18[/C][C]0.488761[/C][C]0.977522[/C][C]0.511239[/C][/ROW]
[ROW][C]19[/C][C]0.41933[/C][C]0.838661[/C][C]0.58067[/C][/ROW]
[ROW][C]20[/C][C]0.336419[/C][C]0.672838[/C][C]0.663581[/C][/ROW]
[ROW][C]21[/C][C]0.305037[/C][C]0.610073[/C][C]0.694963[/C][/ROW]
[ROW][C]22[/C][C]0.253785[/C][C]0.50757[/C][C]0.746215[/C][/ROW]
[ROW][C]23[/C][C]0.19098[/C][C]0.38196[/C][C]0.80902[/C][/ROW]
[ROW][C]24[/C][C]0.258165[/C][C]0.516329[/C][C]0.741835[/C][/ROW]
[ROW][C]25[/C][C]0.21659[/C][C]0.433181[/C][C]0.78341[/C][/ROW]
[ROW][C]26[/C][C]0.166135[/C][C]0.33227[/C][C]0.833865[/C][/ROW]
[ROW][C]27[/C][C]0.231212[/C][C]0.462424[/C][C]0.768788[/C][/ROW]
[ROW][C]28[/C][C]0.192766[/C][C]0.385532[/C][C]0.807234[/C][/ROW]
[ROW][C]29[/C][C]0.148975[/C][C]0.297951[/C][C]0.851025[/C][/ROW]
[ROW][C]30[/C][C]0.110905[/C][C]0.22181[/C][C]0.889095[/C][/ROW]
[ROW][C]31[/C][C]0.119267[/C][C]0.238535[/C][C]0.880733[/C][/ROW]
[ROW][C]32[/C][C]0.0902957[/C][C]0.180591[/C][C]0.909704[/C][/ROW]
[ROW][C]33[/C][C]0.108983[/C][C]0.217966[/C][C]0.891017[/C][/ROW]
[ROW][C]34[/C][C]0.47635[/C][C]0.9527[/C][C]0.52365[/C][/ROW]
[ROW][C]35[/C][C]0.417551[/C][C]0.835102[/C][C]0.582449[/C][/ROW]
[ROW][C]36[/C][C]0.373107[/C][C]0.746214[/C][C]0.626893[/C][/ROW]
[ROW][C]37[/C][C]0.329564[/C][C]0.659128[/C][C]0.670436[/C][/ROW]
[ROW][C]38[/C][C]0.293766[/C][C]0.587531[/C][C]0.706234[/C][/ROW]
[ROW][C]39[/C][C]0.268626[/C][C]0.537251[/C][C]0.731374[/C][/ROW]
[ROW][C]40[/C][C]0.22423[/C][C]0.44846[/C][C]0.77577[/C][/ROW]
[ROW][C]41[/C][C]0.377636[/C][C]0.755273[/C][C]0.622364[/C][/ROW]
[ROW][C]42[/C][C]0.351658[/C][C]0.703316[/C][C]0.648342[/C][/ROW]
[ROW][C]43[/C][C]0.354995[/C][C]0.709989[/C][C]0.645005[/C][/ROW]
[ROW][C]44[/C][C]0.35775[/C][C]0.7155[/C][C]0.64225[/C][/ROW]
[ROW][C]45[/C][C]0.325827[/C][C]0.651653[/C][C]0.674173[/C][/ROW]
[ROW][C]46[/C][C]0.289162[/C][C]0.578323[/C][C]0.710838[/C][/ROW]
[ROW][C]47[/C][C]0.263056[/C][C]0.526112[/C][C]0.736944[/C][/ROW]
[ROW][C]48[/C][C]0.264985[/C][C]0.529969[/C][C]0.735015[/C][/ROW]
[ROW][C]49[/C][C]0.266236[/C][C]0.532473[/C][C]0.733764[/C][/ROW]
[ROW][C]50[/C][C]0.273721[/C][C]0.547441[/C][C]0.726279[/C][/ROW]
[ROW][C]51[/C][C]0.249377[/C][C]0.498755[/C][C]0.750623[/C][/ROW]
[ROW][C]52[/C][C]0.416812[/C][C]0.833625[/C][C]0.583188[/C][/ROW]
[ROW][C]53[/C][C]0.366735[/C][C]0.733471[/C][C]0.633265[/C][/ROW]
[ROW][C]54[/C][C]0.359407[/C][C]0.718815[/C][C]0.640593[/C][/ROW]
[ROW][C]55[/C][C]0.378002[/C][C]0.756004[/C][C]0.621998[/C][/ROW]
[ROW][C]56[/C][C]0.328305[/C][C]0.656609[/C][C]0.671695[/C][/ROW]
[ROW][C]57[/C][C]0.453468[/C][C]0.906935[/C][C]0.546532[/C][/ROW]
[ROW][C]58[/C][C]0.569008[/C][C]0.861984[/C][C]0.430992[/C][/ROW]
[ROW][C]59[/C][C]0.709644[/C][C]0.580712[/C][C]0.290356[/C][/ROW]
[ROW][C]60[/C][C]0.716051[/C][C]0.567899[/C][C]0.283949[/C][/ROW]
[ROW][C]61[/C][C]0.687409[/C][C]0.625181[/C][C]0.312591[/C][/ROW]
[ROW][C]62[/C][C]0.665214[/C][C]0.669571[/C][C]0.334786[/C][/ROW]
[ROW][C]63[/C][C]0.713678[/C][C]0.572645[/C][C]0.286322[/C][/ROW]
[ROW][C]64[/C][C]0.824608[/C][C]0.350785[/C][C]0.175392[/C][/ROW]
[ROW][C]65[/C][C]0.811936[/C][C]0.376129[/C][C]0.188064[/C][/ROW]
[ROW][C]66[/C][C]0.802825[/C][C]0.39435[/C][C]0.197175[/C][/ROW]
[ROW][C]67[/C][C]0.786877[/C][C]0.426246[/C][C]0.213123[/C][/ROW]
[ROW][C]68[/C][C]0.762482[/C][C]0.475037[/C][C]0.237518[/C][/ROW]
[ROW][C]69[/C][C]0.749176[/C][C]0.501648[/C][C]0.250824[/C][/ROW]
[ROW][C]70[/C][C]0.704037[/C][C]0.591925[/C][C]0.295963[/C][/ROW]
[ROW][C]71[/C][C]0.663058[/C][C]0.673884[/C][C]0.336942[/C][/ROW]
[ROW][C]72[/C][C]0.607095[/C][C]0.785811[/C][C]0.392905[/C][/ROW]
[ROW][C]73[/C][C]0.592313[/C][C]0.815374[/C][C]0.407687[/C][/ROW]
[ROW][C]74[/C][C]0.608357[/C][C]0.783286[/C][C]0.391643[/C][/ROW]
[ROW][C]75[/C][C]0.553227[/C][C]0.893545[/C][C]0.446773[/C][/ROW]
[ROW][C]76[/C][C]0.639167[/C][C]0.721666[/C][C]0.360833[/C][/ROW]
[ROW][C]77[/C][C]0.617988[/C][C]0.764024[/C][C]0.382012[/C][/ROW]
[ROW][C]78[/C][C]0.637206[/C][C]0.725587[/C][C]0.362794[/C][/ROW]
[ROW][C]79[/C][C]0.582813[/C][C]0.834373[/C][C]0.417187[/C][/ROW]
[ROW][C]80[/C][C]0.540173[/C][C]0.919653[/C][C]0.459827[/C][/ROW]
[ROW][C]81[/C][C]0.501666[/C][C]0.996668[/C][C]0.498334[/C][/ROW]
[ROW][C]82[/C][C]0.556909[/C][C]0.886181[/C][C]0.443091[/C][/ROW]
[ROW][C]83[/C][C]0.492025[/C][C]0.98405[/C][C]0.507975[/C][/ROW]
[ROW][C]84[/C][C]0.627547[/C][C]0.744905[/C][C]0.372453[/C][/ROW]
[ROW][C]85[/C][C]0.563954[/C][C]0.872092[/C][C]0.436046[/C][/ROW]
[ROW][C]86[/C][C]0.500904[/C][C]0.998193[/C][C]0.499096[/C][/ROW]
[ROW][C]87[/C][C]0.427005[/C][C]0.854009[/C][C]0.572995[/C][/ROW]
[ROW][C]88[/C][C]0.397406[/C][C]0.794812[/C][C]0.602594[/C][/ROW]
[ROW][C]89[/C][C]0.386114[/C][C]0.772228[/C][C]0.613886[/C][/ROW]
[ROW][C]90[/C][C]0.459318[/C][C]0.918635[/C][C]0.540682[/C][/ROW]
[ROW][C]91[/C][C]0.399018[/C][C]0.798036[/C][C]0.600982[/C][/ROW]
[ROW][C]92[/C][C]0.451889[/C][C]0.903779[/C][C]0.548111[/C][/ROW]
[ROW][C]93[/C][C]0.425596[/C][C]0.851191[/C][C]0.574404[/C][/ROW]
[ROW][C]94[/C][C]0.350366[/C][C]0.700733[/C][C]0.649634[/C][/ROW]
[ROW][C]95[/C][C]0.272286[/C][C]0.544571[/C][C]0.727714[/C][/ROW]
[ROW][C]96[/C][C]0.208695[/C][C]0.417391[/C][C]0.791305[/C][/ROW]
[ROW][C]97[/C][C]0.295477[/C][C]0.590955[/C][C]0.704523[/C][/ROW]
[ROW][C]98[/C][C]0.380648[/C][C]0.761295[/C][C]0.619352[/C][/ROW]
[ROW][C]99[/C][C]0.664034[/C][C]0.671932[/C][C]0.335966[/C][/ROW]
[ROW][C]100[/C][C]0.553625[/C][C]0.89275[/C][C]0.446375[/C][/ROW]
[ROW][C]101[/C][C]0.406725[/C][C]0.813451[/C][C]0.593275[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=268254&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=268254&T=5

As an alternative you can also use a QR Code:

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

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
11	0.23004	0.460081	0.76996
12	0.538049	0.923903	0.461951
13	0.476117	0.952234	0.523883
14	0.499085	0.998171	0.500915
15	0.621066	0.757868	0.378934
16	0.597396	0.805209	0.402604
17	0.542375	0.91525	0.457625
18	0.488761	0.977522	0.511239
19	0.41933	0.838661	0.58067
20	0.336419	0.672838	0.663581
21	0.305037	0.610073	0.694963
22	0.253785	0.50757	0.746215
23	0.19098	0.38196	0.80902
24	0.258165	0.516329	0.741835
25	0.21659	0.433181	0.78341
26	0.166135	0.33227	0.833865
27	0.231212	0.462424	0.768788
28	0.192766	0.385532	0.807234
29	0.148975	0.297951	0.851025
30	0.110905	0.22181	0.889095
31	0.119267	0.238535	0.880733
32	0.0902957	0.180591	0.909704
33	0.108983	0.217966	0.891017
34	0.47635	0.9527	0.52365
35	0.417551	0.835102	0.582449
36	0.373107	0.746214	0.626893
37	0.329564	0.659128	0.670436
38	0.293766	0.587531	0.706234
39	0.268626	0.537251	0.731374
40	0.22423	0.44846	0.77577
41	0.377636	0.755273	0.622364
42	0.351658	0.703316	0.648342
43	0.354995	0.709989	0.645005
44	0.35775	0.7155	0.64225
45	0.325827	0.651653	0.674173
46	0.289162	0.578323	0.710838
47	0.263056	0.526112	0.736944
48	0.264985	0.529969	0.735015
49	0.266236	0.532473	0.733764
50	0.273721	0.547441	0.726279
51	0.249377	0.498755	0.750623
52	0.416812	0.833625	0.583188
53	0.366735	0.733471	0.633265
54	0.359407	0.718815	0.640593
55	0.378002	0.756004	0.621998
56	0.328305	0.656609	0.671695
57	0.453468	0.906935	0.546532
58	0.569008	0.861984	0.430992
59	0.709644	0.580712	0.290356
60	0.716051	0.567899	0.283949
61	0.687409	0.625181	0.312591
62	0.665214	0.669571	0.334786
63	0.713678	0.572645	0.286322
64	0.824608	0.350785	0.175392
65	0.811936	0.376129	0.188064
66	0.802825	0.39435	0.197175
67	0.786877	0.426246	0.213123
68	0.762482	0.475037	0.237518
69	0.749176	0.501648	0.250824
70	0.704037	0.591925	0.295963
71	0.663058	0.673884	0.336942
72	0.607095	0.785811	0.392905
73	0.592313	0.815374	0.407687
74	0.608357	0.783286	0.391643
75	0.553227	0.893545	0.446773
76	0.639167	0.721666	0.360833
77	0.617988	0.764024	0.382012
78	0.637206	0.725587	0.362794
79	0.582813	0.834373	0.417187
80	0.540173	0.919653	0.459827
81	0.501666	0.996668	0.498334
82	0.556909	0.886181	0.443091
83	0.492025	0.98405	0.507975
84	0.627547	0.744905	0.372453
85	0.563954	0.872092	0.436046
86	0.500904	0.998193	0.499096
87	0.427005	0.854009	0.572995
88	0.397406	0.794812	0.602594
89	0.386114	0.772228	0.613886
90	0.459318	0.918635	0.540682
91	0.399018	0.798036	0.600982
92	0.451889	0.903779	0.548111
93	0.425596	0.851191	0.574404
94	0.350366	0.700733	0.649634
95	0.272286	0.544571	0.727714
96	0.208695	0.417391	0.791305
97	0.295477	0.590955	0.704523
98	0.380648	0.761295	0.619352
99	0.664034	0.671932	0.335966
100	0.553625	0.89275	0.446375
101	0.406725	0.813451	0.593275

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=268254&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=268254&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=268254&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 2

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Figure 3

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Figure 4

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Figure 5

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Figure 6

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Figure 7

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Figure 8

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Figure 9

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Figure 10

PNG link

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Parameters (Session):

par1 = 8 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 8 ; 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