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R Software Module

rwasp_multipleregression.wasp

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Multiple Regression

Date of computation

Mon, 15 Dec 2014 12:38:14 +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/t14186471358ujagmx648bcoh1.htm/, Retrieved Thu, 16 May 2024 17:05:27 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=268259, Retrieved Thu, 16 May 2024 17:05:27 +0000

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-       [Multiple Regression] [exclusive] [2014-12-15 12:38:14] [4475d2f35de7f19e7f9792645feacf86] [Current]

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11 8 5 18 12 15 3 12.9
19 18 15 23 20 15 3 12.2
16 12 8 22 14 14 3 12.8
24 24 14 22 25 19 3 7.4
15 16 9 19 15 15 3 6.7
17 19 12 25 20 15 7 12.6
19 16 12 28 21 18 6 14.8
19 15 6 16 15 17 6 13.3
28 28 21 28 28 21 3 11.1
26 21 14 21 11 9 3 8.2
15 18 11 22 22 17 4 11.4
26 22 18 24 22 15 3 6.4
16 19 14 24 27 21 6 10.6
24 22 9 26 24 18 3 12
25 25 16 28 23 19 3 6.3
22 20 15 24 24 18 9 11.3
15 16 9 20 21 20 3 11.9
21 19 12 26 20 19 3 9.3
22 18 13 21 19 16 4 9.6
27 26 14 28 25 21 5 10
26 24 9 27 16 21 3 6.4
26 20 12 23 24 17 5 13.8
22 19 13 24 21 19 3 10.8
21 19 13 24 22 20 3 13.8
22 23 13 22 25 17 5 11.7
20 18 11 21 23 21 3 10.9
21 16 14 25 20 17 4 16.1
20 18 8 20 21 15 7 13.4
22 21 15 21 22 19 3 9.9
21 20 8 26 25 18 3 11.5
8 15 5 23 23 19 3 8.3
22 19 9 21 19 17 6 11.7
20 19 14 27 21 19 3 9
24 7 8 25 19 19 4 9.7
17 20 10 23 25 17 6 10.8
20 20 11 25 16 19 3 10.3
23 19 10 23 24 17 3 10.4
20 19 13 19 24 16 7 12.7
22 20 15 22 18 14 5 9.3
19 18 8 24 28 19 3 11.8
15 14 8 19 15 13 3 5.9
20 17 9 21 17 16 3 11.4
22 17 9 27 18 17 3 13
17 8 8 25 26 19 3 10.8
14 9 5 25 18 16 4 12.3
24 22 18 23 22 18 3 11.3
17 20 8 17 19 20 3 11.8
23 20 13 28 17 19 3 7.9
25 22 16 25 26 19 3 12.7
16 22 7 20 21 16 3 12.3
18 22 13 25 26 19 3 11.6
20 16 11 21 21 17 6 6.7
18 14 7 24 12 18 3 10.9
23 24 15 28 20 19 3 12.1
24 21 14 20 20 19 3 13.3
23 20 12 19 24 21 3 10.1
13 20 6 24 24 21 5 5.7
20 18 12 21 22 18 9 14.3
20 14 8 24 21 13 3 8
19 19 7 23 20 15 3 13.3
22 24 13 18 23 21 3 9.3
22 19 13 27 19 21 3 12.5
15 16 5 25 24 21 9 7.6
17 16 6 20 21 14 3 15.9
19 16 14 21 16 17 5 9.2
20 14 7 23 17 15 7 9.1
22 22 16 27 23 18 3 11.1
21 21 11 24 20 13 3 13
21 15 8 27 19 15 3 14.5
16 14 3 24 18 18 3 12.2
20 15 11 23 18 15 6 12.3
21 14 13 24 21 16 3 11.4
20 20 12 21 20 16 6 8.8
23 21 17 23 17 13 3 14.6
18 14 3 27 25 20 3 12.6
16 16 8 25 17 20 3 13
17 13 6 19 17 17 4 12.6
24 26 13 24 24 14 3 13.2
13 13 6 25 21 18 3 9.9
19 18 10 23 22 20 3 7.7
20 15 10 23 18 18 3 10.5
22 18 11 25 22 19 3 13.4
19 21 8 26 20 20 3 10.9
21 17 16 26 21 17 4 4.3
15 18 5 16 21 17 8 10.3
21 20 13 23 20 17 5 11.8
24 18 15 26 18 17 3 11.2
22 25 12 25 25 21 3 11.4
20 20 13 23 23 16 5 8.6
21 19 10 26 21 19 3 13.2
19 18 10 22 20 15 6 12.6
14 12 6 20 21 18 7 5.6
25 22 19 27 20 16 5 9.9
11 16 3 20 22 18 11 8.8
17 18 6 22 15 18 4 7.7
22 23 16 24 24 18 3 9
20 20 14 21 22 17 5 7.3
22 20 12 24 21 18 7 11.4
15 16 9 26 17 16 3 13.6
23 22 15 24 23 20 3 7.9
20 19 14 24 22 18 3 10.7
22 23 16 27 23 20 3 10.3
16 6 3 25 16 21 4 8.3
25 19 14 27 18 21 3 9.6
18 24 8 19 25 17 3 14.2
19 19 9 22 18 21 5 8.5
25 15 11 22 14 18 5 13.5
21 18 5 25 20 21 3 4.9
22 18 14 23 19 19 4 6.4
21 22 10 24 18 15 3 9.6
22 23 13 24 22 17 3 11.6
23 18 15 23 21 18 3 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	'George Udny Yule' @ yule.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 & 'George Udny Yule' @ yule.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=268259&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]'George Udny Yule' @ yule.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=268259&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=268259&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	'George Udny Yule' @ yule.wessa.net

Multiple Linear Regression - Estimated Regression Equation
TOT[t] = + 13.1467 + 0.0886748AMS.I1[t] -0.0522794AMS.I2[t] -0.0830362I3_Min4[t] + 0.00496711AMS.E1[t] + 0.133921AMS.E2[t] -0.272816E3_Min1[t] -0.104515A_Min12[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
TOT[t] =  +  13.1467 +  0.0886748AMS.I1[t] -0.0522794AMS.I2[t] -0.0830362I3_Min4[t] +  0.00496711AMS.E1[t] +  0.133921AMS.E2[t] -0.272816E3_Min1[t] -0.104515A_Min12[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=268259&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]TOT[t] =  +  13.1467 +  0.0886748AMS.I1[t] -0.0522794AMS.I2[t] -0.0830362I3_Min4[t] +  0.00496711AMS.E1[t] +  0.133921AMS.E2[t] -0.272816E3_Min1[t] -0.104515A_Min12[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=268259&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=268259&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] = + 13.1467 + 0.0886748AMS.I1[t] -0.0522794AMS.I2[t] -0.0830362I3_Min4[t] + 0.00496711AMS.E1[t] + 0.133921AMS.E2[t] -0.272816E3_Min1[t] -0.104515A_Min12[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)	13.1467	2.84071	4.628	1.06941e-05	5.34704e-06
AMS.I1	0.0886748	0.0984102	0.9011	0.369631	0.184816
AMS.I2	-0.0522794	0.0855199	-0.6113	0.542326	0.271163
I3_Min4	-0.0830362	0.0947668	-0.8762	0.382932	0.191466
AMS.E1	0.00496711	0.0983617	0.0505	0.959822	0.479911
AMS.E2	0.133921	0.085245	1.571	0.119219	0.0596093
E3_Min1	-0.272816	0.11582	-2.356	0.0203726	0.0101863
A_Min12	-0.104515	0.149074	-0.7011	0.484808	0.242404

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Ordinary Least Squares \tabularnewline
Variable & Parameter & S.D. & T-STATH0: parameter = 0 & 2-tail p-value & 1-tail p-value \tabularnewline
(Intercept) & 13.1467 & 2.84071 & 4.628 & 1.06941e-05 & 5.34704e-06 \tabularnewline
AMS.I1 & 0.0886748 & 0.0984102 & 0.9011 & 0.369631 & 0.184816 \tabularnewline
AMS.I2 & -0.0522794 & 0.0855199 & -0.6113 & 0.542326 & 0.271163 \tabularnewline
I3_Min4 & -0.0830362 & 0.0947668 & -0.8762 & 0.382932 & 0.191466 \tabularnewline
AMS.E1 & 0.00496711 & 0.0983617 & 0.0505 & 0.959822 & 0.479911 \tabularnewline
AMS.E2 & 0.133921 & 0.085245 & 1.571 & 0.119219 & 0.0596093 \tabularnewline
E3_Min1 & -0.272816 & 0.11582 & -2.356 & 0.0203726 & 0.0101863 \tabularnewline
A_Min12 & -0.104515 & 0.149074 & -0.7011 & 0.484808 & 0.242404 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=268259&T=2

[TABLE]
[ROW][C]Multiple Linear Regression - Ordinary Least Squares[/C][/ROW]
[ROW][C]Variable[/C][C]Parameter[/C][C]S.D.[/C][C]T-STATH0: parameter = 0[/C][C]2-tail p-value[/C][C]1-tail p-value[/C][/ROW]
[ROW][C](Intercept)[/C][C]13.1467[/C][C]2.84071[/C][C]4.628[/C][C]1.06941e-05[/C][C]5.34704e-06[/C][/ROW]
[ROW][C]AMS.I1[/C][C]0.0886748[/C][C]0.0984102[/C][C]0.9011[/C][C]0.369631[/C][C]0.184816[/C][/ROW]
[ROW][C]AMS.I2[/C][C]-0.0522794[/C][C]0.0855199[/C][C]-0.6113[/C][C]0.542326[/C][C]0.271163[/C][/ROW]
[ROW][C]I3_Min4[/C][C]-0.0830362[/C][C]0.0947668[/C][C]-0.8762[/C][C]0.382932[/C][C]0.191466[/C][/ROW]
[ROW][C]AMS.E1[/C][C]0.00496711[/C][C]0.0983617[/C][C]0.0505[/C][C]0.959822[/C][C]0.479911[/C][/ROW]
[ROW][C]AMS.E2[/C][C]0.133921[/C][C]0.085245[/C][C]1.571[/C][C]0.119219[/C][C]0.0596093[/C][/ROW]
[ROW][C]E3_Min1[/C][C]-0.272816[/C][C]0.11582[/C][C]-2.356[/C][C]0.0203726[/C][C]0.0101863[/C][/ROW]
[ROW][C]A_Min12[/C][C]-0.104515[/C][C]0.149074[/C][C]-0.7011[/C][C]0.484808[/C][C]0.242404[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=268259&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=268259&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)	13.1467	2.84071	4.628	1.06941e-05	5.34704e-06
AMS.I1	0.0886748	0.0984102	0.9011	0.369631	0.184816
AMS.I2	-0.0522794	0.0855199	-0.6113	0.542326	0.271163
I3_Min4	-0.0830362	0.0947668	-0.8762	0.382932	0.191466
AMS.E1	0.00496711	0.0983617	0.0505	0.959822	0.479911
AMS.E2	0.133921	0.085245	1.571	0.119219	0.0596093
E3_Min1	-0.272816	0.11582	-2.356	0.0203726	0.0101863
A_Min12	-0.104515	0.149074	-0.7011	0.484808	0.242404

Multiple Linear Regression - Regression Statistics
Multiple R	0.252945
R-squared	0.063981
Adjusted R-squared	0.000979676
F-TEST (value)	1.01555
F-TEST (DF numerator)	7
F-TEST (DF denominator)	104
p-value	0.424764
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	2.47013
Sum Squared Residuals	634.56

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.252945 \tabularnewline
R-squared & 0.063981 \tabularnewline
Adjusted R-squared & 0.000979676 \tabularnewline
F-TEST (value) & 1.01555 \tabularnewline
F-TEST (DF numerator) & 7 \tabularnewline
F-TEST (DF denominator) & 104 \tabularnewline
p-value & 0.424764 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.47013 \tabularnewline
Sum Squared Residuals & 634.56 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=268259&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.252945[/C][/ROW]
[ROW][C]R-squared[/C][C]0.063981[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.000979676[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]1.01555[/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]0.424764[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]2.47013[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]634.56[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=268259&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=268259&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	0.252945
R-squared	0.063981
Adjusted R-squared	0.000979676
F-TEST (value)	1.01555
F-TEST (DF numerator)	7
F-TEST (DF denominator)	104
p-value	0.424764
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	2.47013
Sum Squared Residuals	634.56

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	12.9	10.5794	2.32065
2	12.2	11.0318	1.16821
3	12.8	11.125	1.67497
4	7.4	10.8179	-3.4179
5	6.7	10.5904	-3.8904
6	12.6	10.6431	1.95685
7	14.8	10.4122	4.38778
8	13.3	10.3724	2.92759
9	11.1	10.2682	0.831841
10	8.2	12.0004	-3.80039
11	11.4	10.622	0.778034
12	6.4	11.4671	-5.0671
13	10.6	9.78849	0.811506
14	12	11.4964	0.503597
15	6.3	10.4502	-4.15018
16	11.3	10.2884	1.01163
17	11.9	10.0348	1.86519
18	9.3	10.3296	-1.02961
19	9.6	10.9427	-1.3427
20	10	10.2545	-0.254504
21	6.4	9.68435	-3.28435
22	13.8	11.5781	2.22191
23	10.8	10.4592	0.340766
24	13.8	10.2317	3.56834
25	11.7	11.1125	0.587534
26	10.9	10.2075	0.692456
27	16.1	10.7565	5.34347
28	13.4	11.4027	1.99732
29	9.9	10.3076	-0.407623
30	11.5	11.5519	-0.0518936
31	8.3	10.3541	-2.05407
32	11.7	10.7407	0.959277
33	9	10.2138	-1.21375
34	9.7	11.3117	-1.61173
35	10.8	10.9755	-0.17549
36	10.3	9.73104	0.568958
37	10.4	11.7394	-1.33944
38	12.7	11.0592	1.6408
39	9.3	10.9842	-1.68424
40	11.8	11.5981	0.201886
41	5.9	11.3236	-5.42363
42	11.4	10.9865	0.413547
43	13	11.0547	1.94529
44	10.8	11.6807	-0.880685
45	12.3	11.2541	1.04594
46	11.3	10.4663	0.833665
47	11.8	9.80333	1.99667
48	7.9	9.97982	-2.07982
49	12.7	10.9939	1.70612
50	12.3	11.0671	1.23286
51	11.6	10.6223	0.977733
52	6.7	10.822	-4.12198
53	10.9	9.93168	0.968319
54	12.1	10.0064	2.09361
55	13.3	10.2952	3.0048
56	10.1	10.41	-0.30996
57	5.7	9.83723	-4.13723
58	14.3	10.1819	4.11806
59	8	12.5954	-4.59536
60	13.3	11.6438	1.6562
61	9.3	9.89024	-0.590243
62	12.5	9.66066	2.83934
63	7.6	9.89364	-2.29364
64	15.9	12.0982	3.80184
65	9.2	9.91911	-0.719109
66	9.1	11.1741	-2.07405
67	11.1	10.6088	0.491154
68	13	11.935	1.06495
69	14.5	11.8332	2.66682
70	12.2	10.89	1.31
71	12.3	11.0281	1.27194
72	11.4	11.4504	-0.0504053
73	8.8	10.6687	-1.86872
74	14.6	11.2075	3.39255
75	12.6	11.4741	1.12594
76	13	9.69567	3.30433
77	12.6	10.7914	1.80861
78	13.2	12.0365	1.16353
79	9.9	10.8339	-0.933874
80	7.7	10.3507	-2.65074
81	10.5	10.6062	-0.106198
82	13.4	10.8165	2.58353
83	10.9	10.107	0.792971
84	4.3	10.6771	-6.37706
85	10.3	10.5384	-0.238397
86	11.8	10.516	1.28401
87	11.2	10.6766	0.523404
88	11.4	10.2236	1.17639
89	8.6	11.1019	-2.5019
90	13.2	10.6296	2.5704
91	12.6	11.1285	1.47154
92	5.6	10.5319	-4.93193
93	9.9	10.5606	-0.660602
94	8.8	10.0218	-1.22176
95	7.7	10.0042	-2.30424
96	9	10.6756	-1.67559
97	7.3	10.6022	-3.30219
98	11.4	10.3447	1.05525
99	13.6	10.6202	2.97981
100	7.9	10.22	-2.32002
101	10.7	10.6056	0.0944146
102	10.3	10.0109	0.289065
103	8.3	10.1224	-1.8224
104	9.6	9.70973	-0.10973
105	14.2	11.3148	2.8852
106	8.5	9.359	-0.858995
107	13.5	10.2169	3.28314
108	4.9	10.4125	-5.51254
109	6.4	10.0512	-3.65115
110	9.6	11.1523	-1.55233
111	11.6	10.9297	0.67033
112	11.1	10.702	0.398034

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 12.9 & 10.5794 & 2.32065 \tabularnewline
2 & 12.2 & 11.0318 & 1.16821 \tabularnewline
3 & 12.8 & 11.125 & 1.67497 \tabularnewline
4 & 7.4 & 10.8179 & -3.4179 \tabularnewline
5 & 6.7 & 10.5904 & -3.8904 \tabularnewline
6 & 12.6 & 10.6431 & 1.95685 \tabularnewline
7 & 14.8 & 10.4122 & 4.38778 \tabularnewline
8 & 13.3 & 10.3724 & 2.92759 \tabularnewline
9 & 11.1 & 10.2682 & 0.831841 \tabularnewline
10 & 8.2 & 12.0004 & -3.80039 \tabularnewline
11 & 11.4 & 10.622 & 0.778034 \tabularnewline
12 & 6.4 & 11.4671 & -5.0671 \tabularnewline
13 & 10.6 & 9.78849 & 0.811506 \tabularnewline
14 & 12 & 11.4964 & 0.503597 \tabularnewline
15 & 6.3 & 10.4502 & -4.15018 \tabularnewline
16 & 11.3 & 10.2884 & 1.01163 \tabularnewline
17 & 11.9 & 10.0348 & 1.86519 \tabularnewline
18 & 9.3 & 10.3296 & -1.02961 \tabularnewline
19 & 9.6 & 10.9427 & -1.3427 \tabularnewline
20 & 10 & 10.2545 & -0.254504 \tabularnewline
21 & 6.4 & 9.68435 & -3.28435 \tabularnewline
22 & 13.8 & 11.5781 & 2.22191 \tabularnewline
23 & 10.8 & 10.4592 & 0.340766 \tabularnewline
24 & 13.8 & 10.2317 & 3.56834 \tabularnewline
25 & 11.7 & 11.1125 & 0.587534 \tabularnewline
26 & 10.9 & 10.2075 & 0.692456 \tabularnewline
27 & 16.1 & 10.7565 & 5.34347 \tabularnewline
28 & 13.4 & 11.4027 & 1.99732 \tabularnewline
29 & 9.9 & 10.3076 & -0.407623 \tabularnewline
30 & 11.5 & 11.5519 & -0.0518936 \tabularnewline
31 & 8.3 & 10.3541 & -2.05407 \tabularnewline
32 & 11.7 & 10.7407 & 0.959277 \tabularnewline
33 & 9 & 10.2138 & -1.21375 \tabularnewline
34 & 9.7 & 11.3117 & -1.61173 \tabularnewline
35 & 10.8 & 10.9755 & -0.17549 \tabularnewline
36 & 10.3 & 9.73104 & 0.568958 \tabularnewline
37 & 10.4 & 11.7394 & -1.33944 \tabularnewline
38 & 12.7 & 11.0592 & 1.6408 \tabularnewline
39 & 9.3 & 10.9842 & -1.68424 \tabularnewline
40 & 11.8 & 11.5981 & 0.201886 \tabularnewline
41 & 5.9 & 11.3236 & -5.42363 \tabularnewline
42 & 11.4 & 10.9865 & 0.413547 \tabularnewline
43 & 13 & 11.0547 & 1.94529 \tabularnewline
44 & 10.8 & 11.6807 & -0.880685 \tabularnewline
45 & 12.3 & 11.2541 & 1.04594 \tabularnewline
46 & 11.3 & 10.4663 & 0.833665 \tabularnewline
47 & 11.8 & 9.80333 & 1.99667 \tabularnewline
48 & 7.9 & 9.97982 & -2.07982 \tabularnewline
49 & 12.7 & 10.9939 & 1.70612 \tabularnewline
50 & 12.3 & 11.0671 & 1.23286 \tabularnewline
51 & 11.6 & 10.6223 & 0.977733 \tabularnewline
52 & 6.7 & 10.822 & -4.12198 \tabularnewline
53 & 10.9 & 9.93168 & 0.968319 \tabularnewline
54 & 12.1 & 10.0064 & 2.09361 \tabularnewline
55 & 13.3 & 10.2952 & 3.0048 \tabularnewline
56 & 10.1 & 10.41 & -0.30996 \tabularnewline
57 & 5.7 & 9.83723 & -4.13723 \tabularnewline
58 & 14.3 & 10.1819 & 4.11806 \tabularnewline
59 & 8 & 12.5954 & -4.59536 \tabularnewline
60 & 13.3 & 11.6438 & 1.6562 \tabularnewline
61 & 9.3 & 9.89024 & -0.590243 \tabularnewline
62 & 12.5 & 9.66066 & 2.83934 \tabularnewline
63 & 7.6 & 9.89364 & -2.29364 \tabularnewline
64 & 15.9 & 12.0982 & 3.80184 \tabularnewline
65 & 9.2 & 9.91911 & -0.719109 \tabularnewline
66 & 9.1 & 11.1741 & -2.07405 \tabularnewline
67 & 11.1 & 10.6088 & 0.491154 \tabularnewline
68 & 13 & 11.935 & 1.06495 \tabularnewline
69 & 14.5 & 11.8332 & 2.66682 \tabularnewline
70 & 12.2 & 10.89 & 1.31 \tabularnewline
71 & 12.3 & 11.0281 & 1.27194 \tabularnewline
72 & 11.4 & 11.4504 & -0.0504053 \tabularnewline
73 & 8.8 & 10.6687 & -1.86872 \tabularnewline
74 & 14.6 & 11.2075 & 3.39255 \tabularnewline
75 & 12.6 & 11.4741 & 1.12594 \tabularnewline
76 & 13 & 9.69567 & 3.30433 \tabularnewline
77 & 12.6 & 10.7914 & 1.80861 \tabularnewline
78 & 13.2 & 12.0365 & 1.16353 \tabularnewline
79 & 9.9 & 10.8339 & -0.933874 \tabularnewline
80 & 7.7 & 10.3507 & -2.65074 \tabularnewline
81 & 10.5 & 10.6062 & -0.106198 \tabularnewline
82 & 13.4 & 10.8165 & 2.58353 \tabularnewline
83 & 10.9 & 10.107 & 0.792971 \tabularnewline
84 & 4.3 & 10.6771 & -6.37706 \tabularnewline
85 & 10.3 & 10.5384 & -0.238397 \tabularnewline
86 & 11.8 & 10.516 & 1.28401 \tabularnewline
87 & 11.2 & 10.6766 & 0.523404 \tabularnewline
88 & 11.4 & 10.2236 & 1.17639 \tabularnewline
89 & 8.6 & 11.1019 & -2.5019 \tabularnewline
90 & 13.2 & 10.6296 & 2.5704 \tabularnewline
91 & 12.6 & 11.1285 & 1.47154 \tabularnewline
92 & 5.6 & 10.5319 & -4.93193 \tabularnewline
93 & 9.9 & 10.5606 & -0.660602 \tabularnewline
94 & 8.8 & 10.0218 & -1.22176 \tabularnewline
95 & 7.7 & 10.0042 & -2.30424 \tabularnewline
96 & 9 & 10.6756 & -1.67559 \tabularnewline
97 & 7.3 & 10.6022 & -3.30219 \tabularnewline
98 & 11.4 & 10.3447 & 1.05525 \tabularnewline
99 & 13.6 & 10.6202 & 2.97981 \tabularnewline
100 & 7.9 & 10.22 & -2.32002 \tabularnewline
101 & 10.7 & 10.6056 & 0.0944146 \tabularnewline
102 & 10.3 & 10.0109 & 0.289065 \tabularnewline
103 & 8.3 & 10.1224 & -1.8224 \tabularnewline
104 & 9.6 & 9.70973 & -0.10973 \tabularnewline
105 & 14.2 & 11.3148 & 2.8852 \tabularnewline
106 & 8.5 & 9.359 & -0.858995 \tabularnewline
107 & 13.5 & 10.2169 & 3.28314 \tabularnewline
108 & 4.9 & 10.4125 & -5.51254 \tabularnewline
109 & 6.4 & 10.0512 & -3.65115 \tabularnewline
110 & 9.6 & 11.1523 & -1.55233 \tabularnewline
111 & 11.6 & 10.9297 & 0.67033 \tabularnewline
112 & 11.1 & 10.702 & 0.398034 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=268259&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]10.5794[/C][C]2.32065[/C][/ROW]
[ROW][C]2[/C][C]12.2[/C][C]11.0318[/C][C]1.16821[/C][/ROW]
[ROW][C]3[/C][C]12.8[/C][C]11.125[/C][C]1.67497[/C][/ROW]
[ROW][C]4[/C][C]7.4[/C][C]10.8179[/C][C]-3.4179[/C][/ROW]
[ROW][C]5[/C][C]6.7[/C][C]10.5904[/C][C]-3.8904[/C][/ROW]
[ROW][C]6[/C][C]12.6[/C][C]10.6431[/C][C]1.95685[/C][/ROW]
[ROW][C]7[/C][C]14.8[/C][C]10.4122[/C][C]4.38778[/C][/ROW]
[ROW][C]8[/C][C]13.3[/C][C]10.3724[/C][C]2.92759[/C][/ROW]
[ROW][C]9[/C][C]11.1[/C][C]10.2682[/C][C]0.831841[/C][/ROW]
[ROW][C]10[/C][C]8.2[/C][C]12.0004[/C][C]-3.80039[/C][/ROW]
[ROW][C]11[/C][C]11.4[/C][C]10.622[/C][C]0.778034[/C][/ROW]
[ROW][C]12[/C][C]6.4[/C][C]11.4671[/C][C]-5.0671[/C][/ROW]
[ROW][C]13[/C][C]10.6[/C][C]9.78849[/C][C]0.811506[/C][/ROW]
[ROW][C]14[/C][C]12[/C][C]11.4964[/C][C]0.503597[/C][/ROW]
[ROW][C]15[/C][C]6.3[/C][C]10.4502[/C][C]-4.15018[/C][/ROW]
[ROW][C]16[/C][C]11.3[/C][C]10.2884[/C][C]1.01163[/C][/ROW]
[ROW][C]17[/C][C]11.9[/C][C]10.0348[/C][C]1.86519[/C][/ROW]
[ROW][C]18[/C][C]9.3[/C][C]10.3296[/C][C]-1.02961[/C][/ROW]
[ROW][C]19[/C][C]9.6[/C][C]10.9427[/C][C]-1.3427[/C][/ROW]
[ROW][C]20[/C][C]10[/C][C]10.2545[/C][C]-0.254504[/C][/ROW]
[ROW][C]21[/C][C]6.4[/C][C]9.68435[/C][C]-3.28435[/C][/ROW]
[ROW][C]22[/C][C]13.8[/C][C]11.5781[/C][C]2.22191[/C][/ROW]
[ROW][C]23[/C][C]10.8[/C][C]10.4592[/C][C]0.340766[/C][/ROW]
[ROW][C]24[/C][C]13.8[/C][C]10.2317[/C][C]3.56834[/C][/ROW]
[ROW][C]25[/C][C]11.7[/C][C]11.1125[/C][C]0.587534[/C][/ROW]
[ROW][C]26[/C][C]10.9[/C][C]10.2075[/C][C]0.692456[/C][/ROW]
[ROW][C]27[/C][C]16.1[/C][C]10.7565[/C][C]5.34347[/C][/ROW]
[ROW][C]28[/C][C]13.4[/C][C]11.4027[/C][C]1.99732[/C][/ROW]
[ROW][C]29[/C][C]9.9[/C][C]10.3076[/C][C]-0.407623[/C][/ROW]
[ROW][C]30[/C][C]11.5[/C][C]11.5519[/C][C]-0.0518936[/C][/ROW]
[ROW][C]31[/C][C]8.3[/C][C]10.3541[/C][C]-2.05407[/C][/ROW]
[ROW][C]32[/C][C]11.7[/C][C]10.7407[/C][C]0.959277[/C][/ROW]
[ROW][C]33[/C][C]9[/C][C]10.2138[/C][C]-1.21375[/C][/ROW]
[ROW][C]34[/C][C]9.7[/C][C]11.3117[/C][C]-1.61173[/C][/ROW]
[ROW][C]35[/C][C]10.8[/C][C]10.9755[/C][C]-0.17549[/C][/ROW]
[ROW][C]36[/C][C]10.3[/C][C]9.73104[/C][C]0.568958[/C][/ROW]
[ROW][C]37[/C][C]10.4[/C][C]11.7394[/C][C]-1.33944[/C][/ROW]
[ROW][C]38[/C][C]12.7[/C][C]11.0592[/C][C]1.6408[/C][/ROW]
[ROW][C]39[/C][C]9.3[/C][C]10.9842[/C][C]-1.68424[/C][/ROW]
[ROW][C]40[/C][C]11.8[/C][C]11.5981[/C][C]0.201886[/C][/ROW]
[ROW][C]41[/C][C]5.9[/C][C]11.3236[/C][C]-5.42363[/C][/ROW]
[ROW][C]42[/C][C]11.4[/C][C]10.9865[/C][C]0.413547[/C][/ROW]
[ROW][C]43[/C][C]13[/C][C]11.0547[/C][C]1.94529[/C][/ROW]
[ROW][C]44[/C][C]10.8[/C][C]11.6807[/C][C]-0.880685[/C][/ROW]
[ROW][C]45[/C][C]12.3[/C][C]11.2541[/C][C]1.04594[/C][/ROW]
[ROW][C]46[/C][C]11.3[/C][C]10.4663[/C][C]0.833665[/C][/ROW]
[ROW][C]47[/C][C]11.8[/C][C]9.80333[/C][C]1.99667[/C][/ROW]
[ROW][C]48[/C][C]7.9[/C][C]9.97982[/C][C]-2.07982[/C][/ROW]
[ROW][C]49[/C][C]12.7[/C][C]10.9939[/C][C]1.70612[/C][/ROW]
[ROW][C]50[/C][C]12.3[/C][C]11.0671[/C][C]1.23286[/C][/ROW]
[ROW][C]51[/C][C]11.6[/C][C]10.6223[/C][C]0.977733[/C][/ROW]
[ROW][C]52[/C][C]6.7[/C][C]10.822[/C][C]-4.12198[/C][/ROW]
[ROW][C]53[/C][C]10.9[/C][C]9.93168[/C][C]0.968319[/C][/ROW]
[ROW][C]54[/C][C]12.1[/C][C]10.0064[/C][C]2.09361[/C][/ROW]
[ROW][C]55[/C][C]13.3[/C][C]10.2952[/C][C]3.0048[/C][/ROW]
[ROW][C]56[/C][C]10.1[/C][C]10.41[/C][C]-0.30996[/C][/ROW]
[ROW][C]57[/C][C]5.7[/C][C]9.83723[/C][C]-4.13723[/C][/ROW]
[ROW][C]58[/C][C]14.3[/C][C]10.1819[/C][C]4.11806[/C][/ROW]
[ROW][C]59[/C][C]8[/C][C]12.5954[/C][C]-4.59536[/C][/ROW]
[ROW][C]60[/C][C]13.3[/C][C]11.6438[/C][C]1.6562[/C][/ROW]
[ROW][C]61[/C][C]9.3[/C][C]9.89024[/C][C]-0.590243[/C][/ROW]
[ROW][C]62[/C][C]12.5[/C][C]9.66066[/C][C]2.83934[/C][/ROW]
[ROW][C]63[/C][C]7.6[/C][C]9.89364[/C][C]-2.29364[/C][/ROW]
[ROW][C]64[/C][C]15.9[/C][C]12.0982[/C][C]3.80184[/C][/ROW]
[ROW][C]65[/C][C]9.2[/C][C]9.91911[/C][C]-0.719109[/C][/ROW]
[ROW][C]66[/C][C]9.1[/C][C]11.1741[/C][C]-2.07405[/C][/ROW]
[ROW][C]67[/C][C]11.1[/C][C]10.6088[/C][C]0.491154[/C][/ROW]
[ROW][C]68[/C][C]13[/C][C]11.935[/C][C]1.06495[/C][/ROW]
[ROW][C]69[/C][C]14.5[/C][C]11.8332[/C][C]2.66682[/C][/ROW]
[ROW][C]70[/C][C]12.2[/C][C]10.89[/C][C]1.31[/C][/ROW]
[ROW][C]71[/C][C]12.3[/C][C]11.0281[/C][C]1.27194[/C][/ROW]
[ROW][C]72[/C][C]11.4[/C][C]11.4504[/C][C]-0.0504053[/C][/ROW]
[ROW][C]73[/C][C]8.8[/C][C]10.6687[/C][C]-1.86872[/C][/ROW]
[ROW][C]74[/C][C]14.6[/C][C]11.2075[/C][C]3.39255[/C][/ROW]
[ROW][C]75[/C][C]12.6[/C][C]11.4741[/C][C]1.12594[/C][/ROW]
[ROW][C]76[/C][C]13[/C][C]9.69567[/C][C]3.30433[/C][/ROW]
[ROW][C]77[/C][C]12.6[/C][C]10.7914[/C][C]1.80861[/C][/ROW]
[ROW][C]78[/C][C]13.2[/C][C]12.0365[/C][C]1.16353[/C][/ROW]
[ROW][C]79[/C][C]9.9[/C][C]10.8339[/C][C]-0.933874[/C][/ROW]
[ROW][C]80[/C][C]7.7[/C][C]10.3507[/C][C]-2.65074[/C][/ROW]
[ROW][C]81[/C][C]10.5[/C][C]10.6062[/C][C]-0.106198[/C][/ROW]
[ROW][C]82[/C][C]13.4[/C][C]10.8165[/C][C]2.58353[/C][/ROW]
[ROW][C]83[/C][C]10.9[/C][C]10.107[/C][C]0.792971[/C][/ROW]
[ROW][C]84[/C][C]4.3[/C][C]10.6771[/C][C]-6.37706[/C][/ROW]
[ROW][C]85[/C][C]10.3[/C][C]10.5384[/C][C]-0.238397[/C][/ROW]
[ROW][C]86[/C][C]11.8[/C][C]10.516[/C][C]1.28401[/C][/ROW]
[ROW][C]87[/C][C]11.2[/C][C]10.6766[/C][C]0.523404[/C][/ROW]
[ROW][C]88[/C][C]11.4[/C][C]10.2236[/C][C]1.17639[/C][/ROW]
[ROW][C]89[/C][C]8.6[/C][C]11.1019[/C][C]-2.5019[/C][/ROW]
[ROW][C]90[/C][C]13.2[/C][C]10.6296[/C][C]2.5704[/C][/ROW]
[ROW][C]91[/C][C]12.6[/C][C]11.1285[/C][C]1.47154[/C][/ROW]
[ROW][C]92[/C][C]5.6[/C][C]10.5319[/C][C]-4.93193[/C][/ROW]
[ROW][C]93[/C][C]9.9[/C][C]10.5606[/C][C]-0.660602[/C][/ROW]
[ROW][C]94[/C][C]8.8[/C][C]10.0218[/C][C]-1.22176[/C][/ROW]
[ROW][C]95[/C][C]7.7[/C][C]10.0042[/C][C]-2.30424[/C][/ROW]
[ROW][C]96[/C][C]9[/C][C]10.6756[/C][C]-1.67559[/C][/ROW]
[ROW][C]97[/C][C]7.3[/C][C]10.6022[/C][C]-3.30219[/C][/ROW]
[ROW][C]98[/C][C]11.4[/C][C]10.3447[/C][C]1.05525[/C][/ROW]
[ROW][C]99[/C][C]13.6[/C][C]10.6202[/C][C]2.97981[/C][/ROW]
[ROW][C]100[/C][C]7.9[/C][C]10.22[/C][C]-2.32002[/C][/ROW]
[ROW][C]101[/C][C]10.7[/C][C]10.6056[/C][C]0.0944146[/C][/ROW]
[ROW][C]102[/C][C]10.3[/C][C]10.0109[/C][C]0.289065[/C][/ROW]
[ROW][C]103[/C][C]8.3[/C][C]10.1224[/C][C]-1.8224[/C][/ROW]
[ROW][C]104[/C][C]9.6[/C][C]9.70973[/C][C]-0.10973[/C][/ROW]
[ROW][C]105[/C][C]14.2[/C][C]11.3148[/C][C]2.8852[/C][/ROW]
[ROW][C]106[/C][C]8.5[/C][C]9.359[/C][C]-0.858995[/C][/ROW]
[ROW][C]107[/C][C]13.5[/C][C]10.2169[/C][C]3.28314[/C][/ROW]
[ROW][C]108[/C][C]4.9[/C][C]10.4125[/C][C]-5.51254[/C][/ROW]
[ROW][C]109[/C][C]6.4[/C][C]10.0512[/C][C]-3.65115[/C][/ROW]
[ROW][C]110[/C][C]9.6[/C][C]11.1523[/C][C]-1.55233[/C][/ROW]
[ROW][C]111[/C][C]11.6[/C][C]10.9297[/C][C]0.67033[/C][/ROW]
[ROW][C]112[/C][C]11.1[/C][C]10.702[/C][C]0.398034[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=268259&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=268259&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	10.5794	2.32065
2	12.2	11.0318	1.16821
3	12.8	11.125	1.67497
4	7.4	10.8179	-3.4179
5	6.7	10.5904	-3.8904
6	12.6	10.6431	1.95685
7	14.8	10.4122	4.38778
8	13.3	10.3724	2.92759
9	11.1	10.2682	0.831841
10	8.2	12.0004	-3.80039
11	11.4	10.622	0.778034
12	6.4	11.4671	-5.0671
13	10.6	9.78849	0.811506
14	12	11.4964	0.503597
15	6.3	10.4502	-4.15018
16	11.3	10.2884	1.01163
17	11.9	10.0348	1.86519
18	9.3	10.3296	-1.02961
19	9.6	10.9427	-1.3427
20	10	10.2545	-0.254504
21	6.4	9.68435	-3.28435
22	13.8	11.5781	2.22191
23	10.8	10.4592	0.340766
24	13.8	10.2317	3.56834
25	11.7	11.1125	0.587534
26	10.9	10.2075	0.692456
27	16.1	10.7565	5.34347
28	13.4	11.4027	1.99732
29	9.9	10.3076	-0.407623
30	11.5	11.5519	-0.0518936
31	8.3	10.3541	-2.05407
32	11.7	10.7407	0.959277
33	9	10.2138	-1.21375
34	9.7	11.3117	-1.61173
35	10.8	10.9755	-0.17549
36	10.3	9.73104	0.568958
37	10.4	11.7394	-1.33944
38	12.7	11.0592	1.6408
39	9.3	10.9842	-1.68424
40	11.8	11.5981	0.201886
41	5.9	11.3236	-5.42363
42	11.4	10.9865	0.413547
43	13	11.0547	1.94529
44	10.8	11.6807	-0.880685
45	12.3	11.2541	1.04594
46	11.3	10.4663	0.833665
47	11.8	9.80333	1.99667
48	7.9	9.97982	-2.07982
49	12.7	10.9939	1.70612
50	12.3	11.0671	1.23286
51	11.6	10.6223	0.977733
52	6.7	10.822	-4.12198
53	10.9	9.93168	0.968319
54	12.1	10.0064	2.09361
55	13.3	10.2952	3.0048
56	10.1	10.41	-0.30996
57	5.7	9.83723	-4.13723
58	14.3	10.1819	4.11806
59	8	12.5954	-4.59536
60	13.3	11.6438	1.6562
61	9.3	9.89024	-0.590243
62	12.5	9.66066	2.83934
63	7.6	9.89364	-2.29364
64	15.9	12.0982	3.80184
65	9.2	9.91911	-0.719109
66	9.1	11.1741	-2.07405
67	11.1	10.6088	0.491154
68	13	11.935	1.06495
69	14.5	11.8332	2.66682
70	12.2	10.89	1.31
71	12.3	11.0281	1.27194
72	11.4	11.4504	-0.0504053
73	8.8	10.6687	-1.86872
74	14.6	11.2075	3.39255
75	12.6	11.4741	1.12594
76	13	9.69567	3.30433
77	12.6	10.7914	1.80861
78	13.2	12.0365	1.16353
79	9.9	10.8339	-0.933874
80	7.7	10.3507	-2.65074
81	10.5	10.6062	-0.106198
82	13.4	10.8165	2.58353
83	10.9	10.107	0.792971
84	4.3	10.6771	-6.37706
85	10.3	10.5384	-0.238397
86	11.8	10.516	1.28401
87	11.2	10.6766	0.523404
88	11.4	10.2236	1.17639
89	8.6	11.1019	-2.5019
90	13.2	10.6296	2.5704
91	12.6	11.1285	1.47154
92	5.6	10.5319	-4.93193
93	9.9	10.5606	-0.660602
94	8.8	10.0218	-1.22176
95	7.7	10.0042	-2.30424
96	9	10.6756	-1.67559
97	7.3	10.6022	-3.30219
98	11.4	10.3447	1.05525
99	13.6	10.6202	2.97981
100	7.9	10.22	-2.32002
101	10.7	10.6056	0.0944146
102	10.3	10.0109	0.289065
103	8.3	10.1224	-1.8224
104	9.6	9.70973	-0.10973
105	14.2	11.3148	2.8852
106	8.5	9.359	-0.858995
107	13.5	10.2169	3.28314
108	4.9	10.4125	-5.51254
109	6.4	10.0512	-3.65115
110	9.6	11.1523	-1.55233
111	11.6	10.9297	0.67033
112	11.1	10.702	0.398034

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
11	0.282427	0.564853	0.717573
12	0.68288	0.63424	0.31712
13	0.637799	0.724401	0.362201
14	0.516572	0.966856	0.483428
15	0.638812	0.722376	0.361188
16	0.611603	0.776794	0.388397
17	0.535881	0.928238	0.464119
18	0.489195	0.97839	0.510805
19	0.411076	0.822152	0.588924
20	0.324093	0.648186	0.675907
21	0.306113	0.612225	0.693887
22	0.24232	0.48464	0.75768
23	0.18188	0.363759	0.81812
24	0.252568	0.505136	0.747432
25	0.215141	0.430282	0.784859
26	0.166713	0.333427	0.833287
27	0.26133	0.52266	0.73867
28	0.210973	0.421945	0.789027
29	0.169347	0.338694	0.830653
30	0.139882	0.279763	0.860118
31	0.137587	0.275174	0.862413
32	0.102739	0.205479	0.897261
33	0.088047	0.176094	0.911953
34	0.462343	0.924686	0.537657
35	0.41557	0.831141	0.58443
36	0.368816	0.737632	0.631184
37	0.323035	0.646069	0.676965
38	0.285898	0.571796	0.714102
39	0.256373	0.512746	0.743627
40	0.209209	0.418418	0.790791
41	0.395001	0.790002	0.604999
42	0.354299	0.708599	0.645701
43	0.344455	0.68891	0.655545
44	0.34296	0.685921	0.65704
45	0.302984	0.605969	0.697016
46	0.271265	0.542531	0.728735
47	0.252767	0.505534	0.747233
48	0.250491	0.500982	0.749509
49	0.248626	0.497252	0.751374
50	0.235917	0.471833	0.764083
51	0.212212	0.424424	0.787788
52	0.353694	0.707388	0.646306
53	0.304921	0.609843	0.695079
54	0.294009	0.588018	0.705991
55	0.316447	0.632895	0.683553
56	0.27123	0.542461	0.72877
57	0.392222	0.784443	0.607778
58	0.533722	0.932557	0.466278
59	0.674863	0.650274	0.325137
60	0.668558	0.662883	0.331442
61	0.618852	0.762296	0.381148
62	0.650805	0.698389	0.349195
63	0.680966	0.638067	0.319034
64	0.742344	0.515312	0.257656
65	0.702327	0.595347	0.297673
66	0.704832	0.590335	0.295168
67	0.659092	0.681816	0.340908
68	0.631736	0.736528	0.368264
69	0.613278	0.773444	0.386722
70	0.560438	0.879124	0.439562
71	0.513547	0.972906	0.486453
72	0.45502	0.91004	0.54498
73	0.425143	0.850286	0.574857
74	0.441279	0.882558	0.558721
75	0.403485	0.80697	0.596515
76	0.467004	0.934008	0.532996
77	0.447626	0.895252	0.552374
78	0.40578	0.811559	0.59422
79	0.354075	0.70815	0.645925
80	0.327247	0.654493	0.672753
81	0.273031	0.546062	0.726969
82	0.300619	0.601239	0.699381
83	0.247603	0.495207	0.752397
84	0.495089	0.990178	0.504911
85	0.43756	0.875119	0.56244
86	0.388775	0.77755	0.611225
87	0.319929	0.639859	0.680071
88	0.294531	0.589062	0.705469
89	0.286059	0.572118	0.713941
90	0.320189	0.640377	0.679811
91	0.263891	0.527782	0.736109
92	0.36001	0.720021	0.63999
93	0.307068	0.614136	0.692932
94	0.235846	0.471693	0.764154
95	0.218907	0.437814	0.781093
96	0.163104	0.326208	0.836896
97	0.306666	0.613332	0.693334
98	0.215337	0.430673	0.784663
99	0.213549	0.427098	0.786451
100	0.167683	0.335366	0.832317
101	0.0900835	0.180167	0.909916

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
11 & 0.282427 & 0.564853 & 0.717573 \tabularnewline
12 & 0.68288 & 0.63424 & 0.31712 \tabularnewline
13 & 0.637799 & 0.724401 & 0.362201 \tabularnewline
14 & 0.516572 & 0.966856 & 0.483428 \tabularnewline
15 & 0.638812 & 0.722376 & 0.361188 \tabularnewline
16 & 0.611603 & 0.776794 & 0.388397 \tabularnewline
17 & 0.535881 & 0.928238 & 0.464119 \tabularnewline
18 & 0.489195 & 0.97839 & 0.510805 \tabularnewline
19 & 0.411076 & 0.822152 & 0.588924 \tabularnewline
20 & 0.324093 & 0.648186 & 0.675907 \tabularnewline
21 & 0.306113 & 0.612225 & 0.693887 \tabularnewline
22 & 0.24232 & 0.48464 & 0.75768 \tabularnewline
23 & 0.18188 & 0.363759 & 0.81812 \tabularnewline
24 & 0.252568 & 0.505136 & 0.747432 \tabularnewline
25 & 0.215141 & 0.430282 & 0.784859 \tabularnewline
26 & 0.166713 & 0.333427 & 0.833287 \tabularnewline
27 & 0.26133 & 0.52266 & 0.73867 \tabularnewline
28 & 0.210973 & 0.421945 & 0.789027 \tabularnewline
29 & 0.169347 & 0.338694 & 0.830653 \tabularnewline
30 & 0.139882 & 0.279763 & 0.860118 \tabularnewline
31 & 0.137587 & 0.275174 & 0.862413 \tabularnewline
32 & 0.102739 & 0.205479 & 0.897261 \tabularnewline
33 & 0.088047 & 0.176094 & 0.911953 \tabularnewline
34 & 0.462343 & 0.924686 & 0.537657 \tabularnewline
35 & 0.41557 & 0.831141 & 0.58443 \tabularnewline
36 & 0.368816 & 0.737632 & 0.631184 \tabularnewline
37 & 0.323035 & 0.646069 & 0.676965 \tabularnewline
38 & 0.285898 & 0.571796 & 0.714102 \tabularnewline
39 & 0.256373 & 0.512746 & 0.743627 \tabularnewline
40 & 0.209209 & 0.418418 & 0.790791 \tabularnewline
41 & 0.395001 & 0.790002 & 0.604999 \tabularnewline
42 & 0.354299 & 0.708599 & 0.645701 \tabularnewline
43 & 0.344455 & 0.68891 & 0.655545 \tabularnewline
44 & 0.34296 & 0.685921 & 0.65704 \tabularnewline
45 & 0.302984 & 0.605969 & 0.697016 \tabularnewline
46 & 0.271265 & 0.542531 & 0.728735 \tabularnewline
47 & 0.252767 & 0.505534 & 0.747233 \tabularnewline
48 & 0.250491 & 0.500982 & 0.749509 \tabularnewline
49 & 0.248626 & 0.497252 & 0.751374 \tabularnewline
50 & 0.235917 & 0.471833 & 0.764083 \tabularnewline
51 & 0.212212 & 0.424424 & 0.787788 \tabularnewline
52 & 0.353694 & 0.707388 & 0.646306 \tabularnewline
53 & 0.304921 & 0.609843 & 0.695079 \tabularnewline
54 & 0.294009 & 0.588018 & 0.705991 \tabularnewline
55 & 0.316447 & 0.632895 & 0.683553 \tabularnewline
56 & 0.27123 & 0.542461 & 0.72877 \tabularnewline
57 & 0.392222 & 0.784443 & 0.607778 \tabularnewline
58 & 0.533722 & 0.932557 & 0.466278 \tabularnewline
59 & 0.674863 & 0.650274 & 0.325137 \tabularnewline
60 & 0.668558 & 0.662883 & 0.331442 \tabularnewline
61 & 0.618852 & 0.762296 & 0.381148 \tabularnewline
62 & 0.650805 & 0.698389 & 0.349195 \tabularnewline
63 & 0.680966 & 0.638067 & 0.319034 \tabularnewline
64 & 0.742344 & 0.515312 & 0.257656 \tabularnewline
65 & 0.702327 & 0.595347 & 0.297673 \tabularnewline
66 & 0.704832 & 0.590335 & 0.295168 \tabularnewline
67 & 0.659092 & 0.681816 & 0.340908 \tabularnewline
68 & 0.631736 & 0.736528 & 0.368264 \tabularnewline
69 & 0.613278 & 0.773444 & 0.386722 \tabularnewline
70 & 0.560438 & 0.879124 & 0.439562 \tabularnewline
71 & 0.513547 & 0.972906 & 0.486453 \tabularnewline
72 & 0.45502 & 0.91004 & 0.54498 \tabularnewline
73 & 0.425143 & 0.850286 & 0.574857 \tabularnewline
74 & 0.441279 & 0.882558 & 0.558721 \tabularnewline
75 & 0.403485 & 0.80697 & 0.596515 \tabularnewline
76 & 0.467004 & 0.934008 & 0.532996 \tabularnewline
77 & 0.447626 & 0.895252 & 0.552374 \tabularnewline
78 & 0.40578 & 0.811559 & 0.59422 \tabularnewline
79 & 0.354075 & 0.70815 & 0.645925 \tabularnewline
80 & 0.327247 & 0.654493 & 0.672753 \tabularnewline
81 & 0.273031 & 0.546062 & 0.726969 \tabularnewline
82 & 0.300619 & 0.601239 & 0.699381 \tabularnewline
83 & 0.247603 & 0.495207 & 0.752397 \tabularnewline
84 & 0.495089 & 0.990178 & 0.504911 \tabularnewline
85 & 0.43756 & 0.875119 & 0.56244 \tabularnewline
86 & 0.388775 & 0.77755 & 0.611225 \tabularnewline
87 & 0.319929 & 0.639859 & 0.680071 \tabularnewline
88 & 0.294531 & 0.589062 & 0.705469 \tabularnewline
89 & 0.286059 & 0.572118 & 0.713941 \tabularnewline
90 & 0.320189 & 0.640377 & 0.679811 \tabularnewline
91 & 0.263891 & 0.527782 & 0.736109 \tabularnewline
92 & 0.36001 & 0.720021 & 0.63999 \tabularnewline
93 & 0.307068 & 0.614136 & 0.692932 \tabularnewline
94 & 0.235846 & 0.471693 & 0.764154 \tabularnewline
95 & 0.218907 & 0.437814 & 0.781093 \tabularnewline
96 & 0.163104 & 0.326208 & 0.836896 \tabularnewline
97 & 0.306666 & 0.613332 & 0.693334 \tabularnewline
98 & 0.215337 & 0.430673 & 0.784663 \tabularnewline
99 & 0.213549 & 0.427098 & 0.786451 \tabularnewline
100 & 0.167683 & 0.335366 & 0.832317 \tabularnewline
101 & 0.0900835 & 0.180167 & 0.909916 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=268259&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]0.282427[/C][C]0.564853[/C][C]0.717573[/C][/ROW]
[ROW][C]12[/C][C]0.68288[/C][C]0.63424[/C][C]0.31712[/C][/ROW]
[ROW][C]13[/C][C]0.637799[/C][C]0.724401[/C][C]0.362201[/C][/ROW]
[ROW][C]14[/C][C]0.516572[/C][C]0.966856[/C][C]0.483428[/C][/ROW]
[ROW][C]15[/C][C]0.638812[/C][C]0.722376[/C][C]0.361188[/C][/ROW]
[ROW][C]16[/C][C]0.611603[/C][C]0.776794[/C][C]0.388397[/C][/ROW]
[ROW][C]17[/C][C]0.535881[/C][C]0.928238[/C][C]0.464119[/C][/ROW]
[ROW][C]18[/C][C]0.489195[/C][C]0.97839[/C][C]0.510805[/C][/ROW]
[ROW][C]19[/C][C]0.411076[/C][C]0.822152[/C][C]0.588924[/C][/ROW]
[ROW][C]20[/C][C]0.324093[/C][C]0.648186[/C][C]0.675907[/C][/ROW]
[ROW][C]21[/C][C]0.306113[/C][C]0.612225[/C][C]0.693887[/C][/ROW]
[ROW][C]22[/C][C]0.24232[/C][C]0.48464[/C][C]0.75768[/C][/ROW]
[ROW][C]23[/C][C]0.18188[/C][C]0.363759[/C][C]0.81812[/C][/ROW]
[ROW][C]24[/C][C]0.252568[/C][C]0.505136[/C][C]0.747432[/C][/ROW]
[ROW][C]25[/C][C]0.215141[/C][C]0.430282[/C][C]0.784859[/C][/ROW]
[ROW][C]26[/C][C]0.166713[/C][C]0.333427[/C][C]0.833287[/C][/ROW]
[ROW][C]27[/C][C]0.26133[/C][C]0.52266[/C][C]0.73867[/C][/ROW]
[ROW][C]28[/C][C]0.210973[/C][C]0.421945[/C][C]0.789027[/C][/ROW]
[ROW][C]29[/C][C]0.169347[/C][C]0.338694[/C][C]0.830653[/C][/ROW]
[ROW][C]30[/C][C]0.139882[/C][C]0.279763[/C][C]0.860118[/C][/ROW]
[ROW][C]31[/C][C]0.137587[/C][C]0.275174[/C][C]0.862413[/C][/ROW]
[ROW][C]32[/C][C]0.102739[/C][C]0.205479[/C][C]0.897261[/C][/ROW]
[ROW][C]33[/C][C]0.088047[/C][C]0.176094[/C][C]0.911953[/C][/ROW]
[ROW][C]34[/C][C]0.462343[/C][C]0.924686[/C][C]0.537657[/C][/ROW]
[ROW][C]35[/C][C]0.41557[/C][C]0.831141[/C][C]0.58443[/C][/ROW]
[ROW][C]36[/C][C]0.368816[/C][C]0.737632[/C][C]0.631184[/C][/ROW]
[ROW][C]37[/C][C]0.323035[/C][C]0.646069[/C][C]0.676965[/C][/ROW]
[ROW][C]38[/C][C]0.285898[/C][C]0.571796[/C][C]0.714102[/C][/ROW]
[ROW][C]39[/C][C]0.256373[/C][C]0.512746[/C][C]0.743627[/C][/ROW]
[ROW][C]40[/C][C]0.209209[/C][C]0.418418[/C][C]0.790791[/C][/ROW]
[ROW][C]41[/C][C]0.395001[/C][C]0.790002[/C][C]0.604999[/C][/ROW]
[ROW][C]42[/C][C]0.354299[/C][C]0.708599[/C][C]0.645701[/C][/ROW]
[ROW][C]43[/C][C]0.344455[/C][C]0.68891[/C][C]0.655545[/C][/ROW]
[ROW][C]44[/C][C]0.34296[/C][C]0.685921[/C][C]0.65704[/C][/ROW]
[ROW][C]45[/C][C]0.302984[/C][C]0.605969[/C][C]0.697016[/C][/ROW]
[ROW][C]46[/C][C]0.271265[/C][C]0.542531[/C][C]0.728735[/C][/ROW]
[ROW][C]47[/C][C]0.252767[/C][C]0.505534[/C][C]0.747233[/C][/ROW]
[ROW][C]48[/C][C]0.250491[/C][C]0.500982[/C][C]0.749509[/C][/ROW]
[ROW][C]49[/C][C]0.248626[/C][C]0.497252[/C][C]0.751374[/C][/ROW]
[ROW][C]50[/C][C]0.235917[/C][C]0.471833[/C][C]0.764083[/C][/ROW]
[ROW][C]51[/C][C]0.212212[/C][C]0.424424[/C][C]0.787788[/C][/ROW]
[ROW][C]52[/C][C]0.353694[/C][C]0.707388[/C][C]0.646306[/C][/ROW]
[ROW][C]53[/C][C]0.304921[/C][C]0.609843[/C][C]0.695079[/C][/ROW]
[ROW][C]54[/C][C]0.294009[/C][C]0.588018[/C][C]0.705991[/C][/ROW]
[ROW][C]55[/C][C]0.316447[/C][C]0.632895[/C][C]0.683553[/C][/ROW]
[ROW][C]56[/C][C]0.27123[/C][C]0.542461[/C][C]0.72877[/C][/ROW]
[ROW][C]57[/C][C]0.392222[/C][C]0.784443[/C][C]0.607778[/C][/ROW]
[ROW][C]58[/C][C]0.533722[/C][C]0.932557[/C][C]0.466278[/C][/ROW]
[ROW][C]59[/C][C]0.674863[/C][C]0.650274[/C][C]0.325137[/C][/ROW]
[ROW][C]60[/C][C]0.668558[/C][C]0.662883[/C][C]0.331442[/C][/ROW]
[ROW][C]61[/C][C]0.618852[/C][C]0.762296[/C][C]0.381148[/C][/ROW]
[ROW][C]62[/C][C]0.650805[/C][C]0.698389[/C][C]0.349195[/C][/ROW]
[ROW][C]63[/C][C]0.680966[/C][C]0.638067[/C][C]0.319034[/C][/ROW]
[ROW][C]64[/C][C]0.742344[/C][C]0.515312[/C][C]0.257656[/C][/ROW]
[ROW][C]65[/C][C]0.702327[/C][C]0.595347[/C][C]0.297673[/C][/ROW]
[ROW][C]66[/C][C]0.704832[/C][C]0.590335[/C][C]0.295168[/C][/ROW]
[ROW][C]67[/C][C]0.659092[/C][C]0.681816[/C][C]0.340908[/C][/ROW]
[ROW][C]68[/C][C]0.631736[/C][C]0.736528[/C][C]0.368264[/C][/ROW]
[ROW][C]69[/C][C]0.613278[/C][C]0.773444[/C][C]0.386722[/C][/ROW]
[ROW][C]70[/C][C]0.560438[/C][C]0.879124[/C][C]0.439562[/C][/ROW]
[ROW][C]71[/C][C]0.513547[/C][C]0.972906[/C][C]0.486453[/C][/ROW]
[ROW][C]72[/C][C]0.45502[/C][C]0.91004[/C][C]0.54498[/C][/ROW]
[ROW][C]73[/C][C]0.425143[/C][C]0.850286[/C][C]0.574857[/C][/ROW]
[ROW][C]74[/C][C]0.441279[/C][C]0.882558[/C][C]0.558721[/C][/ROW]
[ROW][C]75[/C][C]0.403485[/C][C]0.80697[/C][C]0.596515[/C][/ROW]
[ROW][C]76[/C][C]0.467004[/C][C]0.934008[/C][C]0.532996[/C][/ROW]
[ROW][C]77[/C][C]0.447626[/C][C]0.895252[/C][C]0.552374[/C][/ROW]
[ROW][C]78[/C][C]0.40578[/C][C]0.811559[/C][C]0.59422[/C][/ROW]
[ROW][C]79[/C][C]0.354075[/C][C]0.70815[/C][C]0.645925[/C][/ROW]
[ROW][C]80[/C][C]0.327247[/C][C]0.654493[/C][C]0.672753[/C][/ROW]
[ROW][C]81[/C][C]0.273031[/C][C]0.546062[/C][C]0.726969[/C][/ROW]
[ROW][C]82[/C][C]0.300619[/C][C]0.601239[/C][C]0.699381[/C][/ROW]
[ROW][C]83[/C][C]0.247603[/C][C]0.495207[/C][C]0.752397[/C][/ROW]
[ROW][C]84[/C][C]0.495089[/C][C]0.990178[/C][C]0.504911[/C][/ROW]
[ROW][C]85[/C][C]0.43756[/C][C]0.875119[/C][C]0.56244[/C][/ROW]
[ROW][C]86[/C][C]0.388775[/C][C]0.77755[/C][C]0.611225[/C][/ROW]
[ROW][C]87[/C][C]0.319929[/C][C]0.639859[/C][C]0.680071[/C][/ROW]
[ROW][C]88[/C][C]0.294531[/C][C]0.589062[/C][C]0.705469[/C][/ROW]
[ROW][C]89[/C][C]0.286059[/C][C]0.572118[/C][C]0.713941[/C][/ROW]
[ROW][C]90[/C][C]0.320189[/C][C]0.640377[/C][C]0.679811[/C][/ROW]
[ROW][C]91[/C][C]0.263891[/C][C]0.527782[/C][C]0.736109[/C][/ROW]
[ROW][C]92[/C][C]0.36001[/C][C]0.720021[/C][C]0.63999[/C][/ROW]
[ROW][C]93[/C][C]0.307068[/C][C]0.614136[/C][C]0.692932[/C][/ROW]
[ROW][C]94[/C][C]0.235846[/C][C]0.471693[/C][C]0.764154[/C][/ROW]
[ROW][C]95[/C][C]0.218907[/C][C]0.437814[/C][C]0.781093[/C][/ROW]
[ROW][C]96[/C][C]0.163104[/C][C]0.326208[/C][C]0.836896[/C][/ROW]
[ROW][C]97[/C][C]0.306666[/C][C]0.613332[/C][C]0.693334[/C][/ROW]
[ROW][C]98[/C][C]0.215337[/C][C]0.430673[/C][C]0.784663[/C][/ROW]
[ROW][C]99[/C][C]0.213549[/C][C]0.427098[/C][C]0.786451[/C][/ROW]
[ROW][C]100[/C][C]0.167683[/C][C]0.335366[/C][C]0.832317[/C][/ROW]
[ROW][C]101[/C][C]0.0900835[/C][C]0.180167[/C][C]0.909916[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=268259&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=268259&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	0.282427	0.564853	0.717573
12	0.68288	0.63424	0.31712
13	0.637799	0.724401	0.362201
14	0.516572	0.966856	0.483428
15	0.638812	0.722376	0.361188
16	0.611603	0.776794	0.388397
17	0.535881	0.928238	0.464119
18	0.489195	0.97839	0.510805
19	0.411076	0.822152	0.588924
20	0.324093	0.648186	0.675907
21	0.306113	0.612225	0.693887
22	0.24232	0.48464	0.75768
23	0.18188	0.363759	0.81812
24	0.252568	0.505136	0.747432
25	0.215141	0.430282	0.784859
26	0.166713	0.333427	0.833287
27	0.26133	0.52266	0.73867
28	0.210973	0.421945	0.789027
29	0.169347	0.338694	0.830653
30	0.139882	0.279763	0.860118
31	0.137587	0.275174	0.862413
32	0.102739	0.205479	0.897261
33	0.088047	0.176094	0.911953
34	0.462343	0.924686	0.537657
35	0.41557	0.831141	0.58443
36	0.368816	0.737632	0.631184
37	0.323035	0.646069	0.676965
38	0.285898	0.571796	0.714102
39	0.256373	0.512746	0.743627
40	0.209209	0.418418	0.790791
41	0.395001	0.790002	0.604999
42	0.354299	0.708599	0.645701
43	0.344455	0.68891	0.655545
44	0.34296	0.685921	0.65704
45	0.302984	0.605969	0.697016
46	0.271265	0.542531	0.728735
47	0.252767	0.505534	0.747233
48	0.250491	0.500982	0.749509
49	0.248626	0.497252	0.751374
50	0.235917	0.471833	0.764083
51	0.212212	0.424424	0.787788
52	0.353694	0.707388	0.646306
53	0.304921	0.609843	0.695079
54	0.294009	0.588018	0.705991
55	0.316447	0.632895	0.683553
56	0.27123	0.542461	0.72877
57	0.392222	0.784443	0.607778
58	0.533722	0.932557	0.466278
59	0.674863	0.650274	0.325137
60	0.668558	0.662883	0.331442
61	0.618852	0.762296	0.381148
62	0.650805	0.698389	0.349195
63	0.680966	0.638067	0.319034
64	0.742344	0.515312	0.257656
65	0.702327	0.595347	0.297673
66	0.704832	0.590335	0.295168
67	0.659092	0.681816	0.340908
68	0.631736	0.736528	0.368264
69	0.613278	0.773444	0.386722
70	0.560438	0.879124	0.439562
71	0.513547	0.972906	0.486453
72	0.45502	0.91004	0.54498
73	0.425143	0.850286	0.574857
74	0.441279	0.882558	0.558721
75	0.403485	0.80697	0.596515
76	0.467004	0.934008	0.532996
77	0.447626	0.895252	0.552374
78	0.40578	0.811559	0.59422
79	0.354075	0.70815	0.645925
80	0.327247	0.654493	0.672753
81	0.273031	0.546062	0.726969
82	0.300619	0.601239	0.699381
83	0.247603	0.495207	0.752397
84	0.495089	0.990178	0.504911
85	0.43756	0.875119	0.56244
86	0.388775	0.77755	0.611225
87	0.319929	0.639859	0.680071
88	0.294531	0.589062	0.705469
89	0.286059	0.572118	0.713941
90	0.320189	0.640377	0.679811
91	0.263891	0.527782	0.736109
92	0.36001	0.720021	0.63999
93	0.307068	0.614136	0.692932
94	0.235846	0.471693	0.764154
95	0.218907	0.437814	0.781093
96	0.163104	0.326208	0.836896
97	0.306666	0.613332	0.693334
98	0.215337	0.430673	0.784663
99	0.213549	0.427098	0.786451
100	0.167683	0.335366	0.832317
101	0.0900835	0.180167	0.909916

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=268259&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=268259&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=268259&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