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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Fri, 12 Dec 2014 08:18:07 +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/12/t1418372416lzusak3n9becqk7.htm/, Retrieved Thu, 16 May 2024 11:31:03 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=266438, Retrieved Thu, 16 May 2024 11:31:03 +0000

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Estimated Impact

112

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-       [Multiple Regression] [] [2014-12-12 08:18:07] [d33b7eb92cfcc384850e3711242e8bfe] [Current]

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9	23	4,35
11	22	12,7
12	21	18,1
12	25	17,85
7	30	16,6
12	17	12,6
12	27	17,1
12	23	19,1
10	23	16,1
15	18	13,35
10	18	18,4
15	23	14,7
10	19	10,6
15	15	12,6
9	20	16,2
15	16	13,6
12	24	18,9
13	25	14,1
12	25	14,5
12	19	16,15
8	19	14,75
9	16	14,8
15	19	12,45
12	19	12,65
12	23	17,35
15	21	8,6
11	22	18,4
12	19	16,1
6	20	11,6
14	20	17,75
12	3	15,25
12	23	17,65
12	23	16,35
11	20	17,65
12	15	13,6
12	16	14,35
12	7	14,75
12	24	18,25
8	17	9,9
8	24	16
12	24	18,25
12	19	16,85
11	25	14,6
10	20	13,85
11	28	18,95
12	23	15,6
13	27	14,85
12	18	11,75
12	28	18,45
10	21	15,9
10	19	17,1
11	23	16,1
8	27	19,9
12	22	10,95
9	28	18,45
12	25	15,1
9	21	15
11	22	11,35
15	28	15,95
8	20	18,1
8	29	14,6
11	25	15,4
11	25	15,4
11	20	17,6
13	20	13,35
7	16	19,1
12	20	15,35
8	20	7,6
8	23	13,4
4	18	13,9
11	25	19,1
10	18	15,25
7	19	12,9
12	25	16,1
11	25	17,35
9	25	13,15
10	24	12,15
8	19	12,6
8	26	10,35
11	10	15,4
12	17	9,6
10	13	18,2
10	17	13,6
12	30	14,85
8	25	14,75
11	4	14,1
8	16	14,9
10	21	16,25
14	23	19,25
9	22	13,6
9	17	13,6
10	20	15,65
13	20	12,75
12	22	14,6
13	16	9,85
8	23	12,65
3	0	19,2
8	18	16,6
12	25	11,2
11	23	15,25
9	12	11,9
12	18	13,2
12	24	16,35
12	11	12,4
10	18	15,85
13	23	18,15
9	24	11,15
12	29	15,65
11	18	17,75
14	15	7,65
11	29	12,35
9	16	15,6
12	19	19,3
8	22	15,2
15	16	17,1
12	23	15,6
14	23	18,4
12	19	19,05
9	4	18,55
9	20	19,1
13	24	13,1
13	20	12,85
15	4	9,5
11	24	4,5
7	22	11,85
10	16	13,6
11	3	11,7
14	15	12,4
14	24	13,35
13	17	11,4
12	20	14,9
8	27	19,9
13	26	11,2
9	23	14,6
12	17	17,6
13	20	14,05
11	22	16,1
11	19	13,35
13	24	11,85
12	19	11,95
12	23	14,75
10	15	15,15
9	27	13,2
10	26	16,85
13	22	7,85
13	22	7,7
9	18	12,6
11	15	7,85
12	22	10,95
8	27	12,35
12	10	9,95
12	20	14,9
12	17	16,65
9	23	13,4
12	19	13,95
12	13	15,7
11	27	16,85
12	23	10,95
6	16	15,35
7	25	12,2
10	2	15,1
12	26	17,75
10	20	15,2
12	23	14,6
9	22	16,65

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	7 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 & 7 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=266438&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]7 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=266438&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=266438&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	7 seconds
R Server	'Gwilym Jenkins' @ jenkins.wessa.net

Multiple Linear Regression - Estimated Regression Equation
CONFSOFTTOT[t] = + 11.5058 + 0.0137661NUMERACYTOT[t] -0.0610106TOT[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
CONFSOFTTOT[t] =  +  11.5058 +  0.0137661NUMERACYTOT[t] -0.0610106TOT[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=266438&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]CONFSOFTTOT[t] =  +  11.5058 +  0.0137661NUMERACYTOT[t] -0.0610106TOT[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=266438&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=266438&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
CONFSOFTTOT[t] = + 11.5058 + 0.0137661NUMERACYTOT[t] -0.0610106TOT[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)	11.5058	1.00228	11.48	1.06665e-22	5.33327e-23
NUMERACYTOT	0.0137661	0.0310979	0.4427	0.658595	0.329298
TOT	-0.0610106	0.0573007	-1.065	0.288576	0.144288

\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) & 11.5058 & 1.00228 & 11.48 & 1.06665e-22 & 5.33327e-23 \tabularnewline
NUMERACYTOT & 0.0137661 & 0.0310979 & 0.4427 & 0.658595 & 0.329298 \tabularnewline
TOT & -0.0610106 & 0.0573007 & -1.065 & 0.288576 & 0.144288 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=266438&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]11.5058[/C][C]1.00228[/C][C]11.48[/C][C]1.06665e-22[/C][C]5.33327e-23[/C][/ROW]
[ROW][C]NUMERACYTOT[/C][C]0.0137661[/C][C]0.0310979[/C][C]0.4427[/C][C]0.658595[/C][C]0.329298[/C][/ROW]
[ROW][C]TOT[/C][C]-0.0610106[/C][C]0.0573007[/C][C]-1.065[/C][C]0.288576[/C][C]0.144288[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=266438&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=266438&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)	11.5058	1.00228	11.48	1.06665e-22	5.33327e-23
NUMERACYTOT	0.0137661	0.0310979	0.4427	0.658595	0.329298
TOT	-0.0610106	0.0573007	-1.065	0.288576	0.144288

Multiple Linear Regression - Regression Statistics
Multiple R	0.0871305
R-squared	0.00759173
Adjusted R-squared	-0.00466023
F-TEST (value)	0.619634
F-TEST (DF numerator)	2
F-TEST (DF denominator)	162
p-value	0.539412
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	2.19884
Sum Squared Residuals	783.257

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.0871305 \tabularnewline
R-squared & 0.00759173 \tabularnewline
Adjusted R-squared & -0.00466023 \tabularnewline
F-TEST (value) & 0.619634 \tabularnewline
F-TEST (DF numerator) & 2 \tabularnewline
F-TEST (DF denominator) & 162 \tabularnewline
p-value & 0.539412 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.19884 \tabularnewline
Sum Squared Residuals & 783.257 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=266438&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.0871305[/C][/ROW]
[ROW][C]R-squared[/C][C]0.00759173[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]-0.00466023[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]0.619634[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]2[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]162[/C][/ROW]
[ROW][C]p-value[/C][C]0.539412[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]2.19884[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]783.257[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=266438&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=266438&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	0.0871305
R-squared	0.00759173
Adjusted R-squared	-0.00466023
F-TEST (value)	0.619634
F-TEST (DF numerator)	2
F-TEST (DF denominator)	162
p-value	0.539412
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	2.19884
Sum Squared Residuals	783.257

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	9	11.5571	-2.55706
2	11	11.0339	-0.033857
3	12	10.6906	1.30937
4	12	10.761	1.23905
5	7	10.906	-3.90604
6	12	10.9711	1.02887
7	12	10.8342	1.16576
8	12	10.6572	1.34284
9	10	10.8402	-0.840187
10	15	10.9391	4.06086
11	10	10.631	-0.631032
12	15	10.9256	4.0744
13	10	11.1207	-1.12068
14	15	10.9436	4.0564
15	9	10.7928	-1.79279
16	15	10.8964	4.10365
17	12	10.6831	1.31688
18	13	10.9897	2.01026
19	12	10.9653	1.03466
20	12	10.7821	1.21793
21	8	10.8675	-2.86749
22	9	10.8231	-1.82314
23	15	11.0078	3.99219
24	12	10.9956	1.00439
25	12	10.7639	1.23608
26	15	11.2702	3.72977
27	11	10.6861	0.313904
28	12	10.7851	1.21488
29	6	11.0734	-5.07344
30	14	10.6982	3.30178
31	12	10.6167	1.38328
32	12	10.7456	1.25438
33	12	10.8249	1.17507
34	11	10.7043	0.295678
35	12	10.8826	1.11742
36	12	10.8506	1.14941
37	12	10.7023	1.29771
38	12	10.7228	1.27722
39	8	11.1359	-3.13586
40	8	10.8601	-2.86005
41	12	10.7228	1.27722
42	12	10.7394	1.26064
43	11	10.9592	0.0407649
44	10	10.9362	-0.936163
45	11	10.7351	0.264863
46	12	10.8707	1.12931
47	13	10.9715	2.02849
48	12	11.0368	0.963247
49	12	10.7656	1.23436
50	10	10.8249	-0.824857
51	10	10.7241	-0.724112
52	11	10.8402	0.159813
53	8	10.6634	-2.66341
54	12	11.1406	0.859374
55	9	10.7656	-1.76564
56	12	10.9287	1.07127
57	9	10.8798	-1.87977
58	11	11.1162	-0.116221
59	15	10.9182	4.08183
60	8	10.6769	-2.67687
61	8	11.0143	-3.0143
62	11	10.9104	0.0895734
63	11	10.9104	0.0895734
64	11	10.7074	0.292627
65	13	10.9667	2.03333
66	7	10.5608	-3.56079
67	12	10.8446	1.15535
68	8	11.3175	-3.31748
69	8	11.0049	-3.00492
70	4	10.9056	-6.90558
71	11	10.6847	0.315313
72	10	10.8232	-0.823216
73	7	10.9804	-3.98036
74	12	10.8677	1.13228
75	11	10.7915	0.208544
76	9	11.0477	-2.0477
77	10	11.0949	-1.09495
78	8	10.9987	-2.99866
79	8	11.2323	-3.2323
80	11	10.7039	0.296065
81	12	11.1542	0.84584
82	10	10.5744	-0.574404
83	10	10.9101	-0.910117
84	12	11.0128	0.987187
85	8	10.9501	-2.95008
86	11	10.7007	0.299347
87	8	10.817	-2.81704
88	10	10.8035	-0.803503
89	14	10.648	3.352
90	9	10.9789	-1.97895
91	9	10.9101	-1.91012
92	10	10.8263	-0.826343
93	13	11.0033	1.99673
94	12	10.9179	1.08206
95	13	11.1251	1.87486
96	8	11.0507	-3.05067
97	3	10.3344	-7.33443
98	8	10.7409	-2.74085
99	12	11.1667	0.833329
100	11	10.892	0.107954
101	9	10.945	-1.945
102	12	10.9483	1.05171
103	12	10.8387	1.1613
104	12	10.9007	1.09927
105	10	10.7866	-0.786609
106	13	10.7151	2.28488
107	9	11.156	-2.15596
108	12	10.9502	1.04976
109	11	10.6707	0.329311
110	14	11.2456	2.7544
111	11	11.1516	-0.151573
112	9	10.7743	-1.77433
113	12	10.5899	1.41011
114	8	10.8813	-2.88133
115	15	10.6828	4.31719
116	12	10.8707	1.12931
117	14	10.6999	3.30014
118	12	10.6051	1.39486
119	9	10.4292	-1.42916
120	9	10.6159	-1.61586
121	13	11.037	1.96302
122	13	10.9972	2.00283
123	15	10.9813	4.0187
124	11	11.5617	-0.561676
125	7	11.0857	-4.08572
126	10	10.8964	-0.896351
127	11	10.8333	0.166688
128	14	10.9558	3.0442
129	14	11.0217	2.97827
130	13	11.0443	1.95566
131	12	10.8721	1.1279
132	8	10.6634	-2.66341
133	13	11.1804	1.81956
134	9	10.9317	-1.9317
135	12	10.6661	1.33393
136	13	10.924	2.07604
137	11	10.8264	0.173579
138	11	10.9529	0.0470981
139	13	11.1132	1.88675
140	12	11.0383	0.961683
141	12	10.9226	1.07745
142	10	10.788	-0.788018
143	9	11.0722	-2.07218
144	10	10.8357	-0.835727
145	13	11.3298	1.67024
146	13	11.3389	1.66109
147	9	10.9849	-1.98489
148	11	11.2334	-0.233396
149	12	11.1406	0.859374
150	8	11.124	-3.12404
151	12	11.0364	0.963557
152	12	10.8721	1.1279
153	12	10.724	1.27597
154	9	11.0049	-2.00492
155	12	10.9163	1.0837
156	12	10.7269	1.27307
157	11	10.8495	0.150507
158	12	11.1544	0.845608
159	6	10.7896	-4.78958
160	7	11.1057	-4.10566
161	10	10.6121	-0.61211
162	12	10.7808	1.21918
163	10	10.8538	-0.853798
164	12	10.9317	1.0683
165	9	10.7929	-1.79287

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 9 & 11.5571 & -2.55706 \tabularnewline
2 & 11 & 11.0339 & -0.033857 \tabularnewline
3 & 12 & 10.6906 & 1.30937 \tabularnewline
4 & 12 & 10.761 & 1.23905 \tabularnewline
5 & 7 & 10.906 & -3.90604 \tabularnewline
6 & 12 & 10.9711 & 1.02887 \tabularnewline
7 & 12 & 10.8342 & 1.16576 \tabularnewline
8 & 12 & 10.6572 & 1.34284 \tabularnewline
9 & 10 & 10.8402 & -0.840187 \tabularnewline
10 & 15 & 10.9391 & 4.06086 \tabularnewline
11 & 10 & 10.631 & -0.631032 \tabularnewline
12 & 15 & 10.9256 & 4.0744 \tabularnewline
13 & 10 & 11.1207 & -1.12068 \tabularnewline
14 & 15 & 10.9436 & 4.0564 \tabularnewline
15 & 9 & 10.7928 & -1.79279 \tabularnewline
16 & 15 & 10.8964 & 4.10365 \tabularnewline
17 & 12 & 10.6831 & 1.31688 \tabularnewline
18 & 13 & 10.9897 & 2.01026 \tabularnewline
19 & 12 & 10.9653 & 1.03466 \tabularnewline
20 & 12 & 10.7821 & 1.21793 \tabularnewline
21 & 8 & 10.8675 & -2.86749 \tabularnewline
22 & 9 & 10.8231 & -1.82314 \tabularnewline
23 & 15 & 11.0078 & 3.99219 \tabularnewline
24 & 12 & 10.9956 & 1.00439 \tabularnewline
25 & 12 & 10.7639 & 1.23608 \tabularnewline
26 & 15 & 11.2702 & 3.72977 \tabularnewline
27 & 11 & 10.6861 & 0.313904 \tabularnewline
28 & 12 & 10.7851 & 1.21488 \tabularnewline
29 & 6 & 11.0734 & -5.07344 \tabularnewline
30 & 14 & 10.6982 & 3.30178 \tabularnewline
31 & 12 & 10.6167 & 1.38328 \tabularnewline
32 & 12 & 10.7456 & 1.25438 \tabularnewline
33 & 12 & 10.8249 & 1.17507 \tabularnewline
34 & 11 & 10.7043 & 0.295678 \tabularnewline
35 & 12 & 10.8826 & 1.11742 \tabularnewline
36 & 12 & 10.8506 & 1.14941 \tabularnewline
37 & 12 & 10.7023 & 1.29771 \tabularnewline
38 & 12 & 10.7228 & 1.27722 \tabularnewline
39 & 8 & 11.1359 & -3.13586 \tabularnewline
40 & 8 & 10.8601 & -2.86005 \tabularnewline
41 & 12 & 10.7228 & 1.27722 \tabularnewline
42 & 12 & 10.7394 & 1.26064 \tabularnewline
43 & 11 & 10.9592 & 0.0407649 \tabularnewline
44 & 10 & 10.9362 & -0.936163 \tabularnewline
45 & 11 & 10.7351 & 0.264863 \tabularnewline
46 & 12 & 10.8707 & 1.12931 \tabularnewline
47 & 13 & 10.9715 & 2.02849 \tabularnewline
48 & 12 & 11.0368 & 0.963247 \tabularnewline
49 & 12 & 10.7656 & 1.23436 \tabularnewline
50 & 10 & 10.8249 & -0.824857 \tabularnewline
51 & 10 & 10.7241 & -0.724112 \tabularnewline
52 & 11 & 10.8402 & 0.159813 \tabularnewline
53 & 8 & 10.6634 & -2.66341 \tabularnewline
54 & 12 & 11.1406 & 0.859374 \tabularnewline
55 & 9 & 10.7656 & -1.76564 \tabularnewline
56 & 12 & 10.9287 & 1.07127 \tabularnewline
57 & 9 & 10.8798 & -1.87977 \tabularnewline
58 & 11 & 11.1162 & -0.116221 \tabularnewline
59 & 15 & 10.9182 & 4.08183 \tabularnewline
60 & 8 & 10.6769 & -2.67687 \tabularnewline
61 & 8 & 11.0143 & -3.0143 \tabularnewline
62 & 11 & 10.9104 & 0.0895734 \tabularnewline
63 & 11 & 10.9104 & 0.0895734 \tabularnewline
64 & 11 & 10.7074 & 0.292627 \tabularnewline
65 & 13 & 10.9667 & 2.03333 \tabularnewline
66 & 7 & 10.5608 & -3.56079 \tabularnewline
67 & 12 & 10.8446 & 1.15535 \tabularnewline
68 & 8 & 11.3175 & -3.31748 \tabularnewline
69 & 8 & 11.0049 & -3.00492 \tabularnewline
70 & 4 & 10.9056 & -6.90558 \tabularnewline
71 & 11 & 10.6847 & 0.315313 \tabularnewline
72 & 10 & 10.8232 & -0.823216 \tabularnewline
73 & 7 & 10.9804 & -3.98036 \tabularnewline
74 & 12 & 10.8677 & 1.13228 \tabularnewline
75 & 11 & 10.7915 & 0.208544 \tabularnewline
76 & 9 & 11.0477 & -2.0477 \tabularnewline
77 & 10 & 11.0949 & -1.09495 \tabularnewline
78 & 8 & 10.9987 & -2.99866 \tabularnewline
79 & 8 & 11.2323 & -3.2323 \tabularnewline
80 & 11 & 10.7039 & 0.296065 \tabularnewline
81 & 12 & 11.1542 & 0.84584 \tabularnewline
82 & 10 & 10.5744 & -0.574404 \tabularnewline
83 & 10 & 10.9101 & -0.910117 \tabularnewline
84 & 12 & 11.0128 & 0.987187 \tabularnewline
85 & 8 & 10.9501 & -2.95008 \tabularnewline
86 & 11 & 10.7007 & 0.299347 \tabularnewline
87 & 8 & 10.817 & -2.81704 \tabularnewline
88 & 10 & 10.8035 & -0.803503 \tabularnewline
89 & 14 & 10.648 & 3.352 \tabularnewline
90 & 9 & 10.9789 & -1.97895 \tabularnewline
91 & 9 & 10.9101 & -1.91012 \tabularnewline
92 & 10 & 10.8263 & -0.826343 \tabularnewline
93 & 13 & 11.0033 & 1.99673 \tabularnewline
94 & 12 & 10.9179 & 1.08206 \tabularnewline
95 & 13 & 11.1251 & 1.87486 \tabularnewline
96 & 8 & 11.0507 & -3.05067 \tabularnewline
97 & 3 & 10.3344 & -7.33443 \tabularnewline
98 & 8 & 10.7409 & -2.74085 \tabularnewline
99 & 12 & 11.1667 & 0.833329 \tabularnewline
100 & 11 & 10.892 & 0.107954 \tabularnewline
101 & 9 & 10.945 & -1.945 \tabularnewline
102 & 12 & 10.9483 & 1.05171 \tabularnewline
103 & 12 & 10.8387 & 1.1613 \tabularnewline
104 & 12 & 10.9007 & 1.09927 \tabularnewline
105 & 10 & 10.7866 & -0.786609 \tabularnewline
106 & 13 & 10.7151 & 2.28488 \tabularnewline
107 & 9 & 11.156 & -2.15596 \tabularnewline
108 & 12 & 10.9502 & 1.04976 \tabularnewline
109 & 11 & 10.6707 & 0.329311 \tabularnewline
110 & 14 & 11.2456 & 2.7544 \tabularnewline
111 & 11 & 11.1516 & -0.151573 \tabularnewline
112 & 9 & 10.7743 & -1.77433 \tabularnewline
113 & 12 & 10.5899 & 1.41011 \tabularnewline
114 & 8 & 10.8813 & -2.88133 \tabularnewline
115 & 15 & 10.6828 & 4.31719 \tabularnewline
116 & 12 & 10.8707 & 1.12931 \tabularnewline
117 & 14 & 10.6999 & 3.30014 \tabularnewline
118 & 12 & 10.6051 & 1.39486 \tabularnewline
119 & 9 & 10.4292 & -1.42916 \tabularnewline
120 & 9 & 10.6159 & -1.61586 \tabularnewline
121 & 13 & 11.037 & 1.96302 \tabularnewline
122 & 13 & 10.9972 & 2.00283 \tabularnewline
123 & 15 & 10.9813 & 4.0187 \tabularnewline
124 & 11 & 11.5617 & -0.561676 \tabularnewline
125 & 7 & 11.0857 & -4.08572 \tabularnewline
126 & 10 & 10.8964 & -0.896351 \tabularnewline
127 & 11 & 10.8333 & 0.166688 \tabularnewline
128 & 14 & 10.9558 & 3.0442 \tabularnewline
129 & 14 & 11.0217 & 2.97827 \tabularnewline
130 & 13 & 11.0443 & 1.95566 \tabularnewline
131 & 12 & 10.8721 & 1.1279 \tabularnewline
132 & 8 & 10.6634 & -2.66341 \tabularnewline
133 & 13 & 11.1804 & 1.81956 \tabularnewline
134 & 9 & 10.9317 & -1.9317 \tabularnewline
135 & 12 & 10.6661 & 1.33393 \tabularnewline
136 & 13 & 10.924 & 2.07604 \tabularnewline
137 & 11 & 10.8264 & 0.173579 \tabularnewline
138 & 11 & 10.9529 & 0.0470981 \tabularnewline
139 & 13 & 11.1132 & 1.88675 \tabularnewline
140 & 12 & 11.0383 & 0.961683 \tabularnewline
141 & 12 & 10.9226 & 1.07745 \tabularnewline
142 & 10 & 10.788 & -0.788018 \tabularnewline
143 & 9 & 11.0722 & -2.07218 \tabularnewline
144 & 10 & 10.8357 & -0.835727 \tabularnewline
145 & 13 & 11.3298 & 1.67024 \tabularnewline
146 & 13 & 11.3389 & 1.66109 \tabularnewline
147 & 9 & 10.9849 & -1.98489 \tabularnewline
148 & 11 & 11.2334 & -0.233396 \tabularnewline
149 & 12 & 11.1406 & 0.859374 \tabularnewline
150 & 8 & 11.124 & -3.12404 \tabularnewline
151 & 12 & 11.0364 & 0.963557 \tabularnewline
152 & 12 & 10.8721 & 1.1279 \tabularnewline
153 & 12 & 10.724 & 1.27597 \tabularnewline
154 & 9 & 11.0049 & -2.00492 \tabularnewline
155 & 12 & 10.9163 & 1.0837 \tabularnewline
156 & 12 & 10.7269 & 1.27307 \tabularnewline
157 & 11 & 10.8495 & 0.150507 \tabularnewline
158 & 12 & 11.1544 & 0.845608 \tabularnewline
159 & 6 & 10.7896 & -4.78958 \tabularnewline
160 & 7 & 11.1057 & -4.10566 \tabularnewline
161 & 10 & 10.6121 & -0.61211 \tabularnewline
162 & 12 & 10.7808 & 1.21918 \tabularnewline
163 & 10 & 10.8538 & -0.853798 \tabularnewline
164 & 12 & 10.9317 & 1.0683 \tabularnewline
165 & 9 & 10.7929 & -1.79287 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=266438&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]9[/C][C]11.5571[/C][C]-2.55706[/C][/ROW]
[ROW][C]2[/C][C]11[/C][C]11.0339[/C][C]-0.033857[/C][/ROW]
[ROW][C]3[/C][C]12[/C][C]10.6906[/C][C]1.30937[/C][/ROW]
[ROW][C]4[/C][C]12[/C][C]10.761[/C][C]1.23905[/C][/ROW]
[ROW][C]5[/C][C]7[/C][C]10.906[/C][C]-3.90604[/C][/ROW]
[ROW][C]6[/C][C]12[/C][C]10.9711[/C][C]1.02887[/C][/ROW]
[ROW][C]7[/C][C]12[/C][C]10.8342[/C][C]1.16576[/C][/ROW]
[ROW][C]8[/C][C]12[/C][C]10.6572[/C][C]1.34284[/C][/ROW]
[ROW][C]9[/C][C]10[/C][C]10.8402[/C][C]-0.840187[/C][/ROW]
[ROW][C]10[/C][C]15[/C][C]10.9391[/C][C]4.06086[/C][/ROW]
[ROW][C]11[/C][C]10[/C][C]10.631[/C][C]-0.631032[/C][/ROW]
[ROW][C]12[/C][C]15[/C][C]10.9256[/C][C]4.0744[/C][/ROW]
[ROW][C]13[/C][C]10[/C][C]11.1207[/C][C]-1.12068[/C][/ROW]
[ROW][C]14[/C][C]15[/C][C]10.9436[/C][C]4.0564[/C][/ROW]
[ROW][C]15[/C][C]9[/C][C]10.7928[/C][C]-1.79279[/C][/ROW]
[ROW][C]16[/C][C]15[/C][C]10.8964[/C][C]4.10365[/C][/ROW]
[ROW][C]17[/C][C]12[/C][C]10.6831[/C][C]1.31688[/C][/ROW]
[ROW][C]18[/C][C]13[/C][C]10.9897[/C][C]2.01026[/C][/ROW]
[ROW][C]19[/C][C]12[/C][C]10.9653[/C][C]1.03466[/C][/ROW]
[ROW][C]20[/C][C]12[/C][C]10.7821[/C][C]1.21793[/C][/ROW]
[ROW][C]21[/C][C]8[/C][C]10.8675[/C][C]-2.86749[/C][/ROW]
[ROW][C]22[/C][C]9[/C][C]10.8231[/C][C]-1.82314[/C][/ROW]
[ROW][C]23[/C][C]15[/C][C]11.0078[/C][C]3.99219[/C][/ROW]
[ROW][C]24[/C][C]12[/C][C]10.9956[/C][C]1.00439[/C][/ROW]
[ROW][C]25[/C][C]12[/C][C]10.7639[/C][C]1.23608[/C][/ROW]
[ROW][C]26[/C][C]15[/C][C]11.2702[/C][C]3.72977[/C][/ROW]
[ROW][C]27[/C][C]11[/C][C]10.6861[/C][C]0.313904[/C][/ROW]
[ROW][C]28[/C][C]12[/C][C]10.7851[/C][C]1.21488[/C][/ROW]
[ROW][C]29[/C][C]6[/C][C]11.0734[/C][C]-5.07344[/C][/ROW]
[ROW][C]30[/C][C]14[/C][C]10.6982[/C][C]3.30178[/C][/ROW]
[ROW][C]31[/C][C]12[/C][C]10.6167[/C][C]1.38328[/C][/ROW]
[ROW][C]32[/C][C]12[/C][C]10.7456[/C][C]1.25438[/C][/ROW]
[ROW][C]33[/C][C]12[/C][C]10.8249[/C][C]1.17507[/C][/ROW]
[ROW][C]34[/C][C]11[/C][C]10.7043[/C][C]0.295678[/C][/ROW]
[ROW][C]35[/C][C]12[/C][C]10.8826[/C][C]1.11742[/C][/ROW]
[ROW][C]36[/C][C]12[/C][C]10.8506[/C][C]1.14941[/C][/ROW]
[ROW][C]37[/C][C]12[/C][C]10.7023[/C][C]1.29771[/C][/ROW]
[ROW][C]38[/C][C]12[/C][C]10.7228[/C][C]1.27722[/C][/ROW]
[ROW][C]39[/C][C]8[/C][C]11.1359[/C][C]-3.13586[/C][/ROW]
[ROW][C]40[/C][C]8[/C][C]10.8601[/C][C]-2.86005[/C][/ROW]
[ROW][C]41[/C][C]12[/C][C]10.7228[/C][C]1.27722[/C][/ROW]
[ROW][C]42[/C][C]12[/C][C]10.7394[/C][C]1.26064[/C][/ROW]
[ROW][C]43[/C][C]11[/C][C]10.9592[/C][C]0.0407649[/C][/ROW]
[ROW][C]44[/C][C]10[/C][C]10.9362[/C][C]-0.936163[/C][/ROW]
[ROW][C]45[/C][C]11[/C][C]10.7351[/C][C]0.264863[/C][/ROW]
[ROW][C]46[/C][C]12[/C][C]10.8707[/C][C]1.12931[/C][/ROW]
[ROW][C]47[/C][C]13[/C][C]10.9715[/C][C]2.02849[/C][/ROW]
[ROW][C]48[/C][C]12[/C][C]11.0368[/C][C]0.963247[/C][/ROW]
[ROW][C]49[/C][C]12[/C][C]10.7656[/C][C]1.23436[/C][/ROW]
[ROW][C]50[/C][C]10[/C][C]10.8249[/C][C]-0.824857[/C][/ROW]
[ROW][C]51[/C][C]10[/C][C]10.7241[/C][C]-0.724112[/C][/ROW]
[ROW][C]52[/C][C]11[/C][C]10.8402[/C][C]0.159813[/C][/ROW]
[ROW][C]53[/C][C]8[/C][C]10.6634[/C][C]-2.66341[/C][/ROW]
[ROW][C]54[/C][C]12[/C][C]11.1406[/C][C]0.859374[/C][/ROW]
[ROW][C]55[/C][C]9[/C][C]10.7656[/C][C]-1.76564[/C][/ROW]
[ROW][C]56[/C][C]12[/C][C]10.9287[/C][C]1.07127[/C][/ROW]
[ROW][C]57[/C][C]9[/C][C]10.8798[/C][C]-1.87977[/C][/ROW]
[ROW][C]58[/C][C]11[/C][C]11.1162[/C][C]-0.116221[/C][/ROW]
[ROW][C]59[/C][C]15[/C][C]10.9182[/C][C]4.08183[/C][/ROW]
[ROW][C]60[/C][C]8[/C][C]10.6769[/C][C]-2.67687[/C][/ROW]
[ROW][C]61[/C][C]8[/C][C]11.0143[/C][C]-3.0143[/C][/ROW]
[ROW][C]62[/C][C]11[/C][C]10.9104[/C][C]0.0895734[/C][/ROW]
[ROW][C]63[/C][C]11[/C][C]10.9104[/C][C]0.0895734[/C][/ROW]
[ROW][C]64[/C][C]11[/C][C]10.7074[/C][C]0.292627[/C][/ROW]
[ROW][C]65[/C][C]13[/C][C]10.9667[/C][C]2.03333[/C][/ROW]
[ROW][C]66[/C][C]7[/C][C]10.5608[/C][C]-3.56079[/C][/ROW]
[ROW][C]67[/C][C]12[/C][C]10.8446[/C][C]1.15535[/C][/ROW]
[ROW][C]68[/C][C]8[/C][C]11.3175[/C][C]-3.31748[/C][/ROW]
[ROW][C]69[/C][C]8[/C][C]11.0049[/C][C]-3.00492[/C][/ROW]
[ROW][C]70[/C][C]4[/C][C]10.9056[/C][C]-6.90558[/C][/ROW]
[ROW][C]71[/C][C]11[/C][C]10.6847[/C][C]0.315313[/C][/ROW]
[ROW][C]72[/C][C]10[/C][C]10.8232[/C][C]-0.823216[/C][/ROW]
[ROW][C]73[/C][C]7[/C][C]10.9804[/C][C]-3.98036[/C][/ROW]
[ROW][C]74[/C][C]12[/C][C]10.8677[/C][C]1.13228[/C][/ROW]
[ROW][C]75[/C][C]11[/C][C]10.7915[/C][C]0.208544[/C][/ROW]
[ROW][C]76[/C][C]9[/C][C]11.0477[/C][C]-2.0477[/C][/ROW]
[ROW][C]77[/C][C]10[/C][C]11.0949[/C][C]-1.09495[/C][/ROW]
[ROW][C]78[/C][C]8[/C][C]10.9987[/C][C]-2.99866[/C][/ROW]
[ROW][C]79[/C][C]8[/C][C]11.2323[/C][C]-3.2323[/C][/ROW]
[ROW][C]80[/C][C]11[/C][C]10.7039[/C][C]0.296065[/C][/ROW]
[ROW][C]81[/C][C]12[/C][C]11.1542[/C][C]0.84584[/C][/ROW]
[ROW][C]82[/C][C]10[/C][C]10.5744[/C][C]-0.574404[/C][/ROW]
[ROW][C]83[/C][C]10[/C][C]10.9101[/C][C]-0.910117[/C][/ROW]
[ROW][C]84[/C][C]12[/C][C]11.0128[/C][C]0.987187[/C][/ROW]
[ROW][C]85[/C][C]8[/C][C]10.9501[/C][C]-2.95008[/C][/ROW]
[ROW][C]86[/C][C]11[/C][C]10.7007[/C][C]0.299347[/C][/ROW]
[ROW][C]87[/C][C]8[/C][C]10.817[/C][C]-2.81704[/C][/ROW]
[ROW][C]88[/C][C]10[/C][C]10.8035[/C][C]-0.803503[/C][/ROW]
[ROW][C]89[/C][C]14[/C][C]10.648[/C][C]3.352[/C][/ROW]
[ROW][C]90[/C][C]9[/C][C]10.9789[/C][C]-1.97895[/C][/ROW]
[ROW][C]91[/C][C]9[/C][C]10.9101[/C][C]-1.91012[/C][/ROW]
[ROW][C]92[/C][C]10[/C][C]10.8263[/C][C]-0.826343[/C][/ROW]
[ROW][C]93[/C][C]13[/C][C]11.0033[/C][C]1.99673[/C][/ROW]
[ROW][C]94[/C][C]12[/C][C]10.9179[/C][C]1.08206[/C][/ROW]
[ROW][C]95[/C][C]13[/C][C]11.1251[/C][C]1.87486[/C][/ROW]
[ROW][C]96[/C][C]8[/C][C]11.0507[/C][C]-3.05067[/C][/ROW]
[ROW][C]97[/C][C]3[/C][C]10.3344[/C][C]-7.33443[/C][/ROW]
[ROW][C]98[/C][C]8[/C][C]10.7409[/C][C]-2.74085[/C][/ROW]
[ROW][C]99[/C][C]12[/C][C]11.1667[/C][C]0.833329[/C][/ROW]
[ROW][C]100[/C][C]11[/C][C]10.892[/C][C]0.107954[/C][/ROW]
[ROW][C]101[/C][C]9[/C][C]10.945[/C][C]-1.945[/C][/ROW]
[ROW][C]102[/C][C]12[/C][C]10.9483[/C][C]1.05171[/C][/ROW]
[ROW][C]103[/C][C]12[/C][C]10.8387[/C][C]1.1613[/C][/ROW]
[ROW][C]104[/C][C]12[/C][C]10.9007[/C][C]1.09927[/C][/ROW]
[ROW][C]105[/C][C]10[/C][C]10.7866[/C][C]-0.786609[/C][/ROW]
[ROW][C]106[/C][C]13[/C][C]10.7151[/C][C]2.28488[/C][/ROW]
[ROW][C]107[/C][C]9[/C][C]11.156[/C][C]-2.15596[/C][/ROW]
[ROW][C]108[/C][C]12[/C][C]10.9502[/C][C]1.04976[/C][/ROW]
[ROW][C]109[/C][C]11[/C][C]10.6707[/C][C]0.329311[/C][/ROW]
[ROW][C]110[/C][C]14[/C][C]11.2456[/C][C]2.7544[/C][/ROW]
[ROW][C]111[/C][C]11[/C][C]11.1516[/C][C]-0.151573[/C][/ROW]
[ROW][C]112[/C][C]9[/C][C]10.7743[/C][C]-1.77433[/C][/ROW]
[ROW][C]113[/C][C]12[/C][C]10.5899[/C][C]1.41011[/C][/ROW]
[ROW][C]114[/C][C]8[/C][C]10.8813[/C][C]-2.88133[/C][/ROW]
[ROW][C]115[/C][C]15[/C][C]10.6828[/C][C]4.31719[/C][/ROW]
[ROW][C]116[/C][C]12[/C][C]10.8707[/C][C]1.12931[/C][/ROW]
[ROW][C]117[/C][C]14[/C][C]10.6999[/C][C]3.30014[/C][/ROW]
[ROW][C]118[/C][C]12[/C][C]10.6051[/C][C]1.39486[/C][/ROW]
[ROW][C]119[/C][C]9[/C][C]10.4292[/C][C]-1.42916[/C][/ROW]
[ROW][C]120[/C][C]9[/C][C]10.6159[/C][C]-1.61586[/C][/ROW]
[ROW][C]121[/C][C]13[/C][C]11.037[/C][C]1.96302[/C][/ROW]
[ROW][C]122[/C][C]13[/C][C]10.9972[/C][C]2.00283[/C][/ROW]
[ROW][C]123[/C][C]15[/C][C]10.9813[/C][C]4.0187[/C][/ROW]
[ROW][C]124[/C][C]11[/C][C]11.5617[/C][C]-0.561676[/C][/ROW]
[ROW][C]125[/C][C]7[/C][C]11.0857[/C][C]-4.08572[/C][/ROW]
[ROW][C]126[/C][C]10[/C][C]10.8964[/C][C]-0.896351[/C][/ROW]
[ROW][C]127[/C][C]11[/C][C]10.8333[/C][C]0.166688[/C][/ROW]
[ROW][C]128[/C][C]14[/C][C]10.9558[/C][C]3.0442[/C][/ROW]
[ROW][C]129[/C][C]14[/C][C]11.0217[/C][C]2.97827[/C][/ROW]
[ROW][C]130[/C][C]13[/C][C]11.0443[/C][C]1.95566[/C][/ROW]
[ROW][C]131[/C][C]12[/C][C]10.8721[/C][C]1.1279[/C][/ROW]
[ROW][C]132[/C][C]8[/C][C]10.6634[/C][C]-2.66341[/C][/ROW]
[ROW][C]133[/C][C]13[/C][C]11.1804[/C][C]1.81956[/C][/ROW]
[ROW][C]134[/C][C]9[/C][C]10.9317[/C][C]-1.9317[/C][/ROW]
[ROW][C]135[/C][C]12[/C][C]10.6661[/C][C]1.33393[/C][/ROW]
[ROW][C]136[/C][C]13[/C][C]10.924[/C][C]2.07604[/C][/ROW]
[ROW][C]137[/C][C]11[/C][C]10.8264[/C][C]0.173579[/C][/ROW]
[ROW][C]138[/C][C]11[/C][C]10.9529[/C][C]0.0470981[/C][/ROW]
[ROW][C]139[/C][C]13[/C][C]11.1132[/C][C]1.88675[/C][/ROW]
[ROW][C]140[/C][C]12[/C][C]11.0383[/C][C]0.961683[/C][/ROW]
[ROW][C]141[/C][C]12[/C][C]10.9226[/C][C]1.07745[/C][/ROW]
[ROW][C]142[/C][C]10[/C][C]10.788[/C][C]-0.788018[/C][/ROW]
[ROW][C]143[/C][C]9[/C][C]11.0722[/C][C]-2.07218[/C][/ROW]
[ROW][C]144[/C][C]10[/C][C]10.8357[/C][C]-0.835727[/C][/ROW]
[ROW][C]145[/C][C]13[/C][C]11.3298[/C][C]1.67024[/C][/ROW]
[ROW][C]146[/C][C]13[/C][C]11.3389[/C][C]1.66109[/C][/ROW]
[ROW][C]147[/C][C]9[/C][C]10.9849[/C][C]-1.98489[/C][/ROW]
[ROW][C]148[/C][C]11[/C][C]11.2334[/C][C]-0.233396[/C][/ROW]
[ROW][C]149[/C][C]12[/C][C]11.1406[/C][C]0.859374[/C][/ROW]
[ROW][C]150[/C][C]8[/C][C]11.124[/C][C]-3.12404[/C][/ROW]
[ROW][C]151[/C][C]12[/C][C]11.0364[/C][C]0.963557[/C][/ROW]
[ROW][C]152[/C][C]12[/C][C]10.8721[/C][C]1.1279[/C][/ROW]
[ROW][C]153[/C][C]12[/C][C]10.724[/C][C]1.27597[/C][/ROW]
[ROW][C]154[/C][C]9[/C][C]11.0049[/C][C]-2.00492[/C][/ROW]
[ROW][C]155[/C][C]12[/C][C]10.9163[/C][C]1.0837[/C][/ROW]
[ROW][C]156[/C][C]12[/C][C]10.7269[/C][C]1.27307[/C][/ROW]
[ROW][C]157[/C][C]11[/C][C]10.8495[/C][C]0.150507[/C][/ROW]
[ROW][C]158[/C][C]12[/C][C]11.1544[/C][C]0.845608[/C][/ROW]
[ROW][C]159[/C][C]6[/C][C]10.7896[/C][C]-4.78958[/C][/ROW]
[ROW][C]160[/C][C]7[/C][C]11.1057[/C][C]-4.10566[/C][/ROW]
[ROW][C]161[/C][C]10[/C][C]10.6121[/C][C]-0.61211[/C][/ROW]
[ROW][C]162[/C][C]12[/C][C]10.7808[/C][C]1.21918[/C][/ROW]
[ROW][C]163[/C][C]10[/C][C]10.8538[/C][C]-0.853798[/C][/ROW]
[ROW][C]164[/C][C]12[/C][C]10.9317[/C][C]1.0683[/C][/ROW]
[ROW][C]165[/C][C]9[/C][C]10.7929[/C][C]-1.79287[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=266438&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=266438&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	9	11.5571	-2.55706
2	11	11.0339	-0.033857
3	12	10.6906	1.30937
4	12	10.761	1.23905
5	7	10.906	-3.90604
6	12	10.9711	1.02887
7	12	10.8342	1.16576
8	12	10.6572	1.34284
9	10	10.8402	-0.840187
10	15	10.9391	4.06086
11	10	10.631	-0.631032
12	15	10.9256	4.0744
13	10	11.1207	-1.12068
14	15	10.9436	4.0564
15	9	10.7928	-1.79279
16	15	10.8964	4.10365
17	12	10.6831	1.31688
18	13	10.9897	2.01026
19	12	10.9653	1.03466
20	12	10.7821	1.21793
21	8	10.8675	-2.86749
22	9	10.8231	-1.82314
23	15	11.0078	3.99219
24	12	10.9956	1.00439
25	12	10.7639	1.23608
26	15	11.2702	3.72977
27	11	10.6861	0.313904
28	12	10.7851	1.21488
29	6	11.0734	-5.07344
30	14	10.6982	3.30178
31	12	10.6167	1.38328
32	12	10.7456	1.25438
33	12	10.8249	1.17507
34	11	10.7043	0.295678
35	12	10.8826	1.11742
36	12	10.8506	1.14941
37	12	10.7023	1.29771
38	12	10.7228	1.27722
39	8	11.1359	-3.13586
40	8	10.8601	-2.86005
41	12	10.7228	1.27722
42	12	10.7394	1.26064
43	11	10.9592	0.0407649
44	10	10.9362	-0.936163
45	11	10.7351	0.264863
46	12	10.8707	1.12931
47	13	10.9715	2.02849
48	12	11.0368	0.963247
49	12	10.7656	1.23436
50	10	10.8249	-0.824857
51	10	10.7241	-0.724112
52	11	10.8402	0.159813
53	8	10.6634	-2.66341
54	12	11.1406	0.859374
55	9	10.7656	-1.76564
56	12	10.9287	1.07127
57	9	10.8798	-1.87977
58	11	11.1162	-0.116221
59	15	10.9182	4.08183
60	8	10.6769	-2.67687
61	8	11.0143	-3.0143
62	11	10.9104	0.0895734
63	11	10.9104	0.0895734
64	11	10.7074	0.292627
65	13	10.9667	2.03333
66	7	10.5608	-3.56079
67	12	10.8446	1.15535
68	8	11.3175	-3.31748
69	8	11.0049	-3.00492
70	4	10.9056	-6.90558
71	11	10.6847	0.315313
72	10	10.8232	-0.823216
73	7	10.9804	-3.98036
74	12	10.8677	1.13228
75	11	10.7915	0.208544
76	9	11.0477	-2.0477
77	10	11.0949	-1.09495
78	8	10.9987	-2.99866
79	8	11.2323	-3.2323
80	11	10.7039	0.296065
81	12	11.1542	0.84584
82	10	10.5744	-0.574404
83	10	10.9101	-0.910117
84	12	11.0128	0.987187
85	8	10.9501	-2.95008
86	11	10.7007	0.299347
87	8	10.817	-2.81704
88	10	10.8035	-0.803503
89	14	10.648	3.352
90	9	10.9789	-1.97895
91	9	10.9101	-1.91012
92	10	10.8263	-0.826343
93	13	11.0033	1.99673
94	12	10.9179	1.08206
95	13	11.1251	1.87486
96	8	11.0507	-3.05067
97	3	10.3344	-7.33443
98	8	10.7409	-2.74085
99	12	11.1667	0.833329
100	11	10.892	0.107954
101	9	10.945	-1.945
102	12	10.9483	1.05171
103	12	10.8387	1.1613
104	12	10.9007	1.09927
105	10	10.7866	-0.786609
106	13	10.7151	2.28488
107	9	11.156	-2.15596
108	12	10.9502	1.04976
109	11	10.6707	0.329311
110	14	11.2456	2.7544
111	11	11.1516	-0.151573
112	9	10.7743	-1.77433
113	12	10.5899	1.41011
114	8	10.8813	-2.88133
115	15	10.6828	4.31719
116	12	10.8707	1.12931
117	14	10.6999	3.30014
118	12	10.6051	1.39486
119	9	10.4292	-1.42916
120	9	10.6159	-1.61586
121	13	11.037	1.96302
122	13	10.9972	2.00283
123	15	10.9813	4.0187
124	11	11.5617	-0.561676
125	7	11.0857	-4.08572
126	10	10.8964	-0.896351
127	11	10.8333	0.166688
128	14	10.9558	3.0442
129	14	11.0217	2.97827
130	13	11.0443	1.95566
131	12	10.8721	1.1279
132	8	10.6634	-2.66341
133	13	11.1804	1.81956
134	9	10.9317	-1.9317
135	12	10.6661	1.33393
136	13	10.924	2.07604
137	11	10.8264	0.173579
138	11	10.9529	0.0470981
139	13	11.1132	1.88675
140	12	11.0383	0.961683
141	12	10.9226	1.07745
142	10	10.788	-0.788018
143	9	11.0722	-2.07218
144	10	10.8357	-0.835727
145	13	11.3298	1.67024
146	13	11.3389	1.66109
147	9	10.9849	-1.98489
148	11	11.2334	-0.233396
149	12	11.1406	0.859374
150	8	11.124	-3.12404
151	12	11.0364	0.963557
152	12	10.8721	1.1279
153	12	10.724	1.27597
154	9	11.0049	-2.00492
155	12	10.9163	1.0837
156	12	10.7269	1.27307
157	11	10.8495	0.150507
158	12	11.1544	0.845608
159	6	10.7896	-4.78958
160	7	11.1057	-4.10566
161	10	10.6121	-0.61211
162	12	10.7808	1.21918
163	10	10.8538	-0.853798
164	12	10.9317	1.0683
165	9	10.7929	-1.79287

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
6	0.180349	0.360699	0.819651
7	0.239258	0.478516	0.760742
8	0.132258	0.264515	0.867742
9	0.0975393	0.195079	0.902461
10	0.145597	0.291194	0.854403
11	0.319026	0.638051	0.680974
12	0.560229	0.879543	0.439771
13	0.520604	0.958793	0.479396
14	0.50603	0.987941	0.49397
15	0.591552	0.816897	0.408448
16	0.587657	0.824687	0.412343
17	0.51867	0.96266	0.48133
18	0.539674	0.920652	0.460326
19	0.487325	0.974649	0.512675
20	0.420904	0.841809	0.579096
21	0.609828	0.780344	0.390172
22	0.691631	0.616739	0.308369
23	0.756862	0.486277	0.243138
24	0.704137	0.591726	0.295863
25	0.652396	0.695208	0.347604
26	0.723218	0.553563	0.276782
27	0.670642	0.658716	0.329358
28	0.616495	0.767011	0.383505
29	0.866094	0.267812	0.133906
30	0.872008	0.255985	0.127992
31	0.861203	0.277595	0.138797
32	0.831882	0.336235	0.168118
33	0.798803	0.402393	0.201197
34	0.761568	0.476865	0.238432
35	0.720348	0.559303	0.279652
36	0.676651	0.646697	0.323349
37	0.63824	0.72352	0.36176
38	0.59494	0.810119	0.40506
39	0.672604	0.654792	0.327396
40	0.721081	0.557838	0.278919
41	0.684	0.632001	0.316
42	0.643645	0.712709	0.356355
43	0.594295	0.811409	0.405705
44	0.561288	0.877424	0.438712
45	0.511369	0.977263	0.488631
46	0.469716	0.939431	0.530284
47	0.464534	0.929067	0.535466
48	0.420397	0.840793	0.579603
49	0.383658	0.767315	0.616342
50	0.354982	0.709964	0.645018
51	0.330391	0.660782	0.669609
52	0.288495	0.576991	0.711505
53	0.326665	0.653329	0.673335
54	0.289874	0.579748	0.710126
55	0.277013	0.554025	0.722987
56	0.246982	0.493964	0.753018
57	0.247466	0.494932	0.752534
58	0.211385	0.42277	0.788615
59	0.31697	0.633941	0.68303
60	0.360076	0.720151	0.639924
61	0.392993	0.785985	0.607007
62	0.348954	0.697907	0.651046
63	0.306974	0.613948	0.693026
64	0.269119	0.538238	0.730881
65	0.259225	0.518449	0.740775
66	0.354517	0.709035	0.645483
67	0.321867	0.643733	0.678133
68	0.384094	0.768188	0.615906
69	0.422722	0.845444	0.577278
70	0.799555	0.40089	0.200445
71	0.767621	0.464757	0.232379
72	0.738251	0.523497	0.261749
73	0.813348	0.373304	0.186652
74	0.791177	0.417646	0.208823
75	0.758066	0.483868	0.241934
76	0.749613	0.500775	0.250387
77	0.720253	0.559494	0.279747
78	0.750407	0.499187	0.249593
79	0.784624	0.430751	0.215376
80	0.752026	0.495948	0.247974
81	0.72224	0.555521	0.27776
82	0.690733	0.618533	0.309267
83	0.657328	0.685345	0.342672
84	0.62656	0.746879	0.37344
85	0.65725	0.685499	0.34275
86	0.617157	0.765686	0.382843
87	0.644986	0.710028	0.355014
88	0.607791	0.784419	0.392209
89	0.668057	0.663886	0.331943
90	0.659762	0.680476	0.340238
91	0.649793	0.700414	0.350207
92	0.612662	0.774676	0.387338
93	0.606053	0.787893	0.393947
94	0.573177	0.853646	0.426823
95	0.560844	0.878313	0.439156
96	0.604173	0.791654	0.395827
97	0.927169	0.145662	0.0728311
98	0.937752	0.124496	0.0622482
99	0.924488	0.151023	0.0755116
100	0.906441	0.187119	0.0935595
101	0.908711	0.182578	0.0912892
102	0.892305	0.21539	0.107695
103	0.876336	0.247328	0.123664
104	0.856404	0.287192	0.143596
105	0.833157	0.333685	0.166843
106	0.83885	0.3223	0.16115
107	0.84138	0.31724	0.15862
108	0.821352	0.357297	0.178648
109	0.788839	0.422322	0.211161
110	0.798199	0.403603	0.201801
111	0.762251	0.475497	0.237749
112	0.754657	0.490686	0.245343
113	0.732649	0.534702	0.267351
114	0.763702	0.472595	0.236298
115	0.860114	0.279772	0.139886
116	0.840187	0.319626	0.159813
117	0.893818	0.212363	0.106182
118	0.888836	0.222327	0.111164
119	0.877501	0.244999	0.122499
120	0.859345	0.28131	0.140655
121	0.858443	0.283114	0.141557
122	0.854093	0.291814	0.145907
123	0.890465	0.219071	0.109535
124	0.871712	0.256576	0.128288
125	0.94108	0.11784	0.05892
126	0.927952	0.144095	0.0720475
127	0.909986	0.180027	0.0900136
128	0.924993	0.150014	0.0750068
129	0.946803	0.106394	0.0531971
130	0.941371	0.117258	0.0586289
131	0.93078	0.138439	0.0692196
132	0.92753	0.144941	0.0724704
133	0.924205	0.151591	0.0757953
134	0.917246	0.165509	0.0827544
135	0.907819	0.184362	0.0921809
136	0.91342	0.173159	0.0865796
137	0.888015	0.22397	0.111985
138	0.853908	0.292183	0.146092
139	0.85406	0.291879	0.14594
140	0.824369	0.351261	0.175631
141	0.805887	0.388226	0.194113
142	0.756762	0.486477	0.243238
143	0.730943	0.538114	0.269057
144	0.668659	0.662683	0.331341
145	0.644578	0.710844	0.355422
146	0.650262	0.699476	0.349738
147	0.614928	0.770144	0.385072
148	0.53814	0.923719	0.46186
149	0.509141	0.981719	0.490859
150	0.509187	0.981627	0.490813
151	0.484839	0.969677	0.515161
152	0.439911	0.879822	0.560089
153	0.387991	0.775982	0.612009
154	0.320092	0.640184	0.679908
155	0.287815	0.575631	0.712185
156	0.274976	0.549951	0.725024
157	0.187629	0.375258	0.812371
158	0.276004	0.552008	0.723996
159	0.534003	0.931993	0.465997

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
6 & 0.180349 & 0.360699 & 0.819651 \tabularnewline
7 & 0.239258 & 0.478516 & 0.760742 \tabularnewline
8 & 0.132258 & 0.264515 & 0.867742 \tabularnewline
9 & 0.0975393 & 0.195079 & 0.902461 \tabularnewline
10 & 0.145597 & 0.291194 & 0.854403 \tabularnewline
11 & 0.319026 & 0.638051 & 0.680974 \tabularnewline
12 & 0.560229 & 0.879543 & 0.439771 \tabularnewline
13 & 0.520604 & 0.958793 & 0.479396 \tabularnewline
14 & 0.50603 & 0.987941 & 0.49397 \tabularnewline
15 & 0.591552 & 0.816897 & 0.408448 \tabularnewline
16 & 0.587657 & 0.824687 & 0.412343 \tabularnewline
17 & 0.51867 & 0.96266 & 0.48133 \tabularnewline
18 & 0.539674 & 0.920652 & 0.460326 \tabularnewline
19 & 0.487325 & 0.974649 & 0.512675 \tabularnewline
20 & 0.420904 & 0.841809 & 0.579096 \tabularnewline
21 & 0.609828 & 0.780344 & 0.390172 \tabularnewline
22 & 0.691631 & 0.616739 & 0.308369 \tabularnewline
23 & 0.756862 & 0.486277 & 0.243138 \tabularnewline
24 & 0.704137 & 0.591726 & 0.295863 \tabularnewline
25 & 0.652396 & 0.695208 & 0.347604 \tabularnewline
26 & 0.723218 & 0.553563 & 0.276782 \tabularnewline
27 & 0.670642 & 0.658716 & 0.329358 \tabularnewline
28 & 0.616495 & 0.767011 & 0.383505 \tabularnewline
29 & 0.866094 & 0.267812 & 0.133906 \tabularnewline
30 & 0.872008 & 0.255985 & 0.127992 \tabularnewline
31 & 0.861203 & 0.277595 & 0.138797 \tabularnewline
32 & 0.831882 & 0.336235 & 0.168118 \tabularnewline
33 & 0.798803 & 0.402393 & 0.201197 \tabularnewline
34 & 0.761568 & 0.476865 & 0.238432 \tabularnewline
35 & 0.720348 & 0.559303 & 0.279652 \tabularnewline
36 & 0.676651 & 0.646697 & 0.323349 \tabularnewline
37 & 0.63824 & 0.72352 & 0.36176 \tabularnewline
38 & 0.59494 & 0.810119 & 0.40506 \tabularnewline
39 & 0.672604 & 0.654792 & 0.327396 \tabularnewline
40 & 0.721081 & 0.557838 & 0.278919 \tabularnewline
41 & 0.684 & 0.632001 & 0.316 \tabularnewline
42 & 0.643645 & 0.712709 & 0.356355 \tabularnewline
43 & 0.594295 & 0.811409 & 0.405705 \tabularnewline
44 & 0.561288 & 0.877424 & 0.438712 \tabularnewline
45 & 0.511369 & 0.977263 & 0.488631 \tabularnewline
46 & 0.469716 & 0.939431 & 0.530284 \tabularnewline
47 & 0.464534 & 0.929067 & 0.535466 \tabularnewline
48 & 0.420397 & 0.840793 & 0.579603 \tabularnewline
49 & 0.383658 & 0.767315 & 0.616342 \tabularnewline
50 & 0.354982 & 0.709964 & 0.645018 \tabularnewline
51 & 0.330391 & 0.660782 & 0.669609 \tabularnewline
52 & 0.288495 & 0.576991 & 0.711505 \tabularnewline
53 & 0.326665 & 0.653329 & 0.673335 \tabularnewline
54 & 0.289874 & 0.579748 & 0.710126 \tabularnewline
55 & 0.277013 & 0.554025 & 0.722987 \tabularnewline
56 & 0.246982 & 0.493964 & 0.753018 \tabularnewline
57 & 0.247466 & 0.494932 & 0.752534 \tabularnewline
58 & 0.211385 & 0.42277 & 0.788615 \tabularnewline
59 & 0.31697 & 0.633941 & 0.68303 \tabularnewline
60 & 0.360076 & 0.720151 & 0.639924 \tabularnewline
61 & 0.392993 & 0.785985 & 0.607007 \tabularnewline
62 & 0.348954 & 0.697907 & 0.651046 \tabularnewline
63 & 0.306974 & 0.613948 & 0.693026 \tabularnewline
64 & 0.269119 & 0.538238 & 0.730881 \tabularnewline
65 & 0.259225 & 0.518449 & 0.740775 \tabularnewline
66 & 0.354517 & 0.709035 & 0.645483 \tabularnewline
67 & 0.321867 & 0.643733 & 0.678133 \tabularnewline
68 & 0.384094 & 0.768188 & 0.615906 \tabularnewline
69 & 0.422722 & 0.845444 & 0.577278 \tabularnewline
70 & 0.799555 & 0.40089 & 0.200445 \tabularnewline
71 & 0.767621 & 0.464757 & 0.232379 \tabularnewline
72 & 0.738251 & 0.523497 & 0.261749 \tabularnewline
73 & 0.813348 & 0.373304 & 0.186652 \tabularnewline
74 & 0.791177 & 0.417646 & 0.208823 \tabularnewline
75 & 0.758066 & 0.483868 & 0.241934 \tabularnewline
76 & 0.749613 & 0.500775 & 0.250387 \tabularnewline
77 & 0.720253 & 0.559494 & 0.279747 \tabularnewline
78 & 0.750407 & 0.499187 & 0.249593 \tabularnewline
79 & 0.784624 & 0.430751 & 0.215376 \tabularnewline
80 & 0.752026 & 0.495948 & 0.247974 \tabularnewline
81 & 0.72224 & 0.555521 & 0.27776 \tabularnewline
82 & 0.690733 & 0.618533 & 0.309267 \tabularnewline
83 & 0.657328 & 0.685345 & 0.342672 \tabularnewline
84 & 0.62656 & 0.746879 & 0.37344 \tabularnewline
85 & 0.65725 & 0.685499 & 0.34275 \tabularnewline
86 & 0.617157 & 0.765686 & 0.382843 \tabularnewline
87 & 0.644986 & 0.710028 & 0.355014 \tabularnewline
88 & 0.607791 & 0.784419 & 0.392209 \tabularnewline
89 & 0.668057 & 0.663886 & 0.331943 \tabularnewline
90 & 0.659762 & 0.680476 & 0.340238 \tabularnewline
91 & 0.649793 & 0.700414 & 0.350207 \tabularnewline
92 & 0.612662 & 0.774676 & 0.387338 \tabularnewline
93 & 0.606053 & 0.787893 & 0.393947 \tabularnewline
94 & 0.573177 & 0.853646 & 0.426823 \tabularnewline
95 & 0.560844 & 0.878313 & 0.439156 \tabularnewline
96 & 0.604173 & 0.791654 & 0.395827 \tabularnewline
97 & 0.927169 & 0.145662 & 0.0728311 \tabularnewline
98 & 0.937752 & 0.124496 & 0.0622482 \tabularnewline
99 & 0.924488 & 0.151023 & 0.0755116 \tabularnewline
100 & 0.906441 & 0.187119 & 0.0935595 \tabularnewline
101 & 0.908711 & 0.182578 & 0.0912892 \tabularnewline
102 & 0.892305 & 0.21539 & 0.107695 \tabularnewline
103 & 0.876336 & 0.247328 & 0.123664 \tabularnewline
104 & 0.856404 & 0.287192 & 0.143596 \tabularnewline
105 & 0.833157 & 0.333685 & 0.166843 \tabularnewline
106 & 0.83885 & 0.3223 & 0.16115 \tabularnewline
107 & 0.84138 & 0.31724 & 0.15862 \tabularnewline
108 & 0.821352 & 0.357297 & 0.178648 \tabularnewline
109 & 0.788839 & 0.422322 & 0.211161 \tabularnewline
110 & 0.798199 & 0.403603 & 0.201801 \tabularnewline
111 & 0.762251 & 0.475497 & 0.237749 \tabularnewline
112 & 0.754657 & 0.490686 & 0.245343 \tabularnewline
113 & 0.732649 & 0.534702 & 0.267351 \tabularnewline
114 & 0.763702 & 0.472595 & 0.236298 \tabularnewline
115 & 0.860114 & 0.279772 & 0.139886 \tabularnewline
116 & 0.840187 & 0.319626 & 0.159813 \tabularnewline
117 & 0.893818 & 0.212363 & 0.106182 \tabularnewline
118 & 0.888836 & 0.222327 & 0.111164 \tabularnewline
119 & 0.877501 & 0.244999 & 0.122499 \tabularnewline
120 & 0.859345 & 0.28131 & 0.140655 \tabularnewline
121 & 0.858443 & 0.283114 & 0.141557 \tabularnewline
122 & 0.854093 & 0.291814 & 0.145907 \tabularnewline
123 & 0.890465 & 0.219071 & 0.109535 \tabularnewline
124 & 0.871712 & 0.256576 & 0.128288 \tabularnewline
125 & 0.94108 & 0.11784 & 0.05892 \tabularnewline
126 & 0.927952 & 0.144095 & 0.0720475 \tabularnewline
127 & 0.909986 & 0.180027 & 0.0900136 \tabularnewline
128 & 0.924993 & 0.150014 & 0.0750068 \tabularnewline
129 & 0.946803 & 0.106394 & 0.0531971 \tabularnewline
130 & 0.941371 & 0.117258 & 0.0586289 \tabularnewline
131 & 0.93078 & 0.138439 & 0.0692196 \tabularnewline
132 & 0.92753 & 0.144941 & 0.0724704 \tabularnewline
133 & 0.924205 & 0.151591 & 0.0757953 \tabularnewline
134 & 0.917246 & 0.165509 & 0.0827544 \tabularnewline
135 & 0.907819 & 0.184362 & 0.0921809 \tabularnewline
136 & 0.91342 & 0.173159 & 0.0865796 \tabularnewline
137 & 0.888015 & 0.22397 & 0.111985 \tabularnewline
138 & 0.853908 & 0.292183 & 0.146092 \tabularnewline
139 & 0.85406 & 0.291879 & 0.14594 \tabularnewline
140 & 0.824369 & 0.351261 & 0.175631 \tabularnewline
141 & 0.805887 & 0.388226 & 0.194113 \tabularnewline
142 & 0.756762 & 0.486477 & 0.243238 \tabularnewline
143 & 0.730943 & 0.538114 & 0.269057 \tabularnewline
144 & 0.668659 & 0.662683 & 0.331341 \tabularnewline
145 & 0.644578 & 0.710844 & 0.355422 \tabularnewline
146 & 0.650262 & 0.699476 & 0.349738 \tabularnewline
147 & 0.614928 & 0.770144 & 0.385072 \tabularnewline
148 & 0.53814 & 0.923719 & 0.46186 \tabularnewline
149 & 0.509141 & 0.981719 & 0.490859 \tabularnewline
150 & 0.509187 & 0.981627 & 0.490813 \tabularnewline
151 & 0.484839 & 0.969677 & 0.515161 \tabularnewline
152 & 0.439911 & 0.879822 & 0.560089 \tabularnewline
153 & 0.387991 & 0.775982 & 0.612009 \tabularnewline
154 & 0.320092 & 0.640184 & 0.679908 \tabularnewline
155 & 0.287815 & 0.575631 & 0.712185 \tabularnewline
156 & 0.274976 & 0.549951 & 0.725024 \tabularnewline
157 & 0.187629 & 0.375258 & 0.812371 \tabularnewline
158 & 0.276004 & 0.552008 & 0.723996 \tabularnewline
159 & 0.534003 & 0.931993 & 0.465997 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=266438&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]6[/C][C]0.180349[/C][C]0.360699[/C][C]0.819651[/C][/ROW]
[ROW][C]7[/C][C]0.239258[/C][C]0.478516[/C][C]0.760742[/C][/ROW]
[ROW][C]8[/C][C]0.132258[/C][C]0.264515[/C][C]0.867742[/C][/ROW]
[ROW][C]9[/C][C]0.0975393[/C][C]0.195079[/C][C]0.902461[/C][/ROW]
[ROW][C]10[/C][C]0.145597[/C][C]0.291194[/C][C]0.854403[/C][/ROW]
[ROW][C]11[/C][C]0.319026[/C][C]0.638051[/C][C]0.680974[/C][/ROW]
[ROW][C]12[/C][C]0.560229[/C][C]0.879543[/C][C]0.439771[/C][/ROW]
[ROW][C]13[/C][C]0.520604[/C][C]0.958793[/C][C]0.479396[/C][/ROW]
[ROW][C]14[/C][C]0.50603[/C][C]0.987941[/C][C]0.49397[/C][/ROW]
[ROW][C]15[/C][C]0.591552[/C][C]0.816897[/C][C]0.408448[/C][/ROW]
[ROW][C]16[/C][C]0.587657[/C][C]0.824687[/C][C]0.412343[/C][/ROW]
[ROW][C]17[/C][C]0.51867[/C][C]0.96266[/C][C]0.48133[/C][/ROW]
[ROW][C]18[/C][C]0.539674[/C][C]0.920652[/C][C]0.460326[/C][/ROW]
[ROW][C]19[/C][C]0.487325[/C][C]0.974649[/C][C]0.512675[/C][/ROW]
[ROW][C]20[/C][C]0.420904[/C][C]0.841809[/C][C]0.579096[/C][/ROW]
[ROW][C]21[/C][C]0.609828[/C][C]0.780344[/C][C]0.390172[/C][/ROW]
[ROW][C]22[/C][C]0.691631[/C][C]0.616739[/C][C]0.308369[/C][/ROW]
[ROW][C]23[/C][C]0.756862[/C][C]0.486277[/C][C]0.243138[/C][/ROW]
[ROW][C]24[/C][C]0.704137[/C][C]0.591726[/C][C]0.295863[/C][/ROW]
[ROW][C]25[/C][C]0.652396[/C][C]0.695208[/C][C]0.347604[/C][/ROW]
[ROW][C]26[/C][C]0.723218[/C][C]0.553563[/C][C]0.276782[/C][/ROW]
[ROW][C]27[/C][C]0.670642[/C][C]0.658716[/C][C]0.329358[/C][/ROW]
[ROW][C]28[/C][C]0.616495[/C][C]0.767011[/C][C]0.383505[/C][/ROW]
[ROW][C]29[/C][C]0.866094[/C][C]0.267812[/C][C]0.133906[/C][/ROW]
[ROW][C]30[/C][C]0.872008[/C][C]0.255985[/C][C]0.127992[/C][/ROW]
[ROW][C]31[/C][C]0.861203[/C][C]0.277595[/C][C]0.138797[/C][/ROW]
[ROW][C]32[/C][C]0.831882[/C][C]0.336235[/C][C]0.168118[/C][/ROW]
[ROW][C]33[/C][C]0.798803[/C][C]0.402393[/C][C]0.201197[/C][/ROW]
[ROW][C]34[/C][C]0.761568[/C][C]0.476865[/C][C]0.238432[/C][/ROW]
[ROW][C]35[/C][C]0.720348[/C][C]0.559303[/C][C]0.279652[/C][/ROW]
[ROW][C]36[/C][C]0.676651[/C][C]0.646697[/C][C]0.323349[/C][/ROW]
[ROW][C]37[/C][C]0.63824[/C][C]0.72352[/C][C]0.36176[/C][/ROW]
[ROW][C]38[/C][C]0.59494[/C][C]0.810119[/C][C]0.40506[/C][/ROW]
[ROW][C]39[/C][C]0.672604[/C][C]0.654792[/C][C]0.327396[/C][/ROW]
[ROW][C]40[/C][C]0.721081[/C][C]0.557838[/C][C]0.278919[/C][/ROW]
[ROW][C]41[/C][C]0.684[/C][C]0.632001[/C][C]0.316[/C][/ROW]
[ROW][C]42[/C][C]0.643645[/C][C]0.712709[/C][C]0.356355[/C][/ROW]
[ROW][C]43[/C][C]0.594295[/C][C]0.811409[/C][C]0.405705[/C][/ROW]
[ROW][C]44[/C][C]0.561288[/C][C]0.877424[/C][C]0.438712[/C][/ROW]
[ROW][C]45[/C][C]0.511369[/C][C]0.977263[/C][C]0.488631[/C][/ROW]
[ROW][C]46[/C][C]0.469716[/C][C]0.939431[/C][C]0.530284[/C][/ROW]
[ROW][C]47[/C][C]0.464534[/C][C]0.929067[/C][C]0.535466[/C][/ROW]
[ROW][C]48[/C][C]0.420397[/C][C]0.840793[/C][C]0.579603[/C][/ROW]
[ROW][C]49[/C][C]0.383658[/C][C]0.767315[/C][C]0.616342[/C][/ROW]
[ROW][C]50[/C][C]0.354982[/C][C]0.709964[/C][C]0.645018[/C][/ROW]
[ROW][C]51[/C][C]0.330391[/C][C]0.660782[/C][C]0.669609[/C][/ROW]
[ROW][C]52[/C][C]0.288495[/C][C]0.576991[/C][C]0.711505[/C][/ROW]
[ROW][C]53[/C][C]0.326665[/C][C]0.653329[/C][C]0.673335[/C][/ROW]
[ROW][C]54[/C][C]0.289874[/C][C]0.579748[/C][C]0.710126[/C][/ROW]
[ROW][C]55[/C][C]0.277013[/C][C]0.554025[/C][C]0.722987[/C][/ROW]
[ROW][C]56[/C][C]0.246982[/C][C]0.493964[/C][C]0.753018[/C][/ROW]
[ROW][C]57[/C][C]0.247466[/C][C]0.494932[/C][C]0.752534[/C][/ROW]
[ROW][C]58[/C][C]0.211385[/C][C]0.42277[/C][C]0.788615[/C][/ROW]
[ROW][C]59[/C][C]0.31697[/C][C]0.633941[/C][C]0.68303[/C][/ROW]
[ROW][C]60[/C][C]0.360076[/C][C]0.720151[/C][C]0.639924[/C][/ROW]
[ROW][C]61[/C][C]0.392993[/C][C]0.785985[/C][C]0.607007[/C][/ROW]
[ROW][C]62[/C][C]0.348954[/C][C]0.697907[/C][C]0.651046[/C][/ROW]
[ROW][C]63[/C][C]0.306974[/C][C]0.613948[/C][C]0.693026[/C][/ROW]
[ROW][C]64[/C][C]0.269119[/C][C]0.538238[/C][C]0.730881[/C][/ROW]
[ROW][C]65[/C][C]0.259225[/C][C]0.518449[/C][C]0.740775[/C][/ROW]
[ROW][C]66[/C][C]0.354517[/C][C]0.709035[/C][C]0.645483[/C][/ROW]
[ROW][C]67[/C][C]0.321867[/C][C]0.643733[/C][C]0.678133[/C][/ROW]
[ROW][C]68[/C][C]0.384094[/C][C]0.768188[/C][C]0.615906[/C][/ROW]
[ROW][C]69[/C][C]0.422722[/C][C]0.845444[/C][C]0.577278[/C][/ROW]
[ROW][C]70[/C][C]0.799555[/C][C]0.40089[/C][C]0.200445[/C][/ROW]
[ROW][C]71[/C][C]0.767621[/C][C]0.464757[/C][C]0.232379[/C][/ROW]
[ROW][C]72[/C][C]0.738251[/C][C]0.523497[/C][C]0.261749[/C][/ROW]
[ROW][C]73[/C][C]0.813348[/C][C]0.373304[/C][C]0.186652[/C][/ROW]
[ROW][C]74[/C][C]0.791177[/C][C]0.417646[/C][C]0.208823[/C][/ROW]
[ROW][C]75[/C][C]0.758066[/C][C]0.483868[/C][C]0.241934[/C][/ROW]
[ROW][C]76[/C][C]0.749613[/C][C]0.500775[/C][C]0.250387[/C][/ROW]
[ROW][C]77[/C][C]0.720253[/C][C]0.559494[/C][C]0.279747[/C][/ROW]
[ROW][C]78[/C][C]0.750407[/C][C]0.499187[/C][C]0.249593[/C][/ROW]
[ROW][C]79[/C][C]0.784624[/C][C]0.430751[/C][C]0.215376[/C][/ROW]
[ROW][C]80[/C][C]0.752026[/C][C]0.495948[/C][C]0.247974[/C][/ROW]
[ROW][C]81[/C][C]0.72224[/C][C]0.555521[/C][C]0.27776[/C][/ROW]
[ROW][C]82[/C][C]0.690733[/C][C]0.618533[/C][C]0.309267[/C][/ROW]
[ROW][C]83[/C][C]0.657328[/C][C]0.685345[/C][C]0.342672[/C][/ROW]
[ROW][C]84[/C][C]0.62656[/C][C]0.746879[/C][C]0.37344[/C][/ROW]
[ROW][C]85[/C][C]0.65725[/C][C]0.685499[/C][C]0.34275[/C][/ROW]
[ROW][C]86[/C][C]0.617157[/C][C]0.765686[/C][C]0.382843[/C][/ROW]
[ROW][C]87[/C][C]0.644986[/C][C]0.710028[/C][C]0.355014[/C][/ROW]
[ROW][C]88[/C][C]0.607791[/C][C]0.784419[/C][C]0.392209[/C][/ROW]
[ROW][C]89[/C][C]0.668057[/C][C]0.663886[/C][C]0.331943[/C][/ROW]
[ROW][C]90[/C][C]0.659762[/C][C]0.680476[/C][C]0.340238[/C][/ROW]
[ROW][C]91[/C][C]0.649793[/C][C]0.700414[/C][C]0.350207[/C][/ROW]
[ROW][C]92[/C][C]0.612662[/C][C]0.774676[/C][C]0.387338[/C][/ROW]
[ROW][C]93[/C][C]0.606053[/C][C]0.787893[/C][C]0.393947[/C][/ROW]
[ROW][C]94[/C][C]0.573177[/C][C]0.853646[/C][C]0.426823[/C][/ROW]
[ROW][C]95[/C][C]0.560844[/C][C]0.878313[/C][C]0.439156[/C][/ROW]
[ROW][C]96[/C][C]0.604173[/C][C]0.791654[/C][C]0.395827[/C][/ROW]
[ROW][C]97[/C][C]0.927169[/C][C]0.145662[/C][C]0.0728311[/C][/ROW]
[ROW][C]98[/C][C]0.937752[/C][C]0.124496[/C][C]0.0622482[/C][/ROW]
[ROW][C]99[/C][C]0.924488[/C][C]0.151023[/C][C]0.0755116[/C][/ROW]
[ROW][C]100[/C][C]0.906441[/C][C]0.187119[/C][C]0.0935595[/C][/ROW]
[ROW][C]101[/C][C]0.908711[/C][C]0.182578[/C][C]0.0912892[/C][/ROW]
[ROW][C]102[/C][C]0.892305[/C][C]0.21539[/C][C]0.107695[/C][/ROW]
[ROW][C]103[/C][C]0.876336[/C][C]0.247328[/C][C]0.123664[/C][/ROW]
[ROW][C]104[/C][C]0.856404[/C][C]0.287192[/C][C]0.143596[/C][/ROW]
[ROW][C]105[/C][C]0.833157[/C][C]0.333685[/C][C]0.166843[/C][/ROW]
[ROW][C]106[/C][C]0.83885[/C][C]0.3223[/C][C]0.16115[/C][/ROW]
[ROW][C]107[/C][C]0.84138[/C][C]0.31724[/C][C]0.15862[/C][/ROW]
[ROW][C]108[/C][C]0.821352[/C][C]0.357297[/C][C]0.178648[/C][/ROW]
[ROW][C]109[/C][C]0.788839[/C][C]0.422322[/C][C]0.211161[/C][/ROW]
[ROW][C]110[/C][C]0.798199[/C][C]0.403603[/C][C]0.201801[/C][/ROW]
[ROW][C]111[/C][C]0.762251[/C][C]0.475497[/C][C]0.237749[/C][/ROW]
[ROW][C]112[/C][C]0.754657[/C][C]0.490686[/C][C]0.245343[/C][/ROW]
[ROW][C]113[/C][C]0.732649[/C][C]0.534702[/C][C]0.267351[/C][/ROW]
[ROW][C]114[/C][C]0.763702[/C][C]0.472595[/C][C]0.236298[/C][/ROW]
[ROW][C]115[/C][C]0.860114[/C][C]0.279772[/C][C]0.139886[/C][/ROW]
[ROW][C]116[/C][C]0.840187[/C][C]0.319626[/C][C]0.159813[/C][/ROW]
[ROW][C]117[/C][C]0.893818[/C][C]0.212363[/C][C]0.106182[/C][/ROW]
[ROW][C]118[/C][C]0.888836[/C][C]0.222327[/C][C]0.111164[/C][/ROW]
[ROW][C]119[/C][C]0.877501[/C][C]0.244999[/C][C]0.122499[/C][/ROW]
[ROW][C]120[/C][C]0.859345[/C][C]0.28131[/C][C]0.140655[/C][/ROW]
[ROW][C]121[/C][C]0.858443[/C][C]0.283114[/C][C]0.141557[/C][/ROW]
[ROW][C]122[/C][C]0.854093[/C][C]0.291814[/C][C]0.145907[/C][/ROW]
[ROW][C]123[/C][C]0.890465[/C][C]0.219071[/C][C]0.109535[/C][/ROW]
[ROW][C]124[/C][C]0.871712[/C][C]0.256576[/C][C]0.128288[/C][/ROW]
[ROW][C]125[/C][C]0.94108[/C][C]0.11784[/C][C]0.05892[/C][/ROW]
[ROW][C]126[/C][C]0.927952[/C][C]0.144095[/C][C]0.0720475[/C][/ROW]
[ROW][C]127[/C][C]0.909986[/C][C]0.180027[/C][C]0.0900136[/C][/ROW]
[ROW][C]128[/C][C]0.924993[/C][C]0.150014[/C][C]0.0750068[/C][/ROW]
[ROW][C]129[/C][C]0.946803[/C][C]0.106394[/C][C]0.0531971[/C][/ROW]
[ROW][C]130[/C][C]0.941371[/C][C]0.117258[/C][C]0.0586289[/C][/ROW]
[ROW][C]131[/C][C]0.93078[/C][C]0.138439[/C][C]0.0692196[/C][/ROW]
[ROW][C]132[/C][C]0.92753[/C][C]0.144941[/C][C]0.0724704[/C][/ROW]
[ROW][C]133[/C][C]0.924205[/C][C]0.151591[/C][C]0.0757953[/C][/ROW]
[ROW][C]134[/C][C]0.917246[/C][C]0.165509[/C][C]0.0827544[/C][/ROW]
[ROW][C]135[/C][C]0.907819[/C][C]0.184362[/C][C]0.0921809[/C][/ROW]
[ROW][C]136[/C][C]0.91342[/C][C]0.173159[/C][C]0.0865796[/C][/ROW]
[ROW][C]137[/C][C]0.888015[/C][C]0.22397[/C][C]0.111985[/C][/ROW]
[ROW][C]138[/C][C]0.853908[/C][C]0.292183[/C][C]0.146092[/C][/ROW]
[ROW][C]139[/C][C]0.85406[/C][C]0.291879[/C][C]0.14594[/C][/ROW]
[ROW][C]140[/C][C]0.824369[/C][C]0.351261[/C][C]0.175631[/C][/ROW]
[ROW][C]141[/C][C]0.805887[/C][C]0.388226[/C][C]0.194113[/C][/ROW]
[ROW][C]142[/C][C]0.756762[/C][C]0.486477[/C][C]0.243238[/C][/ROW]
[ROW][C]143[/C][C]0.730943[/C][C]0.538114[/C][C]0.269057[/C][/ROW]
[ROW][C]144[/C][C]0.668659[/C][C]0.662683[/C][C]0.331341[/C][/ROW]
[ROW][C]145[/C][C]0.644578[/C][C]0.710844[/C][C]0.355422[/C][/ROW]
[ROW][C]146[/C][C]0.650262[/C][C]0.699476[/C][C]0.349738[/C][/ROW]
[ROW][C]147[/C][C]0.614928[/C][C]0.770144[/C][C]0.385072[/C][/ROW]
[ROW][C]148[/C][C]0.53814[/C][C]0.923719[/C][C]0.46186[/C][/ROW]
[ROW][C]149[/C][C]0.509141[/C][C]0.981719[/C][C]0.490859[/C][/ROW]
[ROW][C]150[/C][C]0.509187[/C][C]0.981627[/C][C]0.490813[/C][/ROW]
[ROW][C]151[/C][C]0.484839[/C][C]0.969677[/C][C]0.515161[/C][/ROW]
[ROW][C]152[/C][C]0.439911[/C][C]0.879822[/C][C]0.560089[/C][/ROW]
[ROW][C]153[/C][C]0.387991[/C][C]0.775982[/C][C]0.612009[/C][/ROW]
[ROW][C]154[/C][C]0.320092[/C][C]0.640184[/C][C]0.679908[/C][/ROW]
[ROW][C]155[/C][C]0.287815[/C][C]0.575631[/C][C]0.712185[/C][/ROW]
[ROW][C]156[/C][C]0.274976[/C][C]0.549951[/C][C]0.725024[/C][/ROW]
[ROW][C]157[/C][C]0.187629[/C][C]0.375258[/C][C]0.812371[/C][/ROW]
[ROW][C]158[/C][C]0.276004[/C][C]0.552008[/C][C]0.723996[/C][/ROW]
[ROW][C]159[/C][C]0.534003[/C][C]0.931993[/C][C]0.465997[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=266438&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=266438&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
6	0.180349	0.360699	0.819651
7	0.239258	0.478516	0.760742
8	0.132258	0.264515	0.867742
9	0.0975393	0.195079	0.902461
10	0.145597	0.291194	0.854403
11	0.319026	0.638051	0.680974
12	0.560229	0.879543	0.439771
13	0.520604	0.958793	0.479396
14	0.50603	0.987941	0.49397
15	0.591552	0.816897	0.408448
16	0.587657	0.824687	0.412343
17	0.51867	0.96266	0.48133
18	0.539674	0.920652	0.460326
19	0.487325	0.974649	0.512675
20	0.420904	0.841809	0.579096
21	0.609828	0.780344	0.390172
22	0.691631	0.616739	0.308369
23	0.756862	0.486277	0.243138
24	0.704137	0.591726	0.295863
25	0.652396	0.695208	0.347604
26	0.723218	0.553563	0.276782
27	0.670642	0.658716	0.329358
28	0.616495	0.767011	0.383505
29	0.866094	0.267812	0.133906
30	0.872008	0.255985	0.127992
31	0.861203	0.277595	0.138797
32	0.831882	0.336235	0.168118
33	0.798803	0.402393	0.201197
34	0.761568	0.476865	0.238432
35	0.720348	0.559303	0.279652
36	0.676651	0.646697	0.323349
37	0.63824	0.72352	0.36176
38	0.59494	0.810119	0.40506
39	0.672604	0.654792	0.327396
40	0.721081	0.557838	0.278919
41	0.684	0.632001	0.316
42	0.643645	0.712709	0.356355
43	0.594295	0.811409	0.405705
44	0.561288	0.877424	0.438712
45	0.511369	0.977263	0.488631
46	0.469716	0.939431	0.530284
47	0.464534	0.929067	0.535466
48	0.420397	0.840793	0.579603
49	0.383658	0.767315	0.616342
50	0.354982	0.709964	0.645018
51	0.330391	0.660782	0.669609
52	0.288495	0.576991	0.711505
53	0.326665	0.653329	0.673335
54	0.289874	0.579748	0.710126
55	0.277013	0.554025	0.722987
56	0.246982	0.493964	0.753018
57	0.247466	0.494932	0.752534
58	0.211385	0.42277	0.788615
59	0.31697	0.633941	0.68303
60	0.360076	0.720151	0.639924
61	0.392993	0.785985	0.607007
62	0.348954	0.697907	0.651046
63	0.306974	0.613948	0.693026
64	0.269119	0.538238	0.730881
65	0.259225	0.518449	0.740775
66	0.354517	0.709035	0.645483
67	0.321867	0.643733	0.678133
68	0.384094	0.768188	0.615906
69	0.422722	0.845444	0.577278
70	0.799555	0.40089	0.200445
71	0.767621	0.464757	0.232379
72	0.738251	0.523497	0.261749
73	0.813348	0.373304	0.186652
74	0.791177	0.417646	0.208823
75	0.758066	0.483868	0.241934
76	0.749613	0.500775	0.250387
77	0.720253	0.559494	0.279747
78	0.750407	0.499187	0.249593
79	0.784624	0.430751	0.215376
80	0.752026	0.495948	0.247974
81	0.72224	0.555521	0.27776
82	0.690733	0.618533	0.309267
83	0.657328	0.685345	0.342672
84	0.62656	0.746879	0.37344
85	0.65725	0.685499	0.34275
86	0.617157	0.765686	0.382843
87	0.644986	0.710028	0.355014
88	0.607791	0.784419	0.392209
89	0.668057	0.663886	0.331943
90	0.659762	0.680476	0.340238
91	0.649793	0.700414	0.350207
92	0.612662	0.774676	0.387338
93	0.606053	0.787893	0.393947
94	0.573177	0.853646	0.426823
95	0.560844	0.878313	0.439156
96	0.604173	0.791654	0.395827
97	0.927169	0.145662	0.0728311
98	0.937752	0.124496	0.0622482
99	0.924488	0.151023	0.0755116
100	0.906441	0.187119	0.0935595
101	0.908711	0.182578	0.0912892
102	0.892305	0.21539	0.107695
103	0.876336	0.247328	0.123664
104	0.856404	0.287192	0.143596
105	0.833157	0.333685	0.166843
106	0.83885	0.3223	0.16115
107	0.84138	0.31724	0.15862
108	0.821352	0.357297	0.178648
109	0.788839	0.422322	0.211161
110	0.798199	0.403603	0.201801
111	0.762251	0.475497	0.237749
112	0.754657	0.490686	0.245343
113	0.732649	0.534702	0.267351
114	0.763702	0.472595	0.236298
115	0.860114	0.279772	0.139886
116	0.840187	0.319626	0.159813
117	0.893818	0.212363	0.106182
118	0.888836	0.222327	0.111164
119	0.877501	0.244999	0.122499
120	0.859345	0.28131	0.140655
121	0.858443	0.283114	0.141557
122	0.854093	0.291814	0.145907
123	0.890465	0.219071	0.109535
124	0.871712	0.256576	0.128288
125	0.94108	0.11784	0.05892
126	0.927952	0.144095	0.0720475
127	0.909986	0.180027	0.0900136
128	0.924993	0.150014	0.0750068
129	0.946803	0.106394	0.0531971
130	0.941371	0.117258	0.0586289
131	0.93078	0.138439	0.0692196
132	0.92753	0.144941	0.0724704
133	0.924205	0.151591	0.0757953
134	0.917246	0.165509	0.0827544
135	0.907819	0.184362	0.0921809
136	0.91342	0.173159	0.0865796
137	0.888015	0.22397	0.111985
138	0.853908	0.292183	0.146092
139	0.85406	0.291879	0.14594
140	0.824369	0.351261	0.175631
141	0.805887	0.388226	0.194113
142	0.756762	0.486477	0.243238
143	0.730943	0.538114	0.269057
144	0.668659	0.662683	0.331341
145	0.644578	0.710844	0.355422
146	0.650262	0.699476	0.349738
147	0.614928	0.770144	0.385072
148	0.53814	0.923719	0.46186
149	0.509141	0.981719	0.490859
150	0.509187	0.981627	0.490813
151	0.484839	0.969677	0.515161
152	0.439911	0.879822	0.560089
153	0.387991	0.775982	0.612009
154	0.320092	0.640184	0.679908
155	0.287815	0.575631	0.712185
156	0.274976	0.549951	0.725024
157	0.187629	0.375258	0.812371
158	0.276004	0.552008	0.723996
159	0.534003	0.931993	0.465997

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=266438&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=266438&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=266438&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

Postscript link

PDF link

Parameters (Session):

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

Parameters (R input):

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

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

par3 <- 'No Linear Trend'
par2 <- 'Do not include Seasonal Dummies'
par1 <- '1'
library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, signif(mysum$coefficients[i,1],6), sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,signif(mysum$coefficients[i,1],6))
a<-table.element(a, signif(mysum$coefficients[i,2],6))
a<-table.element(a, signif(mysum$coefficients[i,3],4))
a<-table.element(a, signif(mysum$coefficients[i,4],6))
a<-table.element(a, signif(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, signif(sqrt(mysum$r.squared),6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, signif(mysum$r.squared,6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, signif(mysum$adj.r.squared,6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[1],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[2],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[3],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, signif(1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]),6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a, signif(mysum$sigma,6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, signif(sum(myerror*myerror),6))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,signif(x[i],6))
a<-table.element(a,signif(x[i]-mysum$resid[i],6))
a<-table.element(a,signif(mysum$resid[i],6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
if (n > n25) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-values',header=TRUE)
a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'breakpoint index',header=TRUE)
a<-table.element(a,'greater',header=TRUE)
a<-table.element(a,'2-sided',header=TRUE)
a<-table.element(a,'less',header=TRUE)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
a<-table.element(a,mypoint,header=TRUE)
a<-table.element(a,signif(gqarr[mypoint-kp3+1,1],6))
a<-table.element(a,signif(gqarr[mypoint-kp3+1,2],6))
a<-table.element(a,signif(gqarr[mypoint-kp3+1,3],6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,signif(numsignificant1,6))
a<-table.element(a,signif(numsignificant1/numgqtests,6))
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,signif(numsignificant5,6))
a<-table.element(a,signif(numsignificant5/numgqtests,6))
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,signif(numsignificant10,6))
a<-table.element(a,signif(numsignificant10/numgqtests,6))
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code