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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Tue, 19 Nov 2013 06:23:19 -0500

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2013/Nov/19/t1384860310851zas2tiat3fqp.htm/, Retrieved Fri, 03 May 2024 16:07:24 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=226370, Retrieved Fri, 03 May 2024 16:07:24 +0000

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-     [Multiple Regression] [Competence to learn] [2010-11-17 07:43:53] [b98453cac15ba1066b407e146608df68]
- R PD  [Multiple Regression] [] [2013-11-17 14:13:46] [2710f8dea95e981897be7c03387b4566]
- R PD      [Multiple Regression] [] [2013-11-19 11:23:19] [05fc9f73518f9509c56332c989d681e3] [Current]

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Dataseries X:

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Histogram

Boxplots

26	21	23	17	4
20	16	24	17	4
19	19	22	18	6
19	18	20	21	8
20	16	24	20	8
25	23	27	28	4
25	17	28	19	4
22	12	27	22	8
26	19	24	16	5
22	16	23	18	4
17	19	24	25	4
22	20	27	17	4
19	13	27	14	4
24	20	28	11	4
26	27	27	27	4
21	17	23	20	8
13	8	24	22	4
26	25	28	22	4
20	26	27	21	4
22	13	25	23	8
14	19	19	17	4
21	15	24	24	7
7	5	20	14	4
23	16	28	17	4
17	14	26	23	5
25	24	23	24	4
25	24	23	24	4
19	9	20	8	4
20	19	11	22	4
23	19	24	23	4
22	25	25	25	4
22	19	23	21	4
21	18	18	24	15
15	15	20	15	10
20	12	20	22	4
22	21	24	21	8
18	12	23	25	4
20	15	25	16	4
28	28	28	28	4
22	25	26	23	4
18	19	26	21	7
23	20	23	21	4
20	24	22	26	6
25	26	24	22	5
26	25	21	21	4
15	12	20	18	16
17	12	22	12	5
23	15	20	25	12
21	17	25	17	6
13	14	20	24	9
18	16	22	15	9
19	11	23	13	4
22	20	25	26	5
16	11	23	16	4
24	22	23	24	4
18	20	22	21	5
20	19	24	20	4
24	17	25	14	4
14	21	21	25	4
22	23	12	25	5
24	18	17	20	4
18	17	20	22	6
21	27	23	20	4
23	25	23	26	4
17	19	20	18	18
22	22	28	22	4
24	24	24	24	6
21	20	24	17	4
22	19	24	24	4
16	11	24	20	5
21	22	28	19	4
23	22	25	20	4
22	16	21	15	5
24	20	25	23	10
24	24	25	26	5
16	16	18	22	8
16	16	17	20	8
21	22	26	24	5
26	24	28	26	4
15	16	21	21	4
25	27	27	25	4
18	11	22	13	5
23	21	21	20	4
20	20	25	22	4
17	20	22	23	8
25	27	23	28	4
24	20	26	22	5
17	12	19	20	14
19	8	25	6	8
20	21	21	21	8
15	18	13	20	4
27	24	24	18	4
22	16	25	23	6
23	18	26	20	4
16	20	25	24	7
19	20	25	22	7
25	19	22	21	4
19	17	21	18	6
19	16	23	21	4
26	26	25	23	7
21	15	24	23	4
20	22	21	15	4
24	17	21	21	8
22	23	25	24	4
20	21	22	23	4
18	19	20	21	10
18	14	20	21	8
24	17	23	20	6
24	12	28	11	4
22	24	23	22	4
23	18	28	27	4
22	20	24	25	5
20	16	18	18	4
18	20	20	20	6
25	22	28	24	4
18	12	21	10	5
16	16	21	27	7
20	17	25	21	8
19	22	19	21	5
15	12	18	18	8
19	14	21	15	10
19	23	22	24	8
16	15	24	22	5
17	17	15	14	12
28	28	28	28	4
23	20	26	18	5
25	23	23	26	4
20	13	26	17	6
17	18	20	19	4
23	23	22	22	4
16	19	20	18	7
23	23	23	24	7
11	12	22	15	10
18	16	24	18	4
24	23	23	26	5
23	13	22	11	8
21	22	26	26	11
16	18	23	21	7
24	23	27	23	4
23	20	23	23	8
18	10	21	15	6
20	17	26	22	7
9	18	23	26	5
24	15	21	16	4
25	23	27	20	8
20	17	19	18	4
21	17	23	22	8
25	22	25	16	6
22	20	23	19	4
21	20	22	20	9
21	19	22	19	5
22	18	25	23	6
27	22	25	24	4
24	20	28	25	4
24	22	28	21	4
21	18	20	21	5
18	16	25	23	6
16	16	19	27	16
22	16	25	23	6
20	16	22	18	6
18	17	18	16	4
20	18	20	16	4

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	11 seconds
R Server	'Sir Ronald Aylmer Fisher' @ fisher.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 11 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ fisher.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=226370&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]11 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Ronald Aylmer Fisher' @ fisher.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=226370&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=226370&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	11 seconds
R Server	'Sir Ronald Aylmer Fisher' @ fisher.wessa.net

Multiple Linear Regression - Estimated Regression Equation
A[t] = + 11.7299 -0.0827731I1[t] -0.122777I2[t] -0.176683E1[t] + 0.103161E2[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
A[t] =  +  11.7299 -0.0827731I1[t] -0.122777I2[t] -0.176683E1[t] +  0.103161E2[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=226370&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]A[t] =  +  11.7299 -0.0827731I1[t] -0.122777I2[t] -0.176683E1[t] +  0.103161E2[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=226370&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=226370&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
A[t] = + 11.7299 -0.0827731I1[t] -0.122777I2[t] -0.176683E1[t] + 0.103161E2[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)	11.7299	1.64849	7.116	3.73535e-11	1.86767e-11
I1	-0.0827731	0.0729915	-1.134	0.258519	0.12926
I2	-0.122777	0.0648949	-1.892	0.0603405	0.0301702
E1	-0.176683	0.0689964	-2.561	0.0113874	0.0056937
E2	0.103161	0.0568832	1.814	0.0716556	0.0358278

\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) & 11.7299 & 1.64849 & 7.116 & 3.73535e-11 & 1.86767e-11 \tabularnewline
I1 & -0.0827731 & 0.0729915 & -1.134 & 0.258519 & 0.12926 \tabularnewline
I2 & -0.122777 & 0.0648949 & -1.892 & 0.0603405 & 0.0301702 \tabularnewline
E1 & -0.176683 & 0.0689964 & -2.561 & 0.0113874 & 0.0056937 \tabularnewline
E2 & 0.103161 & 0.0568832 & 1.814 & 0.0716556 & 0.0358278 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=226370&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]11.7299[/C][C]1.64849[/C][C]7.116[/C][C]3.73535e-11[/C][C]1.86767e-11[/C][/ROW]
[ROW][C]I1[/C][C]-0.0827731[/C][C]0.0729915[/C][C]-1.134[/C][C]0.258519[/C][C]0.12926[/C][/ROW]
[ROW][C]I2[/C][C]-0.122777[/C][C]0.0648949[/C][C]-1.892[/C][C]0.0603405[/C][C]0.0301702[/C][/ROW]
[ROW][C]E1[/C][C]-0.176683[/C][C]0.0689964[/C][C]-2.561[/C][C]0.0113874[/C][C]0.0056937[/C][/ROW]
[ROW][C]E2[/C][C]0.103161[/C][C]0.0568832[/C][C]1.814[/C][C]0.0716556[/C][C]0.0358278[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=226370&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=226370&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)	11.7299	1.64849	7.116	3.73535e-11	1.86767e-11
I1	-0.0827731	0.0729915	-1.134	0.258519	0.12926
I2	-0.122777	0.0648949	-1.892	0.0603405	0.0301702
E1	-0.176683	0.0689964	-2.561	0.0113874	0.0056937
E2	0.103161	0.0568832	1.814	0.0716556	0.0358278

Multiple Linear Regression - Regression Statistics
Multiple R	0.379605
R-squared	0.1441
Adjusted R-squared	0.122293
F-TEST (value)	6.60815
F-TEST (DF numerator)	4
F-TEST (DF denominator)	157
p-value	6.10295e-05
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	2.46024
Sum Squared Residuals	950.287

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.379605 \tabularnewline
R-squared & 0.1441 \tabularnewline
Adjusted R-squared & 0.122293 \tabularnewline
F-TEST (value) & 6.60815 \tabularnewline
F-TEST (DF numerator) & 4 \tabularnewline
F-TEST (DF denominator) & 157 \tabularnewline
p-value & 6.10295e-05 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.46024 \tabularnewline
Sum Squared Residuals & 950.287 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=226370&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.379605[/C][/ROW]
[ROW][C]R-squared[/C][C]0.1441[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.122293[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]6.60815[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]4[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]157[/C][/ROW]
[ROW][C]p-value[/C][C]6.10295e-05[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]2.46024[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]950.287[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=226370&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=226370&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	0.379605
R-squared	0.1441
Adjusted R-squared	0.122293
F-TEST (value)	6.60815
F-TEST (DF numerator)	4
F-TEST (DF denominator)	157
p-value	6.10295e-05
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	2.46024
Sum Squared Residuals	950.287

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	4	4.68951	-0.689513
2	4	5.62336	-1.62336
3	6	5.79432	0.205677
4	8	6.57995	1.42005
5	8	5.93284	2.06716
6	4	4.95477	-0.954771
7	4	4.5863	-0.586304
8	8	5.93467	2.06533
9	5	4.65522	0.344776
10	4	5.73765	-1.73765
11	4	6.32863	-2.32863
12	4	4.43665	-0.436653
13	4	5.23493	-1.23493
14	4	3.47546	0.524541
15	4	4.27773	-0.277729
16	8	5.90397	2.09603
17	4	7.70079	-3.70079
18	4	3.8308	0.169204
19	4	4.27818	-0.278179
20	8	6.26842	1.73158
21	4	6.63507	-2.63507
22	7	6.38549	0.614515
23	4	8.4472	-4.4472
24	4	4.66831	-0.668306
25	5	6.38283	-1.38283
26	4	5.12608	-1.12608
27	4	5.12608	-1.12608
28	4	6.34385	-2.34385
29	4	8.0677	-4.0677
30	4	5.62567	-1.62567
31	4	5.00142	-1.00142
32	4	5.6788	-1.6788
33	15	7.07725	7.92275
34	10	6.66041	3.33959
35	4	7.337	-3.337
36	8	5.25657	2.74343
37	4	7.28198	-3.28198
38	4	5.46629	-1.46629
39	4	3.91588	0.0841161
40	4	4.61841	-0.618415
41	7	5.47985	1.52015
42	4	5.47325	-1.47325
43	6	5.92295	0.0770493
44	5	4.49752	0.502478
45	4	4.96441	-0.964413
46	16	7.33822	8.66178
47	5	6.20034	-1.20034
48	12	7.02983	4.97017
49	6	5.24112	0.758878
50	9	7.87718	1.12282
51	9	5.93594	3.06406
52	4	6.08405	-2.08405
53	5	5.71847	-0.718465
54	4	6.64185	-2.64185
55	4	5.45441	-1.45441
56	5	6.0638	-1.0638
57	4	5.56451	-1.56451
58	4	4.68332	-0.68332
59	4	6.86144	-2.86144
60	5	7.54385	-2.54385
61	4	6.59297	-2.59297
62	6	6.88866	-0.888659
63	4	4.6762	-0.676198
64	4	5.37517	-1.37517
65	18	6.31323	11.6868
66	4	4.53022	-0.53022
67	6	5.03217	0.967828
68	4	5.04947	-1.04947
69	4	5.8116	-1.8116
70	5	6.87782	-1.87782
71	4	4.30351	-0.30351
72	4	4.77117	-0.771173
73	5	5.78153	-0.781535
74	10	5.24344	4.75656
75	5	5.06181	-0.0618107
76	8	7.53035	0.469653
77	8	7.50071	0.499292
78	5	5.17268	-0.17268
79	4	4.36622	-0.366217
80	4	6.97991	-2.97991
81	4	4.15418	-0.15418
82	5	6.34351	-1.34351
83	4	5.60068	-1.60068
84	4	5.47137	-1.47137
85	8	6.3529	1.6471
86	4	5.17039	-1.17039
87	5	4.96359	0.0364066
88	14	7.55568	6.44432
89	8	5.37689	2.62311
90	8	5.95216	2.04784
91	4	8.04466	-4.04466
92	4	4.16489	-0.164887
93	6	5.90009	0.0999085
94	4	5.0856	-1.0856
95	7	6.00878	0.991218
96	7	5.55414	1.44586
97	4	5.60717	-1.60717
98	6	6.21656	-0.216559
99	4	6.29545	-2.29545
100	7	4.34123	2.65877
101	4	6.28232	-2.28232
102	4	5.21042	-1.21042
103	8	6.11218	1.88782
104	4	5.14381	-1.14381
105	4	5.9818	-1.9818
106	10	6.53994	3.46006
107	8	7.15383	0.846171
108	6	5.65565	0.344349
109	4	4.45768	-0.457676
110	4	5.16808	-1.16808
111	4	5.45436	-1.45436
112	5	5.79199	-0.791987
113	4	6.78661	-2.78661
114	6	6.31401	-0.314005
115	4	4.48822	-0.488223
116	5	6.08793	-1.08793
117	7	7.5161	-0.516104
118	8	5.73654	2.26346
119	5	6.26552	-1.26552
120	8	7.69159	0.308415
121	10	6.27541	3.72459
122	8	5.92218	2.07782
123	5	6.59303	-1.59303
124	12	7.02956	4.97044
125	4	3.91588	0.0841161
126	5	4.63372	0.366277
127	4	5.45518	-1.45518
128	6	5.63832	0.361679
129	4	6.53917	-2.53917
130	4	5.38477	-1.38477
131	7	6.39601	0.603993
132	7	5.4144	1.5856
133	10	7.00646	2.99354
134	4	5.89206	-1.89206
135	5	5.53795	-0.537953
136	8	5.47777	2.52223
137	11	5.379	5.621
138	7	6.29822	0.701781
139	4	4.52174	-0.52174
140	8	5.67957	2.32043
141	6	6.84929	-0.84929
142	7	5.66302	1.33698
143	5	7.39343	-2.39343
144	4	5.84193	-1.84193
145	8	4.12948	3.87052
146	4	6.48715	-2.48715
147	8	6.11029	1.88971
148	6	4.19298	1.80702
149	4	5.3497	-1.3497
150	9	5.71232	3.28768
151	5	5.73194	-0.731937
152	6	5.65454	0.345463
153	4	4.85272	-0.852724
154	4	4.91971	-0.919711
155	4	4.26151	-0.261513
156	5	6.4144	-1.4144
157	6	6.23118	-0.231184
158	16	7.86947	8.13053
159	6	5.90009	0.0999085
160	6	6.07988	-0.079881
161	4	6.62306	-2.62306
162	4	5.98137	-1.98137

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 4 & 4.68951 & -0.689513 \tabularnewline
2 & 4 & 5.62336 & -1.62336 \tabularnewline
3 & 6 & 5.79432 & 0.205677 \tabularnewline
4 & 8 & 6.57995 & 1.42005 \tabularnewline
5 & 8 & 5.93284 & 2.06716 \tabularnewline
6 & 4 & 4.95477 & -0.954771 \tabularnewline
7 & 4 & 4.5863 & -0.586304 \tabularnewline
8 & 8 & 5.93467 & 2.06533 \tabularnewline
9 & 5 & 4.65522 & 0.344776 \tabularnewline
10 & 4 & 5.73765 & -1.73765 \tabularnewline
11 & 4 & 6.32863 & -2.32863 \tabularnewline
12 & 4 & 4.43665 & -0.436653 \tabularnewline
13 & 4 & 5.23493 & -1.23493 \tabularnewline
14 & 4 & 3.47546 & 0.524541 \tabularnewline
15 & 4 & 4.27773 & -0.277729 \tabularnewline
16 & 8 & 5.90397 & 2.09603 \tabularnewline
17 & 4 & 7.70079 & -3.70079 \tabularnewline
18 & 4 & 3.8308 & 0.169204 \tabularnewline
19 & 4 & 4.27818 & -0.278179 \tabularnewline
20 & 8 & 6.26842 & 1.73158 \tabularnewline
21 & 4 & 6.63507 & -2.63507 \tabularnewline
22 & 7 & 6.38549 & 0.614515 \tabularnewline
23 & 4 & 8.4472 & -4.4472 \tabularnewline
24 & 4 & 4.66831 & -0.668306 \tabularnewline
25 & 5 & 6.38283 & -1.38283 \tabularnewline
26 & 4 & 5.12608 & -1.12608 \tabularnewline
27 & 4 & 5.12608 & -1.12608 \tabularnewline
28 & 4 & 6.34385 & -2.34385 \tabularnewline
29 & 4 & 8.0677 & -4.0677 \tabularnewline
30 & 4 & 5.62567 & -1.62567 \tabularnewline
31 & 4 & 5.00142 & -1.00142 \tabularnewline
32 & 4 & 5.6788 & -1.6788 \tabularnewline
33 & 15 & 7.07725 & 7.92275 \tabularnewline
34 & 10 & 6.66041 & 3.33959 \tabularnewline
35 & 4 & 7.337 & -3.337 \tabularnewline
36 & 8 & 5.25657 & 2.74343 \tabularnewline
37 & 4 & 7.28198 & -3.28198 \tabularnewline
38 & 4 & 5.46629 & -1.46629 \tabularnewline
39 & 4 & 3.91588 & 0.0841161 \tabularnewline
40 & 4 & 4.61841 & -0.618415 \tabularnewline
41 & 7 & 5.47985 & 1.52015 \tabularnewline
42 & 4 & 5.47325 & -1.47325 \tabularnewline
43 & 6 & 5.92295 & 0.0770493 \tabularnewline
44 & 5 & 4.49752 & 0.502478 \tabularnewline
45 & 4 & 4.96441 & -0.964413 \tabularnewline
46 & 16 & 7.33822 & 8.66178 \tabularnewline
47 & 5 & 6.20034 & -1.20034 \tabularnewline
48 & 12 & 7.02983 & 4.97017 \tabularnewline
49 & 6 & 5.24112 & 0.758878 \tabularnewline
50 & 9 & 7.87718 & 1.12282 \tabularnewline
51 & 9 & 5.93594 & 3.06406 \tabularnewline
52 & 4 & 6.08405 & -2.08405 \tabularnewline
53 & 5 & 5.71847 & -0.718465 \tabularnewline
54 & 4 & 6.64185 & -2.64185 \tabularnewline
55 & 4 & 5.45441 & -1.45441 \tabularnewline
56 & 5 & 6.0638 & -1.0638 \tabularnewline
57 & 4 & 5.56451 & -1.56451 \tabularnewline
58 & 4 & 4.68332 & -0.68332 \tabularnewline
59 & 4 & 6.86144 & -2.86144 \tabularnewline
60 & 5 & 7.54385 & -2.54385 \tabularnewline
61 & 4 & 6.59297 & -2.59297 \tabularnewline
62 & 6 & 6.88866 & -0.888659 \tabularnewline
63 & 4 & 4.6762 & -0.676198 \tabularnewline
64 & 4 & 5.37517 & -1.37517 \tabularnewline
65 & 18 & 6.31323 & 11.6868 \tabularnewline
66 & 4 & 4.53022 & -0.53022 \tabularnewline
67 & 6 & 5.03217 & 0.967828 \tabularnewline
68 & 4 & 5.04947 & -1.04947 \tabularnewline
69 & 4 & 5.8116 & -1.8116 \tabularnewline
70 & 5 & 6.87782 & -1.87782 \tabularnewline
71 & 4 & 4.30351 & -0.30351 \tabularnewline
72 & 4 & 4.77117 & -0.771173 \tabularnewline
73 & 5 & 5.78153 & -0.781535 \tabularnewline
74 & 10 & 5.24344 & 4.75656 \tabularnewline
75 & 5 & 5.06181 & -0.0618107 \tabularnewline
76 & 8 & 7.53035 & 0.469653 \tabularnewline
77 & 8 & 7.50071 & 0.499292 \tabularnewline
78 & 5 & 5.17268 & -0.17268 \tabularnewline
79 & 4 & 4.36622 & -0.366217 \tabularnewline
80 & 4 & 6.97991 & -2.97991 \tabularnewline
81 & 4 & 4.15418 & -0.15418 \tabularnewline
82 & 5 & 6.34351 & -1.34351 \tabularnewline
83 & 4 & 5.60068 & -1.60068 \tabularnewline
84 & 4 & 5.47137 & -1.47137 \tabularnewline
85 & 8 & 6.3529 & 1.6471 \tabularnewline
86 & 4 & 5.17039 & -1.17039 \tabularnewline
87 & 5 & 4.96359 & 0.0364066 \tabularnewline
88 & 14 & 7.55568 & 6.44432 \tabularnewline
89 & 8 & 5.37689 & 2.62311 \tabularnewline
90 & 8 & 5.95216 & 2.04784 \tabularnewline
91 & 4 & 8.04466 & -4.04466 \tabularnewline
92 & 4 & 4.16489 & -0.164887 \tabularnewline
93 & 6 & 5.90009 & 0.0999085 \tabularnewline
94 & 4 & 5.0856 & -1.0856 \tabularnewline
95 & 7 & 6.00878 & 0.991218 \tabularnewline
96 & 7 & 5.55414 & 1.44586 \tabularnewline
97 & 4 & 5.60717 & -1.60717 \tabularnewline
98 & 6 & 6.21656 & -0.216559 \tabularnewline
99 & 4 & 6.29545 & -2.29545 \tabularnewline
100 & 7 & 4.34123 & 2.65877 \tabularnewline
101 & 4 & 6.28232 & -2.28232 \tabularnewline
102 & 4 & 5.21042 & -1.21042 \tabularnewline
103 & 8 & 6.11218 & 1.88782 \tabularnewline
104 & 4 & 5.14381 & -1.14381 \tabularnewline
105 & 4 & 5.9818 & -1.9818 \tabularnewline
106 & 10 & 6.53994 & 3.46006 \tabularnewline
107 & 8 & 7.15383 & 0.846171 \tabularnewline
108 & 6 & 5.65565 & 0.344349 \tabularnewline
109 & 4 & 4.45768 & -0.457676 \tabularnewline
110 & 4 & 5.16808 & -1.16808 \tabularnewline
111 & 4 & 5.45436 & -1.45436 \tabularnewline
112 & 5 & 5.79199 & -0.791987 \tabularnewline
113 & 4 & 6.78661 & -2.78661 \tabularnewline
114 & 6 & 6.31401 & -0.314005 \tabularnewline
115 & 4 & 4.48822 & -0.488223 \tabularnewline
116 & 5 & 6.08793 & -1.08793 \tabularnewline
117 & 7 & 7.5161 & -0.516104 \tabularnewline
118 & 8 & 5.73654 & 2.26346 \tabularnewline
119 & 5 & 6.26552 & -1.26552 \tabularnewline
120 & 8 & 7.69159 & 0.308415 \tabularnewline
121 & 10 & 6.27541 & 3.72459 \tabularnewline
122 & 8 & 5.92218 & 2.07782 \tabularnewline
123 & 5 & 6.59303 & -1.59303 \tabularnewline
124 & 12 & 7.02956 & 4.97044 \tabularnewline
125 & 4 & 3.91588 & 0.0841161 \tabularnewline
126 & 5 & 4.63372 & 0.366277 \tabularnewline
127 & 4 & 5.45518 & -1.45518 \tabularnewline
128 & 6 & 5.63832 & 0.361679 \tabularnewline
129 & 4 & 6.53917 & -2.53917 \tabularnewline
130 & 4 & 5.38477 & -1.38477 \tabularnewline
131 & 7 & 6.39601 & 0.603993 \tabularnewline
132 & 7 & 5.4144 & 1.5856 \tabularnewline
133 & 10 & 7.00646 & 2.99354 \tabularnewline
134 & 4 & 5.89206 & -1.89206 \tabularnewline
135 & 5 & 5.53795 & -0.537953 \tabularnewline
136 & 8 & 5.47777 & 2.52223 \tabularnewline
137 & 11 & 5.379 & 5.621 \tabularnewline
138 & 7 & 6.29822 & 0.701781 \tabularnewline
139 & 4 & 4.52174 & -0.52174 \tabularnewline
140 & 8 & 5.67957 & 2.32043 \tabularnewline
141 & 6 & 6.84929 & -0.84929 \tabularnewline
142 & 7 & 5.66302 & 1.33698 \tabularnewline
143 & 5 & 7.39343 & -2.39343 \tabularnewline
144 & 4 & 5.84193 & -1.84193 \tabularnewline
145 & 8 & 4.12948 & 3.87052 \tabularnewline
146 & 4 & 6.48715 & -2.48715 \tabularnewline
147 & 8 & 6.11029 & 1.88971 \tabularnewline
148 & 6 & 4.19298 & 1.80702 \tabularnewline
149 & 4 & 5.3497 & -1.3497 \tabularnewline
150 & 9 & 5.71232 & 3.28768 \tabularnewline
151 & 5 & 5.73194 & -0.731937 \tabularnewline
152 & 6 & 5.65454 & 0.345463 \tabularnewline
153 & 4 & 4.85272 & -0.852724 \tabularnewline
154 & 4 & 4.91971 & -0.919711 \tabularnewline
155 & 4 & 4.26151 & -0.261513 \tabularnewline
156 & 5 & 6.4144 & -1.4144 \tabularnewline
157 & 6 & 6.23118 & -0.231184 \tabularnewline
158 & 16 & 7.86947 & 8.13053 \tabularnewline
159 & 6 & 5.90009 & 0.0999085 \tabularnewline
160 & 6 & 6.07988 & -0.079881 \tabularnewline
161 & 4 & 6.62306 & -2.62306 \tabularnewline
162 & 4 & 5.98137 & -1.98137 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=226370&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]4[/C][C]4.68951[/C][C]-0.689513[/C][/ROW]
[ROW][C]2[/C][C]4[/C][C]5.62336[/C][C]-1.62336[/C][/ROW]
[ROW][C]3[/C][C]6[/C][C]5.79432[/C][C]0.205677[/C][/ROW]
[ROW][C]4[/C][C]8[/C][C]6.57995[/C][C]1.42005[/C][/ROW]
[ROW][C]5[/C][C]8[/C][C]5.93284[/C][C]2.06716[/C][/ROW]
[ROW][C]6[/C][C]4[/C][C]4.95477[/C][C]-0.954771[/C][/ROW]
[ROW][C]7[/C][C]4[/C][C]4.5863[/C][C]-0.586304[/C][/ROW]
[ROW][C]8[/C][C]8[/C][C]5.93467[/C][C]2.06533[/C][/ROW]
[ROW][C]9[/C][C]5[/C][C]4.65522[/C][C]0.344776[/C][/ROW]
[ROW][C]10[/C][C]4[/C][C]5.73765[/C][C]-1.73765[/C][/ROW]
[ROW][C]11[/C][C]4[/C][C]6.32863[/C][C]-2.32863[/C][/ROW]
[ROW][C]12[/C][C]4[/C][C]4.43665[/C][C]-0.436653[/C][/ROW]
[ROW][C]13[/C][C]4[/C][C]5.23493[/C][C]-1.23493[/C][/ROW]
[ROW][C]14[/C][C]4[/C][C]3.47546[/C][C]0.524541[/C][/ROW]
[ROW][C]15[/C][C]4[/C][C]4.27773[/C][C]-0.277729[/C][/ROW]
[ROW][C]16[/C][C]8[/C][C]5.90397[/C][C]2.09603[/C][/ROW]
[ROW][C]17[/C][C]4[/C][C]7.70079[/C][C]-3.70079[/C][/ROW]
[ROW][C]18[/C][C]4[/C][C]3.8308[/C][C]0.169204[/C][/ROW]
[ROW][C]19[/C][C]4[/C][C]4.27818[/C][C]-0.278179[/C][/ROW]
[ROW][C]20[/C][C]8[/C][C]6.26842[/C][C]1.73158[/C][/ROW]
[ROW][C]21[/C][C]4[/C][C]6.63507[/C][C]-2.63507[/C][/ROW]
[ROW][C]22[/C][C]7[/C][C]6.38549[/C][C]0.614515[/C][/ROW]
[ROW][C]23[/C][C]4[/C][C]8.4472[/C][C]-4.4472[/C][/ROW]
[ROW][C]24[/C][C]4[/C][C]4.66831[/C][C]-0.668306[/C][/ROW]
[ROW][C]25[/C][C]5[/C][C]6.38283[/C][C]-1.38283[/C][/ROW]
[ROW][C]26[/C][C]4[/C][C]5.12608[/C][C]-1.12608[/C][/ROW]
[ROW][C]27[/C][C]4[/C][C]5.12608[/C][C]-1.12608[/C][/ROW]
[ROW][C]28[/C][C]4[/C][C]6.34385[/C][C]-2.34385[/C][/ROW]
[ROW][C]29[/C][C]4[/C][C]8.0677[/C][C]-4.0677[/C][/ROW]
[ROW][C]30[/C][C]4[/C][C]5.62567[/C][C]-1.62567[/C][/ROW]
[ROW][C]31[/C][C]4[/C][C]5.00142[/C][C]-1.00142[/C][/ROW]
[ROW][C]32[/C][C]4[/C][C]5.6788[/C][C]-1.6788[/C][/ROW]
[ROW][C]33[/C][C]15[/C][C]7.07725[/C][C]7.92275[/C][/ROW]
[ROW][C]34[/C][C]10[/C][C]6.66041[/C][C]3.33959[/C][/ROW]
[ROW][C]35[/C][C]4[/C][C]7.337[/C][C]-3.337[/C][/ROW]
[ROW][C]36[/C][C]8[/C][C]5.25657[/C][C]2.74343[/C][/ROW]
[ROW][C]37[/C][C]4[/C][C]7.28198[/C][C]-3.28198[/C][/ROW]
[ROW][C]38[/C][C]4[/C][C]5.46629[/C][C]-1.46629[/C][/ROW]
[ROW][C]39[/C][C]4[/C][C]3.91588[/C][C]0.0841161[/C][/ROW]
[ROW][C]40[/C][C]4[/C][C]4.61841[/C][C]-0.618415[/C][/ROW]
[ROW][C]41[/C][C]7[/C][C]5.47985[/C][C]1.52015[/C][/ROW]
[ROW][C]42[/C][C]4[/C][C]5.47325[/C][C]-1.47325[/C][/ROW]
[ROW][C]43[/C][C]6[/C][C]5.92295[/C][C]0.0770493[/C][/ROW]
[ROW][C]44[/C][C]5[/C][C]4.49752[/C][C]0.502478[/C][/ROW]
[ROW][C]45[/C][C]4[/C][C]4.96441[/C][C]-0.964413[/C][/ROW]
[ROW][C]46[/C][C]16[/C][C]7.33822[/C][C]8.66178[/C][/ROW]
[ROW][C]47[/C][C]5[/C][C]6.20034[/C][C]-1.20034[/C][/ROW]
[ROW][C]48[/C][C]12[/C][C]7.02983[/C][C]4.97017[/C][/ROW]
[ROW][C]49[/C][C]6[/C][C]5.24112[/C][C]0.758878[/C][/ROW]
[ROW][C]50[/C][C]9[/C][C]7.87718[/C][C]1.12282[/C][/ROW]
[ROW][C]51[/C][C]9[/C][C]5.93594[/C][C]3.06406[/C][/ROW]
[ROW][C]52[/C][C]4[/C][C]6.08405[/C][C]-2.08405[/C][/ROW]
[ROW][C]53[/C][C]5[/C][C]5.71847[/C][C]-0.718465[/C][/ROW]
[ROW][C]54[/C][C]4[/C][C]6.64185[/C][C]-2.64185[/C][/ROW]
[ROW][C]55[/C][C]4[/C][C]5.45441[/C][C]-1.45441[/C][/ROW]
[ROW][C]56[/C][C]5[/C][C]6.0638[/C][C]-1.0638[/C][/ROW]
[ROW][C]57[/C][C]4[/C][C]5.56451[/C][C]-1.56451[/C][/ROW]
[ROW][C]58[/C][C]4[/C][C]4.68332[/C][C]-0.68332[/C][/ROW]
[ROW][C]59[/C][C]4[/C][C]6.86144[/C][C]-2.86144[/C][/ROW]
[ROW][C]60[/C][C]5[/C][C]7.54385[/C][C]-2.54385[/C][/ROW]
[ROW][C]61[/C][C]4[/C][C]6.59297[/C][C]-2.59297[/C][/ROW]
[ROW][C]62[/C][C]6[/C][C]6.88866[/C][C]-0.888659[/C][/ROW]
[ROW][C]63[/C][C]4[/C][C]4.6762[/C][C]-0.676198[/C][/ROW]
[ROW][C]64[/C][C]4[/C][C]5.37517[/C][C]-1.37517[/C][/ROW]
[ROW][C]65[/C][C]18[/C][C]6.31323[/C][C]11.6868[/C][/ROW]
[ROW][C]66[/C][C]4[/C][C]4.53022[/C][C]-0.53022[/C][/ROW]
[ROW][C]67[/C][C]6[/C][C]5.03217[/C][C]0.967828[/C][/ROW]
[ROW][C]68[/C][C]4[/C][C]5.04947[/C][C]-1.04947[/C][/ROW]
[ROW][C]69[/C][C]4[/C][C]5.8116[/C][C]-1.8116[/C][/ROW]
[ROW][C]70[/C][C]5[/C][C]6.87782[/C][C]-1.87782[/C][/ROW]
[ROW][C]71[/C][C]4[/C][C]4.30351[/C][C]-0.30351[/C][/ROW]
[ROW][C]72[/C][C]4[/C][C]4.77117[/C][C]-0.771173[/C][/ROW]
[ROW][C]73[/C][C]5[/C][C]5.78153[/C][C]-0.781535[/C][/ROW]
[ROW][C]74[/C][C]10[/C][C]5.24344[/C][C]4.75656[/C][/ROW]
[ROW][C]75[/C][C]5[/C][C]5.06181[/C][C]-0.0618107[/C][/ROW]
[ROW][C]76[/C][C]8[/C][C]7.53035[/C][C]0.469653[/C][/ROW]
[ROW][C]77[/C][C]8[/C][C]7.50071[/C][C]0.499292[/C][/ROW]
[ROW][C]78[/C][C]5[/C][C]5.17268[/C][C]-0.17268[/C][/ROW]
[ROW][C]79[/C][C]4[/C][C]4.36622[/C][C]-0.366217[/C][/ROW]
[ROW][C]80[/C][C]4[/C][C]6.97991[/C][C]-2.97991[/C][/ROW]
[ROW][C]81[/C][C]4[/C][C]4.15418[/C][C]-0.15418[/C][/ROW]
[ROW][C]82[/C][C]5[/C][C]6.34351[/C][C]-1.34351[/C][/ROW]
[ROW][C]83[/C][C]4[/C][C]5.60068[/C][C]-1.60068[/C][/ROW]
[ROW][C]84[/C][C]4[/C][C]5.47137[/C][C]-1.47137[/C][/ROW]
[ROW][C]85[/C][C]8[/C][C]6.3529[/C][C]1.6471[/C][/ROW]
[ROW][C]86[/C][C]4[/C][C]5.17039[/C][C]-1.17039[/C][/ROW]
[ROW][C]87[/C][C]5[/C][C]4.96359[/C][C]0.0364066[/C][/ROW]
[ROW][C]88[/C][C]14[/C][C]7.55568[/C][C]6.44432[/C][/ROW]
[ROW][C]89[/C][C]8[/C][C]5.37689[/C][C]2.62311[/C][/ROW]
[ROW][C]90[/C][C]8[/C][C]5.95216[/C][C]2.04784[/C][/ROW]
[ROW][C]91[/C][C]4[/C][C]8.04466[/C][C]-4.04466[/C][/ROW]
[ROW][C]92[/C][C]4[/C][C]4.16489[/C][C]-0.164887[/C][/ROW]
[ROW][C]93[/C][C]6[/C][C]5.90009[/C][C]0.0999085[/C][/ROW]
[ROW][C]94[/C][C]4[/C][C]5.0856[/C][C]-1.0856[/C][/ROW]
[ROW][C]95[/C][C]7[/C][C]6.00878[/C][C]0.991218[/C][/ROW]
[ROW][C]96[/C][C]7[/C][C]5.55414[/C][C]1.44586[/C][/ROW]
[ROW][C]97[/C][C]4[/C][C]5.60717[/C][C]-1.60717[/C][/ROW]
[ROW][C]98[/C][C]6[/C][C]6.21656[/C][C]-0.216559[/C][/ROW]
[ROW][C]99[/C][C]4[/C][C]6.29545[/C][C]-2.29545[/C][/ROW]
[ROW][C]100[/C][C]7[/C][C]4.34123[/C][C]2.65877[/C][/ROW]
[ROW][C]101[/C][C]4[/C][C]6.28232[/C][C]-2.28232[/C][/ROW]
[ROW][C]102[/C][C]4[/C][C]5.21042[/C][C]-1.21042[/C][/ROW]
[ROW][C]103[/C][C]8[/C][C]6.11218[/C][C]1.88782[/C][/ROW]
[ROW][C]104[/C][C]4[/C][C]5.14381[/C][C]-1.14381[/C][/ROW]
[ROW][C]105[/C][C]4[/C][C]5.9818[/C][C]-1.9818[/C][/ROW]
[ROW][C]106[/C][C]10[/C][C]6.53994[/C][C]3.46006[/C][/ROW]
[ROW][C]107[/C][C]8[/C][C]7.15383[/C][C]0.846171[/C][/ROW]
[ROW][C]108[/C][C]6[/C][C]5.65565[/C][C]0.344349[/C][/ROW]
[ROW][C]109[/C][C]4[/C][C]4.45768[/C][C]-0.457676[/C][/ROW]
[ROW][C]110[/C][C]4[/C][C]5.16808[/C][C]-1.16808[/C][/ROW]
[ROW][C]111[/C][C]4[/C][C]5.45436[/C][C]-1.45436[/C][/ROW]
[ROW][C]112[/C][C]5[/C][C]5.79199[/C][C]-0.791987[/C][/ROW]
[ROW][C]113[/C][C]4[/C][C]6.78661[/C][C]-2.78661[/C][/ROW]
[ROW][C]114[/C][C]6[/C][C]6.31401[/C][C]-0.314005[/C][/ROW]
[ROW][C]115[/C][C]4[/C][C]4.48822[/C][C]-0.488223[/C][/ROW]
[ROW][C]116[/C][C]5[/C][C]6.08793[/C][C]-1.08793[/C][/ROW]
[ROW][C]117[/C][C]7[/C][C]7.5161[/C][C]-0.516104[/C][/ROW]
[ROW][C]118[/C][C]8[/C][C]5.73654[/C][C]2.26346[/C][/ROW]
[ROW][C]119[/C][C]5[/C][C]6.26552[/C][C]-1.26552[/C][/ROW]
[ROW][C]120[/C][C]8[/C][C]7.69159[/C][C]0.308415[/C][/ROW]
[ROW][C]121[/C][C]10[/C][C]6.27541[/C][C]3.72459[/C][/ROW]
[ROW][C]122[/C][C]8[/C][C]5.92218[/C][C]2.07782[/C][/ROW]
[ROW][C]123[/C][C]5[/C][C]6.59303[/C][C]-1.59303[/C][/ROW]
[ROW][C]124[/C][C]12[/C][C]7.02956[/C][C]4.97044[/C][/ROW]
[ROW][C]125[/C][C]4[/C][C]3.91588[/C][C]0.0841161[/C][/ROW]
[ROW][C]126[/C][C]5[/C][C]4.63372[/C][C]0.366277[/C][/ROW]
[ROW][C]127[/C][C]4[/C][C]5.45518[/C][C]-1.45518[/C][/ROW]
[ROW][C]128[/C][C]6[/C][C]5.63832[/C][C]0.361679[/C][/ROW]
[ROW][C]129[/C][C]4[/C][C]6.53917[/C][C]-2.53917[/C][/ROW]
[ROW][C]130[/C][C]4[/C][C]5.38477[/C][C]-1.38477[/C][/ROW]
[ROW][C]131[/C][C]7[/C][C]6.39601[/C][C]0.603993[/C][/ROW]
[ROW][C]132[/C][C]7[/C][C]5.4144[/C][C]1.5856[/C][/ROW]
[ROW][C]133[/C][C]10[/C][C]7.00646[/C][C]2.99354[/C][/ROW]
[ROW][C]134[/C][C]4[/C][C]5.89206[/C][C]-1.89206[/C][/ROW]
[ROW][C]135[/C][C]5[/C][C]5.53795[/C][C]-0.537953[/C][/ROW]
[ROW][C]136[/C][C]8[/C][C]5.47777[/C][C]2.52223[/C][/ROW]
[ROW][C]137[/C][C]11[/C][C]5.379[/C][C]5.621[/C][/ROW]
[ROW][C]138[/C][C]7[/C][C]6.29822[/C][C]0.701781[/C][/ROW]
[ROW][C]139[/C][C]4[/C][C]4.52174[/C][C]-0.52174[/C][/ROW]
[ROW][C]140[/C][C]8[/C][C]5.67957[/C][C]2.32043[/C][/ROW]
[ROW][C]141[/C][C]6[/C][C]6.84929[/C][C]-0.84929[/C][/ROW]
[ROW][C]142[/C][C]7[/C][C]5.66302[/C][C]1.33698[/C][/ROW]
[ROW][C]143[/C][C]5[/C][C]7.39343[/C][C]-2.39343[/C][/ROW]
[ROW][C]144[/C][C]4[/C][C]5.84193[/C][C]-1.84193[/C][/ROW]
[ROW][C]145[/C][C]8[/C][C]4.12948[/C][C]3.87052[/C][/ROW]
[ROW][C]146[/C][C]4[/C][C]6.48715[/C][C]-2.48715[/C][/ROW]
[ROW][C]147[/C][C]8[/C][C]6.11029[/C][C]1.88971[/C][/ROW]
[ROW][C]148[/C][C]6[/C][C]4.19298[/C][C]1.80702[/C][/ROW]
[ROW][C]149[/C][C]4[/C][C]5.3497[/C][C]-1.3497[/C][/ROW]
[ROW][C]150[/C][C]9[/C][C]5.71232[/C][C]3.28768[/C][/ROW]
[ROW][C]151[/C][C]5[/C][C]5.73194[/C][C]-0.731937[/C][/ROW]
[ROW][C]152[/C][C]6[/C][C]5.65454[/C][C]0.345463[/C][/ROW]
[ROW][C]153[/C][C]4[/C][C]4.85272[/C][C]-0.852724[/C][/ROW]
[ROW][C]154[/C][C]4[/C][C]4.91971[/C][C]-0.919711[/C][/ROW]
[ROW][C]155[/C][C]4[/C][C]4.26151[/C][C]-0.261513[/C][/ROW]
[ROW][C]156[/C][C]5[/C][C]6.4144[/C][C]-1.4144[/C][/ROW]
[ROW][C]157[/C][C]6[/C][C]6.23118[/C][C]-0.231184[/C][/ROW]
[ROW][C]158[/C][C]16[/C][C]7.86947[/C][C]8.13053[/C][/ROW]
[ROW][C]159[/C][C]6[/C][C]5.90009[/C][C]0.0999085[/C][/ROW]
[ROW][C]160[/C][C]6[/C][C]6.07988[/C][C]-0.079881[/C][/ROW]
[ROW][C]161[/C][C]4[/C][C]6.62306[/C][C]-2.62306[/C][/ROW]
[ROW][C]162[/C][C]4[/C][C]5.98137[/C][C]-1.98137[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=226370&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=226370&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	4	4.68951	-0.689513
2	4	5.62336	-1.62336
3	6	5.79432	0.205677
4	8	6.57995	1.42005
5	8	5.93284	2.06716
6	4	4.95477	-0.954771
7	4	4.5863	-0.586304
8	8	5.93467	2.06533
9	5	4.65522	0.344776
10	4	5.73765	-1.73765
11	4	6.32863	-2.32863
12	4	4.43665	-0.436653
13	4	5.23493	-1.23493
14	4	3.47546	0.524541
15	4	4.27773	-0.277729
16	8	5.90397	2.09603
17	4	7.70079	-3.70079
18	4	3.8308	0.169204
19	4	4.27818	-0.278179
20	8	6.26842	1.73158
21	4	6.63507	-2.63507
22	7	6.38549	0.614515
23	4	8.4472	-4.4472
24	4	4.66831	-0.668306
25	5	6.38283	-1.38283
26	4	5.12608	-1.12608
27	4	5.12608	-1.12608
28	4	6.34385	-2.34385
29	4	8.0677	-4.0677
30	4	5.62567	-1.62567
31	4	5.00142	-1.00142
32	4	5.6788	-1.6788
33	15	7.07725	7.92275
34	10	6.66041	3.33959
35	4	7.337	-3.337
36	8	5.25657	2.74343
37	4	7.28198	-3.28198
38	4	5.46629	-1.46629
39	4	3.91588	0.0841161
40	4	4.61841	-0.618415
41	7	5.47985	1.52015
42	4	5.47325	-1.47325
43	6	5.92295	0.0770493
44	5	4.49752	0.502478
45	4	4.96441	-0.964413
46	16	7.33822	8.66178
47	5	6.20034	-1.20034
48	12	7.02983	4.97017
49	6	5.24112	0.758878
50	9	7.87718	1.12282
51	9	5.93594	3.06406
52	4	6.08405	-2.08405
53	5	5.71847	-0.718465
54	4	6.64185	-2.64185
55	4	5.45441	-1.45441
56	5	6.0638	-1.0638
57	4	5.56451	-1.56451
58	4	4.68332	-0.68332
59	4	6.86144	-2.86144
60	5	7.54385	-2.54385
61	4	6.59297	-2.59297
62	6	6.88866	-0.888659
63	4	4.6762	-0.676198
64	4	5.37517	-1.37517
65	18	6.31323	11.6868
66	4	4.53022	-0.53022
67	6	5.03217	0.967828
68	4	5.04947	-1.04947
69	4	5.8116	-1.8116
70	5	6.87782	-1.87782
71	4	4.30351	-0.30351
72	4	4.77117	-0.771173
73	5	5.78153	-0.781535
74	10	5.24344	4.75656
75	5	5.06181	-0.0618107
76	8	7.53035	0.469653
77	8	7.50071	0.499292
78	5	5.17268	-0.17268
79	4	4.36622	-0.366217
80	4	6.97991	-2.97991
81	4	4.15418	-0.15418
82	5	6.34351	-1.34351
83	4	5.60068	-1.60068
84	4	5.47137	-1.47137
85	8	6.3529	1.6471
86	4	5.17039	-1.17039
87	5	4.96359	0.0364066
88	14	7.55568	6.44432
89	8	5.37689	2.62311
90	8	5.95216	2.04784
91	4	8.04466	-4.04466
92	4	4.16489	-0.164887
93	6	5.90009	0.0999085
94	4	5.0856	-1.0856
95	7	6.00878	0.991218
96	7	5.55414	1.44586
97	4	5.60717	-1.60717
98	6	6.21656	-0.216559
99	4	6.29545	-2.29545
100	7	4.34123	2.65877
101	4	6.28232	-2.28232
102	4	5.21042	-1.21042
103	8	6.11218	1.88782
104	4	5.14381	-1.14381
105	4	5.9818	-1.9818
106	10	6.53994	3.46006
107	8	7.15383	0.846171
108	6	5.65565	0.344349
109	4	4.45768	-0.457676
110	4	5.16808	-1.16808
111	4	5.45436	-1.45436
112	5	5.79199	-0.791987
113	4	6.78661	-2.78661
114	6	6.31401	-0.314005
115	4	4.48822	-0.488223
116	5	6.08793	-1.08793
117	7	7.5161	-0.516104
118	8	5.73654	2.26346
119	5	6.26552	-1.26552
120	8	7.69159	0.308415
121	10	6.27541	3.72459
122	8	5.92218	2.07782
123	5	6.59303	-1.59303
124	12	7.02956	4.97044
125	4	3.91588	0.0841161
126	5	4.63372	0.366277
127	4	5.45518	-1.45518
128	6	5.63832	0.361679
129	4	6.53917	-2.53917
130	4	5.38477	-1.38477
131	7	6.39601	0.603993
132	7	5.4144	1.5856
133	10	7.00646	2.99354
134	4	5.89206	-1.89206
135	5	5.53795	-0.537953
136	8	5.47777	2.52223
137	11	5.379	5.621
138	7	6.29822	0.701781
139	4	4.52174	-0.52174
140	8	5.67957	2.32043
141	6	6.84929	-0.84929
142	7	5.66302	1.33698
143	5	7.39343	-2.39343
144	4	5.84193	-1.84193
145	8	4.12948	3.87052
146	4	6.48715	-2.48715
147	8	6.11029	1.88971
148	6	4.19298	1.80702
149	4	5.3497	-1.3497
150	9	5.71232	3.28768
151	5	5.73194	-0.731937
152	6	5.65454	0.345463
153	4	4.85272	-0.852724
154	4	4.91971	-0.919711
155	4	4.26151	-0.261513
156	5	6.4144	-1.4144
157	6	6.23118	-0.231184
158	16	7.86947	8.13053
159	6	5.90009	0.0999085
160	6	6.07988	-0.079881
161	4	6.62306	-2.62306
162	4	5.98137	-1.98137

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
8	0.143396	0.286792	0.856604
9	0.0675805	0.135161	0.93242
10	0.140164	0.280329	0.859836
11	0.122457	0.244914	0.877543
12	0.0971469	0.194294	0.902853
13	0.0589228	0.117846	0.941077
14	0.0548184	0.109637	0.945182
15	0.0327688	0.0655375	0.967231
16	0.0327018	0.0654036	0.967298
17	0.0554249	0.11085	0.944575
18	0.0338683	0.0677366	0.966132
19	0.0221978	0.0443956	0.977802
20	0.0190203	0.0380405	0.98098
21	0.0135608	0.0271215	0.986439
22	0.00798543	0.0159709	0.992015
23	0.00668298	0.013366	0.993317
24	0.00390019	0.00780038	0.9961
25	0.00217352	0.00434704	0.997826
26	0.00200751	0.00401502	0.997992
27	0.00156594	0.00313188	0.998434
28	0.00138635	0.0027727	0.998614
29	0.00185829	0.00371659	0.998142
30	0.00139483	0.00278966	0.998605
31	0.000791953	0.00158391	0.999208
32	0.000513774	0.00102755	0.999486
33	0.204387	0.408774	0.795613
34	0.367758	0.735515	0.632242
35	0.409293	0.818585	0.590707
36	0.435127	0.870254	0.564873
37	0.448666	0.897332	0.551334
38	0.403983	0.807967	0.596017
39	0.354273	0.708546	0.645727
40	0.305088	0.610176	0.694912
41	0.297492	0.594984	0.702508
42	0.266171	0.532343	0.733829
43	0.224768	0.449535	0.775232
44	0.187437	0.374874	0.812563
45	0.161088	0.322177	0.838912
46	0.768147	0.463706	0.231853
47	0.733839	0.532322	0.266161
48	0.830521	0.338957	0.169479
49	0.80163	0.39674	0.19837
50	0.776198	0.447605	0.223802
51	0.798773	0.402454	0.201227
52	0.785552	0.428896	0.214448
53	0.751299	0.497402	0.248701
54	0.75079	0.498419	0.24921
55	0.726222	0.547556	0.273778
56	0.690713	0.618575	0.309287
57	0.662211	0.675577	0.337789
58	0.620451	0.759098	0.379549
59	0.625496	0.749009	0.374504
60	0.626538	0.746924	0.373462
61	0.626496	0.747008	0.373504
62	0.58521	0.82958	0.41479
63	0.541183	0.917635	0.458817
64	0.50602	0.98796	0.49398
65	0.99375	0.0125009	0.00625045
66	0.991495	0.0170108	0.00850538
67	0.988972	0.0220566	0.0110283
68	0.985847	0.0283068	0.0141534
69	0.983757	0.032485	0.0162425
70	0.982174	0.0356526	0.0178263
71	0.976553	0.0468941	0.023447
72	0.970078	0.0598431	0.0299215
73	0.962305	0.0753897	0.0376948
74	0.981483	0.0370331	0.0185166
75	0.975575	0.0488509	0.0244255
76	0.968518	0.0629636	0.0314818
77	0.959873	0.0802535	0.0401267
78	0.948977	0.102045	0.0510226
79	0.936023	0.127953	0.0639765
80	0.943705	0.11259	0.0562951
81	0.929392	0.141216	0.0706078
82	0.919625	0.16075	0.080375
83	0.908484	0.183033	0.0915163
84	0.896817	0.206366	0.103183
85	0.884309	0.231383	0.115691
86	0.864716	0.270567	0.135284
87	0.838049	0.323902	0.161951
88	0.951824	0.096352	0.048176
89	0.951514	0.0969723	0.0484861
90	0.947552	0.104897	0.0524485
91	0.966251	0.0674972	0.0337486
92	0.956465	0.0870704	0.0435352
93	0.944423	0.111154	0.0555771
94	0.933085	0.133831	0.0669153
95	0.918677	0.162646	0.0813232
96	0.90534	0.189321	0.0946604
97	0.893953	0.212095	0.106047
98	0.870906	0.258187	0.129094
99	0.87013	0.259739	0.12987
100	0.874027	0.251947	0.125973
101	0.875597	0.248806	0.124403
102	0.855155	0.289691	0.144845
103	0.841685	0.316629	0.158315
104	0.81918	0.36164	0.18082
105	0.812862	0.374276	0.187138
106	0.837682	0.324637	0.162318
107	0.808117	0.383765	0.191883
108	0.772835	0.454329	0.227165
109	0.735066	0.529869	0.264934
110	0.705202	0.589597	0.294798
111	0.682942	0.634117	0.317058
112	0.645627	0.708745	0.354373
113	0.665585	0.66883	0.334415
114	0.620592	0.758816	0.379408
115	0.575531	0.848938	0.424469
116	0.537441	0.925117	0.462559
117	0.497521	0.995041	0.502479
118	0.477063	0.954127	0.522937
119	0.448034	0.896067	0.551966
120	0.398034	0.796067	0.601966
121	0.450267	0.900534	0.549733
122	0.419797	0.839594	0.580203
123	0.40343	0.806859	0.59657
124	0.602726	0.794548	0.397274
125	0.551275	0.89745	0.448725
126	0.494969	0.989938	0.505031
127	0.475241	0.950482	0.524759
128	0.418581	0.837161	0.581419
129	0.41687	0.83374	0.58313
130	0.38352	0.76704	0.61648
131	0.33147	0.662939	0.66853
132	0.287768	0.575537	0.712232
133	0.346137	0.692274	0.653863
134	0.31184	0.62368	0.68816
135	0.295976	0.591953	0.704024
136	0.434216	0.868431	0.565784
137	0.54767	0.90466	0.45233
138	0.491513	0.983025	0.508487
139	0.442251	0.884502	0.557749
140	0.386702	0.773404	0.613298
141	0.34761	0.695219	0.65239
142	0.31334	0.626681	0.68666
143	0.495705	0.99141	0.504295
144	0.558568	0.882864	0.441432
145	0.625208	0.749583	0.374792
146	0.585555	0.82889	0.414445
147	0.54924	0.90152	0.45076
148	0.848166	0.303669	0.151834
149	0.777325	0.44535	0.222675
150	0.858586	0.282827	0.141414
151	0.775726	0.448548	0.224274
152	0.659921	0.680158	0.340079
153	0.514081	0.971839	0.485919
154	0.428408	0.856817	0.571592

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
8 & 0.143396 & 0.286792 & 0.856604 \tabularnewline
9 & 0.0675805 & 0.135161 & 0.93242 \tabularnewline
10 & 0.140164 & 0.280329 & 0.859836 \tabularnewline
11 & 0.122457 & 0.244914 & 0.877543 \tabularnewline
12 & 0.0971469 & 0.194294 & 0.902853 \tabularnewline
13 & 0.0589228 & 0.117846 & 0.941077 \tabularnewline
14 & 0.0548184 & 0.109637 & 0.945182 \tabularnewline
15 & 0.0327688 & 0.0655375 & 0.967231 \tabularnewline
16 & 0.0327018 & 0.0654036 & 0.967298 \tabularnewline
17 & 0.0554249 & 0.11085 & 0.944575 \tabularnewline
18 & 0.0338683 & 0.0677366 & 0.966132 \tabularnewline
19 & 0.0221978 & 0.0443956 & 0.977802 \tabularnewline
20 & 0.0190203 & 0.0380405 & 0.98098 \tabularnewline
21 & 0.0135608 & 0.0271215 & 0.986439 \tabularnewline
22 & 0.00798543 & 0.0159709 & 0.992015 \tabularnewline
23 & 0.00668298 & 0.013366 & 0.993317 \tabularnewline
24 & 0.00390019 & 0.00780038 & 0.9961 \tabularnewline
25 & 0.00217352 & 0.00434704 & 0.997826 \tabularnewline
26 & 0.00200751 & 0.00401502 & 0.997992 \tabularnewline
27 & 0.00156594 & 0.00313188 & 0.998434 \tabularnewline
28 & 0.00138635 & 0.0027727 & 0.998614 \tabularnewline
29 & 0.00185829 & 0.00371659 & 0.998142 \tabularnewline
30 & 0.00139483 & 0.00278966 & 0.998605 \tabularnewline
31 & 0.000791953 & 0.00158391 & 0.999208 \tabularnewline
32 & 0.000513774 & 0.00102755 & 0.999486 \tabularnewline
33 & 0.204387 & 0.408774 & 0.795613 \tabularnewline
34 & 0.367758 & 0.735515 & 0.632242 \tabularnewline
35 & 0.409293 & 0.818585 & 0.590707 \tabularnewline
36 & 0.435127 & 0.870254 & 0.564873 \tabularnewline
37 & 0.448666 & 0.897332 & 0.551334 \tabularnewline
38 & 0.403983 & 0.807967 & 0.596017 \tabularnewline
39 & 0.354273 & 0.708546 & 0.645727 \tabularnewline
40 & 0.305088 & 0.610176 & 0.694912 \tabularnewline
41 & 0.297492 & 0.594984 & 0.702508 \tabularnewline
42 & 0.266171 & 0.532343 & 0.733829 \tabularnewline
43 & 0.224768 & 0.449535 & 0.775232 \tabularnewline
44 & 0.187437 & 0.374874 & 0.812563 \tabularnewline
45 & 0.161088 & 0.322177 & 0.838912 \tabularnewline
46 & 0.768147 & 0.463706 & 0.231853 \tabularnewline
47 & 0.733839 & 0.532322 & 0.266161 \tabularnewline
48 & 0.830521 & 0.338957 & 0.169479 \tabularnewline
49 & 0.80163 & 0.39674 & 0.19837 \tabularnewline
50 & 0.776198 & 0.447605 & 0.223802 \tabularnewline
51 & 0.798773 & 0.402454 & 0.201227 \tabularnewline
52 & 0.785552 & 0.428896 & 0.214448 \tabularnewline
53 & 0.751299 & 0.497402 & 0.248701 \tabularnewline
54 & 0.75079 & 0.498419 & 0.24921 \tabularnewline
55 & 0.726222 & 0.547556 & 0.273778 \tabularnewline
56 & 0.690713 & 0.618575 & 0.309287 \tabularnewline
57 & 0.662211 & 0.675577 & 0.337789 \tabularnewline
58 & 0.620451 & 0.759098 & 0.379549 \tabularnewline
59 & 0.625496 & 0.749009 & 0.374504 \tabularnewline
60 & 0.626538 & 0.746924 & 0.373462 \tabularnewline
61 & 0.626496 & 0.747008 & 0.373504 \tabularnewline
62 & 0.58521 & 0.82958 & 0.41479 \tabularnewline
63 & 0.541183 & 0.917635 & 0.458817 \tabularnewline
64 & 0.50602 & 0.98796 & 0.49398 \tabularnewline
65 & 0.99375 & 0.0125009 & 0.00625045 \tabularnewline
66 & 0.991495 & 0.0170108 & 0.00850538 \tabularnewline
67 & 0.988972 & 0.0220566 & 0.0110283 \tabularnewline
68 & 0.985847 & 0.0283068 & 0.0141534 \tabularnewline
69 & 0.983757 & 0.032485 & 0.0162425 \tabularnewline
70 & 0.982174 & 0.0356526 & 0.0178263 \tabularnewline
71 & 0.976553 & 0.0468941 & 0.023447 \tabularnewline
72 & 0.970078 & 0.0598431 & 0.0299215 \tabularnewline
73 & 0.962305 & 0.0753897 & 0.0376948 \tabularnewline
74 & 0.981483 & 0.0370331 & 0.0185166 \tabularnewline
75 & 0.975575 & 0.0488509 & 0.0244255 \tabularnewline
76 & 0.968518 & 0.0629636 & 0.0314818 \tabularnewline
77 & 0.959873 & 0.0802535 & 0.0401267 \tabularnewline
78 & 0.948977 & 0.102045 & 0.0510226 \tabularnewline
79 & 0.936023 & 0.127953 & 0.0639765 \tabularnewline
80 & 0.943705 & 0.11259 & 0.0562951 \tabularnewline
81 & 0.929392 & 0.141216 & 0.0706078 \tabularnewline
82 & 0.919625 & 0.16075 & 0.080375 \tabularnewline
83 & 0.908484 & 0.183033 & 0.0915163 \tabularnewline
84 & 0.896817 & 0.206366 & 0.103183 \tabularnewline
85 & 0.884309 & 0.231383 & 0.115691 \tabularnewline
86 & 0.864716 & 0.270567 & 0.135284 \tabularnewline
87 & 0.838049 & 0.323902 & 0.161951 \tabularnewline
88 & 0.951824 & 0.096352 & 0.048176 \tabularnewline
89 & 0.951514 & 0.0969723 & 0.0484861 \tabularnewline
90 & 0.947552 & 0.104897 & 0.0524485 \tabularnewline
91 & 0.966251 & 0.0674972 & 0.0337486 \tabularnewline
92 & 0.956465 & 0.0870704 & 0.0435352 \tabularnewline
93 & 0.944423 & 0.111154 & 0.0555771 \tabularnewline
94 & 0.933085 & 0.133831 & 0.0669153 \tabularnewline
95 & 0.918677 & 0.162646 & 0.0813232 \tabularnewline
96 & 0.90534 & 0.189321 & 0.0946604 \tabularnewline
97 & 0.893953 & 0.212095 & 0.106047 \tabularnewline
98 & 0.870906 & 0.258187 & 0.129094 \tabularnewline
99 & 0.87013 & 0.259739 & 0.12987 \tabularnewline
100 & 0.874027 & 0.251947 & 0.125973 \tabularnewline
101 & 0.875597 & 0.248806 & 0.124403 \tabularnewline
102 & 0.855155 & 0.289691 & 0.144845 \tabularnewline
103 & 0.841685 & 0.316629 & 0.158315 \tabularnewline
104 & 0.81918 & 0.36164 & 0.18082 \tabularnewline
105 & 0.812862 & 0.374276 & 0.187138 \tabularnewline
106 & 0.837682 & 0.324637 & 0.162318 \tabularnewline
107 & 0.808117 & 0.383765 & 0.191883 \tabularnewline
108 & 0.772835 & 0.454329 & 0.227165 \tabularnewline
109 & 0.735066 & 0.529869 & 0.264934 \tabularnewline
110 & 0.705202 & 0.589597 & 0.294798 \tabularnewline
111 & 0.682942 & 0.634117 & 0.317058 \tabularnewline
112 & 0.645627 & 0.708745 & 0.354373 \tabularnewline
113 & 0.665585 & 0.66883 & 0.334415 \tabularnewline
114 & 0.620592 & 0.758816 & 0.379408 \tabularnewline
115 & 0.575531 & 0.848938 & 0.424469 \tabularnewline
116 & 0.537441 & 0.925117 & 0.462559 \tabularnewline
117 & 0.497521 & 0.995041 & 0.502479 \tabularnewline
118 & 0.477063 & 0.954127 & 0.522937 \tabularnewline
119 & 0.448034 & 0.896067 & 0.551966 \tabularnewline
120 & 0.398034 & 0.796067 & 0.601966 \tabularnewline
121 & 0.450267 & 0.900534 & 0.549733 \tabularnewline
122 & 0.419797 & 0.839594 & 0.580203 \tabularnewline
123 & 0.40343 & 0.806859 & 0.59657 \tabularnewline
124 & 0.602726 & 0.794548 & 0.397274 \tabularnewline
125 & 0.551275 & 0.89745 & 0.448725 \tabularnewline
126 & 0.494969 & 0.989938 & 0.505031 \tabularnewline
127 & 0.475241 & 0.950482 & 0.524759 \tabularnewline
128 & 0.418581 & 0.837161 & 0.581419 \tabularnewline
129 & 0.41687 & 0.83374 & 0.58313 \tabularnewline
130 & 0.38352 & 0.76704 & 0.61648 \tabularnewline
131 & 0.33147 & 0.662939 & 0.66853 \tabularnewline
132 & 0.287768 & 0.575537 & 0.712232 \tabularnewline
133 & 0.346137 & 0.692274 & 0.653863 \tabularnewline
134 & 0.31184 & 0.62368 & 0.68816 \tabularnewline
135 & 0.295976 & 0.591953 & 0.704024 \tabularnewline
136 & 0.434216 & 0.868431 & 0.565784 \tabularnewline
137 & 0.54767 & 0.90466 & 0.45233 \tabularnewline
138 & 0.491513 & 0.983025 & 0.508487 \tabularnewline
139 & 0.442251 & 0.884502 & 0.557749 \tabularnewline
140 & 0.386702 & 0.773404 & 0.613298 \tabularnewline
141 & 0.34761 & 0.695219 & 0.65239 \tabularnewline
142 & 0.31334 & 0.626681 & 0.68666 \tabularnewline
143 & 0.495705 & 0.99141 & 0.504295 \tabularnewline
144 & 0.558568 & 0.882864 & 0.441432 \tabularnewline
145 & 0.625208 & 0.749583 & 0.374792 \tabularnewline
146 & 0.585555 & 0.82889 & 0.414445 \tabularnewline
147 & 0.54924 & 0.90152 & 0.45076 \tabularnewline
148 & 0.848166 & 0.303669 & 0.151834 \tabularnewline
149 & 0.777325 & 0.44535 & 0.222675 \tabularnewline
150 & 0.858586 & 0.282827 & 0.141414 \tabularnewline
151 & 0.775726 & 0.448548 & 0.224274 \tabularnewline
152 & 0.659921 & 0.680158 & 0.340079 \tabularnewline
153 & 0.514081 & 0.971839 & 0.485919 \tabularnewline
154 & 0.428408 & 0.856817 & 0.571592 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=226370&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]8[/C][C]0.143396[/C][C]0.286792[/C][C]0.856604[/C][/ROW]
[ROW][C]9[/C][C]0.0675805[/C][C]0.135161[/C][C]0.93242[/C][/ROW]
[ROW][C]10[/C][C]0.140164[/C][C]0.280329[/C][C]0.859836[/C][/ROW]
[ROW][C]11[/C][C]0.122457[/C][C]0.244914[/C][C]0.877543[/C][/ROW]
[ROW][C]12[/C][C]0.0971469[/C][C]0.194294[/C][C]0.902853[/C][/ROW]
[ROW][C]13[/C][C]0.0589228[/C][C]0.117846[/C][C]0.941077[/C][/ROW]
[ROW][C]14[/C][C]0.0548184[/C][C]0.109637[/C][C]0.945182[/C][/ROW]
[ROW][C]15[/C][C]0.0327688[/C][C]0.0655375[/C][C]0.967231[/C][/ROW]
[ROW][C]16[/C][C]0.0327018[/C][C]0.0654036[/C][C]0.967298[/C][/ROW]
[ROW][C]17[/C][C]0.0554249[/C][C]0.11085[/C][C]0.944575[/C][/ROW]
[ROW][C]18[/C][C]0.0338683[/C][C]0.0677366[/C][C]0.966132[/C][/ROW]
[ROW][C]19[/C][C]0.0221978[/C][C]0.0443956[/C][C]0.977802[/C][/ROW]
[ROW][C]20[/C][C]0.0190203[/C][C]0.0380405[/C][C]0.98098[/C][/ROW]
[ROW][C]21[/C][C]0.0135608[/C][C]0.0271215[/C][C]0.986439[/C][/ROW]
[ROW][C]22[/C][C]0.00798543[/C][C]0.0159709[/C][C]0.992015[/C][/ROW]
[ROW][C]23[/C][C]0.00668298[/C][C]0.013366[/C][C]0.993317[/C][/ROW]
[ROW][C]24[/C][C]0.00390019[/C][C]0.00780038[/C][C]0.9961[/C][/ROW]
[ROW][C]25[/C][C]0.00217352[/C][C]0.00434704[/C][C]0.997826[/C][/ROW]
[ROW][C]26[/C][C]0.00200751[/C][C]0.00401502[/C][C]0.997992[/C][/ROW]
[ROW][C]27[/C][C]0.00156594[/C][C]0.00313188[/C][C]0.998434[/C][/ROW]
[ROW][C]28[/C][C]0.00138635[/C][C]0.0027727[/C][C]0.998614[/C][/ROW]
[ROW][C]29[/C][C]0.00185829[/C][C]0.00371659[/C][C]0.998142[/C][/ROW]
[ROW][C]30[/C][C]0.00139483[/C][C]0.00278966[/C][C]0.998605[/C][/ROW]
[ROW][C]31[/C][C]0.000791953[/C][C]0.00158391[/C][C]0.999208[/C][/ROW]
[ROW][C]32[/C][C]0.000513774[/C][C]0.00102755[/C][C]0.999486[/C][/ROW]
[ROW][C]33[/C][C]0.204387[/C][C]0.408774[/C][C]0.795613[/C][/ROW]
[ROW][C]34[/C][C]0.367758[/C][C]0.735515[/C][C]0.632242[/C][/ROW]
[ROW][C]35[/C][C]0.409293[/C][C]0.818585[/C][C]0.590707[/C][/ROW]
[ROW][C]36[/C][C]0.435127[/C][C]0.870254[/C][C]0.564873[/C][/ROW]
[ROW][C]37[/C][C]0.448666[/C][C]0.897332[/C][C]0.551334[/C][/ROW]
[ROW][C]38[/C][C]0.403983[/C][C]0.807967[/C][C]0.596017[/C][/ROW]
[ROW][C]39[/C][C]0.354273[/C][C]0.708546[/C][C]0.645727[/C][/ROW]
[ROW][C]40[/C][C]0.305088[/C][C]0.610176[/C][C]0.694912[/C][/ROW]
[ROW][C]41[/C][C]0.297492[/C][C]0.594984[/C][C]0.702508[/C][/ROW]
[ROW][C]42[/C][C]0.266171[/C][C]0.532343[/C][C]0.733829[/C][/ROW]
[ROW][C]43[/C][C]0.224768[/C][C]0.449535[/C][C]0.775232[/C][/ROW]
[ROW][C]44[/C][C]0.187437[/C][C]0.374874[/C][C]0.812563[/C][/ROW]
[ROW][C]45[/C][C]0.161088[/C][C]0.322177[/C][C]0.838912[/C][/ROW]
[ROW][C]46[/C][C]0.768147[/C][C]0.463706[/C][C]0.231853[/C][/ROW]
[ROW][C]47[/C][C]0.733839[/C][C]0.532322[/C][C]0.266161[/C][/ROW]
[ROW][C]48[/C][C]0.830521[/C][C]0.338957[/C][C]0.169479[/C][/ROW]
[ROW][C]49[/C][C]0.80163[/C][C]0.39674[/C][C]0.19837[/C][/ROW]
[ROW][C]50[/C][C]0.776198[/C][C]0.447605[/C][C]0.223802[/C][/ROW]
[ROW][C]51[/C][C]0.798773[/C][C]0.402454[/C][C]0.201227[/C][/ROW]
[ROW][C]52[/C][C]0.785552[/C][C]0.428896[/C][C]0.214448[/C][/ROW]
[ROW][C]53[/C][C]0.751299[/C][C]0.497402[/C][C]0.248701[/C][/ROW]
[ROW][C]54[/C][C]0.75079[/C][C]0.498419[/C][C]0.24921[/C][/ROW]
[ROW][C]55[/C][C]0.726222[/C][C]0.547556[/C][C]0.273778[/C][/ROW]
[ROW][C]56[/C][C]0.690713[/C][C]0.618575[/C][C]0.309287[/C][/ROW]
[ROW][C]57[/C][C]0.662211[/C][C]0.675577[/C][C]0.337789[/C][/ROW]
[ROW][C]58[/C][C]0.620451[/C][C]0.759098[/C][C]0.379549[/C][/ROW]
[ROW][C]59[/C][C]0.625496[/C][C]0.749009[/C][C]0.374504[/C][/ROW]
[ROW][C]60[/C][C]0.626538[/C][C]0.746924[/C][C]0.373462[/C][/ROW]
[ROW][C]61[/C][C]0.626496[/C][C]0.747008[/C][C]0.373504[/C][/ROW]
[ROW][C]62[/C][C]0.58521[/C][C]0.82958[/C][C]0.41479[/C][/ROW]
[ROW][C]63[/C][C]0.541183[/C][C]0.917635[/C][C]0.458817[/C][/ROW]
[ROW][C]64[/C][C]0.50602[/C][C]0.98796[/C][C]0.49398[/C][/ROW]
[ROW][C]65[/C][C]0.99375[/C][C]0.0125009[/C][C]0.00625045[/C][/ROW]
[ROW][C]66[/C][C]0.991495[/C][C]0.0170108[/C][C]0.00850538[/C][/ROW]
[ROW][C]67[/C][C]0.988972[/C][C]0.0220566[/C][C]0.0110283[/C][/ROW]
[ROW][C]68[/C][C]0.985847[/C][C]0.0283068[/C][C]0.0141534[/C][/ROW]
[ROW][C]69[/C][C]0.983757[/C][C]0.032485[/C][C]0.0162425[/C][/ROW]
[ROW][C]70[/C][C]0.982174[/C][C]0.0356526[/C][C]0.0178263[/C][/ROW]
[ROW][C]71[/C][C]0.976553[/C][C]0.0468941[/C][C]0.023447[/C][/ROW]
[ROW][C]72[/C][C]0.970078[/C][C]0.0598431[/C][C]0.0299215[/C][/ROW]
[ROW][C]73[/C][C]0.962305[/C][C]0.0753897[/C][C]0.0376948[/C][/ROW]
[ROW][C]74[/C][C]0.981483[/C][C]0.0370331[/C][C]0.0185166[/C][/ROW]
[ROW][C]75[/C][C]0.975575[/C][C]0.0488509[/C][C]0.0244255[/C][/ROW]
[ROW][C]76[/C][C]0.968518[/C][C]0.0629636[/C][C]0.0314818[/C][/ROW]
[ROW][C]77[/C][C]0.959873[/C][C]0.0802535[/C][C]0.0401267[/C][/ROW]
[ROW][C]78[/C][C]0.948977[/C][C]0.102045[/C][C]0.0510226[/C][/ROW]
[ROW][C]79[/C][C]0.936023[/C][C]0.127953[/C][C]0.0639765[/C][/ROW]
[ROW][C]80[/C][C]0.943705[/C][C]0.11259[/C][C]0.0562951[/C][/ROW]
[ROW][C]81[/C][C]0.929392[/C][C]0.141216[/C][C]0.0706078[/C][/ROW]
[ROW][C]82[/C][C]0.919625[/C][C]0.16075[/C][C]0.080375[/C][/ROW]
[ROW][C]83[/C][C]0.908484[/C][C]0.183033[/C][C]0.0915163[/C][/ROW]
[ROW][C]84[/C][C]0.896817[/C][C]0.206366[/C][C]0.103183[/C][/ROW]
[ROW][C]85[/C][C]0.884309[/C][C]0.231383[/C][C]0.115691[/C][/ROW]
[ROW][C]86[/C][C]0.864716[/C][C]0.270567[/C][C]0.135284[/C][/ROW]
[ROW][C]87[/C][C]0.838049[/C][C]0.323902[/C][C]0.161951[/C][/ROW]
[ROW][C]88[/C][C]0.951824[/C][C]0.096352[/C][C]0.048176[/C][/ROW]
[ROW][C]89[/C][C]0.951514[/C][C]0.0969723[/C][C]0.0484861[/C][/ROW]
[ROW][C]90[/C][C]0.947552[/C][C]0.104897[/C][C]0.0524485[/C][/ROW]
[ROW][C]91[/C][C]0.966251[/C][C]0.0674972[/C][C]0.0337486[/C][/ROW]
[ROW][C]92[/C][C]0.956465[/C][C]0.0870704[/C][C]0.0435352[/C][/ROW]
[ROW][C]93[/C][C]0.944423[/C][C]0.111154[/C][C]0.0555771[/C][/ROW]
[ROW][C]94[/C][C]0.933085[/C][C]0.133831[/C][C]0.0669153[/C][/ROW]
[ROW][C]95[/C][C]0.918677[/C][C]0.162646[/C][C]0.0813232[/C][/ROW]
[ROW][C]96[/C][C]0.90534[/C][C]0.189321[/C][C]0.0946604[/C][/ROW]
[ROW][C]97[/C][C]0.893953[/C][C]0.212095[/C][C]0.106047[/C][/ROW]
[ROW][C]98[/C][C]0.870906[/C][C]0.258187[/C][C]0.129094[/C][/ROW]
[ROW][C]99[/C][C]0.87013[/C][C]0.259739[/C][C]0.12987[/C][/ROW]
[ROW][C]100[/C][C]0.874027[/C][C]0.251947[/C][C]0.125973[/C][/ROW]
[ROW][C]101[/C][C]0.875597[/C][C]0.248806[/C][C]0.124403[/C][/ROW]
[ROW][C]102[/C][C]0.855155[/C][C]0.289691[/C][C]0.144845[/C][/ROW]
[ROW][C]103[/C][C]0.841685[/C][C]0.316629[/C][C]0.158315[/C][/ROW]
[ROW][C]104[/C][C]0.81918[/C][C]0.36164[/C][C]0.18082[/C][/ROW]
[ROW][C]105[/C][C]0.812862[/C][C]0.374276[/C][C]0.187138[/C][/ROW]
[ROW][C]106[/C][C]0.837682[/C][C]0.324637[/C][C]0.162318[/C][/ROW]
[ROW][C]107[/C][C]0.808117[/C][C]0.383765[/C][C]0.191883[/C][/ROW]
[ROW][C]108[/C][C]0.772835[/C][C]0.454329[/C][C]0.227165[/C][/ROW]
[ROW][C]109[/C][C]0.735066[/C][C]0.529869[/C][C]0.264934[/C][/ROW]
[ROW][C]110[/C][C]0.705202[/C][C]0.589597[/C][C]0.294798[/C][/ROW]
[ROW][C]111[/C][C]0.682942[/C][C]0.634117[/C][C]0.317058[/C][/ROW]
[ROW][C]112[/C][C]0.645627[/C][C]0.708745[/C][C]0.354373[/C][/ROW]
[ROW][C]113[/C][C]0.665585[/C][C]0.66883[/C][C]0.334415[/C][/ROW]
[ROW][C]114[/C][C]0.620592[/C][C]0.758816[/C][C]0.379408[/C][/ROW]
[ROW][C]115[/C][C]0.575531[/C][C]0.848938[/C][C]0.424469[/C][/ROW]
[ROW][C]116[/C][C]0.537441[/C][C]0.925117[/C][C]0.462559[/C][/ROW]
[ROW][C]117[/C][C]0.497521[/C][C]0.995041[/C][C]0.502479[/C][/ROW]
[ROW][C]118[/C][C]0.477063[/C][C]0.954127[/C][C]0.522937[/C][/ROW]
[ROW][C]119[/C][C]0.448034[/C][C]0.896067[/C][C]0.551966[/C][/ROW]
[ROW][C]120[/C][C]0.398034[/C][C]0.796067[/C][C]0.601966[/C][/ROW]
[ROW][C]121[/C][C]0.450267[/C][C]0.900534[/C][C]0.549733[/C][/ROW]
[ROW][C]122[/C][C]0.419797[/C][C]0.839594[/C][C]0.580203[/C][/ROW]
[ROW][C]123[/C][C]0.40343[/C][C]0.806859[/C][C]0.59657[/C][/ROW]
[ROW][C]124[/C][C]0.602726[/C][C]0.794548[/C][C]0.397274[/C][/ROW]
[ROW][C]125[/C][C]0.551275[/C][C]0.89745[/C][C]0.448725[/C][/ROW]
[ROW][C]126[/C][C]0.494969[/C][C]0.989938[/C][C]0.505031[/C][/ROW]
[ROW][C]127[/C][C]0.475241[/C][C]0.950482[/C][C]0.524759[/C][/ROW]
[ROW][C]128[/C][C]0.418581[/C][C]0.837161[/C][C]0.581419[/C][/ROW]
[ROW][C]129[/C][C]0.41687[/C][C]0.83374[/C][C]0.58313[/C][/ROW]
[ROW][C]130[/C][C]0.38352[/C][C]0.76704[/C][C]0.61648[/C][/ROW]
[ROW][C]131[/C][C]0.33147[/C][C]0.662939[/C][C]0.66853[/C][/ROW]
[ROW][C]132[/C][C]0.287768[/C][C]0.575537[/C][C]0.712232[/C][/ROW]
[ROW][C]133[/C][C]0.346137[/C][C]0.692274[/C][C]0.653863[/C][/ROW]
[ROW][C]134[/C][C]0.31184[/C][C]0.62368[/C][C]0.68816[/C][/ROW]
[ROW][C]135[/C][C]0.295976[/C][C]0.591953[/C][C]0.704024[/C][/ROW]
[ROW][C]136[/C][C]0.434216[/C][C]0.868431[/C][C]0.565784[/C][/ROW]
[ROW][C]137[/C][C]0.54767[/C][C]0.90466[/C][C]0.45233[/C][/ROW]
[ROW][C]138[/C][C]0.491513[/C][C]0.983025[/C][C]0.508487[/C][/ROW]
[ROW][C]139[/C][C]0.442251[/C][C]0.884502[/C][C]0.557749[/C][/ROW]
[ROW][C]140[/C][C]0.386702[/C][C]0.773404[/C][C]0.613298[/C][/ROW]
[ROW][C]141[/C][C]0.34761[/C][C]0.695219[/C][C]0.65239[/C][/ROW]
[ROW][C]142[/C][C]0.31334[/C][C]0.626681[/C][C]0.68666[/C][/ROW]
[ROW][C]143[/C][C]0.495705[/C][C]0.99141[/C][C]0.504295[/C][/ROW]
[ROW][C]144[/C][C]0.558568[/C][C]0.882864[/C][C]0.441432[/C][/ROW]
[ROW][C]145[/C][C]0.625208[/C][C]0.749583[/C][C]0.374792[/C][/ROW]
[ROW][C]146[/C][C]0.585555[/C][C]0.82889[/C][C]0.414445[/C][/ROW]
[ROW][C]147[/C][C]0.54924[/C][C]0.90152[/C][C]0.45076[/C][/ROW]
[ROW][C]148[/C][C]0.848166[/C][C]0.303669[/C][C]0.151834[/C][/ROW]
[ROW][C]149[/C][C]0.777325[/C][C]0.44535[/C][C]0.222675[/C][/ROW]
[ROW][C]150[/C][C]0.858586[/C][C]0.282827[/C][C]0.141414[/C][/ROW]
[ROW][C]151[/C][C]0.775726[/C][C]0.448548[/C][C]0.224274[/C][/ROW]
[ROW][C]152[/C][C]0.659921[/C][C]0.680158[/C][C]0.340079[/C][/ROW]
[ROW][C]153[/C][C]0.514081[/C][C]0.971839[/C][C]0.485919[/C][/ROW]
[ROW][C]154[/C][C]0.428408[/C][C]0.856817[/C][C]0.571592[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=226370&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=226370&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
8	0.143396	0.286792	0.856604
9	0.0675805	0.135161	0.93242
10	0.140164	0.280329	0.859836
11	0.122457	0.244914	0.877543
12	0.0971469	0.194294	0.902853
13	0.0589228	0.117846	0.941077
14	0.0548184	0.109637	0.945182
15	0.0327688	0.0655375	0.967231
16	0.0327018	0.0654036	0.967298
17	0.0554249	0.11085	0.944575
18	0.0338683	0.0677366	0.966132
19	0.0221978	0.0443956	0.977802
20	0.0190203	0.0380405	0.98098
21	0.0135608	0.0271215	0.986439
22	0.00798543	0.0159709	0.992015
23	0.00668298	0.013366	0.993317
24	0.00390019	0.00780038	0.9961
25	0.00217352	0.00434704	0.997826
26	0.00200751	0.00401502	0.997992
27	0.00156594	0.00313188	0.998434
28	0.00138635	0.0027727	0.998614
29	0.00185829	0.00371659	0.998142
30	0.00139483	0.00278966	0.998605
31	0.000791953	0.00158391	0.999208
32	0.000513774	0.00102755	0.999486
33	0.204387	0.408774	0.795613
34	0.367758	0.735515	0.632242
35	0.409293	0.818585	0.590707
36	0.435127	0.870254	0.564873
37	0.448666	0.897332	0.551334
38	0.403983	0.807967	0.596017
39	0.354273	0.708546	0.645727
40	0.305088	0.610176	0.694912
41	0.297492	0.594984	0.702508
42	0.266171	0.532343	0.733829
43	0.224768	0.449535	0.775232
44	0.187437	0.374874	0.812563
45	0.161088	0.322177	0.838912
46	0.768147	0.463706	0.231853
47	0.733839	0.532322	0.266161
48	0.830521	0.338957	0.169479
49	0.80163	0.39674	0.19837
50	0.776198	0.447605	0.223802
51	0.798773	0.402454	0.201227
52	0.785552	0.428896	0.214448
53	0.751299	0.497402	0.248701
54	0.75079	0.498419	0.24921
55	0.726222	0.547556	0.273778
56	0.690713	0.618575	0.309287
57	0.662211	0.675577	0.337789
58	0.620451	0.759098	0.379549
59	0.625496	0.749009	0.374504
60	0.626538	0.746924	0.373462
61	0.626496	0.747008	0.373504
62	0.58521	0.82958	0.41479
63	0.541183	0.917635	0.458817
64	0.50602	0.98796	0.49398
65	0.99375	0.0125009	0.00625045
66	0.991495	0.0170108	0.00850538
67	0.988972	0.0220566	0.0110283
68	0.985847	0.0283068	0.0141534
69	0.983757	0.032485	0.0162425
70	0.982174	0.0356526	0.0178263
71	0.976553	0.0468941	0.023447
72	0.970078	0.0598431	0.0299215
73	0.962305	0.0753897	0.0376948
74	0.981483	0.0370331	0.0185166
75	0.975575	0.0488509	0.0244255
76	0.968518	0.0629636	0.0314818
77	0.959873	0.0802535	0.0401267
78	0.948977	0.102045	0.0510226
79	0.936023	0.127953	0.0639765
80	0.943705	0.11259	0.0562951
81	0.929392	0.141216	0.0706078
82	0.919625	0.16075	0.080375
83	0.908484	0.183033	0.0915163
84	0.896817	0.206366	0.103183
85	0.884309	0.231383	0.115691
86	0.864716	0.270567	0.135284
87	0.838049	0.323902	0.161951
88	0.951824	0.096352	0.048176
89	0.951514	0.0969723	0.0484861
90	0.947552	0.104897	0.0524485
91	0.966251	0.0674972	0.0337486
92	0.956465	0.0870704	0.0435352
93	0.944423	0.111154	0.0555771
94	0.933085	0.133831	0.0669153
95	0.918677	0.162646	0.0813232
96	0.90534	0.189321	0.0946604
97	0.893953	0.212095	0.106047
98	0.870906	0.258187	0.129094
99	0.87013	0.259739	0.12987
100	0.874027	0.251947	0.125973
101	0.875597	0.248806	0.124403
102	0.855155	0.289691	0.144845
103	0.841685	0.316629	0.158315
104	0.81918	0.36164	0.18082
105	0.812862	0.374276	0.187138
106	0.837682	0.324637	0.162318
107	0.808117	0.383765	0.191883
108	0.772835	0.454329	0.227165
109	0.735066	0.529869	0.264934
110	0.705202	0.589597	0.294798
111	0.682942	0.634117	0.317058
112	0.645627	0.708745	0.354373
113	0.665585	0.66883	0.334415
114	0.620592	0.758816	0.379408
115	0.575531	0.848938	0.424469
116	0.537441	0.925117	0.462559
117	0.497521	0.995041	0.502479
118	0.477063	0.954127	0.522937
119	0.448034	0.896067	0.551966
120	0.398034	0.796067	0.601966
121	0.450267	0.900534	0.549733
122	0.419797	0.839594	0.580203
123	0.40343	0.806859	0.59657
124	0.602726	0.794548	0.397274
125	0.551275	0.89745	0.448725
126	0.494969	0.989938	0.505031
127	0.475241	0.950482	0.524759
128	0.418581	0.837161	0.581419
129	0.41687	0.83374	0.58313
130	0.38352	0.76704	0.61648
131	0.33147	0.662939	0.66853
132	0.287768	0.575537	0.712232
133	0.346137	0.692274	0.653863
134	0.31184	0.62368	0.68816
135	0.295976	0.591953	0.704024
136	0.434216	0.868431	0.565784
137	0.54767	0.90466	0.45233
138	0.491513	0.983025	0.508487
139	0.442251	0.884502	0.557749
140	0.386702	0.773404	0.613298
141	0.34761	0.695219	0.65239
142	0.31334	0.626681	0.68666
143	0.495705	0.99141	0.504295
144	0.558568	0.882864	0.441432
145	0.625208	0.749583	0.374792
146	0.585555	0.82889	0.414445
147	0.54924	0.90152	0.45076
148	0.848166	0.303669	0.151834
149	0.777325	0.44535	0.222675
150	0.858586	0.282827	0.141414
151	0.775726	0.448548	0.224274
152	0.659921	0.680158	0.340079
153	0.514081	0.971839	0.485919
154	0.428408	0.856817	0.571592

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	9	0.0612245	NOK
5% type I error level	23	0.156463	NOK
10% type I error level	34	0.231293	NOK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 9 & 0.0612245 & NOK \tabularnewline
5% type I error level & 23 & 0.156463 & NOK \tabularnewline
10% type I error level & 34 & 0.231293 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=226370&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]9[/C][C]0.0612245[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]23[/C][C]0.156463[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]34[/C][C]0.231293[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=226370&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=226370&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	9	0.0612245	NOK
5% type I error level	23	0.156463	NOK
10% type I error level	34	0.231293	NOK

Figure 1

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Figure 3

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Figure 4

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Figure 5

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Figure 6

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Figure 7

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Figure 8

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Figure 9

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Figure 10

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Parameters (Session):

par1 = 5 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 5 ; 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