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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationMon, 05 Nov 2012 14:19:42 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/Nov/05/t13521432573jt7w8wguv7ylk1.htm/, Retrieved Wed, 01 Feb 2023 15:37:37 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=186241, Retrieved Wed, 01 Feb 2023 15:37:37 +0000
QR Codes:

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IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact86
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [] [2010-11-17 09:14:55] [b98453cac15ba1066b407e146608df68]
-   PD    [Multiple Regression] [maandeffect t] [2012-11-05 19:19:42] [b4b733de199089e913cc2b6ea19b06b9] [Current]
- R         [Multiple Regression] [maandeffect?] [2012-11-05 19:32:43] [2c4ddb4bf62114b8025bb962e2c7a2b5]
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Dataseries X:
1	-19	-3	53	14	24	20	-9	-2	20	6	-29	17
2	-20	-4	50	16	24	19	-12	-4	21	6	-29	13
3	-21	-7	50	19	31	21	-10	-5	20	5	-27	12
4	-19	-7	51	18	25	17	-10	-2	21	5	-29	13
5	-17	-7	53	19	28	15	-11	-4	19	3	-24	10
6	-16	-3	49	20	24	18	-11	-4	22	5	-29	14
7	-10	0	54	20	25	19	-10	-5	20	5	-21	13
8	-16	-5	57	24	16	16	-13	-7	18	5	-20	10
9	-10	-3	58	18	17	21	-10	-5	16	3	-26	11
10	-8	3	56	15	11	26	-6	-6	17	6	-19	12
11	-7	2	60	25	12	23	-9	-4	18	6	-22	7
12	-15	-7	55	23	39	24	-8	-2	19	4	-22	11
13	-7	-1	54	20	19	23	-12	-3	18	6	-15	9
14	-6	0	52	20	14	19	-10	0	20	5	-16	13
15	-6	-3	55	22	15	25	-11	-4	21	4	-22	12
16	2	4	56	25	7	21	-13	-3	18	5	-21	5
17	-4	2	54	22	12	19	-10	-3	19	5	-11	13
18	-4	3	53	26	12	20	-10	-3	19	4	-10	11
19	-8	0	59	27	14	20	-11	-4	19	3	-6	8
20	-10	-10	62	41	9	17	-11	-5	21	2	-8	8
21	-16	-10	63	29	8	25	-11	-5	19	3	-15	8
22	-14	-9	64	33	4	19	-10	-6	19	2	-16	8
23	-30	-22	75	39	7	13	-13	-10	17	-1	-24	0
24	-33	-16	77	27	3	15	-12	-11	16	0	-27	3
25	-40	-18	79	27	5	15	-13	-13	16	-2	-33	0
26	-38	-14	77	25	0	13	-15	-12	17	1	-29	-1
27	-39	-12	82	19	-2	11	-16	-13	16	-2	-34	-1
28	-46	-17	83	15	6	9	-18	-12	15	-2	-37	-4
29	-50	-23	81	19	11	2	-17	-15	16	-2	-31	1
30	-55	-28	78	23	9	-2	-18	-14	16	-6	-33	-1
31	-66	-31	79	23	17	-4	-20	-16	16	-4	-25	0
32	-63	-21	79	7	21	-2	-22	-16	18	-2	-27	-1
33	-56	-19	73	1	21	1	-17	-12	19	0	-21	6
34	-66	-22	72	7	41	-13	-19	-16	16	-5	-32	0
35	-63	-22	67	4	57	-11	-18	-15	16	-4	-31	-3
36	-69	-25	67	-8	65	-14	-26	-17	16	-5	-32	-3
37	-69	-16	50	-14	68	-4	-19	-15	18	-1	-30	4
38	-72	-22	45	-10	73	-9	-23	-14	16	-2	-34	1
39	-69	-21	39	-11	71	-5	-21	-15	15	-4	-35	0
40	-67	-10	39	-10	71	-4	-27	-14	15	-1	-37	-4
41	-64	-7	37	-8	70	-8	-27	-16	16	1	-32	-2
42	-61	-5	30	-8	69	-1	-21	-11	18	1	-28	3
43	-58	-4	24	-7	65	-2	-22	-14	16	-2	-26	2
44	-47	7	27	-8	57	-1	-24	-12	19	1	-24	5
45	-44	6	19	-4	57	8	-21	-11	19	1	-27	6
46	-42	3	19	3	57	8	-21	-13	18	3	-26	6
47	-34	10	25	-5	55	6	-22	-12	17	3	-27	3
48	-38	0	16	-4	65	7	-25	-12	19	1	-27	4
49	-41	-2	20	5	65	2	-21	-10	22	1	-24	7
50	-38	-1	25	3	64	3	-26	-12	19	0	-28	5
51	-37	2	34	6	60	0	-27	-11	19	2	-23	6
52	-22	8	39	10	43	5	-22	-10	16	2	-23	1
53	-37	-6	40	16	47	-1	-22	-12	18	-1	-29	3
54	-36	-4	38	11	40	3	-20	-12	20	1	-25	6
55	-25	4	42	10	31	4	-21	-11	17	0	-24	0
56	-15	7	46	21	27	8	-16	-12	17	1	-20	3
57	-17	3	48	18	24	10	-17	-9	17	1	-22	4
58	-19	3	51	20	23	14	-19	-6	20	3	-24	7
59	-12	8	55	18	17	15	-20	-7	21	2	-27	6
60	-17	3	52	23	16	9	-20	-7	19	0	-25	6
61	-21	-3	55	28	15	8	-20	-10	18	0	-26	6
62	-10	4	58	31	8	10	-19	-8	20	3	-24	6
63	-19	-5	72	38	5	5	-20	-11	17	-2	-26	2
64	-14	-1	70	27	6	4	-25	-12	15	0	-22	2
65	-8	5	70	21	5	8	-25	-11	17	1	-20	2
66	-16	0	63	31	12	8	-22	-11	18	-1	-26	3
67	-14	-6	66	31	8	10	-19	-9	20	-2	-22	-1
68	-30	-13	65	29	17	8	-20	-9	19	-1	-29	-4
69	-33	-15	55	24	22	10	-18	-12	20	-1	-30	4
70	-37	-8	57	27	24	-8	-17	-10	22	1	-26	5
71	-47	-20	60	36	36	-6	-17	-10	20	-2	-30	3
72	-48	-10	63	35	31	-10	-21	-13	21	-5	-33	-1
73	-50	-22	65	44	34	-15	-17	-13	19	-5	-33	-4
74	-56	-25	61	39	47	-21	-22	-12	22	-6	-31	0
75	-47	-10	65	26	33	-24	-24	-14	19	-4	-36	-1
76	-37	-8	63	27	35	-15	-18	-9	21	-3	-43	-1
77	-35	-9	59	17	31	-12	-20	-12	19	-3	-40	3
78	-29	-5	56	20	35	-11	-21	-10	21	-1	-38	2
79	-28	-7	54	22	39	-11	-17	-13	18	-2	-41	-4
80	-29	-11	56	32	46	-13	-17	-11	18	-3	-38	-3
81	-33	-11	54	28	40	-10	-17	-11	20	-3	-40	-1
82	-41	-16	58	30	50	-9	-21	-11	19	-3	-41	3




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time8 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 & 8 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ fisher.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186241&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]8 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=186241&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186241&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 Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time8 seconds
R Server'Sir Ronald Aylmer Fisher' @ fisher.wessa.net







Multiple Linear Regression - Estimated Regression Equation
maand[t] = + 3.75902714069415e-15 -1.09730656292003e-16X_1t[t] -1.29673007892416e-16Yt[t] + 5.64261283981446e-17X_2t[t] -9.6274052844502e-17X_3t[t] + 5.68713539993411e-18X_4t[t] + 1.46630317988021e-16X_5t[t] + 2.68490208439456e-16X_6t[t] + 3.20663515391684e-16X_7t[t] -6.00835963321108e-16X_8t[t] + 5.21026437727825e-16X_9t[t] + 7.37830649995089e-17X_10t[t] + 1.99827229686305e-16X_11t[t] + 1t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
maand[t] =  +  3.75902714069415e-15 -1.09730656292003e-16X_1t[t] -1.29673007892416e-16Yt[t] +  5.64261283981446e-17X_2t[t] -9.6274052844502e-17X_3t[t] +  5.68713539993411e-18X_4t[t] +  1.46630317988021e-16X_5t[t] +  2.68490208439456e-16X_6t[t] +  3.20663515391684e-16X_7t[t] -6.00835963321108e-16X_8t[t] +  5.21026437727825e-16X_9t[t] +  7.37830649995089e-17X_10t[t] +  1.99827229686305e-16X_11t[t] +  1t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186241&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]maand[t] =  +  3.75902714069415e-15 -1.09730656292003e-16X_1t[t] -1.29673007892416e-16Yt[t] +  5.64261283981446e-17X_2t[t] -9.6274052844502e-17X_3t[t] +  5.68713539993411e-18X_4t[t] +  1.46630317988021e-16X_5t[t] +  2.68490208439456e-16X_6t[t] +  3.20663515391684e-16X_7t[t] -6.00835963321108e-16X_8t[t] +  5.21026437727825e-16X_9t[t] +  7.37830649995089e-17X_10t[t] +  1.99827229686305e-16X_11t[t] +  1t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186241&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186241&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
maand[t] = + 3.75902714069415e-15 -1.09730656292003e-16X_1t[t] -1.29673007892416e-16Yt[t] + 5.64261283981446e-17X_2t[t] -9.6274052844502e-17X_3t[t] + 5.68713539993411e-18X_4t[t] + 1.46630317988021e-16X_5t[t] + 2.68490208439456e-16X_6t[t] + 3.20663515391684e-16X_7t[t] -6.00835963321108e-16X_8t[t] + 5.21026437727825e-16X_9t[t] + 7.37830649995089e-17X_10t[t] + 1.99827229686305e-16X_11t[t] + 1t + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)3.75902714069415e-1501.03580.3039820.151991
X_1t-1.09730656292003e-160-3.86410.0002510.000126
Yt-1.29673007892416e-160-2.92620.0046620.002331
X_2t5.64261283981446e-1702.10060.0393860.019693
X_3t-9.6274052844502e-170-3.80140.000310.000155
X_4t5.68713539993411e-1800.25350.8006570.400329
X_5t1.46630317988021e-1604.45763.2e-051.6e-05
X_6t2.68490208439456e-1604.90946e-063e-06
X_7t3.20663515391684e-1603.17790.0022320.001116
X_8t-6.00835963321108e-160-5.18982e-061e-06
X_9t5.21026437727825e-1604.16778.9e-054.4e-05
X_10t7.37830649995089e-1703.00940.003670.001835
X_11t1.99827229686305e-1603.31730.0014620.000731
t106554462620310908000

\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) & 3.75902714069415e-15 & 0 & 1.0358 & 0.303982 & 0.151991 \tabularnewline
X_1t & -1.09730656292003e-16 & 0 & -3.8641 & 0.000251 & 0.000126 \tabularnewline
Yt & -1.29673007892416e-16 & 0 & -2.9262 & 0.004662 & 0.002331 \tabularnewline
X_2t & 5.64261283981446e-17 & 0 & 2.1006 & 0.039386 & 0.019693 \tabularnewline
X_3t & -9.6274052844502e-17 & 0 & -3.8014 & 0.00031 & 0.000155 \tabularnewline
X_4t & 5.68713539993411e-18 & 0 & 0.2535 & 0.800657 & 0.400329 \tabularnewline
X_5t & 1.46630317988021e-16 & 0 & 4.4576 & 3.2e-05 & 1.6e-05 \tabularnewline
X_6t & 2.68490208439456e-16 & 0 & 4.9094 & 6e-06 & 3e-06 \tabularnewline
X_7t & 3.20663515391684e-16 & 0 & 3.1779 & 0.002232 & 0.001116 \tabularnewline
X_8t & -6.00835963321108e-16 & 0 & -5.1898 & 2e-06 & 1e-06 \tabularnewline
X_9t & 5.21026437727825e-16 & 0 & 4.1677 & 8.9e-05 & 4.4e-05 \tabularnewline
X_10t & 7.37830649995089e-17 & 0 & 3.0094 & 0.00367 & 0.001835 \tabularnewline
X_11t & 1.99827229686305e-16 & 0 & 3.3173 & 0.001462 & 0.000731 \tabularnewline
t & 1 & 0 & 65544626203109080 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186241&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]3.75902714069415e-15[/C][C]0[/C][C]1.0358[/C][C]0.303982[/C][C]0.151991[/C][/ROW]
[ROW][C]X_1t[/C][C]-1.09730656292003e-16[/C][C]0[/C][C]-3.8641[/C][C]0.000251[/C][C]0.000126[/C][/ROW]
[ROW][C]Yt[/C][C]-1.29673007892416e-16[/C][C]0[/C][C]-2.9262[/C][C]0.004662[/C][C]0.002331[/C][/ROW]
[ROW][C]X_2t[/C][C]5.64261283981446e-17[/C][C]0[/C][C]2.1006[/C][C]0.039386[/C][C]0.019693[/C][/ROW]
[ROW][C]X_3t[/C][C]-9.6274052844502e-17[/C][C]0[/C][C]-3.8014[/C][C]0.00031[/C][C]0.000155[/C][/ROW]
[ROW][C]X_4t[/C][C]5.68713539993411e-18[/C][C]0[/C][C]0.2535[/C][C]0.800657[/C][C]0.400329[/C][/ROW]
[ROW][C]X_5t[/C][C]1.46630317988021e-16[/C][C]0[/C][C]4.4576[/C][C]3.2e-05[/C][C]1.6e-05[/C][/ROW]
[ROW][C]X_6t[/C][C]2.68490208439456e-16[/C][C]0[/C][C]4.9094[/C][C]6e-06[/C][C]3e-06[/C][/ROW]
[ROW][C]X_7t[/C][C]3.20663515391684e-16[/C][C]0[/C][C]3.1779[/C][C]0.002232[/C][C]0.001116[/C][/ROW]
[ROW][C]X_8t[/C][C]-6.00835963321108e-16[/C][C]0[/C][C]-5.1898[/C][C]2e-06[/C][C]1e-06[/C][/ROW]
[ROW][C]X_9t[/C][C]5.21026437727825e-16[/C][C]0[/C][C]4.1677[/C][C]8.9e-05[/C][C]4.4e-05[/C][/ROW]
[ROW][C]X_10t[/C][C]7.37830649995089e-17[/C][C]0[/C][C]3.0094[/C][C]0.00367[/C][C]0.001835[/C][/ROW]
[ROW][C]X_11t[/C][C]1.99827229686305e-16[/C][C]0[/C][C]3.3173[/C][C]0.001462[/C][C]0.000731[/C][/ROW]
[ROW][C]t[/C][C]1[/C][C]0[/C][C]65544626203109080[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186241&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186241&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
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)3.75902714069415e-1501.03580.3039820.151991
X_1t-1.09730656292003e-160-3.86410.0002510.000126
Yt-1.29673007892416e-160-2.92620.0046620.002331
X_2t5.64261283981446e-1702.10060.0393860.019693
X_3t-9.6274052844502e-170-3.80140.000310.000155
X_4t5.68713539993411e-1800.25350.8006570.400329
X_5t1.46630317988021e-1604.45763.2e-051.6e-05
X_6t2.68490208439456e-1604.90946e-063e-06
X_7t3.20663515391684e-1603.17790.0022320.001116
X_8t-6.00835963321108e-160-5.18982e-061e-06
X_9t5.21026437727825e-1604.16778.9e-054.4e-05
X_10t7.37830649995089e-1703.00940.003670.001835
X_11t1.99827229686305e-1603.31730.0014620.000731
t106554462620310908000







Multiple Linear Regression - Regression Statistics
Multiple R1
R-squared1
Adjusted R-squared1
F-TEST (value)2.9296165392591e+33
F-TEST (DF numerator)13
F-TEST (DF denominator)68
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.09829951860756e-15
Sum Squared Residuals8.20258046150048e-29

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 1 \tabularnewline
R-squared & 1 \tabularnewline
Adjusted R-squared & 1 \tabularnewline
F-TEST (value) & 2.9296165392591e+33 \tabularnewline
F-TEST (DF numerator) & 13 \tabularnewline
F-TEST (DF denominator) & 68 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 1.09829951860756e-15 \tabularnewline
Sum Squared Residuals & 8.20258046150048e-29 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186241&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]1[/C][/ROW]
[ROW][C]R-squared[/C][C]1[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]1[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]2.9296165392591e+33[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]13[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]68[/C][/ROW]
[ROW][C]p-value[/C][C]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]1.09829951860756e-15[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]8.20258046150048e-29[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186241&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186241&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 R1
R-squared1
Adjusted R-squared1
F-TEST (value)2.9296165392591e+33
F-TEST (DF numerator)13
F-TEST (DF denominator)68
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.09829951860756e-15
Sum Squared Residuals8.20258046150048e-29







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
111-9.51232828942849e-16
222-1.43874244480495e-15
3336.54044811802134e-16
444-4.56524039394615e-16
555-1.86875318642681e-15
666-3.66844444095979e-16
7771.15333993241805e-15
8886.22492106059019e-16
9999.44745573095916e-16
1010101.09356295120206e-15
111111-3.45634408722318e-17
121212-1.89590850788152e-15
131313-4.21751123478041e-16
1414146.82029117825761e-15
1515151.16533710010282e-16
161616-1.53581215804394e-15
171717-1.13719690415603e-15
181818-1.33101350476712e-15
191919-5.46203384312603e-16
2020202.15256525340445e-17
2121212.85289861786971e-16
222222-4.35636399648386e-16
2323236.30648351760257e-16
242424-4.93820212015549e-17
2525258.60504654617179e-16
2626264.59488698467876e-16
2727271.8515162068357e-17
282828-1.43783742884245e-17
2929291.47791446406699e-16
303030-5.94233004380331e-16
313131-1.79298795524996e-16
3232323.96231873004825e-16
3333336.74587791838043e-18
343434-1.52331088081177e-16
353535-5.80139334560492e-16
3636367.50670932500488e-16
373737-1.10960003944072e-16
383838-7.64694926978831e-16
3939392.37397864667883e-16
4040402.18275377353383e-16
4141411.63515111861505e-16
4242421.38245214722014e-16
434343-5.23089558689502e-16
4444441.79715941056111e-16
4545451.70305404068671e-17
4646462.53210578795393e-16
474747-5.0261757196383e-17
4848485.75506321544438e-16
494949-7.86619326457259e-16
5050505.78962120323504e-16
5151513.75672453917209e-16
525252-6.32229027719334e-17
5353533.23759431324033e-16
5454549.90243364330694e-17
555555-3.53462286056539e-16
5656565.28991723294676e-17
575757-7.42842380015916e-16
5858586.72801838991667e-16
5959598.98907839218018e-17
606060-7.81263239446812e-16
616161-3.66706301745246e-16
6262624.34755948775423e-16
6363638.2638006668242e-17
646464-4.38886205772478e-16
656565-2.29082915913908e-16
6666663.27046719143833e-16
6767673.06748673802436e-17
686868-4.13579296843554e-16
696969-1.29636338311076e-15
707070-5.44511355771099e-16
717171-3.97058613447162e-16
7272726.75214053038262e-16
7373732.43001961098468e-16
7474749.24509577162011e-16
757575-1.24521763679478e-15
767676-4.28696791398346e-16
777777-2.78131832004764e-16
787878-5.25613362678292e-17
7979796.71791467903539e-16
8080803.53727036941602e-16
8181814.98414858878753e-16
8282826.5705267694034e-16

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 1 & 1 & -9.51232828942849e-16 \tabularnewline
2 & 2 & 2 & -1.43874244480495e-15 \tabularnewline
3 & 3 & 3 & 6.54044811802134e-16 \tabularnewline
4 & 4 & 4 & -4.56524039394615e-16 \tabularnewline
5 & 5 & 5 & -1.86875318642681e-15 \tabularnewline
6 & 6 & 6 & -3.66844444095979e-16 \tabularnewline
7 & 7 & 7 & 1.15333993241805e-15 \tabularnewline
8 & 8 & 8 & 6.22492106059019e-16 \tabularnewline
9 & 9 & 9 & 9.44745573095916e-16 \tabularnewline
10 & 10 & 10 & 1.09356295120206e-15 \tabularnewline
11 & 11 & 11 & -3.45634408722318e-17 \tabularnewline
12 & 12 & 12 & -1.89590850788152e-15 \tabularnewline
13 & 13 & 13 & -4.21751123478041e-16 \tabularnewline
14 & 14 & 14 & 6.82029117825761e-15 \tabularnewline
15 & 15 & 15 & 1.16533710010282e-16 \tabularnewline
16 & 16 & 16 & -1.53581215804394e-15 \tabularnewline
17 & 17 & 17 & -1.13719690415603e-15 \tabularnewline
18 & 18 & 18 & -1.33101350476712e-15 \tabularnewline
19 & 19 & 19 & -5.46203384312603e-16 \tabularnewline
20 & 20 & 20 & 2.15256525340445e-17 \tabularnewline
21 & 21 & 21 & 2.85289861786971e-16 \tabularnewline
22 & 22 & 22 & -4.35636399648386e-16 \tabularnewline
23 & 23 & 23 & 6.30648351760257e-16 \tabularnewline
24 & 24 & 24 & -4.93820212015549e-17 \tabularnewline
25 & 25 & 25 & 8.60504654617179e-16 \tabularnewline
26 & 26 & 26 & 4.59488698467876e-16 \tabularnewline
27 & 27 & 27 & 1.8515162068357e-17 \tabularnewline
28 & 28 & 28 & -1.43783742884245e-17 \tabularnewline
29 & 29 & 29 & 1.47791446406699e-16 \tabularnewline
30 & 30 & 30 & -5.94233004380331e-16 \tabularnewline
31 & 31 & 31 & -1.79298795524996e-16 \tabularnewline
32 & 32 & 32 & 3.96231873004825e-16 \tabularnewline
33 & 33 & 33 & 6.74587791838043e-18 \tabularnewline
34 & 34 & 34 & -1.52331088081177e-16 \tabularnewline
35 & 35 & 35 & -5.80139334560492e-16 \tabularnewline
36 & 36 & 36 & 7.50670932500488e-16 \tabularnewline
37 & 37 & 37 & -1.10960003944072e-16 \tabularnewline
38 & 38 & 38 & -7.64694926978831e-16 \tabularnewline
39 & 39 & 39 & 2.37397864667883e-16 \tabularnewline
40 & 40 & 40 & 2.18275377353383e-16 \tabularnewline
41 & 41 & 41 & 1.63515111861505e-16 \tabularnewline
42 & 42 & 42 & 1.38245214722014e-16 \tabularnewline
43 & 43 & 43 & -5.23089558689502e-16 \tabularnewline
44 & 44 & 44 & 1.79715941056111e-16 \tabularnewline
45 & 45 & 45 & 1.70305404068671e-17 \tabularnewline
46 & 46 & 46 & 2.53210578795393e-16 \tabularnewline
47 & 47 & 47 & -5.0261757196383e-17 \tabularnewline
48 & 48 & 48 & 5.75506321544438e-16 \tabularnewline
49 & 49 & 49 & -7.86619326457259e-16 \tabularnewline
50 & 50 & 50 & 5.78962120323504e-16 \tabularnewline
51 & 51 & 51 & 3.75672453917209e-16 \tabularnewline
52 & 52 & 52 & -6.32229027719334e-17 \tabularnewline
53 & 53 & 53 & 3.23759431324033e-16 \tabularnewline
54 & 54 & 54 & 9.90243364330694e-17 \tabularnewline
55 & 55 & 55 & -3.53462286056539e-16 \tabularnewline
56 & 56 & 56 & 5.28991723294676e-17 \tabularnewline
57 & 57 & 57 & -7.42842380015916e-16 \tabularnewline
58 & 58 & 58 & 6.72801838991667e-16 \tabularnewline
59 & 59 & 59 & 8.98907839218018e-17 \tabularnewline
60 & 60 & 60 & -7.81263239446812e-16 \tabularnewline
61 & 61 & 61 & -3.66706301745246e-16 \tabularnewline
62 & 62 & 62 & 4.34755948775423e-16 \tabularnewline
63 & 63 & 63 & 8.2638006668242e-17 \tabularnewline
64 & 64 & 64 & -4.38886205772478e-16 \tabularnewline
65 & 65 & 65 & -2.29082915913908e-16 \tabularnewline
66 & 66 & 66 & 3.27046719143833e-16 \tabularnewline
67 & 67 & 67 & 3.06748673802436e-17 \tabularnewline
68 & 68 & 68 & -4.13579296843554e-16 \tabularnewline
69 & 69 & 69 & -1.29636338311076e-15 \tabularnewline
70 & 70 & 70 & -5.44511355771099e-16 \tabularnewline
71 & 71 & 71 & -3.97058613447162e-16 \tabularnewline
72 & 72 & 72 & 6.75214053038262e-16 \tabularnewline
73 & 73 & 73 & 2.43001961098468e-16 \tabularnewline
74 & 74 & 74 & 9.24509577162011e-16 \tabularnewline
75 & 75 & 75 & -1.24521763679478e-15 \tabularnewline
76 & 76 & 76 & -4.28696791398346e-16 \tabularnewline
77 & 77 & 77 & -2.78131832004764e-16 \tabularnewline
78 & 78 & 78 & -5.25613362678292e-17 \tabularnewline
79 & 79 & 79 & 6.71791467903539e-16 \tabularnewline
80 & 80 & 80 & 3.53727036941602e-16 \tabularnewline
81 & 81 & 81 & 4.98414858878753e-16 \tabularnewline
82 & 82 & 82 & 6.5705267694034e-16 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186241&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]1[/C][C]1[/C][C]-9.51232828942849e-16[/C][/ROW]
[ROW][C]2[/C][C]2[/C][C]2[/C][C]-1.43874244480495e-15[/C][/ROW]
[ROW][C]3[/C][C]3[/C][C]3[/C][C]6.54044811802134e-16[/C][/ROW]
[ROW][C]4[/C][C]4[/C][C]4[/C][C]-4.56524039394615e-16[/C][/ROW]
[ROW][C]5[/C][C]5[/C][C]5[/C][C]-1.86875318642681e-15[/C][/ROW]
[ROW][C]6[/C][C]6[/C][C]6[/C][C]-3.66844444095979e-16[/C][/ROW]
[ROW][C]7[/C][C]7[/C][C]7[/C][C]1.15333993241805e-15[/C][/ROW]
[ROW][C]8[/C][C]8[/C][C]8[/C][C]6.22492106059019e-16[/C][/ROW]
[ROW][C]9[/C][C]9[/C][C]9[/C][C]9.44745573095916e-16[/C][/ROW]
[ROW][C]10[/C][C]10[/C][C]10[/C][C]1.09356295120206e-15[/C][/ROW]
[ROW][C]11[/C][C]11[/C][C]11[/C][C]-3.45634408722318e-17[/C][/ROW]
[ROW][C]12[/C][C]12[/C][C]12[/C][C]-1.89590850788152e-15[/C][/ROW]
[ROW][C]13[/C][C]13[/C][C]13[/C][C]-4.21751123478041e-16[/C][/ROW]
[ROW][C]14[/C][C]14[/C][C]14[/C][C]6.82029117825761e-15[/C][/ROW]
[ROW][C]15[/C][C]15[/C][C]15[/C][C]1.16533710010282e-16[/C][/ROW]
[ROW][C]16[/C][C]16[/C][C]16[/C][C]-1.53581215804394e-15[/C][/ROW]
[ROW][C]17[/C][C]17[/C][C]17[/C][C]-1.13719690415603e-15[/C][/ROW]
[ROW][C]18[/C][C]18[/C][C]18[/C][C]-1.33101350476712e-15[/C][/ROW]
[ROW][C]19[/C][C]19[/C][C]19[/C][C]-5.46203384312603e-16[/C][/ROW]
[ROW][C]20[/C][C]20[/C][C]20[/C][C]2.15256525340445e-17[/C][/ROW]
[ROW][C]21[/C][C]21[/C][C]21[/C][C]2.85289861786971e-16[/C][/ROW]
[ROW][C]22[/C][C]22[/C][C]22[/C][C]-4.35636399648386e-16[/C][/ROW]
[ROW][C]23[/C][C]23[/C][C]23[/C][C]6.30648351760257e-16[/C][/ROW]
[ROW][C]24[/C][C]24[/C][C]24[/C][C]-4.93820212015549e-17[/C][/ROW]
[ROW][C]25[/C][C]25[/C][C]25[/C][C]8.60504654617179e-16[/C][/ROW]
[ROW][C]26[/C][C]26[/C][C]26[/C][C]4.59488698467876e-16[/C][/ROW]
[ROW][C]27[/C][C]27[/C][C]27[/C][C]1.8515162068357e-17[/C][/ROW]
[ROW][C]28[/C][C]28[/C][C]28[/C][C]-1.43783742884245e-17[/C][/ROW]
[ROW][C]29[/C][C]29[/C][C]29[/C][C]1.47791446406699e-16[/C][/ROW]
[ROW][C]30[/C][C]30[/C][C]30[/C][C]-5.94233004380331e-16[/C][/ROW]
[ROW][C]31[/C][C]31[/C][C]31[/C][C]-1.79298795524996e-16[/C][/ROW]
[ROW][C]32[/C][C]32[/C][C]32[/C][C]3.96231873004825e-16[/C][/ROW]
[ROW][C]33[/C][C]33[/C][C]33[/C][C]6.74587791838043e-18[/C][/ROW]
[ROW][C]34[/C][C]34[/C][C]34[/C][C]-1.52331088081177e-16[/C][/ROW]
[ROW][C]35[/C][C]35[/C][C]35[/C][C]-5.80139334560492e-16[/C][/ROW]
[ROW][C]36[/C][C]36[/C][C]36[/C][C]7.50670932500488e-16[/C][/ROW]
[ROW][C]37[/C][C]37[/C][C]37[/C][C]-1.10960003944072e-16[/C][/ROW]
[ROW][C]38[/C][C]38[/C][C]38[/C][C]-7.64694926978831e-16[/C][/ROW]
[ROW][C]39[/C][C]39[/C][C]39[/C][C]2.37397864667883e-16[/C][/ROW]
[ROW][C]40[/C][C]40[/C][C]40[/C][C]2.18275377353383e-16[/C][/ROW]
[ROW][C]41[/C][C]41[/C][C]41[/C][C]1.63515111861505e-16[/C][/ROW]
[ROW][C]42[/C][C]42[/C][C]42[/C][C]1.38245214722014e-16[/C][/ROW]
[ROW][C]43[/C][C]43[/C][C]43[/C][C]-5.23089558689502e-16[/C][/ROW]
[ROW][C]44[/C][C]44[/C][C]44[/C][C]1.79715941056111e-16[/C][/ROW]
[ROW][C]45[/C][C]45[/C][C]45[/C][C]1.70305404068671e-17[/C][/ROW]
[ROW][C]46[/C][C]46[/C][C]46[/C][C]2.53210578795393e-16[/C][/ROW]
[ROW][C]47[/C][C]47[/C][C]47[/C][C]-5.0261757196383e-17[/C][/ROW]
[ROW][C]48[/C][C]48[/C][C]48[/C][C]5.75506321544438e-16[/C][/ROW]
[ROW][C]49[/C][C]49[/C][C]49[/C][C]-7.86619326457259e-16[/C][/ROW]
[ROW][C]50[/C][C]50[/C][C]50[/C][C]5.78962120323504e-16[/C][/ROW]
[ROW][C]51[/C][C]51[/C][C]51[/C][C]3.75672453917209e-16[/C][/ROW]
[ROW][C]52[/C][C]52[/C][C]52[/C][C]-6.32229027719334e-17[/C][/ROW]
[ROW][C]53[/C][C]53[/C][C]53[/C][C]3.23759431324033e-16[/C][/ROW]
[ROW][C]54[/C][C]54[/C][C]54[/C][C]9.90243364330694e-17[/C][/ROW]
[ROW][C]55[/C][C]55[/C][C]55[/C][C]-3.53462286056539e-16[/C][/ROW]
[ROW][C]56[/C][C]56[/C][C]56[/C][C]5.28991723294676e-17[/C][/ROW]
[ROW][C]57[/C][C]57[/C][C]57[/C][C]-7.42842380015916e-16[/C][/ROW]
[ROW][C]58[/C][C]58[/C][C]58[/C][C]6.72801838991667e-16[/C][/ROW]
[ROW][C]59[/C][C]59[/C][C]59[/C][C]8.98907839218018e-17[/C][/ROW]
[ROW][C]60[/C][C]60[/C][C]60[/C][C]-7.81263239446812e-16[/C][/ROW]
[ROW][C]61[/C][C]61[/C][C]61[/C][C]-3.66706301745246e-16[/C][/ROW]
[ROW][C]62[/C][C]62[/C][C]62[/C][C]4.34755948775423e-16[/C][/ROW]
[ROW][C]63[/C][C]63[/C][C]63[/C][C]8.2638006668242e-17[/C][/ROW]
[ROW][C]64[/C][C]64[/C][C]64[/C][C]-4.38886205772478e-16[/C][/ROW]
[ROW][C]65[/C][C]65[/C][C]65[/C][C]-2.29082915913908e-16[/C][/ROW]
[ROW][C]66[/C][C]66[/C][C]66[/C][C]3.27046719143833e-16[/C][/ROW]
[ROW][C]67[/C][C]67[/C][C]67[/C][C]3.06748673802436e-17[/C][/ROW]
[ROW][C]68[/C][C]68[/C][C]68[/C][C]-4.13579296843554e-16[/C][/ROW]
[ROW][C]69[/C][C]69[/C][C]69[/C][C]-1.29636338311076e-15[/C][/ROW]
[ROW][C]70[/C][C]70[/C][C]70[/C][C]-5.44511355771099e-16[/C][/ROW]
[ROW][C]71[/C][C]71[/C][C]71[/C][C]-3.97058613447162e-16[/C][/ROW]
[ROW][C]72[/C][C]72[/C][C]72[/C][C]6.75214053038262e-16[/C][/ROW]
[ROW][C]73[/C][C]73[/C][C]73[/C][C]2.43001961098468e-16[/C][/ROW]
[ROW][C]74[/C][C]74[/C][C]74[/C][C]9.24509577162011e-16[/C][/ROW]
[ROW][C]75[/C][C]75[/C][C]75[/C][C]-1.24521763679478e-15[/C][/ROW]
[ROW][C]76[/C][C]76[/C][C]76[/C][C]-4.28696791398346e-16[/C][/ROW]
[ROW][C]77[/C][C]77[/C][C]77[/C][C]-2.78131832004764e-16[/C][/ROW]
[ROW][C]78[/C][C]78[/C][C]78[/C][C]-5.25613362678292e-17[/C][/ROW]
[ROW][C]79[/C][C]79[/C][C]79[/C][C]6.71791467903539e-16[/C][/ROW]
[ROW][C]80[/C][C]80[/C][C]80[/C][C]3.53727036941602e-16[/C][/ROW]
[ROW][C]81[/C][C]81[/C][C]81[/C][C]4.98414858878753e-16[/C][/ROW]
[ROW][C]82[/C][C]82[/C][C]82[/C][C]6.5705267694034e-16[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186241&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186241&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 IndexActualsInterpolationForecastResidualsPrediction Error
111-9.51232828942849e-16
222-1.43874244480495e-15
3336.54044811802134e-16
444-4.56524039394615e-16
555-1.86875318642681e-15
666-3.66844444095979e-16
7771.15333993241805e-15
8886.22492106059019e-16
9999.44745573095916e-16
1010101.09356295120206e-15
111111-3.45634408722318e-17
121212-1.89590850788152e-15
131313-4.21751123478041e-16
1414146.82029117825761e-15
1515151.16533710010282e-16
161616-1.53581215804394e-15
171717-1.13719690415603e-15
181818-1.33101350476712e-15
191919-5.46203384312603e-16
2020202.15256525340445e-17
2121212.85289861786971e-16
222222-4.35636399648386e-16
2323236.30648351760257e-16
242424-4.93820212015549e-17
2525258.60504654617179e-16
2626264.59488698467876e-16
2727271.8515162068357e-17
282828-1.43783742884245e-17
2929291.47791446406699e-16
303030-5.94233004380331e-16
313131-1.79298795524996e-16
3232323.96231873004825e-16
3333336.74587791838043e-18
343434-1.52331088081177e-16
353535-5.80139334560492e-16
3636367.50670932500488e-16
373737-1.10960003944072e-16
383838-7.64694926978831e-16
3939392.37397864667883e-16
4040402.18275377353383e-16
4141411.63515111861505e-16
4242421.38245214722014e-16
434343-5.23089558689502e-16
4444441.79715941056111e-16
4545451.70305404068671e-17
4646462.53210578795393e-16
474747-5.0261757196383e-17
4848485.75506321544438e-16
494949-7.86619326457259e-16
5050505.78962120323504e-16
5151513.75672453917209e-16
525252-6.32229027719334e-17
5353533.23759431324033e-16
5454549.90243364330694e-17
555555-3.53462286056539e-16
5656565.28991723294676e-17
575757-7.42842380015916e-16
5858586.72801838991667e-16
5959598.98907839218018e-17
606060-7.81263239446812e-16
616161-3.66706301745246e-16
6262624.34755948775423e-16
6363638.2638006668242e-17
646464-4.38886205772478e-16
656565-2.29082915913908e-16
6666663.27046719143833e-16
6767673.06748673802436e-17
686868-4.13579296843554e-16
696969-1.29636338311076e-15
707070-5.44511355771099e-16
717171-3.97058613447162e-16
7272726.75214053038262e-16
7373732.43001961098468e-16
7474749.24509577162011e-16
757575-1.24521763679478e-15
767676-4.28696791398346e-16
777777-2.78131832004764e-16
787878-5.25613362678292e-17
7979796.71791467903539e-16
8080803.53727036941602e-16
8181814.98414858878753e-16
8282826.5705267694034e-16







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
170.001582704457246060.003165408914492130.998417295542754
180.000690560427396740.001381120854793480.999309439572603
190.0001095697304482880.0002191394608965770.999890430269552
200.005069617704106080.01013923540821220.994930382295894
219.95537478238447e-071.99107495647689e-060.999999004462522
220.02124280284868910.04248560569737810.978757197151311
230.0001235268173062430.0002470536346124860.999876473182694
244.69269946767927e-089.38539893535855e-080.999999953073005
250.0005528817977727130.001105763595545430.999447118202227
269.2700783736469e-050.0001854015674729380.999907299216264
270.6774803781876560.6450392436246890.322519621812344
280.6678282546293280.6643434907413440.332171745370672
290.1752219045062010.3504438090124010.824778095493799
301.80257844614975e-063.60515689229951e-060.999998197421554
310.6235338754613830.7529322490772340.376466124538617
322.02496160265124e-074.04992320530248e-070.99999979750384
330.02727994103979070.05455988207958140.972720058960209
340.08960965422134180.1792193084426840.910390345778658
350.9975042161827680.004991567634463340.00249578381723167
360.8724266271387430.2551467457225140.127573372861257
370.3781979833893630.7563959667787260.621802016610637
380.9506961282220260.09860774355594720.0493038717779736
390.2761201532959150.5522403065918310.723879846704085
400.8843474738531020.2313050522937950.115652526146898
410.9999999992576361.48472776907079e-097.42363884535394e-10
420.785387015598430.429225968803140.21461298440157
431.66169707203077e-093.32339414406154e-090.999999998338303
440.1206843362293150.2413686724586310.879315663770685
450.006365880047886650.01273176009577330.993634119952113
460.9999999999577978.4405038695986e-114.2202519347993e-11
470.9965139735325690.006972052934862690.00348602646743135
480.0002964174144619550.000592834828923910.999703582585538
490.9999423125256950.0001153749486091935.76874743045966e-05
500.9962267376006610.007546524798677360.00377326239933868
510.9999327811884410.0001344376231173976.72188115586983e-05
520.9527499687744730.09450006245105460.0472500312255273
530.9933407533098840.01331849338023150.00665924669011574
540.03268790520575650.06537581041151310.967312094794244
550.9864947274919490.02701054501610250.0135052725080512
560.1104077503913170.2208155007826340.889592249608683
570.2316888279289710.4633776558579430.768311172071029
580.9999961003124947.79937501167889e-063.89968750583944e-06
590.9999999854604912.90790184905025e-081.45395092452513e-08
600.713154230277070.5736915394458610.28684576972293
610.9999458143155290.0001083713689413475.41856844706733e-05
620.9844534442338170.03109311153236680.0155465557661834
630.9946319212966250.01073615740675010.00536807870337503
640.9990785924216990.001842815156601150.000921407578300575
650.9998432479555490.0003135040889025620.000156752044451281

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
17 & 0.00158270445724606 & 0.00316540891449213 & 0.998417295542754 \tabularnewline
18 & 0.00069056042739674 & 0.00138112085479348 & 0.999309439572603 \tabularnewline
19 & 0.000109569730448288 & 0.000219139460896577 & 0.999890430269552 \tabularnewline
20 & 0.00506961770410608 & 0.0101392354082122 & 0.994930382295894 \tabularnewline
21 & 9.95537478238447e-07 & 1.99107495647689e-06 & 0.999999004462522 \tabularnewline
22 & 0.0212428028486891 & 0.0424856056973781 & 0.978757197151311 \tabularnewline
23 & 0.000123526817306243 & 0.000247053634612486 & 0.999876473182694 \tabularnewline
24 & 4.69269946767927e-08 & 9.38539893535855e-08 & 0.999999953073005 \tabularnewline
25 & 0.000552881797772713 & 0.00110576359554543 & 0.999447118202227 \tabularnewline
26 & 9.2700783736469e-05 & 0.000185401567472938 & 0.999907299216264 \tabularnewline
27 & 0.677480378187656 & 0.645039243624689 & 0.322519621812344 \tabularnewline
28 & 0.667828254629328 & 0.664343490741344 & 0.332171745370672 \tabularnewline
29 & 0.175221904506201 & 0.350443809012401 & 0.824778095493799 \tabularnewline
30 & 1.80257844614975e-06 & 3.60515689229951e-06 & 0.999998197421554 \tabularnewline
31 & 0.623533875461383 & 0.752932249077234 & 0.376466124538617 \tabularnewline
32 & 2.02496160265124e-07 & 4.04992320530248e-07 & 0.99999979750384 \tabularnewline
33 & 0.0272799410397907 & 0.0545598820795814 & 0.972720058960209 \tabularnewline
34 & 0.0896096542213418 & 0.179219308442684 & 0.910390345778658 \tabularnewline
35 & 0.997504216182768 & 0.00499156763446334 & 0.00249578381723167 \tabularnewline
36 & 0.872426627138743 & 0.255146745722514 & 0.127573372861257 \tabularnewline
37 & 0.378197983389363 & 0.756395966778726 & 0.621802016610637 \tabularnewline
38 & 0.950696128222026 & 0.0986077435559472 & 0.0493038717779736 \tabularnewline
39 & 0.276120153295915 & 0.552240306591831 & 0.723879846704085 \tabularnewline
40 & 0.884347473853102 & 0.231305052293795 & 0.115652526146898 \tabularnewline
41 & 0.999999999257636 & 1.48472776907079e-09 & 7.42363884535394e-10 \tabularnewline
42 & 0.78538701559843 & 0.42922596880314 & 0.21461298440157 \tabularnewline
43 & 1.66169707203077e-09 & 3.32339414406154e-09 & 0.999999998338303 \tabularnewline
44 & 0.120684336229315 & 0.241368672458631 & 0.879315663770685 \tabularnewline
45 & 0.00636588004788665 & 0.0127317600957733 & 0.993634119952113 \tabularnewline
46 & 0.999999999957797 & 8.4405038695986e-11 & 4.2202519347993e-11 \tabularnewline
47 & 0.996513973532569 & 0.00697205293486269 & 0.00348602646743135 \tabularnewline
48 & 0.000296417414461955 & 0.00059283482892391 & 0.999703582585538 \tabularnewline
49 & 0.999942312525695 & 0.000115374948609193 & 5.76874743045966e-05 \tabularnewline
50 & 0.996226737600661 & 0.00754652479867736 & 0.00377326239933868 \tabularnewline
51 & 0.999932781188441 & 0.000134437623117397 & 6.72188115586983e-05 \tabularnewline
52 & 0.952749968774473 & 0.0945000624510546 & 0.0472500312255273 \tabularnewline
53 & 0.993340753309884 & 0.0133184933802315 & 0.00665924669011574 \tabularnewline
54 & 0.0326879052057565 & 0.0653758104115131 & 0.967312094794244 \tabularnewline
55 & 0.986494727491949 & 0.0270105450161025 & 0.0135052725080512 \tabularnewline
56 & 0.110407750391317 & 0.220815500782634 & 0.889592249608683 \tabularnewline
57 & 0.231688827928971 & 0.463377655857943 & 0.768311172071029 \tabularnewline
58 & 0.999996100312494 & 7.79937501167889e-06 & 3.89968750583944e-06 \tabularnewline
59 & 0.999999985460491 & 2.90790184905025e-08 & 1.45395092452513e-08 \tabularnewline
60 & 0.71315423027707 & 0.573691539445861 & 0.28684576972293 \tabularnewline
61 & 0.999945814315529 & 0.000108371368941347 & 5.41856844706733e-05 \tabularnewline
62 & 0.984453444233817 & 0.0310931115323668 & 0.0155465557661834 \tabularnewline
63 & 0.994631921296625 & 0.0107361574067501 & 0.00536807870337503 \tabularnewline
64 & 0.999078592421699 & 0.00184281515660115 & 0.000921407578300575 \tabularnewline
65 & 0.999843247955549 & 0.000313504088902562 & 0.000156752044451281 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186241&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]17[/C][C]0.00158270445724606[/C][C]0.00316540891449213[/C][C]0.998417295542754[/C][/ROW]
[ROW][C]18[/C][C]0.00069056042739674[/C][C]0.00138112085479348[/C][C]0.999309439572603[/C][/ROW]
[ROW][C]19[/C][C]0.000109569730448288[/C][C]0.000219139460896577[/C][C]0.999890430269552[/C][/ROW]
[ROW][C]20[/C][C]0.00506961770410608[/C][C]0.0101392354082122[/C][C]0.994930382295894[/C][/ROW]
[ROW][C]21[/C][C]9.95537478238447e-07[/C][C]1.99107495647689e-06[/C][C]0.999999004462522[/C][/ROW]
[ROW][C]22[/C][C]0.0212428028486891[/C][C]0.0424856056973781[/C][C]0.978757197151311[/C][/ROW]
[ROW][C]23[/C][C]0.000123526817306243[/C][C]0.000247053634612486[/C][C]0.999876473182694[/C][/ROW]
[ROW][C]24[/C][C]4.69269946767927e-08[/C][C]9.38539893535855e-08[/C][C]0.999999953073005[/C][/ROW]
[ROW][C]25[/C][C]0.000552881797772713[/C][C]0.00110576359554543[/C][C]0.999447118202227[/C][/ROW]
[ROW][C]26[/C][C]9.2700783736469e-05[/C][C]0.000185401567472938[/C][C]0.999907299216264[/C][/ROW]
[ROW][C]27[/C][C]0.677480378187656[/C][C]0.645039243624689[/C][C]0.322519621812344[/C][/ROW]
[ROW][C]28[/C][C]0.667828254629328[/C][C]0.664343490741344[/C][C]0.332171745370672[/C][/ROW]
[ROW][C]29[/C][C]0.175221904506201[/C][C]0.350443809012401[/C][C]0.824778095493799[/C][/ROW]
[ROW][C]30[/C][C]1.80257844614975e-06[/C][C]3.60515689229951e-06[/C][C]0.999998197421554[/C][/ROW]
[ROW][C]31[/C][C]0.623533875461383[/C][C]0.752932249077234[/C][C]0.376466124538617[/C][/ROW]
[ROW][C]32[/C][C]2.02496160265124e-07[/C][C]4.04992320530248e-07[/C][C]0.99999979750384[/C][/ROW]
[ROW][C]33[/C][C]0.0272799410397907[/C][C]0.0545598820795814[/C][C]0.972720058960209[/C][/ROW]
[ROW][C]34[/C][C]0.0896096542213418[/C][C]0.179219308442684[/C][C]0.910390345778658[/C][/ROW]
[ROW][C]35[/C][C]0.997504216182768[/C][C]0.00499156763446334[/C][C]0.00249578381723167[/C][/ROW]
[ROW][C]36[/C][C]0.872426627138743[/C][C]0.255146745722514[/C][C]0.127573372861257[/C][/ROW]
[ROW][C]37[/C][C]0.378197983389363[/C][C]0.756395966778726[/C][C]0.621802016610637[/C][/ROW]
[ROW][C]38[/C][C]0.950696128222026[/C][C]0.0986077435559472[/C][C]0.0493038717779736[/C][/ROW]
[ROW][C]39[/C][C]0.276120153295915[/C][C]0.552240306591831[/C][C]0.723879846704085[/C][/ROW]
[ROW][C]40[/C][C]0.884347473853102[/C][C]0.231305052293795[/C][C]0.115652526146898[/C][/ROW]
[ROW][C]41[/C][C]0.999999999257636[/C][C]1.48472776907079e-09[/C][C]7.42363884535394e-10[/C][/ROW]
[ROW][C]42[/C][C]0.78538701559843[/C][C]0.42922596880314[/C][C]0.21461298440157[/C][/ROW]
[ROW][C]43[/C][C]1.66169707203077e-09[/C][C]3.32339414406154e-09[/C][C]0.999999998338303[/C][/ROW]
[ROW][C]44[/C][C]0.120684336229315[/C][C]0.241368672458631[/C][C]0.879315663770685[/C][/ROW]
[ROW][C]45[/C][C]0.00636588004788665[/C][C]0.0127317600957733[/C][C]0.993634119952113[/C][/ROW]
[ROW][C]46[/C][C]0.999999999957797[/C][C]8.4405038695986e-11[/C][C]4.2202519347993e-11[/C][/ROW]
[ROW][C]47[/C][C]0.996513973532569[/C][C]0.00697205293486269[/C][C]0.00348602646743135[/C][/ROW]
[ROW][C]48[/C][C]0.000296417414461955[/C][C]0.00059283482892391[/C][C]0.999703582585538[/C][/ROW]
[ROW][C]49[/C][C]0.999942312525695[/C][C]0.000115374948609193[/C][C]5.76874743045966e-05[/C][/ROW]
[ROW][C]50[/C][C]0.996226737600661[/C][C]0.00754652479867736[/C][C]0.00377326239933868[/C][/ROW]
[ROW][C]51[/C][C]0.999932781188441[/C][C]0.000134437623117397[/C][C]6.72188115586983e-05[/C][/ROW]
[ROW][C]52[/C][C]0.952749968774473[/C][C]0.0945000624510546[/C][C]0.0472500312255273[/C][/ROW]
[ROW][C]53[/C][C]0.993340753309884[/C][C]0.0133184933802315[/C][C]0.00665924669011574[/C][/ROW]
[ROW][C]54[/C][C]0.0326879052057565[/C][C]0.0653758104115131[/C][C]0.967312094794244[/C][/ROW]
[ROW][C]55[/C][C]0.986494727491949[/C][C]0.0270105450161025[/C][C]0.0135052725080512[/C][/ROW]
[ROW][C]56[/C][C]0.110407750391317[/C][C]0.220815500782634[/C][C]0.889592249608683[/C][/ROW]
[ROW][C]57[/C][C]0.231688827928971[/C][C]0.463377655857943[/C][C]0.768311172071029[/C][/ROW]
[ROW][C]58[/C][C]0.999996100312494[/C][C]7.79937501167889e-06[/C][C]3.89968750583944e-06[/C][/ROW]
[ROW][C]59[/C][C]0.999999985460491[/C][C]2.90790184905025e-08[/C][C]1.45395092452513e-08[/C][/ROW]
[ROW][C]60[/C][C]0.71315423027707[/C][C]0.573691539445861[/C][C]0.28684576972293[/C][/ROW]
[ROW][C]61[/C][C]0.999945814315529[/C][C]0.000108371368941347[/C][C]5.41856844706733e-05[/C][/ROW]
[ROW][C]62[/C][C]0.984453444233817[/C][C]0.0310931115323668[/C][C]0.0155465557661834[/C][/ROW]
[ROW][C]63[/C][C]0.994631921296625[/C][C]0.0107361574067501[/C][C]0.00536807870337503[/C][/ROW]
[ROW][C]64[/C][C]0.999078592421699[/C][C]0.00184281515660115[/C][C]0.000921407578300575[/C][/ROW]
[ROW][C]65[/C][C]0.999843247955549[/C][C]0.000313504088902562[/C][C]0.000156752044451281[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186241&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186241&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-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
170.001582704457246060.003165408914492130.998417295542754
180.000690560427396740.001381120854793480.999309439572603
190.0001095697304482880.0002191394608965770.999890430269552
200.005069617704106080.01013923540821220.994930382295894
219.95537478238447e-071.99107495647689e-060.999999004462522
220.02124280284868910.04248560569737810.978757197151311
230.0001235268173062430.0002470536346124860.999876473182694
244.69269946767927e-089.38539893535855e-080.999999953073005
250.0005528817977727130.001105763595545430.999447118202227
269.2700783736469e-050.0001854015674729380.999907299216264
270.6774803781876560.6450392436246890.322519621812344
280.6678282546293280.6643434907413440.332171745370672
290.1752219045062010.3504438090124010.824778095493799
301.80257844614975e-063.60515689229951e-060.999998197421554
310.6235338754613830.7529322490772340.376466124538617
322.02496160265124e-074.04992320530248e-070.99999979750384
330.02727994103979070.05455988207958140.972720058960209
340.08960965422134180.1792193084426840.910390345778658
350.9975042161827680.004991567634463340.00249578381723167
360.8724266271387430.2551467457225140.127573372861257
370.3781979833893630.7563959667787260.621802016610637
380.9506961282220260.09860774355594720.0493038717779736
390.2761201532959150.5522403065918310.723879846704085
400.8843474738531020.2313050522937950.115652526146898
410.9999999992576361.48472776907079e-097.42363884535394e-10
420.785387015598430.429225968803140.21461298440157
431.66169707203077e-093.32339414406154e-090.999999998338303
440.1206843362293150.2413686724586310.879315663770685
450.006365880047886650.01273176009577330.993634119952113
460.9999999999577978.4405038695986e-114.2202519347993e-11
470.9965139735325690.006972052934862690.00348602646743135
480.0002964174144619550.000592834828923910.999703582585538
490.9999423125256950.0001153749486091935.76874743045966e-05
500.9962267376006610.007546524798677360.00377326239933868
510.9999327811884410.0001344376231173976.72188115586983e-05
520.9527499687744730.09450006245105460.0472500312255273
530.9933407533098840.01331849338023150.00665924669011574
540.03268790520575650.06537581041151310.967312094794244
550.9864947274919490.02701054501610250.0135052725080512
560.1104077503913170.2208155007826340.889592249608683
570.2316888279289710.4633776558579430.768311172071029
580.9999961003124947.79937501167889e-063.89968750583944e-06
590.9999999854604912.90790184905025e-081.45395092452513e-08
600.713154230277070.5736915394458610.28684576972293
610.9999458143155290.0001083713689413475.41856844706733e-05
620.9844534442338170.03109311153236680.0155465557661834
630.9946319212966250.01073615740675010.00536807870337503
640.9990785924216990.001842815156601150.000921407578300575
650.9998432479555490.0003135040889025620.000156752044451281







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level240.489795918367347NOK
5% type I error level310.63265306122449NOK
10% type I error level350.714285714285714NOK

\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 & 24 & 0.489795918367347 & NOK \tabularnewline
5% type I error level & 31 & 0.63265306122449 & NOK \tabularnewline
10% type I error level & 35 & 0.714285714285714 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186241&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]24[/C][C]0.489795918367347[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]31[/C][C]0.63265306122449[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]35[/C][C]0.714285714285714[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186241&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186241&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 testsOK/NOK
1% type I error level240.489795918367347NOK
5% type I error level310.63265306122449NOK
10% type I error level350.714285714285714NOK



Parameters (Session):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;
Parameters (R input):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = 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$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$coefficients[i,1], 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,mysum$coefficients[i,1])
a<-table.element(a, round(mysum$coefficients[i,2],6))
a<-table.element(a, round(mysum$coefficients[i,3],4))
a<-table.element(a, round(mysum$coefficients[i,4],6))
a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, sqrt(mysum$r.squared))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, mysum$r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, mysum$adj.r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, mysum$fstatistic[1])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[3])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
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, mysum$sigma)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, sum(myerror*myerror))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,x[i])
a<-table.element(a,x[i]-mysum$resid[i])
a<-table.element(a,mysum$resid[i])
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,gqarr[mypoint-kp3+1,1])
a<-table.element(a,gqarr[mypoint-kp3+1,2])
a<-table.element(a,gqarr[mypoint-kp3+1,3])
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,numsignificant1)
a<-table.element(a,numsignificant1/numgqtests)
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,numsignificant5)
a<-table.element(a,numsignificant5/numgqtests)
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,numsignificant10)
a<-table.element(a,numsignificant10/numgqtests)
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')
}