## Free Statistics

of Irreproducible Research!

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:

Original text written by user:
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 Input view raw input (R code) Raw Output view raw output of R engine Computing time 8 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 Input view raw input (R code) Raw Output view raw output of R engine Computing time 8 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 Variable Parameter S.D. T-STATH0: parameter = 0 2-tail p-value 1-tail p-value (Intercept) 3.75902714069415e-15 0 1.0358 0.303982 0.151991 X_1t -1.09730656292003e-16 0 -3.8641 0.000251 0.000126 Yt -1.29673007892416e-16 0 -2.9262 0.004662 0.002331 X_2t 5.64261283981446e-17 0 2.1006 0.039386 0.019693 X_3t -9.6274052844502e-17 0 -3.8014 0.00031 0.000155 X_4t 5.68713539993411e-18 0 0.2535 0.800657 0.400329 X_5t 1.46630317988021e-16 0 4.4576 3.2e-05 1.6e-05 X_6t 2.68490208439456e-16 0 4.9094 6e-06 3e-06 X_7t 3.20663515391684e-16 0 3.1779 0.002232 0.001116 X_8t -6.00835963321108e-16 0 -5.1898 2e-06 1e-06 X_9t 5.21026437727825e-16 0 4.1677 8.9e-05 4.4e-05 X_10t 7.37830649995089e-17 0 3.0094 0.00367 0.001835 X_11t 1.99827229686305e-16 0 3.3173 0.001462 0.000731 t 1 0 65544626203109080 0 0

\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 Variable Parameter S.D. T-STATH0: parameter = 0 2-tail p-value 1-tail p-value (Intercept) 3.75902714069415e-15 0 1.0358 0.303982 0.151991 X_1t -1.09730656292003e-16 0 -3.8641 0.000251 0.000126 Yt -1.29673007892416e-16 0 -2.9262 0.004662 0.002331 X_2t 5.64261283981446e-17 0 2.1006 0.039386 0.019693 X_3t -9.6274052844502e-17 0 -3.8014 0.00031 0.000155 X_4t 5.68713539993411e-18 0 0.2535 0.800657 0.400329 X_5t 1.46630317988021e-16 0 4.4576 3.2e-05 1.6e-05 X_6t 2.68490208439456e-16 0 4.9094 6e-06 3e-06 X_7t 3.20663515391684e-16 0 3.1779 0.002232 0.001116 X_8t -6.00835963321108e-16 0 -5.1898 2e-06 1e-06 X_9t 5.21026437727825e-16 0 4.1677 8.9e-05 4.4e-05 X_10t 7.37830649995089e-17 0 3.0094 0.00367 0.001835 X_11t 1.99827229686305e-16 0 3.3173 0.001462 0.000731 t 1 0 65544626203109080 0 0

 Multiple Linear Regression - Regression Statistics Multiple R 1 R-squared 1 Adjusted R-squared 1 F-TEST (value) 2.9296165392591e+33 F-TEST (DF numerator) 13 F-TEST (DF denominator) 68 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 1.09829951860756e-15 Sum Squared Residuals 8.20258046150048e-29

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 1 \tabularnewline
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]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 R 1 R-squared 1 Adjusted R-squared 1 F-TEST (value) 2.9296165392591e+33 F-TEST (DF numerator) 13 F-TEST (DF denominator) 68 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 1.09829951860756e-15 Sum Squared Residuals 8.20258046150048e-29

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

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

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

 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 24 0.489795918367347 NOK 5% type I error level 31 0.63265306122449 NOK 10% type I error level 35 0.714285714285714 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 & 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 tests OK/NOK 1% type I error level 24 0.489795918367347 NOK 5% type I error level 31 0.63265306122449 NOK 10% type I error level 35 0.714285714285714 NOK

library(lattice)library(lmtest)n25 <- 25 #minimum number of obs. for Goldfeld-Quandt testpar1 <- 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 <- x1if (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'}xk <- length(x[1,])df <- as.data.frame(x)(mylm <- lm(df))(mysum <- summary(mylm))if (n > n25) {kp3 <- k + 3nmkm3 <- n - k - 3gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))numgqtests <- 0numsignificant1 <- 0numsignificant5 <- 0numsignificant10 <- 0for (mypoint in kp3:nmkm3) {j <- 0numgqtests <- numgqtests + 1for (myalt in c('greater', 'two.sided', 'less')) {j <- j + 1gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value}if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1}gqarr}bitmap(file='test0.png')plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')points(x[,1]-mysum$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)dumdum1 <- dum[2:length(myerror),]dum1z <- as.data.frame(dum1)zplot(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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')}}myeq <- paste(myeq, ' + e[t]')a<-table.row.start(a)a<-table.element(a, myeq)a<-table.row.end(a)a<-table.end(a)table.save(a,file='mytable1.tab')a<-table.start()a<-table.row.start(a)a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)a<-table.row.end(a)a<-table.row.start(a)a<-table.element(a,'Variable',header=TRUE)a<-table.element(a,'Parameter',header=TRUE)a<-table.element(a,'S.D.',header=TRUE)a<-table.element(a,'T-STATH0: parameter = 0',header=TRUE)a<-table.element(a,'2-tail p-value',header=TRUE)a<-table.element(a,'1-tail p-value',header=TRUE)a<-table.row.end(a)for (i in 1:k){a<-table.row.start(a)a<-table.element(a,rownames(mysum$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$sigma)a<-table.row.end(a)a<-table.row.start(a)a<-table.element(a, 'Sum Squared Residuals',1,TRUE)a<-table.element(a, sum(myerror*myerror))a<-table.row.end(a)a<-table.end(a)table.save(a,file='mytable3.tab')a<-table.start()a<-table.row.start(a)a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)a<-table.row.end(a)a<-table.row.start(a)a<-table.element(a, 'Time or Index', 1, TRUE)a<-table.element(a, 'Actuals', 1, TRUE)a<-table.element(a, 'InterpolationForecast', 1, TRUE)a<-table.element(a, 'ResidualsPrediction Error', 1, TRUE)a<-table.row.end(a)for (i in 1:n) {a<-table.row.start(a)a<-table.element(a,i, 1, TRUE)a<-table.element(a,x[i])a<-table.element(a,x[i]-mysum$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')}