## 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 13:54:01 -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/t1352141664bh0kcirf8ncfuim.htm/, Retrieved Wed, 01 Feb 2023 16:36:01 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=186221, Retrieved Wed, 01 Feb 2023 16:36:01 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact46
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
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- R PD    [Multiple Regression] [Maand effect] [2012-11-05 18:54:01] [b4b733de199089e913cc2b6ea19b06b9] [Current]
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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 Maurice George Kendall' @ kendall.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 Maurice George Kendall' @ kendall.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186221&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 Maurice George Kendall' @ kendall.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186221&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186221&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 Maurice George Kendall' @ kendall.wessa.net

 Multiple Linear Regression - Estimated Regression Equation Yt[t] = + 31.2523403078176 + 0.0692637846218022maand[t] + 0.392490573829635X_1t[t] -0.330411918643864X_2t[t] -0.281962571399898X_3t[t] -0.197169139762377X_4t[t] -0.217565819885181X_5t[t] -0.230285224824777X_6t[t] -0.101101280702886X_7t[t] -0.143272681608277X_8t[t] + 1.0143440658791X_9t[t] + 0.0225520935682711X_10t[t] -0.169656575930437X_11t[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Yt[t] =  +  31.2523403078176 +  0.0692637846218022maand[t] +  0.392490573829635X_1t[t] -0.330411918643864X_2t[t] -0.281962571399898X_3t[t] -0.197169139762377X_4t[t] -0.217565819885181X_5t[t] -0.230285224824777X_6t[t] -0.101101280702886X_7t[t] -0.143272681608277X_8t[t] +  1.0143440658791X_9t[t] +  0.0225520935682711X_10t[t] -0.169656575930437X_11t[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186221&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Yt[t] =  +  31.2523403078176 +  0.0692637846218022maand[t] +  0.392490573829635X_1t[t] -0.330411918643864X_2t[t] -0.281962571399898X_3t[t] -0.197169139762377X_4t[t] -0.217565819885181X_5t[t] -0.230285224824777X_6t[t] -0.101101280702886X_7t[t] -0.143272681608277X_8t[t] +  1.0143440658791X_9t[t] +  0.0225520935682711X_10t[t] -0.169656575930437X_11t[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186221&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186221&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 Yt[t] = + 31.2523403078176 + 0.0692637846218022maand[t] + 0.392490573829635X_1t[t] -0.330411918643864X_2t[t] -0.281962571399898X_3t[t] -0.197169139762377X_4t[t] -0.217565819885181X_5t[t] -0.230285224824777X_6t[t] -0.101101280702886X_7t[t] -0.143272681608277X_8t[t] + 1.0143440658791X_9t[t] + 0.0225520935682711X_10t[t] -0.169656575930437X_11t[t] + e[t]

 Multiple Linear Regression - Ordinary Least Squares Variable Parameter S.D. T-STATH0: parameter = 0 2-tail p-value 1-tail p-value (Intercept) 31.2523403078176 9.113002 3.4294 0.001025 0.000512 maand 0.0692637846218022 0.040599 1.7061 0.092496 0.046248 X_1t 0.392490573829635 0.06098 6.4364 0 0 X_2t -0.330411918643864 0.061178 -5.4008 1e-06 0 X_3t -0.281962571399898 0.059844 -4.7116 1.2e-05 6e-06 X_4t -0.197169139762377 0.056136 -3.5123 0.000788 0.000394 X_5t -0.217565819885181 0.085436 -2.5465 0.013115 0.006558 X_6t -0.230285224824777 0.145957 -1.5778 0.119196 0.059598 X_7t -0.101101280702886 0.273841 -0.3692 0.713111 0.356556 X_8t -0.143272681608277 0.31403 -0.4562 0.649651 0.324826 X_9t 1.0143440658791 0.3169 3.2008 0.002072 0.001036 X_10t 0.0225520935682711 0.066548 0.3389 0.735727 0.367864 X_11t -0.169656575930437 0.162364 -1.0449 0.299708 0.149854

\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) & 31.2523403078176 & 9.113002 & 3.4294 & 0.001025 & 0.000512 \tabularnewline
maand & 0.0692637846218022 & 0.040599 & 1.7061 & 0.092496 & 0.046248 \tabularnewline
X_1t & 0.392490573829635 & 0.06098 & 6.4364 & 0 & 0 \tabularnewline
X_2t & -0.330411918643864 & 0.061178 & -5.4008 & 1e-06 & 0 \tabularnewline
X_3t & -0.281962571399898 & 0.059844 & -4.7116 & 1.2e-05 & 6e-06 \tabularnewline
X_4t & -0.197169139762377 & 0.056136 & -3.5123 & 0.000788 & 0.000394 \tabularnewline
X_5t & -0.217565819885181 & 0.085436 & -2.5465 & 0.013115 & 0.006558 \tabularnewline
X_6t & -0.230285224824777 & 0.145957 & -1.5778 & 0.119196 & 0.059598 \tabularnewline
X_7t & -0.101101280702886 & 0.273841 & -0.3692 & 0.713111 & 0.356556 \tabularnewline
X_8t & -0.143272681608277 & 0.31403 & -0.4562 & 0.649651 & 0.324826 \tabularnewline
X_9t & 1.0143440658791 & 0.3169 & 3.2008 & 0.002072 & 0.001036 \tabularnewline
X_10t & 0.0225520935682711 & 0.066548 & 0.3389 & 0.735727 & 0.367864 \tabularnewline
X_11t & -0.169656575930437 & 0.162364 & -1.0449 & 0.299708 & 0.149854 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186221&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]31.2523403078176[/C][C]9.113002[/C][C]3.4294[/C][C]0.001025[/C][C]0.000512[/C][/ROW]
[ROW][C]maand[/C][C]0.0692637846218022[/C][C]0.040599[/C][C]1.7061[/C][C]0.092496[/C][C]0.046248[/C][/ROW]
[ROW][C]X_1t[/C][C]0.392490573829635[/C][C]0.06098[/C][C]6.4364[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]X_2t[/C][C]-0.330411918643864[/C][C]0.061178[/C][C]-5.4008[/C][C]1e-06[/C][C]0[/C][/ROW]
[ROW][C]X_3t[/C][C]-0.281962571399898[/C][C]0.059844[/C][C]-4.7116[/C][C]1.2e-05[/C][C]6e-06[/C][/ROW]
[ROW][C]X_4t[/C][C]-0.197169139762377[/C][C]0.056136[/C][C]-3.5123[/C][C]0.000788[/C][C]0.000394[/C][/ROW]
[ROW][C]X_5t[/C][C]-0.217565819885181[/C][C]0.085436[/C][C]-2.5465[/C][C]0.013115[/C][C]0.006558[/C][/ROW]
[ROW][C]X_6t[/C][C]-0.230285224824777[/C][C]0.145957[/C][C]-1.5778[/C][C]0.119196[/C][C]0.059598[/C][/ROW]
[ROW][C]X_7t[/C][C]-0.101101280702886[/C][C]0.273841[/C][C]-0.3692[/C][C]0.713111[/C][C]0.356556[/C][/ROW]
[ROW][C]X_8t[/C][C]-0.143272681608277[/C][C]0.31403[/C][C]-0.4562[/C][C]0.649651[/C][C]0.324826[/C][/ROW]
[ROW][C]X_9t[/C][C]1.0143440658791[/C][C]0.3169[/C][C]3.2008[/C][C]0.002072[/C][C]0.001036[/C][/ROW]
[ROW][C]X_10t[/C][C]0.0225520935682711[/C][C]0.066548[/C][C]0.3389[/C][C]0.735727[/C][C]0.367864[/C][/ROW]
[ROW][C]X_11t[/C][C]-0.169656575930437[/C][C]0.162364[/C][C]-1.0449[/C][C]0.299708[/C][C]0.149854[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186221&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186221&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) 31.2523403078176 9.113002 3.4294 0.001025 0.000512 maand 0.0692637846218022 0.040599 1.7061 0.092496 0.046248 X_1t 0.392490573829635 0.06098 6.4364 0 0 X_2t -0.330411918643864 0.061178 -5.4008 1e-06 0 X_3t -0.281962571399898 0.059844 -4.7116 1.2e-05 6e-06 X_4t -0.197169139762377 0.056136 -3.5123 0.000788 0.000394 X_5t -0.217565819885181 0.085436 -2.5465 0.013115 0.006558 X_6t -0.230285224824777 0.145957 -1.5778 0.119196 0.059598 X_7t -0.101101280702886 0.273841 -0.3692 0.713111 0.356556 X_8t -0.143272681608277 0.31403 -0.4562 0.649651 0.324826 X_9t 1.0143440658791 0.3169 3.2008 0.002072 0.001036 X_10t 0.0225520935682711 0.066548 0.3389 0.735727 0.367864 X_11t -0.169656575930437 0.162364 -1.0449 0.299708 0.149854

 Multiple Linear Regression - Regression Statistics Multiple R 0.958890447905324 R-squared 0.919470891084072 Adjusted R-squared 0.90546582866391 F-TEST (value) 65.6527520905883 F-TEST (DF numerator) 12 F-TEST (DF denominator) 69 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 2.98362133428308 Sum Squared Residuals 614.237742380849

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.958890447905324 \tabularnewline
R-squared & 0.919470891084072 \tabularnewline
Adjusted R-squared & 0.90546582866391 \tabularnewline
F-TEST (value) & 65.6527520905883 \tabularnewline
F-TEST (DF numerator) & 12 \tabularnewline
F-TEST (DF denominator) & 69 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.98362133428308 \tabularnewline
Sum Squared Residuals & 614.237742380849 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186221&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.958890447905324[/C][/ROW]
[ROW][C]R-squared[/C][C]0.919470891084072[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.90546582866391[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]65.6527520905883[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]12[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]69[/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]2.98362133428308[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]614.237742380849[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186221&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186221&T=3

As an alternative you can also use a QR Code:

The GUIDs for individual cells are displayed in the table below:

 Multiple Linear Regression - Regression Statistics Multiple R 0.958890447905324 R-squared 0.919470891084072 Adjusted R-squared 0.90546582866391 F-TEST (value) 65.6527520905883 F-TEST (DF numerator) 12 F-TEST (DF denominator) 69 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 2.98362133428308 Sum Squared Residuals 614.237742380849

 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals InterpolationForecast ResidualsPrediction Error 1 -3 -4.72119240640717 1.72119240640717 2 -4 -2.97113090460443 -1.02886909539557 3 -7 -6.97134081626948 -0.0286591837305171 4 -7 -4.7736043999013 -2.2263956000987 5 -7 -5.70644644250306 -1.29355355749694 6 -3 -3.26154456611182 0.261544566111821 7 0 -2.39669714780782 2.39669714780782 8 -5 -2.66311770972184 -2.33688229027816 9 -3 -3.1027153533168 0.102715353316797 10 3 1.42135484608033 1.57864515391967 11 2 -0.676628833741848 2.67662883374185 12 -7 -10.3555124005262 3.35551240052616 13 -1 1.88230502291626 -2.88230502291626 14 0 2.09505007886414 -2.09505007886414 15 -3 -1.38199347964915 -1.61800652035085 16 4 6.11229106513785 -2.11229106513785 17 2 2.81674887138434 -0.816748871384343 18 3 1.21852964871528 1.78147035128472 19 0 -3.02472068305634 3.02472068305634 20 -10 -8.28549897868586 -1.71450102131414 21 -10 -7.91837234408827 -2.08162765591173 22 -9 -7.59439824125933 -1.40560175874068 23 -22 -18.9017928496072 -3.09820715039282 24 -16 -16.4819220528147 0.481922052814727 25 -18 -21.4377975809588 3.43779758095883 26 -14 -14.4187326948288 0.418732694828763 27 -12 -16.5539072079041 4.55390720790414 28 -17 -18.6328052538213 1.63280525382127 29 -23 -20.8466395550125 -2.15336044498755 30 -28 -25.2458249648115 -2.75417503518846 31 -31 -28.264369573859 -2.73563042614102 32 -21 -21.4027755171847 0.402775517184676 33 -19 -16.2872275020373 -2.71277249796267 34 -22 -25.4087545681004 3.40875456810037 35 -22 -24.039430248828 2.03943024882802 36 -25 -22.8586265082721 -2.14137349172788 37 -16 -17.4336103757 1.43361037570005 38 -22 -17.4046233348815 -4.5953766651185 39 -21 -16.4671588822581 -4.53284111774188 40 -10 -11.1552779587938 1.15527795879383 41 -7 -6.88314601135591 -0.116853988644091 42 -5 -7.58115629523078 2.58115629523078 43 -4 -5.63580647414244 1.63580647414244 44 7 1.80908423668899 5.19091576331101 45 6 1.58390261557192 4.41609738442808 46 3 2.86112501639423 0.138874983605766 47 10 7.93188663023744 2.06811336976256 48 0 5.13964120199576 -5.13964120199576 49 -2 -0.734523405236185 -1.26547659476382 50 -1 0.421888412395371 -1.42188841239537 51 2 0.606397775348408 1.39360222465159 52 8 6.07273008478058 1.92726991521942 53 -6 -4.85283443754242 -1.14716556245758 54 -4 -0.947711720633971 -3.05228827936603 55 4 4.54136918313299 -0.541369183132991 56 7 3.57597389452548 3.42402610547452 57 3 2.91391680756589 0.0860831924341132 58 3 1.1720071855651 1.8279928144349 59 8 3.47220152717368 4.52779847282632 60 3 0.965960819247762 2.03403918075224 61 -3 -2.09702691458384 -0.902973085416163 62 4 3.7666652859257 0.233334714074305 63 -5 -8.09144553707161 3.09144553707161 64 -1 1.38104919358239 -2.38104919358239 65 5 5.49573931889926 -0.49573931889926 66 0 -2.62963337412548 2.62963337412548 67 -6 -3.83819226547778 -2.16180773452222 68 -13 -8.75482417365195 -4.24517582634805 69 -15 -8.25042139582411 -6.74957860417589 70 -8 -6.50577819894602 -1.49422180105398 71 -20 -19.1988624249031 -0.801137575096897 72 -10 -19.7261435351109 9.72614353511091 73 -22 -23.2696520061877 1.26965200618766 74 -25 -25.1090344144367 0.109034414436684 75 -10 -12.5722489996687 2.57224899966868 76 -8 -11.8689325741325 3.86893257413251 77 -9 -6.2979855219252 -2.7020144780748 78 -5 -2.74968643650074 -2.25031356349926 79 -7 -3.2317898459778 -3.7682101540222 80 -11 -9.29906544754912 -1.70093455245088 81 -11 -9.15173515865379 -1.84826484134621 82 -16 -15.9339908159359 -0.066009184064096

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & -3 & -4.72119240640717 & 1.72119240640717 \tabularnewline
2 & -4 & -2.97113090460443 & -1.02886909539557 \tabularnewline
3 & -7 & -6.97134081626948 & -0.0286591837305171 \tabularnewline
4 & -7 & -4.7736043999013 & -2.2263956000987 \tabularnewline
5 & -7 & -5.70644644250306 & -1.29355355749694 \tabularnewline
6 & -3 & -3.26154456611182 & 0.261544566111821 \tabularnewline
7 & 0 & -2.39669714780782 & 2.39669714780782 \tabularnewline
8 & -5 & -2.66311770972184 & -2.33688229027816 \tabularnewline
9 & -3 & -3.1027153533168 & 0.102715353316797 \tabularnewline
10 & 3 & 1.42135484608033 & 1.57864515391967 \tabularnewline
11 & 2 & -0.676628833741848 & 2.67662883374185 \tabularnewline
12 & -7 & -10.3555124005262 & 3.35551240052616 \tabularnewline
13 & -1 & 1.88230502291626 & -2.88230502291626 \tabularnewline
14 & 0 & 2.09505007886414 & -2.09505007886414 \tabularnewline
15 & -3 & -1.38199347964915 & -1.61800652035085 \tabularnewline
16 & 4 & 6.11229106513785 & -2.11229106513785 \tabularnewline
17 & 2 & 2.81674887138434 & -0.816748871384343 \tabularnewline
18 & 3 & 1.21852964871528 & 1.78147035128472 \tabularnewline
19 & 0 & -3.02472068305634 & 3.02472068305634 \tabularnewline
20 & -10 & -8.28549897868586 & -1.71450102131414 \tabularnewline
21 & -10 & -7.91837234408827 & -2.08162765591173 \tabularnewline
22 & -9 & -7.59439824125933 & -1.40560175874068 \tabularnewline
23 & -22 & -18.9017928496072 & -3.09820715039282 \tabularnewline
24 & -16 & -16.4819220528147 & 0.481922052814727 \tabularnewline
25 & -18 & -21.4377975809588 & 3.43779758095883 \tabularnewline
26 & -14 & -14.4187326948288 & 0.418732694828763 \tabularnewline
27 & -12 & -16.5539072079041 & 4.55390720790414 \tabularnewline
28 & -17 & -18.6328052538213 & 1.63280525382127 \tabularnewline
29 & -23 & -20.8466395550125 & -2.15336044498755 \tabularnewline
30 & -28 & -25.2458249648115 & -2.75417503518846 \tabularnewline
31 & -31 & -28.264369573859 & -2.73563042614102 \tabularnewline
32 & -21 & -21.4027755171847 & 0.402775517184676 \tabularnewline
33 & -19 & -16.2872275020373 & -2.71277249796267 \tabularnewline
34 & -22 & -25.4087545681004 & 3.40875456810037 \tabularnewline
35 & -22 & -24.039430248828 & 2.03943024882802 \tabularnewline
36 & -25 & -22.8586265082721 & -2.14137349172788 \tabularnewline
37 & -16 & -17.4336103757 & 1.43361037570005 \tabularnewline
38 & -22 & -17.4046233348815 & -4.5953766651185 \tabularnewline
39 & -21 & -16.4671588822581 & -4.53284111774188 \tabularnewline
40 & -10 & -11.1552779587938 & 1.15527795879383 \tabularnewline
41 & -7 & -6.88314601135591 & -0.116853988644091 \tabularnewline
42 & -5 & -7.58115629523078 & 2.58115629523078 \tabularnewline
43 & -4 & -5.63580647414244 & 1.63580647414244 \tabularnewline
44 & 7 & 1.80908423668899 & 5.19091576331101 \tabularnewline
45 & 6 & 1.58390261557192 & 4.41609738442808 \tabularnewline
46 & 3 & 2.86112501639423 & 0.138874983605766 \tabularnewline
47 & 10 & 7.93188663023744 & 2.06811336976256 \tabularnewline
48 & 0 & 5.13964120199576 & -5.13964120199576 \tabularnewline
49 & -2 & -0.734523405236185 & -1.26547659476382 \tabularnewline
50 & -1 & 0.421888412395371 & -1.42188841239537 \tabularnewline
51 & 2 & 0.606397775348408 & 1.39360222465159 \tabularnewline
52 & 8 & 6.07273008478058 & 1.92726991521942 \tabularnewline
53 & -6 & -4.85283443754242 & -1.14716556245758 \tabularnewline
54 & -4 & -0.947711720633971 & -3.05228827936603 \tabularnewline
55 & 4 & 4.54136918313299 & -0.541369183132991 \tabularnewline
56 & 7 & 3.57597389452548 & 3.42402610547452 \tabularnewline
57 & 3 & 2.91391680756589 & 0.0860831924341132 \tabularnewline
58 & 3 & 1.1720071855651 & 1.8279928144349 \tabularnewline
59 & 8 & 3.47220152717368 & 4.52779847282632 \tabularnewline
60 & 3 & 0.965960819247762 & 2.03403918075224 \tabularnewline
61 & -3 & -2.09702691458384 & -0.902973085416163 \tabularnewline
62 & 4 & 3.7666652859257 & 0.233334714074305 \tabularnewline
63 & -5 & -8.09144553707161 & 3.09144553707161 \tabularnewline
64 & -1 & 1.38104919358239 & -2.38104919358239 \tabularnewline
65 & 5 & 5.49573931889926 & -0.49573931889926 \tabularnewline
66 & 0 & -2.62963337412548 & 2.62963337412548 \tabularnewline
67 & -6 & -3.83819226547778 & -2.16180773452222 \tabularnewline
68 & -13 & -8.75482417365195 & -4.24517582634805 \tabularnewline
69 & -15 & -8.25042139582411 & -6.74957860417589 \tabularnewline
70 & -8 & -6.50577819894602 & -1.49422180105398 \tabularnewline
71 & -20 & -19.1988624249031 & -0.801137575096897 \tabularnewline
72 & -10 & -19.7261435351109 & 9.72614353511091 \tabularnewline
73 & -22 & -23.2696520061877 & 1.26965200618766 \tabularnewline
74 & -25 & -25.1090344144367 & 0.109034414436684 \tabularnewline
75 & -10 & -12.5722489996687 & 2.57224899966868 \tabularnewline
76 & -8 & -11.8689325741325 & 3.86893257413251 \tabularnewline
77 & -9 & -6.2979855219252 & -2.7020144780748 \tabularnewline
78 & -5 & -2.74968643650074 & -2.25031356349926 \tabularnewline
79 & -7 & -3.2317898459778 & -3.7682101540222 \tabularnewline
80 & -11 & -9.29906544754912 & -1.70093455245088 \tabularnewline
81 & -11 & -9.15173515865379 & -1.84826484134621 \tabularnewline
82 & -16 & -15.9339908159359 & -0.066009184064096 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186221&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]-3[/C][C]-4.72119240640717[/C][C]1.72119240640717[/C][/ROW]
[ROW][C]2[/C][C]-4[/C][C]-2.97113090460443[/C][C]-1.02886909539557[/C][/ROW]
[ROW][C]3[/C][C]-7[/C][C]-6.97134081626948[/C][C]-0.0286591837305171[/C][/ROW]
[ROW][C]4[/C][C]-7[/C][C]-4.7736043999013[/C][C]-2.2263956000987[/C][/ROW]
[ROW][C]5[/C][C]-7[/C][C]-5.70644644250306[/C][C]-1.29355355749694[/C][/ROW]
[ROW][C]6[/C][C]-3[/C][C]-3.26154456611182[/C][C]0.261544566111821[/C][/ROW]
[ROW][C]7[/C][C]0[/C][C]-2.39669714780782[/C][C]2.39669714780782[/C][/ROW]
[ROW][C]8[/C][C]-5[/C][C]-2.66311770972184[/C][C]-2.33688229027816[/C][/ROW]
[ROW][C]9[/C][C]-3[/C][C]-3.1027153533168[/C][C]0.102715353316797[/C][/ROW]
[ROW][C]10[/C][C]3[/C][C]1.42135484608033[/C][C]1.57864515391967[/C][/ROW]
[ROW][C]11[/C][C]2[/C][C]-0.676628833741848[/C][C]2.67662883374185[/C][/ROW]
[ROW][C]12[/C][C]-7[/C][C]-10.3555124005262[/C][C]3.35551240052616[/C][/ROW]
[ROW][C]13[/C][C]-1[/C][C]1.88230502291626[/C][C]-2.88230502291626[/C][/ROW]
[ROW][C]14[/C][C]0[/C][C]2.09505007886414[/C][C]-2.09505007886414[/C][/ROW]
[ROW][C]15[/C][C]-3[/C][C]-1.38199347964915[/C][C]-1.61800652035085[/C][/ROW]
[ROW][C]16[/C][C]4[/C][C]6.11229106513785[/C][C]-2.11229106513785[/C][/ROW]
[ROW][C]17[/C][C]2[/C][C]2.81674887138434[/C][C]-0.816748871384343[/C][/ROW]
[ROW][C]18[/C][C]3[/C][C]1.21852964871528[/C][C]1.78147035128472[/C][/ROW]
[ROW][C]19[/C][C]0[/C][C]-3.02472068305634[/C][C]3.02472068305634[/C][/ROW]
[ROW][C]20[/C][C]-10[/C][C]-8.28549897868586[/C][C]-1.71450102131414[/C][/ROW]
[ROW][C]21[/C][C]-10[/C][C]-7.91837234408827[/C][C]-2.08162765591173[/C][/ROW]
[ROW][C]22[/C][C]-9[/C][C]-7.59439824125933[/C][C]-1.40560175874068[/C][/ROW]
[ROW][C]23[/C][C]-22[/C][C]-18.9017928496072[/C][C]-3.09820715039282[/C][/ROW]
[ROW][C]24[/C][C]-16[/C][C]-16.4819220528147[/C][C]0.481922052814727[/C][/ROW]
[ROW][C]25[/C][C]-18[/C][C]-21.4377975809588[/C][C]3.43779758095883[/C][/ROW]
[ROW][C]26[/C][C]-14[/C][C]-14.4187326948288[/C][C]0.418732694828763[/C][/ROW]
[ROW][C]27[/C][C]-12[/C][C]-16.5539072079041[/C][C]4.55390720790414[/C][/ROW]
[ROW][C]28[/C][C]-17[/C][C]-18.6328052538213[/C][C]1.63280525382127[/C][/ROW]
[ROW][C]29[/C][C]-23[/C][C]-20.8466395550125[/C][C]-2.15336044498755[/C][/ROW]
[ROW][C]30[/C][C]-28[/C][C]-25.2458249648115[/C][C]-2.75417503518846[/C][/ROW]
[ROW][C]31[/C][C]-31[/C][C]-28.264369573859[/C][C]-2.73563042614102[/C][/ROW]
[ROW][C]32[/C][C]-21[/C][C]-21.4027755171847[/C][C]0.402775517184676[/C][/ROW]
[ROW][C]33[/C][C]-19[/C][C]-16.2872275020373[/C][C]-2.71277249796267[/C][/ROW]
[ROW][C]34[/C][C]-22[/C][C]-25.4087545681004[/C][C]3.40875456810037[/C][/ROW]
[ROW][C]35[/C][C]-22[/C][C]-24.039430248828[/C][C]2.03943024882802[/C][/ROW]
[ROW][C]36[/C][C]-25[/C][C]-22.8586265082721[/C][C]-2.14137349172788[/C][/ROW]
[ROW][C]37[/C][C]-16[/C][C]-17.4336103757[/C][C]1.43361037570005[/C][/ROW]
[ROW][C]38[/C][C]-22[/C][C]-17.4046233348815[/C][C]-4.5953766651185[/C][/ROW]
[ROW][C]39[/C][C]-21[/C][C]-16.4671588822581[/C][C]-4.53284111774188[/C][/ROW]
[ROW][C]40[/C][C]-10[/C][C]-11.1552779587938[/C][C]1.15527795879383[/C][/ROW]
[ROW][C]41[/C][C]-7[/C][C]-6.88314601135591[/C][C]-0.116853988644091[/C][/ROW]
[ROW][C]42[/C][C]-5[/C][C]-7.58115629523078[/C][C]2.58115629523078[/C][/ROW]
[ROW][C]43[/C][C]-4[/C][C]-5.63580647414244[/C][C]1.63580647414244[/C][/ROW]
[ROW][C]44[/C][C]7[/C][C]1.80908423668899[/C][C]5.19091576331101[/C][/ROW]
[ROW][C]45[/C][C]6[/C][C]1.58390261557192[/C][C]4.41609738442808[/C][/ROW]
[ROW][C]46[/C][C]3[/C][C]2.86112501639423[/C][C]0.138874983605766[/C][/ROW]
[ROW][C]47[/C][C]10[/C][C]7.93188663023744[/C][C]2.06811336976256[/C][/ROW]
[ROW][C]48[/C][C]0[/C][C]5.13964120199576[/C][C]-5.13964120199576[/C][/ROW]
[ROW][C]49[/C][C]-2[/C][C]-0.734523405236185[/C][C]-1.26547659476382[/C][/ROW]
[ROW][C]50[/C][C]-1[/C][C]0.421888412395371[/C][C]-1.42188841239537[/C][/ROW]
[ROW][C]51[/C][C]2[/C][C]0.606397775348408[/C][C]1.39360222465159[/C][/ROW]
[ROW][C]52[/C][C]8[/C][C]6.07273008478058[/C][C]1.92726991521942[/C][/ROW]
[ROW][C]53[/C][C]-6[/C][C]-4.85283443754242[/C][C]-1.14716556245758[/C][/ROW]
[ROW][C]54[/C][C]-4[/C][C]-0.947711720633971[/C][C]-3.05228827936603[/C][/ROW]
[ROW][C]55[/C][C]4[/C][C]4.54136918313299[/C][C]-0.541369183132991[/C][/ROW]
[ROW][C]56[/C][C]7[/C][C]3.57597389452548[/C][C]3.42402610547452[/C][/ROW]
[ROW][C]57[/C][C]3[/C][C]2.91391680756589[/C][C]0.0860831924341132[/C][/ROW]
[ROW][C]58[/C][C]3[/C][C]1.1720071855651[/C][C]1.8279928144349[/C][/ROW]
[ROW][C]59[/C][C]8[/C][C]3.47220152717368[/C][C]4.52779847282632[/C][/ROW]
[ROW][C]60[/C][C]3[/C][C]0.965960819247762[/C][C]2.03403918075224[/C][/ROW]
[ROW][C]61[/C][C]-3[/C][C]-2.09702691458384[/C][C]-0.902973085416163[/C][/ROW]
[ROW][C]62[/C][C]4[/C][C]3.7666652859257[/C][C]0.233334714074305[/C][/ROW]
[ROW][C]63[/C][C]-5[/C][C]-8.09144553707161[/C][C]3.09144553707161[/C][/ROW]
[ROW][C]64[/C][C]-1[/C][C]1.38104919358239[/C][C]-2.38104919358239[/C][/ROW]
[ROW][C]65[/C][C]5[/C][C]5.49573931889926[/C][C]-0.49573931889926[/C][/ROW]
[ROW][C]66[/C][C]0[/C][C]-2.62963337412548[/C][C]2.62963337412548[/C][/ROW]
[ROW][C]67[/C][C]-6[/C][C]-3.83819226547778[/C][C]-2.16180773452222[/C][/ROW]
[ROW][C]68[/C][C]-13[/C][C]-8.75482417365195[/C][C]-4.24517582634805[/C][/ROW]
[ROW][C]69[/C][C]-15[/C][C]-8.25042139582411[/C][C]-6.74957860417589[/C][/ROW]
[ROW][C]70[/C][C]-8[/C][C]-6.50577819894602[/C][C]-1.49422180105398[/C][/ROW]
[ROW][C]71[/C][C]-20[/C][C]-19.1988624249031[/C][C]-0.801137575096897[/C][/ROW]
[ROW][C]72[/C][C]-10[/C][C]-19.7261435351109[/C][C]9.72614353511091[/C][/ROW]
[ROW][C]73[/C][C]-22[/C][C]-23.2696520061877[/C][C]1.26965200618766[/C][/ROW]
[ROW][C]74[/C][C]-25[/C][C]-25.1090344144367[/C][C]0.109034414436684[/C][/ROW]
[ROW][C]75[/C][C]-10[/C][C]-12.5722489996687[/C][C]2.57224899966868[/C][/ROW]
[ROW][C]76[/C][C]-8[/C][C]-11.8689325741325[/C][C]3.86893257413251[/C][/ROW]
[ROW][C]77[/C][C]-9[/C][C]-6.2979855219252[/C][C]-2.7020144780748[/C][/ROW]
[ROW][C]78[/C][C]-5[/C][C]-2.74968643650074[/C][C]-2.25031356349926[/C][/ROW]
[ROW][C]79[/C][C]-7[/C][C]-3.2317898459778[/C][C]-3.7682101540222[/C][/ROW]
[ROW][C]80[/C][C]-11[/C][C]-9.29906544754912[/C][C]-1.70093455245088[/C][/ROW]
[ROW][C]81[/C][C]-11[/C][C]-9.15173515865379[/C][C]-1.84826484134621[/C][/ROW]
[ROW][C]82[/C][C]-16[/C][C]-15.9339908159359[/C][C]-0.066009184064096[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186221&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186221&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 -3 -4.72119240640717 1.72119240640717 2 -4 -2.97113090460443 -1.02886909539557 3 -7 -6.97134081626948 -0.0286591837305171 4 -7 -4.7736043999013 -2.2263956000987 5 -7 -5.70644644250306 -1.29355355749694 6 -3 -3.26154456611182 0.261544566111821 7 0 -2.39669714780782 2.39669714780782 8 -5 -2.66311770972184 -2.33688229027816 9 -3 -3.1027153533168 0.102715353316797 10 3 1.42135484608033 1.57864515391967 11 2 -0.676628833741848 2.67662883374185 12 -7 -10.3555124005262 3.35551240052616 13 -1 1.88230502291626 -2.88230502291626 14 0 2.09505007886414 -2.09505007886414 15 -3 -1.38199347964915 -1.61800652035085 16 4 6.11229106513785 -2.11229106513785 17 2 2.81674887138434 -0.816748871384343 18 3 1.21852964871528 1.78147035128472 19 0 -3.02472068305634 3.02472068305634 20 -10 -8.28549897868586 -1.71450102131414 21 -10 -7.91837234408827 -2.08162765591173 22 -9 -7.59439824125933 -1.40560175874068 23 -22 -18.9017928496072 -3.09820715039282 24 -16 -16.4819220528147 0.481922052814727 25 -18 -21.4377975809588 3.43779758095883 26 -14 -14.4187326948288 0.418732694828763 27 -12 -16.5539072079041 4.55390720790414 28 -17 -18.6328052538213 1.63280525382127 29 -23 -20.8466395550125 -2.15336044498755 30 -28 -25.2458249648115 -2.75417503518846 31 -31 -28.264369573859 -2.73563042614102 32 -21 -21.4027755171847 0.402775517184676 33 -19 -16.2872275020373 -2.71277249796267 34 -22 -25.4087545681004 3.40875456810037 35 -22 -24.039430248828 2.03943024882802 36 -25 -22.8586265082721 -2.14137349172788 37 -16 -17.4336103757 1.43361037570005 38 -22 -17.4046233348815 -4.5953766651185 39 -21 -16.4671588822581 -4.53284111774188 40 -10 -11.1552779587938 1.15527795879383 41 -7 -6.88314601135591 -0.116853988644091 42 -5 -7.58115629523078 2.58115629523078 43 -4 -5.63580647414244 1.63580647414244 44 7 1.80908423668899 5.19091576331101 45 6 1.58390261557192 4.41609738442808 46 3 2.86112501639423 0.138874983605766 47 10 7.93188663023744 2.06811336976256 48 0 5.13964120199576 -5.13964120199576 49 -2 -0.734523405236185 -1.26547659476382 50 -1 0.421888412395371 -1.42188841239537 51 2 0.606397775348408 1.39360222465159 52 8 6.07273008478058 1.92726991521942 53 -6 -4.85283443754242 -1.14716556245758 54 -4 -0.947711720633971 -3.05228827936603 55 4 4.54136918313299 -0.541369183132991 56 7 3.57597389452548 3.42402610547452 57 3 2.91391680756589 0.0860831924341132 58 3 1.1720071855651 1.8279928144349 59 8 3.47220152717368 4.52779847282632 60 3 0.965960819247762 2.03403918075224 61 -3 -2.09702691458384 -0.902973085416163 62 4 3.7666652859257 0.233334714074305 63 -5 -8.09144553707161 3.09144553707161 64 -1 1.38104919358239 -2.38104919358239 65 5 5.49573931889926 -0.49573931889926 66 0 -2.62963337412548 2.62963337412548 67 -6 -3.83819226547778 -2.16180773452222 68 -13 -8.75482417365195 -4.24517582634805 69 -15 -8.25042139582411 -6.74957860417589 70 -8 -6.50577819894602 -1.49422180105398 71 -20 -19.1988624249031 -0.801137575096897 72 -10 -19.7261435351109 9.72614353511091 73 -22 -23.2696520061877 1.26965200618766 74 -25 -25.1090344144367 0.109034414436684 75 -10 -12.5722489996687 2.57224899966868 76 -8 -11.8689325741325 3.86893257413251 77 -9 -6.2979855219252 -2.7020144780748 78 -5 -2.74968643650074 -2.25031356349926 79 -7 -3.2317898459778 -3.7682101540222 80 -11 -9.29906544754912 -1.70093455245088 81 -11 -9.15173515865379 -1.84826484134621 82 -16 -15.9339908159359 -0.066009184064096

 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 16 0.0677809001898002 0.1355618003796 0.9322190998102 17 0.0253955605516903 0.0507911211033806 0.97460443944831 18 0.0236998522143747 0.0473997044287495 0.976300147785625 19 0.0515492313123149 0.10309846262463 0.948450768687685 20 0.149687818633611 0.299375637267223 0.850312181366389 21 0.0905169402625558 0.181033880525112 0.909483059737444 22 0.0540109208046697 0.108021841609339 0.94598907919533 23 0.0378696388637156 0.0757392777274312 0.962130361136284 24 0.0287784287202537 0.0575568574405075 0.971221571279746 25 0.0409808615602266 0.0819617231204532 0.959019138439773 26 0.0233526040710799 0.0467052081421597 0.97664739592892 27 0.0192152677844629 0.0384305355689258 0.980784732215537 28 0.0131738022092497 0.0263476044184993 0.98682619779075 29 0.0212896227993876 0.0425792455987752 0.978710377200612 30 0.0152791150034611 0.0305582300069221 0.984720884996539 31 0.0147499323685368 0.0294998647370735 0.985250067631463 32 0.00919100598800534 0.0183820119760107 0.990808994011995 33 0.0185525591516544 0.0371051183033088 0.981447440848346 34 0.0209346804476103 0.0418693608952207 0.97906531955239 35 0.0149308673552814 0.0298617347105628 0.985069132644719 36 0.0134601147267481 0.0269202294534961 0.986539885273252 37 0.0122000019528373 0.0244000039056746 0.987799998047163 38 0.0111773877351889 0.0223547754703778 0.988822612264811 39 0.0205967119159435 0.0411934238318869 0.979403288084057 40 0.0900605242582304 0.180121048516461 0.90993947574177 41 0.102137055266294 0.204274110532589 0.897862944733706 42 0.131795576587806 0.263591153175612 0.868204423412194 43 0.127772731181717 0.255545462363435 0.872227268818283 44 0.175319271358085 0.350638542716169 0.824680728641915 45 0.218215385308712 0.436430770617424 0.781784614691288 46 0.220327713392509 0.440655426785018 0.779672286607491 47 0.249960133187923 0.499920266375846 0.750039866812077 48 0.322816390498991 0.645632780997981 0.677183609501009 49 0.271893279311488 0.543786558622976 0.728106720688512 50 0.225799326749575 0.45159865349915 0.774200673250425 51 0.201966426559479 0.403932853118957 0.798033573440521 52 0.17866466604221 0.35732933208442 0.82133533395779 53 0.224649343695278 0.449298687390556 0.775350656304722 54 0.42063418590503 0.84126837181006 0.57936581409497 55 0.362797538961266 0.725595077922532 0.637202461038734 56 0.300453229465664 0.600906458931329 0.699546770534336 57 0.24927484334788 0.49854968669576 0.75072515665212 58 0.204255031351732 0.408510062703464 0.795744968648268 59 0.175556208762809 0.351112417525617 0.824443791237191 60 0.253773682966365 0.50754736593273 0.746226317033635 61 0.264959256608082 0.529918513216163 0.735040743391918 62 0.18597294069229 0.371945881384579 0.81402705930771 63 0.183237693686484 0.366475387372968 0.816762306313516 64 0.133307873406489 0.266615746812977 0.866692126593511 65 0.0763206158442311 0.152641231688462 0.923679384155769 66 0.038957413080411 0.077914826160822 0.961042586919589

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
16 & 0.0677809001898002 & 0.1355618003796 & 0.9322190998102 \tabularnewline
17 & 0.0253955605516903 & 0.0507911211033806 & 0.97460443944831 \tabularnewline
18 & 0.0236998522143747 & 0.0473997044287495 & 0.976300147785625 \tabularnewline
19 & 0.0515492313123149 & 0.10309846262463 & 0.948450768687685 \tabularnewline
20 & 0.149687818633611 & 0.299375637267223 & 0.850312181366389 \tabularnewline
21 & 0.0905169402625558 & 0.181033880525112 & 0.909483059737444 \tabularnewline
22 & 0.0540109208046697 & 0.108021841609339 & 0.94598907919533 \tabularnewline
23 & 0.0378696388637156 & 0.0757392777274312 & 0.962130361136284 \tabularnewline
24 & 0.0287784287202537 & 0.0575568574405075 & 0.971221571279746 \tabularnewline
25 & 0.0409808615602266 & 0.0819617231204532 & 0.959019138439773 \tabularnewline
26 & 0.0233526040710799 & 0.0467052081421597 & 0.97664739592892 \tabularnewline
27 & 0.0192152677844629 & 0.0384305355689258 & 0.980784732215537 \tabularnewline
28 & 0.0131738022092497 & 0.0263476044184993 & 0.98682619779075 \tabularnewline
29 & 0.0212896227993876 & 0.0425792455987752 & 0.978710377200612 \tabularnewline
30 & 0.0152791150034611 & 0.0305582300069221 & 0.984720884996539 \tabularnewline
31 & 0.0147499323685368 & 0.0294998647370735 & 0.985250067631463 \tabularnewline
32 & 0.00919100598800534 & 0.0183820119760107 & 0.990808994011995 \tabularnewline
33 & 0.0185525591516544 & 0.0371051183033088 & 0.981447440848346 \tabularnewline
34 & 0.0209346804476103 & 0.0418693608952207 & 0.97906531955239 \tabularnewline
35 & 0.0149308673552814 & 0.0298617347105628 & 0.985069132644719 \tabularnewline
36 & 0.0134601147267481 & 0.0269202294534961 & 0.986539885273252 \tabularnewline
37 & 0.0122000019528373 & 0.0244000039056746 & 0.987799998047163 \tabularnewline
38 & 0.0111773877351889 & 0.0223547754703778 & 0.988822612264811 \tabularnewline
39 & 0.0205967119159435 & 0.0411934238318869 & 0.979403288084057 \tabularnewline
40 & 0.0900605242582304 & 0.180121048516461 & 0.90993947574177 \tabularnewline
41 & 0.102137055266294 & 0.204274110532589 & 0.897862944733706 \tabularnewline
42 & 0.131795576587806 & 0.263591153175612 & 0.868204423412194 \tabularnewline
43 & 0.127772731181717 & 0.255545462363435 & 0.872227268818283 \tabularnewline
44 & 0.175319271358085 & 0.350638542716169 & 0.824680728641915 \tabularnewline
45 & 0.218215385308712 & 0.436430770617424 & 0.781784614691288 \tabularnewline
46 & 0.220327713392509 & 0.440655426785018 & 0.779672286607491 \tabularnewline
47 & 0.249960133187923 & 0.499920266375846 & 0.750039866812077 \tabularnewline
48 & 0.322816390498991 & 0.645632780997981 & 0.677183609501009 \tabularnewline
49 & 0.271893279311488 & 0.543786558622976 & 0.728106720688512 \tabularnewline
50 & 0.225799326749575 & 0.45159865349915 & 0.774200673250425 \tabularnewline
51 & 0.201966426559479 & 0.403932853118957 & 0.798033573440521 \tabularnewline
52 & 0.17866466604221 & 0.35732933208442 & 0.82133533395779 \tabularnewline
53 & 0.224649343695278 & 0.449298687390556 & 0.775350656304722 \tabularnewline
54 & 0.42063418590503 & 0.84126837181006 & 0.57936581409497 \tabularnewline
55 & 0.362797538961266 & 0.725595077922532 & 0.637202461038734 \tabularnewline
56 & 0.300453229465664 & 0.600906458931329 & 0.699546770534336 \tabularnewline
57 & 0.24927484334788 & 0.49854968669576 & 0.75072515665212 \tabularnewline
58 & 0.204255031351732 & 0.408510062703464 & 0.795744968648268 \tabularnewline
59 & 0.175556208762809 & 0.351112417525617 & 0.824443791237191 \tabularnewline
60 & 0.253773682966365 & 0.50754736593273 & 0.746226317033635 \tabularnewline
61 & 0.264959256608082 & 0.529918513216163 & 0.735040743391918 \tabularnewline
62 & 0.18597294069229 & 0.371945881384579 & 0.81402705930771 \tabularnewline
63 & 0.183237693686484 & 0.366475387372968 & 0.816762306313516 \tabularnewline
64 & 0.133307873406489 & 0.266615746812977 & 0.866692126593511 \tabularnewline
65 & 0.0763206158442311 & 0.152641231688462 & 0.923679384155769 \tabularnewline
66 & 0.038957413080411 & 0.077914826160822 & 0.961042586919589 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186221&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]16[/C][C]0.0677809001898002[/C][C]0.1355618003796[/C][C]0.9322190998102[/C][/ROW]
[ROW][C]17[/C][C]0.0253955605516903[/C][C]0.0507911211033806[/C][C]0.97460443944831[/C][/ROW]
[ROW][C]18[/C][C]0.0236998522143747[/C][C]0.0473997044287495[/C][C]0.976300147785625[/C][/ROW]
[ROW][C]19[/C][C]0.0515492313123149[/C][C]0.10309846262463[/C][C]0.948450768687685[/C][/ROW]
[ROW][C]20[/C][C]0.149687818633611[/C][C]0.299375637267223[/C][C]0.850312181366389[/C][/ROW]
[ROW][C]21[/C][C]0.0905169402625558[/C][C]0.181033880525112[/C][C]0.909483059737444[/C][/ROW]
[ROW][C]22[/C][C]0.0540109208046697[/C][C]0.108021841609339[/C][C]0.94598907919533[/C][/ROW]
[ROW][C]23[/C][C]0.0378696388637156[/C][C]0.0757392777274312[/C][C]0.962130361136284[/C][/ROW]
[ROW][C]24[/C][C]0.0287784287202537[/C][C]0.0575568574405075[/C][C]0.971221571279746[/C][/ROW]
[ROW][C]25[/C][C]0.0409808615602266[/C][C]0.0819617231204532[/C][C]0.959019138439773[/C][/ROW]
[ROW][C]26[/C][C]0.0233526040710799[/C][C]0.0467052081421597[/C][C]0.97664739592892[/C][/ROW]
[ROW][C]27[/C][C]0.0192152677844629[/C][C]0.0384305355689258[/C][C]0.980784732215537[/C][/ROW]
[ROW][C]28[/C][C]0.0131738022092497[/C][C]0.0263476044184993[/C][C]0.98682619779075[/C][/ROW]
[ROW][C]29[/C][C]0.0212896227993876[/C][C]0.0425792455987752[/C][C]0.978710377200612[/C][/ROW]
[ROW][C]30[/C][C]0.0152791150034611[/C][C]0.0305582300069221[/C][C]0.984720884996539[/C][/ROW]
[ROW][C]31[/C][C]0.0147499323685368[/C][C]0.0294998647370735[/C][C]0.985250067631463[/C][/ROW]
[ROW][C]32[/C][C]0.00919100598800534[/C][C]0.0183820119760107[/C][C]0.990808994011995[/C][/ROW]
[ROW][C]33[/C][C]0.0185525591516544[/C][C]0.0371051183033088[/C][C]0.981447440848346[/C][/ROW]
[ROW][C]34[/C][C]0.0209346804476103[/C][C]0.0418693608952207[/C][C]0.97906531955239[/C][/ROW]
[ROW][C]35[/C][C]0.0149308673552814[/C][C]0.0298617347105628[/C][C]0.985069132644719[/C][/ROW]
[ROW][C]36[/C][C]0.0134601147267481[/C][C]0.0269202294534961[/C][C]0.986539885273252[/C][/ROW]
[ROW][C]37[/C][C]0.0122000019528373[/C][C]0.0244000039056746[/C][C]0.987799998047163[/C][/ROW]
[ROW][C]38[/C][C]0.0111773877351889[/C][C]0.0223547754703778[/C][C]0.988822612264811[/C][/ROW]
[ROW][C]39[/C][C]0.0205967119159435[/C][C]0.0411934238318869[/C][C]0.979403288084057[/C][/ROW]
[ROW][C]40[/C][C]0.0900605242582304[/C][C]0.180121048516461[/C][C]0.90993947574177[/C][/ROW]
[ROW][C]41[/C][C]0.102137055266294[/C][C]0.204274110532589[/C][C]0.897862944733706[/C][/ROW]
[ROW][C]42[/C][C]0.131795576587806[/C][C]0.263591153175612[/C][C]0.868204423412194[/C][/ROW]
[ROW][C]43[/C][C]0.127772731181717[/C][C]0.255545462363435[/C][C]0.872227268818283[/C][/ROW]
[ROW][C]44[/C][C]0.175319271358085[/C][C]0.350638542716169[/C][C]0.824680728641915[/C][/ROW]
[ROW][C]45[/C][C]0.218215385308712[/C][C]0.436430770617424[/C][C]0.781784614691288[/C][/ROW]
[ROW][C]46[/C][C]0.220327713392509[/C][C]0.440655426785018[/C][C]0.779672286607491[/C][/ROW]
[ROW][C]47[/C][C]0.249960133187923[/C][C]0.499920266375846[/C][C]0.750039866812077[/C][/ROW]
[ROW][C]48[/C][C]0.322816390498991[/C][C]0.645632780997981[/C][C]0.677183609501009[/C][/ROW]
[ROW][C]49[/C][C]0.271893279311488[/C][C]0.543786558622976[/C][C]0.728106720688512[/C][/ROW]
[ROW][C]50[/C][C]0.225799326749575[/C][C]0.45159865349915[/C][C]0.774200673250425[/C][/ROW]
[ROW][C]51[/C][C]0.201966426559479[/C][C]0.403932853118957[/C][C]0.798033573440521[/C][/ROW]
[ROW][C]52[/C][C]0.17866466604221[/C][C]0.35732933208442[/C][C]0.82133533395779[/C][/ROW]
[ROW][C]53[/C][C]0.224649343695278[/C][C]0.449298687390556[/C][C]0.775350656304722[/C][/ROW]
[ROW][C]54[/C][C]0.42063418590503[/C][C]0.84126837181006[/C][C]0.57936581409497[/C][/ROW]
[ROW][C]55[/C][C]0.362797538961266[/C][C]0.725595077922532[/C][C]0.637202461038734[/C][/ROW]
[ROW][C]56[/C][C]0.300453229465664[/C][C]0.600906458931329[/C][C]0.699546770534336[/C][/ROW]
[ROW][C]57[/C][C]0.24927484334788[/C][C]0.49854968669576[/C][C]0.75072515665212[/C][/ROW]
[ROW][C]58[/C][C]0.204255031351732[/C][C]0.408510062703464[/C][C]0.795744968648268[/C][/ROW]
[ROW][C]59[/C][C]0.175556208762809[/C][C]0.351112417525617[/C][C]0.824443791237191[/C][/ROW]
[ROW][C]60[/C][C]0.253773682966365[/C][C]0.50754736593273[/C][C]0.746226317033635[/C][/ROW]
[ROW][C]61[/C][C]0.264959256608082[/C][C]0.529918513216163[/C][C]0.735040743391918[/C][/ROW]
[ROW][C]62[/C][C]0.18597294069229[/C][C]0.371945881384579[/C][C]0.81402705930771[/C][/ROW]
[ROW][C]63[/C][C]0.183237693686484[/C][C]0.366475387372968[/C][C]0.816762306313516[/C][/ROW]
[ROW][C]64[/C][C]0.133307873406489[/C][C]0.266615746812977[/C][C]0.866692126593511[/C][/ROW]
[ROW][C]65[/C][C]0.0763206158442311[/C][C]0.152641231688462[/C][C]0.923679384155769[/C][/ROW]
[ROW][C]66[/C][C]0.038957413080411[/C][C]0.077914826160822[/C][C]0.961042586919589[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186221&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186221&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 16 0.0677809001898002 0.1355618003796 0.9322190998102 17 0.0253955605516903 0.0507911211033806 0.97460443944831 18 0.0236998522143747 0.0473997044287495 0.976300147785625 19 0.0515492313123149 0.10309846262463 0.948450768687685 20 0.149687818633611 0.299375637267223 0.850312181366389 21 0.0905169402625558 0.181033880525112 0.909483059737444 22 0.0540109208046697 0.108021841609339 0.94598907919533 23 0.0378696388637156 0.0757392777274312 0.962130361136284 24 0.0287784287202537 0.0575568574405075 0.971221571279746 25 0.0409808615602266 0.0819617231204532 0.959019138439773 26 0.0233526040710799 0.0467052081421597 0.97664739592892 27 0.0192152677844629 0.0384305355689258 0.980784732215537 28 0.0131738022092497 0.0263476044184993 0.98682619779075 29 0.0212896227993876 0.0425792455987752 0.978710377200612 30 0.0152791150034611 0.0305582300069221 0.984720884996539 31 0.0147499323685368 0.0294998647370735 0.985250067631463 32 0.00919100598800534 0.0183820119760107 0.990808994011995 33 0.0185525591516544 0.0371051183033088 0.981447440848346 34 0.0209346804476103 0.0418693608952207 0.97906531955239 35 0.0149308673552814 0.0298617347105628 0.985069132644719 36 0.0134601147267481 0.0269202294534961 0.986539885273252 37 0.0122000019528373 0.0244000039056746 0.987799998047163 38 0.0111773877351889 0.0223547754703778 0.988822612264811 39 0.0205967119159435 0.0411934238318869 0.979403288084057 40 0.0900605242582304 0.180121048516461 0.90993947574177 41 0.102137055266294 0.204274110532589 0.897862944733706 42 0.131795576587806 0.263591153175612 0.868204423412194 43 0.127772731181717 0.255545462363435 0.872227268818283 44 0.175319271358085 0.350638542716169 0.824680728641915 45 0.218215385308712 0.436430770617424 0.781784614691288 46 0.220327713392509 0.440655426785018 0.779672286607491 47 0.249960133187923 0.499920266375846 0.750039866812077 48 0.322816390498991 0.645632780997981 0.677183609501009 49 0.271893279311488 0.543786558622976 0.728106720688512 50 0.225799326749575 0.45159865349915 0.774200673250425 51 0.201966426559479 0.403932853118957 0.798033573440521 52 0.17866466604221 0.35732933208442 0.82133533395779 53 0.224649343695278 0.449298687390556 0.775350656304722 54 0.42063418590503 0.84126837181006 0.57936581409497 55 0.362797538961266 0.725595077922532 0.637202461038734 56 0.300453229465664 0.600906458931329 0.699546770534336 57 0.24927484334788 0.49854968669576 0.75072515665212 58 0.204255031351732 0.408510062703464 0.795744968648268 59 0.175556208762809 0.351112417525617 0.824443791237191 60 0.253773682966365 0.50754736593273 0.746226317033635 61 0.264959256608082 0.529918513216163 0.735040743391918 62 0.18597294069229 0.371945881384579 0.81402705930771 63 0.183237693686484 0.366475387372968 0.816762306313516 64 0.133307873406489 0.266615746812977 0.866692126593511 65 0.0763206158442311 0.152641231688462 0.923679384155769 66 0.038957413080411 0.077914826160822 0.961042586919589

 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 0 0 OK 5% type I error level 15 0.294117647058824 NOK 10% type I error level 20 0.392156862745098 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 & 0 & 0 & OK \tabularnewline
5% type I error level & 15 & 0.294117647058824 & NOK \tabularnewline
10% type I error level & 20 & 0.392156862745098 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186221&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]15[/C][C]0.294117647058824[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]20[/C][C]0.392156862745098[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186221&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186221&T=6

As an alternative you can also use a QR Code:

The GUIDs for individual cells are displayed in the table below:

 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 0 0 OK 5% type I error level 15 0.294117647058824 NOK 10% type I error level 20 0.392156862745098 NOK

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Parameters (Session):
par1 = 3 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 3 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
R code (references can be found in the software module):
library(lattice)library(lmtest)n25 <- 25 #minimum number of obs. for Goldfeld-Quandt 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')}