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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
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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)
-     [Multiple Regression] [Decreasing Compet...] [2010-11-17 09:04:39] [b98453cac15ba1066b407e146608df68]
- 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 Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time8 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 Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time8 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
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)31.25234030781769.1130023.42940.0010250.000512
maand0.06926378462180220.0405991.70610.0924960.046248
X_1t0.3924905738296350.060986.436400
X_2t-0.3304119186438640.061178-5.40081e-060
X_3t-0.2819625713998980.059844-4.71161.2e-056e-06
X_4t-0.1971691397623770.056136-3.51230.0007880.000394
X_5t-0.2175658198851810.085436-2.54650.0131150.006558
X_6t-0.2302852248247770.145957-1.57780.1191960.059598
X_7t-0.1011012807028860.273841-0.36920.7131110.356556
X_8t-0.1432726816082770.31403-0.45620.6496510.324826
X_9t1.01434406587910.31693.20080.0020720.001036
X_10t0.02255209356827110.0665480.33890.7357270.367864
X_11t-0.1696565759304370.162364-1.04490.2997080.149854

\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
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)31.25234030781769.1130023.42940.0010250.000512
maand0.06926378462180220.0405991.70610.0924960.046248
X_1t0.3924905738296350.060986.436400
X_2t-0.3304119186438640.061178-5.40081e-060
X_3t-0.2819625713998980.059844-4.71161.2e-056e-06
X_4t-0.1971691397623770.056136-3.51230.0007880.000394
X_5t-0.2175658198851810.085436-2.54650.0131150.006558
X_6t-0.2302852248247770.145957-1.57780.1191960.059598
X_7t-0.1011012807028860.273841-0.36920.7131110.356556
X_8t-0.1432726816082770.31403-0.45620.6496510.324826
X_9t1.01434406587910.31693.20080.0020720.001036
X_10t0.02255209356827110.0665480.33890.7357270.367864
X_11t-0.1696565759304370.162364-1.04490.2997080.149854







Multiple Linear Regression - Regression Statistics
Multiple R0.958890447905324
R-squared0.919470891084072
Adjusted R-squared0.90546582866391
F-TEST (value)65.6527520905883
F-TEST (DF numerator)12
F-TEST (DF denominator)69
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.98362133428308
Sum Squared Residuals614.237742380849

\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 R0.958890447905324
R-squared0.919470891084072
Adjusted R-squared0.90546582866391
F-TEST (value)65.6527520905883
F-TEST (DF numerator)12
F-TEST (DF denominator)69
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.98362133428308
Sum Squared Residuals614.237742380849







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1-3-4.721192406407171.72119240640717
2-4-2.97113090460443-1.02886909539557
3-7-6.97134081626948-0.0286591837305171
4-7-4.7736043999013-2.2263956000987
5-7-5.70644644250306-1.29355355749694
6-3-3.261544566111820.261544566111821
70-2.396697147807822.39669714780782
8-5-2.66311770972184-2.33688229027816
9-3-3.10271535331680.102715353316797
1031.421354846080331.57864515391967
112-0.6766288337418482.67662883374185
12-7-10.35551240052623.35551240052616
13-11.88230502291626-2.88230502291626
1402.09505007886414-2.09505007886414
15-3-1.38199347964915-1.61800652035085
1646.11229106513785-2.11229106513785
1722.81674887138434-0.816748871384343
1831.218529648715281.78147035128472
190-3.024720683056343.02472068305634
20-10-8.28549897868586-1.71450102131414
21-10-7.91837234408827-2.08162765591173
22-9-7.59439824125933-1.40560175874068
23-22-18.9017928496072-3.09820715039282
24-16-16.48192205281470.481922052814727
25-18-21.43779758095883.43779758095883
26-14-14.41873269482880.418732694828763
27-12-16.55390720790414.55390720790414
28-17-18.63280525382131.63280525382127
29-23-20.8466395550125-2.15336044498755
30-28-25.2458249648115-2.75417503518846
31-31-28.264369573859-2.73563042614102
32-21-21.40277551718470.402775517184676
33-19-16.2872275020373-2.71277249796267
34-22-25.40875456810043.40875456810037
35-22-24.0394302488282.03943024882802
36-25-22.8586265082721-2.14137349172788
37-16-17.43361037571.43361037570005
38-22-17.4046233348815-4.5953766651185
39-21-16.4671588822581-4.53284111774188
40-10-11.15527795879381.15527795879383
41-7-6.88314601135591-0.116853988644091
42-5-7.581156295230782.58115629523078
43-4-5.635806474142441.63580647414244
4471.809084236688995.19091576331101
4561.583902615571924.41609738442808
4632.861125016394230.138874983605766
47107.931886630237442.06811336976256
4805.13964120199576-5.13964120199576
49-2-0.734523405236185-1.26547659476382
50-10.421888412395371-1.42188841239537
5120.6063977753484081.39360222465159
5286.072730084780581.92726991521942
53-6-4.85283443754242-1.14716556245758
54-4-0.947711720633971-3.05228827936603
5544.54136918313299-0.541369183132991
5673.575973894525483.42402610547452
5732.913916807565890.0860831924341132
5831.17200718556511.8279928144349
5983.472201527173684.52779847282632
6030.9659608192477622.03403918075224
61-3-2.09702691458384-0.902973085416163
6243.76666528592570.233334714074305
63-5-8.091445537071613.09144553707161
64-11.38104919358239-2.38104919358239
6555.49573931889926-0.49573931889926
660-2.629633374125482.62963337412548
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
72-10-19.72614353511099.72614353511091
73-22-23.26965200618771.26965200618766
74-25-25.10903441443670.109034414436684
75-10-12.57224899966872.57224899966868
76-8-11.86893257413253.86893257413251
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 IndexActualsInterpolationForecastResidualsPrediction Error
1-3-4.721192406407171.72119240640717
2-4-2.97113090460443-1.02886909539557
3-7-6.97134081626948-0.0286591837305171
4-7-4.7736043999013-2.2263956000987
5-7-5.70644644250306-1.29355355749694
6-3-3.261544566111820.261544566111821
70-2.396697147807822.39669714780782
8-5-2.66311770972184-2.33688229027816
9-3-3.10271535331680.102715353316797
1031.421354846080331.57864515391967
112-0.6766288337418482.67662883374185
12-7-10.35551240052623.35551240052616
13-11.88230502291626-2.88230502291626
1402.09505007886414-2.09505007886414
15-3-1.38199347964915-1.61800652035085
1646.11229106513785-2.11229106513785
1722.81674887138434-0.816748871384343
1831.218529648715281.78147035128472
190-3.024720683056343.02472068305634
20-10-8.28549897868586-1.71450102131414
21-10-7.91837234408827-2.08162765591173
22-9-7.59439824125933-1.40560175874068
23-22-18.9017928496072-3.09820715039282
24-16-16.48192205281470.481922052814727
25-18-21.43779758095883.43779758095883
26-14-14.41873269482880.418732694828763
27-12-16.55390720790414.55390720790414
28-17-18.63280525382131.63280525382127
29-23-20.8466395550125-2.15336044498755
30-28-25.2458249648115-2.75417503518846
31-31-28.264369573859-2.73563042614102
32-21-21.40277551718470.402775517184676
33-19-16.2872275020373-2.71277249796267
34-22-25.40875456810043.40875456810037
35-22-24.0394302488282.03943024882802
36-25-22.8586265082721-2.14137349172788
37-16-17.43361037571.43361037570005
38-22-17.4046233348815-4.5953766651185
39-21-16.4671588822581-4.53284111774188
40-10-11.15527795879381.15527795879383
41-7-6.88314601135591-0.116853988644091
42-5-7.581156295230782.58115629523078
43-4-5.635806474142441.63580647414244
4471.809084236688995.19091576331101
4561.583902615571924.41609738442808
4632.861125016394230.138874983605766
47107.931886630237442.06811336976256
4805.13964120199576-5.13964120199576
49-2-0.734523405236185-1.26547659476382
50-10.421888412395371-1.42188841239537
5120.6063977753484081.39360222465159
5286.072730084780581.92726991521942
53-6-4.85283443754242-1.14716556245758
54-4-0.947711720633971-3.05228827936603
5544.54136918313299-0.541369183132991
5673.575973894525483.42402610547452
5732.913916807565890.0860831924341132
5831.17200718556511.8279928144349
5983.472201527173684.52779847282632
6030.9659608192477622.03403918075224
61-3-2.09702691458384-0.902973085416163
6243.76666528592570.233334714074305
63-5-8.091445537071613.09144553707161
64-11.38104919358239-2.38104919358239
6555.49573931889926-0.49573931889926
660-2.629633374125482.62963337412548
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
72-10-19.72614353511099.72614353511091
73-22-23.26965200618771.26965200618766
74-25-25.10903441443670.109034414436684
75-10-12.57224899966872.57224899966868
76-8-11.86893257413253.86893257413251
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-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
160.06778090018980020.13556180037960.9322190998102
170.02539556055169030.05079112110338060.97460443944831
180.02369985221437470.04739970442874950.976300147785625
190.05154923131231490.103098462624630.948450768687685
200.1496878186336110.2993756372672230.850312181366389
210.09051694026255580.1810338805251120.909483059737444
220.05401092080466970.1080218416093390.94598907919533
230.03786963886371560.07573927772743120.962130361136284
240.02877842872025370.05755685744050750.971221571279746
250.04098086156022660.08196172312045320.959019138439773
260.02335260407107990.04670520814215970.97664739592892
270.01921526778446290.03843053556892580.980784732215537
280.01317380220924970.02634760441849930.98682619779075
290.02128962279938760.04257924559877520.978710377200612
300.01527911500346110.03055823000692210.984720884996539
310.01474993236853680.02949986473707350.985250067631463
320.009191005988005340.01838201197601070.990808994011995
330.01855255915165440.03710511830330880.981447440848346
340.02093468044761030.04186936089522070.97906531955239
350.01493086735528140.02986173471056280.985069132644719
360.01346011472674810.02692022945349610.986539885273252
370.01220000195283730.02440000390567460.987799998047163
380.01117738773518890.02235477547037780.988822612264811
390.02059671191594350.04119342383188690.979403288084057
400.09006052425823040.1801210485164610.90993947574177
410.1021370552662940.2042741105325890.897862944733706
420.1317955765878060.2635911531756120.868204423412194
430.1277727311817170.2555454623634350.872227268818283
440.1753192713580850.3506385427161690.824680728641915
450.2182153853087120.4364307706174240.781784614691288
460.2203277133925090.4406554267850180.779672286607491
470.2499601331879230.4999202663758460.750039866812077
480.3228163904989910.6456327809979810.677183609501009
490.2718932793114880.5437865586229760.728106720688512
500.2257993267495750.451598653499150.774200673250425
510.2019664265594790.4039328531189570.798033573440521
520.178664666042210.357329332084420.82133533395779
530.2246493436952780.4492986873905560.775350656304722
540.420634185905030.841268371810060.57936581409497
550.3627975389612660.7255950779225320.637202461038734
560.3004532294656640.6009064589313290.699546770534336
570.249274843347880.498549686695760.75072515665212
580.2042550313517320.4085100627034640.795744968648268
590.1755562087628090.3511124175256170.824443791237191
600.2537736829663650.507547365932730.746226317033635
610.2649592566080820.5299185132161630.735040743391918
620.185972940692290.3719458813845790.81402705930771
630.1832376936864840.3664753873729680.816762306313516
640.1333078734064890.2666157468129770.866692126593511
650.07632061584423110.1526412316884620.923679384155769
660.0389574130804110.0779148261608220.961042586919589

\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-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
160.06778090018980020.13556180037960.9322190998102
170.02539556055169030.05079112110338060.97460443944831
180.02369985221437470.04739970442874950.976300147785625
190.05154923131231490.103098462624630.948450768687685
200.1496878186336110.2993756372672230.850312181366389
210.09051694026255580.1810338805251120.909483059737444
220.05401092080466970.1080218416093390.94598907919533
230.03786963886371560.07573927772743120.962130361136284
240.02877842872025370.05755685744050750.971221571279746
250.04098086156022660.08196172312045320.959019138439773
260.02335260407107990.04670520814215970.97664739592892
270.01921526778446290.03843053556892580.980784732215537
280.01317380220924970.02634760441849930.98682619779075
290.02128962279938760.04257924559877520.978710377200612
300.01527911500346110.03055823000692210.984720884996539
310.01474993236853680.02949986473707350.985250067631463
320.009191005988005340.01838201197601070.990808994011995
330.01855255915165440.03710511830330880.981447440848346
340.02093468044761030.04186936089522070.97906531955239
350.01493086735528140.02986173471056280.985069132644719
360.01346011472674810.02692022945349610.986539885273252
370.01220000195283730.02440000390567460.987799998047163
380.01117738773518890.02235477547037780.988822612264811
390.02059671191594350.04119342383188690.979403288084057
400.09006052425823040.1801210485164610.90993947574177
410.1021370552662940.2042741105325890.897862944733706
420.1317955765878060.2635911531756120.868204423412194
430.1277727311817170.2555454623634350.872227268818283
440.1753192713580850.3506385427161690.824680728641915
450.2182153853087120.4364307706174240.781784614691288
460.2203277133925090.4406554267850180.779672286607491
470.2499601331879230.4999202663758460.750039866812077
480.3228163904989910.6456327809979810.677183609501009
490.2718932793114880.5437865586229760.728106720688512
500.2257993267495750.451598653499150.774200673250425
510.2019664265594790.4039328531189570.798033573440521
520.178664666042210.357329332084420.82133533395779
530.2246493436952780.4492986873905560.775350656304722
540.420634185905030.841268371810060.57936581409497
550.3627975389612660.7255950779225320.637202461038734
560.3004532294656640.6009064589313290.699546770534336
570.249274843347880.498549686695760.75072515665212
580.2042550313517320.4085100627034640.795744968648268
590.1755562087628090.3511124175256170.824443791237191
600.2537736829663650.507547365932730.746226317033635
610.2649592566080820.5299185132161630.735040743391918
620.185972940692290.3719458813845790.81402705930771
630.1832376936864840.3664753873729680.816762306313516
640.1333078734064890.2666157468129770.866692126593511
650.07632061584423110.1526412316884620.923679384155769
660.0389574130804110.0779148261608220.961042586919589







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level150.294117647058824NOK
10% type I error level200.392156862745098NOK

\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 testsOK/NOK
1% type I error level00OK
5% type I error level150.294117647058824NOK
10% type I error level200.392156862745098NOK



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 test
par1 <- as.numeric(par1)
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,mysum$coefficients[i,1])
a<-table.element(a, round(mysum$coefficients[i,2],6))
a<-table.element(a, round(mysum$coefficients[i,3],4))
a<-table.element(a, round(mysum$coefficients[i,4],6))
a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, sqrt(mysum$r.squared))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, mysum$r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, mysum$adj.r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, mysum$fstatistic[1])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[3])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a, mysum$sigma)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, sum(myerror*myerror))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,x[i])
a<-table.element(a,x[i]-mysum$resid[i])
a<-table.element(a,mysum$resid[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
if (n > n25) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-values',header=TRUE)
a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'breakpoint index',header=TRUE)
a<-table.element(a,'greater',header=TRUE)
a<-table.element(a,'2-sided',header=TRUE)
a<-table.element(a,'less',header=TRUE)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
a<-table.element(a,mypoint,header=TRUE)
a<-table.element(a,gqarr[mypoint-kp3+1,1])
a<-table.element(a,gqarr[mypoint-kp3+1,2])
a<-table.element(a,gqarr[mypoint-kp3+1,3])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,numsignificant1)
a<-table.element(a,numsignificant1/numgqtests)
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,numsignificant5)
a<-table.element(a,numsignificant5/numgqtests)
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,numsignificant10)
a<-table.element(a,numsignificant10/numgqtests)
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}