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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Wed, 25 Nov 2009 13:43:40 -0700

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2009/Nov/25/t1259182167rgbqgisboi4nf7u.htm/, Retrieved Tue, 07 May 2024 21:06:08 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=59637, Retrieved Tue, 07 May 2024 21:06:08 +0000

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Estimated Impact

128

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)

-     [Multiple Regression] [WS72] [2009-11-22 18:36:31] [b51f1812716cf982962b910446b09b55]
-    D    [Multiple Regression] [Revieuw ws7] [2009-11-25 20:43:40] [ac86848d66148c9c4c9404e0c9a511eb] [Current]

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105.62	125.03	105.57	105.24	105.15	104.89
106.17	130.09	105.62	105.57	105.24	105.15
106.27	126.65	106.17	105.62	105.57	105.24
106.41	121.7	106.27	106.17	105.62	105.57
106.94	119.24	106.41	106.27	106.17	105.62
107.16	122.63	106.94	106.41	106.27	106.17
107.32	116.66	107.16	106.94	106.41	106.27
107.32	114.12	107.32	107.16	106.94	106.41
107.35	113.11	107.32	107.32	107.16	106.94
107.55	112.61	107.35	107.32	107.32	107.16
107.87	113.4	107.55	107.35	107.32	107.32
108.37	115.18	107.87	107.55	107.35	107.32
108.38	121.01	108.37	107.87	107.55	107.35
107.92	119.44	108.38	108.37	107.87	107.55
108.03	116.68	107.92	108.38	108.37	107.87
108.14	117.07	108.03	107.92	108.38	108.37
108.3	117.41	108.14	108.03	107.92	108.38
108.64	119.58	108.3	108.14	108.03	107.92
108.66	120.92	108.64	108.3	108.14	108.03
109.04	117.09	108.66	108.64	108.3	108.14
109.03	116.77	109.04	108.66	108.64	108.3
109.03	119.39	109.03	109.04	108.66	108.64
109.54	122.49	109.03	109.03	109.04	108.66
109.75	124.08	109.54	109.03	109.03	109.04
109.83	118.29	109.75	109.54	109.03	109.03
109.65	112.94	109.83	109.75	109.54	109.03
109.82	113.79	109.65	109.83	109.75	109.54
109.95	114.43	109.82	109.65	109.83	109.75
110.12	118.7	109.95	109.82	109.65	109.83
110.15	120.36	110.12	109.95	109.82	109.65
110.21	118.27	110.15	110.12	109.95	109.82
109.99	118.34	110.21	110.15	110.12	109.95
110.14	117.82	109.99	110.21	110.15	110.12
110.14	117.65	110.14	109.99	110.21	110.15
110.81	118.18	110.14	110.14	109.99	110.21
110.97	121.02	110.81	110.14	110.14	109.99
110.99	124.78	110.97	110.81	110.14	110.14
109.73	131.16	110.99	110.97	110.81	110.14
109.81	130.14	109.73	110.99	110.97	110.81
110.02	131.75	109.81	109.73	110.99	110.97
110.18	134.73	110.02	109.81	109.73	110.99
110.21	135.35	110.18	110.02	109.81	109.73
110.25	140.32	110.21	110.18	110.02	109.81
110.36	136.35	110.25	110.21	110.18	110.02
110.51	131.6	110.36	110.25	110.21	110.18
110.6	128.9	110.51	110.36	110.25	110.21
110.95	133.89	110.6	110.51	110.36	110.25
111.18	138.25	110.95	110.6	110.51	110.36
111.19	146.23	111.18	110.95	110.6	110.51
111.69	144.76	111.19	111.18	110.95	110.6
111.7	149.3	111.69	111.19	111.18	110.95
111.83	156.8	111.7	111.69	111.19	111.18
111.77	159.08	111.83	111.7	111.69	111.19
111.73	165.12	111.77	111.83	111.7	111.69
112.01	163.14	111.73	111.77	111.83	111.7
111.86	153.43	112.01	111.73	111.77	111.83
112.04	151.01	111.86	112.01	111.73	111.77

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	6 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

\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 & 6 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=59637&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]6 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=59637&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=59637&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	6 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

Multiple Linear Regression - Estimated Regression Equation
Y[t] = + 24.9913736952300 -0.00450103556579507X[t] + 0.832983649674458`Y(t-1)`[t] -0.0571738582741973`Y(t-2)`[t] -0.232786626019946`Y(t-3)`[t] + 0.228951294811920`Y(t-4)`[t] + 0.0226526075029203t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Y[t] =  +  24.9913736952300 -0.00450103556579507X[t] +  0.832983649674458`Y(t-1)`[t] -0.0571738582741973`Y(t-2)`[t] -0.232786626019946`Y(t-3)`[t] +  0.228951294811920`Y(t-4)`[t] +  0.0226526075029203t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=59637&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Y[t] =  +  24.9913736952300 -0.00450103556579507X[t] +  0.832983649674458`Y(t-1)`[t] -0.0571738582741973`Y(t-2)`[t] -0.232786626019946`Y(t-3)`[t] +  0.228951294811920`Y(t-4)`[t] +  0.0226526075029203t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=59637&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=59637&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
Y[t] = + 24.9913736952300 -0.00450103556579507X[t] + 0.832983649674458`Y(t-1)`[t] -0.0571738582741973`Y(t-2)`[t] -0.232786626019946`Y(t-3)`[t] + 0.228951294811920`Y(t-4)`[t] + 0.0226526075029203t + 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)	24.9913736952300	10.294385	2.4277	0.018839	0.009419
X	-0.00450103556579507	0.004947	-0.9099	0.367258	0.183629
`Y(t-1)`	0.832983649674458	0.135743	6.1365	0	0
`Y(t-2)`	-0.0571738582741973	0.178307	-0.3206	0.749813	0.374906
`Y(t-3)`	-0.232786626019946	0.180137	-1.2923	0.202203	0.101102
`Y(t-4)`	0.228951294811920	0.13434	1.7043	0.09454	0.04727
t	0.0226526075029203	0.012086	1.8743	0.066741	0.03337

\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) & 24.9913736952300 & 10.294385 & 2.4277 & 0.018839 & 0.009419 \tabularnewline
X & -0.00450103556579507 & 0.004947 & -0.9099 & 0.367258 & 0.183629 \tabularnewline
`Y(t-1)` & 0.832983649674458 & 0.135743 & 6.1365 & 0 & 0 \tabularnewline
`Y(t-2)` & -0.0571738582741973 & 0.178307 & -0.3206 & 0.749813 & 0.374906 \tabularnewline
`Y(t-3)` & -0.232786626019946 & 0.180137 & -1.2923 & 0.202203 & 0.101102 \tabularnewline
`Y(t-4)` & 0.228951294811920 & 0.13434 & 1.7043 & 0.09454 & 0.04727 \tabularnewline
t & 0.0226526075029203 & 0.012086 & 1.8743 & 0.066741 & 0.03337 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=59637&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]24.9913736952300[/C][C]10.294385[/C][C]2.4277[/C][C]0.018839[/C][C]0.009419[/C][/ROW]
[ROW][C]X[/C][C]-0.00450103556579507[/C][C]0.004947[/C][C]-0.9099[/C][C]0.367258[/C][C]0.183629[/C][/ROW]
[ROW][C]`Y(t-1)`[/C][C]0.832983649674458[/C][C]0.135743[/C][C]6.1365[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]`Y(t-2)`[/C][C]-0.0571738582741973[/C][C]0.178307[/C][C]-0.3206[/C][C]0.749813[/C][C]0.374906[/C][/ROW]
[ROW][C]`Y(t-3)`[/C][C]-0.232786626019946[/C][C]0.180137[/C][C]-1.2923[/C][C]0.202203[/C][C]0.101102[/C][/ROW]
[ROW][C]`Y(t-4)`[/C][C]0.228951294811920[/C][C]0.13434[/C][C]1.7043[/C][C]0.09454[/C][C]0.04727[/C][/ROW]
[ROW][C]t[/C][C]0.0226526075029203[/C][C]0.012086[/C][C]1.8743[/C][C]0.066741[/C][C]0.03337[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=59637&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=59637&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)	24.9913736952300	10.294385	2.4277	0.018839	0.009419
X	-0.00450103556579507	0.004947	-0.9099	0.367258	0.183629
`Y(t-1)`	0.832983649674458	0.135743	6.1365	0	0
`Y(t-2)`	-0.0571738582741973	0.178307	-0.3206	0.749813	0.374906
`Y(t-3)`	-0.232786626019946	0.180137	-1.2923	0.202203	0.101102
`Y(t-4)`	0.228951294811920	0.13434	1.7043	0.09454	0.04727
t	0.0226526075029203	0.012086	1.8743	0.066741	0.03337

Multiple Linear Regression - Regression Statistics
Multiple R	0.989693018832834
R-squared	0.979492271526448
Adjusted R-squared	0.977031344109622
F-TEST (value)	398.017537953093
F-TEST (DF numerator)	6
F-TEST (DF denominator)	50
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.253351340812442
Sum Squared Residuals	3.2093450945731

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.989693018832834 \tabularnewline
R-squared & 0.979492271526448 \tabularnewline
Adjusted R-squared & 0.977031344109622 \tabularnewline
F-TEST (value) & 398.017537953093 \tabularnewline
F-TEST (DF numerator) & 6 \tabularnewline
F-TEST (DF denominator) & 50 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.253351340812442 \tabularnewline
Sum Squared Residuals & 3.2093450945731 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=59637&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.989693018832834[/C][/ROW]
[ROW][C]R-squared[/C][C]0.979492271526448[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.977031344109622[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]398.017537953093[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]6[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]50[/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]0.253351340812442[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]3.2093450945731[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=59637&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=59637&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	0.989693018832834
R-squared	0.979492271526448
Adjusted R-squared	0.977031344109622
F-TEST (value)	398.017537953093
F-TEST (DF numerator)	6
F-TEST (DF denominator)	50
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.253351340812442
Sum Squared Residuals	3.2093450945731

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	105.62	105.909556464123	-0.289556464122704
2	106.17	105.970792181225	0.199207818774814
3	106.27	106.407996695428	-0.137996695428173
4	106.41	106.568696767885	-0.158696767885347
5	106.94	106.596737168437	0.343262831563244
6	107.16	107.140252809085	0.0197471909147292
7	107.32	107.333035858797	-0.0130358587974443
8	107.32	107.396496501248	-0.0764965012481687
9	107.35	107.484678465875	-0.134678465874600
10	107.55	107.547694525346	0.00230547465391893
11	107.87	107.768305036109	0.101694963891410
12	108.37	108.031082197765	0.33891780223521
13	108.38	108.386001171749	-0.00600117174898755
14	107.92	108.366761851086	-0.446761851085839
15	108.03	107.974964200647	0.05503579935279
16	108.14	108.225937361696	-0.0859373616955538
17	108.3	108.441770055077	-0.141770055077426
18	108.64	108.450719550465	0.189280449535359
19	108.66	108.740985507442	-0.0809855074419642
20	109.04	108.766036424608	0.273963575391751
21	109.03	109.063004427526	-0.0330044275261714
22	109.03	109.116996126921	-0.0869961269214201
23	109.54	109.042387370762	0.497612629238232
24	109.75	109.572034351338	0.177965648662209
25	109.83	109.764226340530	0.0657736594696631
26	109.65	109.746870490776	-0.0968704907764538
27	109.82	109.679066221335	0.140933778664978
28	109.95	109.880193522839	0.069806477161267
29	110.12	110.042412723295	0.0775872767046766
30	110.15	110.110983271139	0.0390167288614961
31	110.21	110.166972455293	0.0430275447070031
32	109.99	110.227763735441	-0.237763735440703
33	110.14	110.098008168350	0.0419918316495721
34	110.14	110.251853089454	-0.111853089454194
35	110.81	110.328494204779	0.481505795220788
36	110.97	110.811175637796	0.158824362204449
37	110.99	110.946217944697	0.0437820553029272
38	109.73	110.791698761526	-1.06169876152646
39	109.81	109.884391056912	-0.0743910569119973
40	110.02	110.070451225203	-0.0504512252029452
41	110.18	110.547933579171	-0.367933579170848
42	110.21	110.381964856789	-0.171964856788717
43	110.25	110.367519921817	-0.117519921816746
44	110.36	110.450479682502	-0.0904796825019399
45	110.51	110.613502064465	-0.103502064464917
46	110.6	110.76452296484	-0.164522964840062
47	110.95	110.814659377530	0.135340622470497
48	111.18	111.094352748633	0.0856472513667392
49	111.19	111.266054379230	-0.0760543792303016
50	111.69	111.229633655535	0.460366344465301
51	111.7	111.674363677023	0.025636322977022
52	111.83	111.693332356689	0.136667643311365
53	111.77	111.699334938915	0.0706650610853777
54	111.73	111.749537452190	-0.0195374521897772
55	112.01	111.723240447188	0.286759552812021
56	111.86	112.068851352161	-0.208851352161338
57	112.04	111.957014625318	0.0829853746823928

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 105.62 & 105.909556464123 & -0.289556464122704 \tabularnewline
2 & 106.17 & 105.970792181225 & 0.199207818774814 \tabularnewline
3 & 106.27 & 106.407996695428 & -0.137996695428173 \tabularnewline
4 & 106.41 & 106.568696767885 & -0.158696767885347 \tabularnewline
5 & 106.94 & 106.596737168437 & 0.343262831563244 \tabularnewline
6 & 107.16 & 107.140252809085 & 0.0197471909147292 \tabularnewline
7 & 107.32 & 107.333035858797 & -0.0130358587974443 \tabularnewline
8 & 107.32 & 107.396496501248 & -0.0764965012481687 \tabularnewline
9 & 107.35 & 107.484678465875 & -0.134678465874600 \tabularnewline
10 & 107.55 & 107.547694525346 & 0.00230547465391893 \tabularnewline
11 & 107.87 & 107.768305036109 & 0.101694963891410 \tabularnewline
12 & 108.37 & 108.031082197765 & 0.33891780223521 \tabularnewline
13 & 108.38 & 108.386001171749 & -0.00600117174898755 \tabularnewline
14 & 107.92 & 108.366761851086 & -0.446761851085839 \tabularnewline
15 & 108.03 & 107.974964200647 & 0.05503579935279 \tabularnewline
16 & 108.14 & 108.225937361696 & -0.0859373616955538 \tabularnewline
17 & 108.3 & 108.441770055077 & -0.141770055077426 \tabularnewline
18 & 108.64 & 108.450719550465 & 0.189280449535359 \tabularnewline
19 & 108.66 & 108.740985507442 & -0.0809855074419642 \tabularnewline
20 & 109.04 & 108.766036424608 & 0.273963575391751 \tabularnewline
21 & 109.03 & 109.063004427526 & -0.0330044275261714 \tabularnewline
22 & 109.03 & 109.116996126921 & -0.0869961269214201 \tabularnewline
23 & 109.54 & 109.042387370762 & 0.497612629238232 \tabularnewline
24 & 109.75 & 109.572034351338 & 0.177965648662209 \tabularnewline
25 & 109.83 & 109.764226340530 & 0.0657736594696631 \tabularnewline
26 & 109.65 & 109.746870490776 & -0.0968704907764538 \tabularnewline
27 & 109.82 & 109.679066221335 & 0.140933778664978 \tabularnewline
28 & 109.95 & 109.880193522839 & 0.069806477161267 \tabularnewline
29 & 110.12 & 110.042412723295 & 0.0775872767046766 \tabularnewline
30 & 110.15 & 110.110983271139 & 0.0390167288614961 \tabularnewline
31 & 110.21 & 110.166972455293 & 0.0430275447070031 \tabularnewline
32 & 109.99 & 110.227763735441 & -0.237763735440703 \tabularnewline
33 & 110.14 & 110.098008168350 & 0.0419918316495721 \tabularnewline
34 & 110.14 & 110.251853089454 & -0.111853089454194 \tabularnewline
35 & 110.81 & 110.328494204779 & 0.481505795220788 \tabularnewline
36 & 110.97 & 110.811175637796 & 0.158824362204449 \tabularnewline
37 & 110.99 & 110.946217944697 & 0.0437820553029272 \tabularnewline
38 & 109.73 & 110.791698761526 & -1.06169876152646 \tabularnewline
39 & 109.81 & 109.884391056912 & -0.0743910569119973 \tabularnewline
40 & 110.02 & 110.070451225203 & -0.0504512252029452 \tabularnewline
41 & 110.18 & 110.547933579171 & -0.367933579170848 \tabularnewline
42 & 110.21 & 110.381964856789 & -0.171964856788717 \tabularnewline
43 & 110.25 & 110.367519921817 & -0.117519921816746 \tabularnewline
44 & 110.36 & 110.450479682502 & -0.0904796825019399 \tabularnewline
45 & 110.51 & 110.613502064465 & -0.103502064464917 \tabularnewline
46 & 110.6 & 110.76452296484 & -0.164522964840062 \tabularnewline
47 & 110.95 & 110.814659377530 & 0.135340622470497 \tabularnewline
48 & 111.18 & 111.094352748633 & 0.0856472513667392 \tabularnewline
49 & 111.19 & 111.266054379230 & -0.0760543792303016 \tabularnewline
50 & 111.69 & 111.229633655535 & 0.460366344465301 \tabularnewline
51 & 111.7 & 111.674363677023 & 0.025636322977022 \tabularnewline
52 & 111.83 & 111.693332356689 & 0.136667643311365 \tabularnewline
53 & 111.77 & 111.699334938915 & 0.0706650610853777 \tabularnewline
54 & 111.73 & 111.749537452190 & -0.0195374521897772 \tabularnewline
55 & 112.01 & 111.723240447188 & 0.286759552812021 \tabularnewline
56 & 111.86 & 112.068851352161 & -0.208851352161338 \tabularnewline
57 & 112.04 & 111.957014625318 & 0.0829853746823928 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=59637&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]105.62[/C][C]105.909556464123[/C][C]-0.289556464122704[/C][/ROW]
[ROW][C]2[/C][C]106.17[/C][C]105.970792181225[/C][C]0.199207818774814[/C][/ROW]
[ROW][C]3[/C][C]106.27[/C][C]106.407996695428[/C][C]-0.137996695428173[/C][/ROW]
[ROW][C]4[/C][C]106.41[/C][C]106.568696767885[/C][C]-0.158696767885347[/C][/ROW]
[ROW][C]5[/C][C]106.94[/C][C]106.596737168437[/C][C]0.343262831563244[/C][/ROW]
[ROW][C]6[/C][C]107.16[/C][C]107.140252809085[/C][C]0.0197471909147292[/C][/ROW]
[ROW][C]7[/C][C]107.32[/C][C]107.333035858797[/C][C]-0.0130358587974443[/C][/ROW]
[ROW][C]8[/C][C]107.32[/C][C]107.396496501248[/C][C]-0.0764965012481687[/C][/ROW]
[ROW][C]9[/C][C]107.35[/C][C]107.484678465875[/C][C]-0.134678465874600[/C][/ROW]
[ROW][C]10[/C][C]107.55[/C][C]107.547694525346[/C][C]0.00230547465391893[/C][/ROW]
[ROW][C]11[/C][C]107.87[/C][C]107.768305036109[/C][C]0.101694963891410[/C][/ROW]
[ROW][C]12[/C][C]108.37[/C][C]108.031082197765[/C][C]0.33891780223521[/C][/ROW]
[ROW][C]13[/C][C]108.38[/C][C]108.386001171749[/C][C]-0.00600117174898755[/C][/ROW]
[ROW][C]14[/C][C]107.92[/C][C]108.366761851086[/C][C]-0.446761851085839[/C][/ROW]
[ROW][C]15[/C][C]108.03[/C][C]107.974964200647[/C][C]0.05503579935279[/C][/ROW]
[ROW][C]16[/C][C]108.14[/C][C]108.225937361696[/C][C]-0.0859373616955538[/C][/ROW]
[ROW][C]17[/C][C]108.3[/C][C]108.441770055077[/C][C]-0.141770055077426[/C][/ROW]
[ROW][C]18[/C][C]108.64[/C][C]108.450719550465[/C][C]0.189280449535359[/C][/ROW]
[ROW][C]19[/C][C]108.66[/C][C]108.740985507442[/C][C]-0.0809855074419642[/C][/ROW]
[ROW][C]20[/C][C]109.04[/C][C]108.766036424608[/C][C]0.273963575391751[/C][/ROW]
[ROW][C]21[/C][C]109.03[/C][C]109.063004427526[/C][C]-0.0330044275261714[/C][/ROW]
[ROW][C]22[/C][C]109.03[/C][C]109.116996126921[/C][C]-0.0869961269214201[/C][/ROW]
[ROW][C]23[/C][C]109.54[/C][C]109.042387370762[/C][C]0.497612629238232[/C][/ROW]
[ROW][C]24[/C][C]109.75[/C][C]109.572034351338[/C][C]0.177965648662209[/C][/ROW]
[ROW][C]25[/C][C]109.83[/C][C]109.764226340530[/C][C]0.0657736594696631[/C][/ROW]
[ROW][C]26[/C][C]109.65[/C][C]109.746870490776[/C][C]-0.0968704907764538[/C][/ROW]
[ROW][C]27[/C][C]109.82[/C][C]109.679066221335[/C][C]0.140933778664978[/C][/ROW]
[ROW][C]28[/C][C]109.95[/C][C]109.880193522839[/C][C]0.069806477161267[/C][/ROW]
[ROW][C]29[/C][C]110.12[/C][C]110.042412723295[/C][C]0.0775872767046766[/C][/ROW]
[ROW][C]30[/C][C]110.15[/C][C]110.110983271139[/C][C]0.0390167288614961[/C][/ROW]
[ROW][C]31[/C][C]110.21[/C][C]110.166972455293[/C][C]0.0430275447070031[/C][/ROW]
[ROW][C]32[/C][C]109.99[/C][C]110.227763735441[/C][C]-0.237763735440703[/C][/ROW]
[ROW][C]33[/C][C]110.14[/C][C]110.098008168350[/C][C]0.0419918316495721[/C][/ROW]
[ROW][C]34[/C][C]110.14[/C][C]110.251853089454[/C][C]-0.111853089454194[/C][/ROW]
[ROW][C]35[/C][C]110.81[/C][C]110.328494204779[/C][C]0.481505795220788[/C][/ROW]
[ROW][C]36[/C][C]110.97[/C][C]110.811175637796[/C][C]0.158824362204449[/C][/ROW]
[ROW][C]37[/C][C]110.99[/C][C]110.946217944697[/C][C]0.0437820553029272[/C][/ROW]
[ROW][C]38[/C][C]109.73[/C][C]110.791698761526[/C][C]-1.06169876152646[/C][/ROW]
[ROW][C]39[/C][C]109.81[/C][C]109.884391056912[/C][C]-0.0743910569119973[/C][/ROW]
[ROW][C]40[/C][C]110.02[/C][C]110.070451225203[/C][C]-0.0504512252029452[/C][/ROW]
[ROW][C]41[/C][C]110.18[/C][C]110.547933579171[/C][C]-0.367933579170848[/C][/ROW]
[ROW][C]42[/C][C]110.21[/C][C]110.381964856789[/C][C]-0.171964856788717[/C][/ROW]
[ROW][C]43[/C][C]110.25[/C][C]110.367519921817[/C][C]-0.117519921816746[/C][/ROW]
[ROW][C]44[/C][C]110.36[/C][C]110.450479682502[/C][C]-0.0904796825019399[/C][/ROW]
[ROW][C]45[/C][C]110.51[/C][C]110.613502064465[/C][C]-0.103502064464917[/C][/ROW]
[ROW][C]46[/C][C]110.6[/C][C]110.76452296484[/C][C]-0.164522964840062[/C][/ROW]
[ROW][C]47[/C][C]110.95[/C][C]110.814659377530[/C][C]0.135340622470497[/C][/ROW]
[ROW][C]48[/C][C]111.18[/C][C]111.094352748633[/C][C]0.0856472513667392[/C][/ROW]
[ROW][C]49[/C][C]111.19[/C][C]111.266054379230[/C][C]-0.0760543792303016[/C][/ROW]
[ROW][C]50[/C][C]111.69[/C][C]111.229633655535[/C][C]0.460366344465301[/C][/ROW]
[ROW][C]51[/C][C]111.7[/C][C]111.674363677023[/C][C]0.025636322977022[/C][/ROW]
[ROW][C]52[/C][C]111.83[/C][C]111.693332356689[/C][C]0.136667643311365[/C][/ROW]
[ROW][C]53[/C][C]111.77[/C][C]111.699334938915[/C][C]0.0706650610853777[/C][/ROW]
[ROW][C]54[/C][C]111.73[/C][C]111.749537452190[/C][C]-0.0195374521897772[/C][/ROW]
[ROW][C]55[/C][C]112.01[/C][C]111.723240447188[/C][C]0.286759552812021[/C][/ROW]
[ROW][C]56[/C][C]111.86[/C][C]112.068851352161[/C][C]-0.208851352161338[/C][/ROW]
[ROW][C]57[/C][C]112.04[/C][C]111.957014625318[/C][C]0.0829853746823928[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=59637&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=59637&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	105.62	105.909556464123	-0.289556464122704
2	106.17	105.970792181225	0.199207818774814
3	106.27	106.407996695428	-0.137996695428173
4	106.41	106.568696767885	-0.158696767885347
5	106.94	106.596737168437	0.343262831563244
6	107.16	107.140252809085	0.0197471909147292
7	107.32	107.333035858797	-0.0130358587974443
8	107.32	107.396496501248	-0.0764965012481687
9	107.35	107.484678465875	-0.134678465874600
10	107.55	107.547694525346	0.00230547465391893
11	107.87	107.768305036109	0.101694963891410
12	108.37	108.031082197765	0.33891780223521
13	108.38	108.386001171749	-0.00600117174898755
14	107.92	108.366761851086	-0.446761851085839
15	108.03	107.974964200647	0.05503579935279
16	108.14	108.225937361696	-0.0859373616955538
17	108.3	108.441770055077	-0.141770055077426
18	108.64	108.450719550465	0.189280449535359
19	108.66	108.740985507442	-0.0809855074419642
20	109.04	108.766036424608	0.273963575391751
21	109.03	109.063004427526	-0.0330044275261714
22	109.03	109.116996126921	-0.0869961269214201
23	109.54	109.042387370762	0.497612629238232
24	109.75	109.572034351338	0.177965648662209
25	109.83	109.764226340530	0.0657736594696631
26	109.65	109.746870490776	-0.0968704907764538
27	109.82	109.679066221335	0.140933778664978
28	109.95	109.880193522839	0.069806477161267
29	110.12	110.042412723295	0.0775872767046766
30	110.15	110.110983271139	0.0390167288614961
31	110.21	110.166972455293	0.0430275447070031
32	109.99	110.227763735441	-0.237763735440703
33	110.14	110.098008168350	0.0419918316495721
34	110.14	110.251853089454	-0.111853089454194
35	110.81	110.328494204779	0.481505795220788
36	110.97	110.811175637796	0.158824362204449
37	110.99	110.946217944697	0.0437820553029272
38	109.73	110.791698761526	-1.06169876152646
39	109.81	109.884391056912	-0.0743910569119973
40	110.02	110.070451225203	-0.0504512252029452
41	110.18	110.547933579171	-0.367933579170848
42	110.21	110.381964856789	-0.171964856788717
43	110.25	110.367519921817	-0.117519921816746
44	110.36	110.450479682502	-0.0904796825019399
45	110.51	110.613502064465	-0.103502064464917
46	110.6	110.76452296484	-0.164522964840062
47	110.95	110.814659377530	0.135340622470497
48	111.18	111.094352748633	0.0856472513667392
49	111.19	111.266054379230	-0.0760543792303016
50	111.69	111.229633655535	0.460366344465301
51	111.7	111.674363677023	0.025636322977022
52	111.83	111.693332356689	0.136667643311365
53	111.77	111.699334938915	0.0706650610853777
54	111.73	111.749537452190	-0.0195374521897772
55	112.01	111.723240447188	0.286759552812021
56	111.86	112.068851352161	-0.208851352161338
57	112.04	111.957014625318	0.0829853746823928

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
10	0.343887050882068	0.687774101764135	0.656112949117932
11	0.196242850758297	0.392485701516594	0.803757149241703
12	0.114542358901213	0.229084717802426	0.885457641098787
13	0.2824376980459	0.5648753960918	0.7175623019541
14	0.536132660446009	0.927734679107981	0.463867339553991
15	0.419048359201166	0.838096718402332	0.580951640798834
16	0.40486546848887	0.80973093697774	0.59513453151113
17	0.371756543957006	0.743513087914013	0.628243456042993
18	0.282944096634609	0.565888193269219	0.71705590336539
19	0.236031448776554	0.472062897553109	0.763968551223446
20	0.187305799334444	0.374611598668888	0.812694200665556
21	0.151174015487692	0.302348030975384	0.848825984512308
22	0.113990647804410	0.227981295608820	0.88600935219559
23	0.198583095752682	0.397166191505364	0.801416904247318
24	0.159705287650408	0.319410575300815	0.840294712349593
25	0.113388983117610	0.226777966235220	0.88661101688239
26	0.105177652581467	0.210355305162934	0.894822347418533
27	0.0769462346036761	0.153892469207352	0.923053765396324
28	0.0553633046730464	0.110726609346093	0.944636695326954
29	0.0380159159246459	0.0760318318492917	0.961984084075354
30	0.0277570839664933	0.0555141679329866	0.972242916033507
31	0.0196627503197175	0.0393255006394349	0.980337249680282
32	0.0240029822852979	0.0480059645705958	0.975997017714702
33	0.0154650809415682	0.0309301618831364	0.984534919058432
34	0.010736817713614	0.021473635427228	0.989263182286386
35	0.0494832176992929	0.0989664353985858	0.950516782300707
36	0.092245672564766	0.184491345129532	0.907754327435234
37	0.659991608712508	0.680016782574985	0.340008391287493
38	0.938116116677975	0.123767766644049	0.0618838833220246
39	0.927453616026723	0.145092767946555	0.0725463839732774
40	0.882991167406407	0.234017665187186	0.117008832593593
41	0.956163381780883	0.087673236438233	0.0438366182191165
42	0.91865955185206	0.162680896295879	0.0813404481479394
43	0.903011464580366	0.193977070839269	0.0969885354196343
44	0.855042917075347	0.289914165849305	0.144957082924653
45	0.756375786452976	0.487248427094048	0.243624213547024
46	0.715817940718264	0.568364118563471	0.284182059281736
47	0.655616975448154	0.688766049103692	0.344383024551846

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
10 & 0.343887050882068 & 0.687774101764135 & 0.656112949117932 \tabularnewline
11 & 0.196242850758297 & 0.392485701516594 & 0.803757149241703 \tabularnewline
12 & 0.114542358901213 & 0.229084717802426 & 0.885457641098787 \tabularnewline
13 & 0.2824376980459 & 0.5648753960918 & 0.7175623019541 \tabularnewline
14 & 0.536132660446009 & 0.927734679107981 & 0.463867339553991 \tabularnewline
15 & 0.419048359201166 & 0.838096718402332 & 0.580951640798834 \tabularnewline
16 & 0.40486546848887 & 0.80973093697774 & 0.59513453151113 \tabularnewline
17 & 0.371756543957006 & 0.743513087914013 & 0.628243456042993 \tabularnewline
18 & 0.282944096634609 & 0.565888193269219 & 0.71705590336539 \tabularnewline
19 & 0.236031448776554 & 0.472062897553109 & 0.763968551223446 \tabularnewline
20 & 0.187305799334444 & 0.374611598668888 & 0.812694200665556 \tabularnewline
21 & 0.151174015487692 & 0.302348030975384 & 0.848825984512308 \tabularnewline
22 & 0.113990647804410 & 0.227981295608820 & 0.88600935219559 \tabularnewline
23 & 0.198583095752682 & 0.397166191505364 & 0.801416904247318 \tabularnewline
24 & 0.159705287650408 & 0.319410575300815 & 0.840294712349593 \tabularnewline
25 & 0.113388983117610 & 0.226777966235220 & 0.88661101688239 \tabularnewline
26 & 0.105177652581467 & 0.210355305162934 & 0.894822347418533 \tabularnewline
27 & 0.0769462346036761 & 0.153892469207352 & 0.923053765396324 \tabularnewline
28 & 0.0553633046730464 & 0.110726609346093 & 0.944636695326954 \tabularnewline
29 & 0.0380159159246459 & 0.0760318318492917 & 0.961984084075354 \tabularnewline
30 & 0.0277570839664933 & 0.0555141679329866 & 0.972242916033507 \tabularnewline
31 & 0.0196627503197175 & 0.0393255006394349 & 0.980337249680282 \tabularnewline
32 & 0.0240029822852979 & 0.0480059645705958 & 0.975997017714702 \tabularnewline
33 & 0.0154650809415682 & 0.0309301618831364 & 0.984534919058432 \tabularnewline
34 & 0.010736817713614 & 0.021473635427228 & 0.989263182286386 \tabularnewline
35 & 0.0494832176992929 & 0.0989664353985858 & 0.950516782300707 \tabularnewline
36 & 0.092245672564766 & 0.184491345129532 & 0.907754327435234 \tabularnewline
37 & 0.659991608712508 & 0.680016782574985 & 0.340008391287493 \tabularnewline
38 & 0.938116116677975 & 0.123767766644049 & 0.0618838833220246 \tabularnewline
39 & 0.927453616026723 & 0.145092767946555 & 0.0725463839732774 \tabularnewline
40 & 0.882991167406407 & 0.234017665187186 & 0.117008832593593 \tabularnewline
41 & 0.956163381780883 & 0.087673236438233 & 0.0438366182191165 \tabularnewline
42 & 0.91865955185206 & 0.162680896295879 & 0.0813404481479394 \tabularnewline
43 & 0.903011464580366 & 0.193977070839269 & 0.0969885354196343 \tabularnewline
44 & 0.855042917075347 & 0.289914165849305 & 0.144957082924653 \tabularnewline
45 & 0.756375786452976 & 0.487248427094048 & 0.243624213547024 \tabularnewline
46 & 0.715817940718264 & 0.568364118563471 & 0.284182059281736 \tabularnewline
47 & 0.655616975448154 & 0.688766049103692 & 0.344383024551846 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=59637&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]10[/C][C]0.343887050882068[/C][C]0.687774101764135[/C][C]0.656112949117932[/C][/ROW]
[ROW][C]11[/C][C]0.196242850758297[/C][C]0.392485701516594[/C][C]0.803757149241703[/C][/ROW]
[ROW][C]12[/C][C]0.114542358901213[/C][C]0.229084717802426[/C][C]0.885457641098787[/C][/ROW]
[ROW][C]13[/C][C]0.2824376980459[/C][C]0.5648753960918[/C][C]0.7175623019541[/C][/ROW]
[ROW][C]14[/C][C]0.536132660446009[/C][C]0.927734679107981[/C][C]0.463867339553991[/C][/ROW]
[ROW][C]15[/C][C]0.419048359201166[/C][C]0.838096718402332[/C][C]0.580951640798834[/C][/ROW]
[ROW][C]16[/C][C]0.40486546848887[/C][C]0.80973093697774[/C][C]0.59513453151113[/C][/ROW]
[ROW][C]17[/C][C]0.371756543957006[/C][C]0.743513087914013[/C][C]0.628243456042993[/C][/ROW]
[ROW][C]18[/C][C]0.282944096634609[/C][C]0.565888193269219[/C][C]0.71705590336539[/C][/ROW]
[ROW][C]19[/C][C]0.236031448776554[/C][C]0.472062897553109[/C][C]0.763968551223446[/C][/ROW]
[ROW][C]20[/C][C]0.187305799334444[/C][C]0.374611598668888[/C][C]0.812694200665556[/C][/ROW]
[ROW][C]21[/C][C]0.151174015487692[/C][C]0.302348030975384[/C][C]0.848825984512308[/C][/ROW]
[ROW][C]22[/C][C]0.113990647804410[/C][C]0.227981295608820[/C][C]0.88600935219559[/C][/ROW]
[ROW][C]23[/C][C]0.198583095752682[/C][C]0.397166191505364[/C][C]0.801416904247318[/C][/ROW]
[ROW][C]24[/C][C]0.159705287650408[/C][C]0.319410575300815[/C][C]0.840294712349593[/C][/ROW]
[ROW][C]25[/C][C]0.113388983117610[/C][C]0.226777966235220[/C][C]0.88661101688239[/C][/ROW]
[ROW][C]26[/C][C]0.105177652581467[/C][C]0.210355305162934[/C][C]0.894822347418533[/C][/ROW]
[ROW][C]27[/C][C]0.0769462346036761[/C][C]0.153892469207352[/C][C]0.923053765396324[/C][/ROW]
[ROW][C]28[/C][C]0.0553633046730464[/C][C]0.110726609346093[/C][C]0.944636695326954[/C][/ROW]
[ROW][C]29[/C][C]0.0380159159246459[/C][C]0.0760318318492917[/C][C]0.961984084075354[/C][/ROW]
[ROW][C]30[/C][C]0.0277570839664933[/C][C]0.0555141679329866[/C][C]0.972242916033507[/C][/ROW]
[ROW][C]31[/C][C]0.0196627503197175[/C][C]0.0393255006394349[/C][C]0.980337249680282[/C][/ROW]
[ROW][C]32[/C][C]0.0240029822852979[/C][C]0.0480059645705958[/C][C]0.975997017714702[/C][/ROW]
[ROW][C]33[/C][C]0.0154650809415682[/C][C]0.0309301618831364[/C][C]0.984534919058432[/C][/ROW]
[ROW][C]34[/C][C]0.010736817713614[/C][C]0.021473635427228[/C][C]0.989263182286386[/C][/ROW]
[ROW][C]35[/C][C]0.0494832176992929[/C][C]0.0989664353985858[/C][C]0.950516782300707[/C][/ROW]
[ROW][C]36[/C][C]0.092245672564766[/C][C]0.184491345129532[/C][C]0.907754327435234[/C][/ROW]
[ROW][C]37[/C][C]0.659991608712508[/C][C]0.680016782574985[/C][C]0.340008391287493[/C][/ROW]
[ROW][C]38[/C][C]0.938116116677975[/C][C]0.123767766644049[/C][C]0.0618838833220246[/C][/ROW]
[ROW][C]39[/C][C]0.927453616026723[/C][C]0.145092767946555[/C][C]0.0725463839732774[/C][/ROW]
[ROW][C]40[/C][C]0.882991167406407[/C][C]0.234017665187186[/C][C]0.117008832593593[/C][/ROW]
[ROW][C]41[/C][C]0.956163381780883[/C][C]0.087673236438233[/C][C]0.0438366182191165[/C][/ROW]
[ROW][C]42[/C][C]0.91865955185206[/C][C]0.162680896295879[/C][C]0.0813404481479394[/C][/ROW]
[ROW][C]43[/C][C]0.903011464580366[/C][C]0.193977070839269[/C][C]0.0969885354196343[/C][/ROW]
[ROW][C]44[/C][C]0.855042917075347[/C][C]0.289914165849305[/C][C]0.144957082924653[/C][/ROW]
[ROW][C]45[/C][C]0.756375786452976[/C][C]0.487248427094048[/C][C]0.243624213547024[/C][/ROW]
[ROW][C]46[/C][C]0.715817940718264[/C][C]0.568364118563471[/C][C]0.284182059281736[/C][/ROW]
[ROW][C]47[/C][C]0.655616975448154[/C][C]0.688766049103692[/C][C]0.344383024551846[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=59637&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=59637&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
10	0.343887050882068	0.687774101764135	0.656112949117932
11	0.196242850758297	0.392485701516594	0.803757149241703
12	0.114542358901213	0.229084717802426	0.885457641098787
13	0.2824376980459	0.5648753960918	0.7175623019541
14	0.536132660446009	0.927734679107981	0.463867339553991
15	0.419048359201166	0.838096718402332	0.580951640798834
16	0.40486546848887	0.80973093697774	0.59513453151113
17	0.371756543957006	0.743513087914013	0.628243456042993
18	0.282944096634609	0.565888193269219	0.71705590336539
19	0.236031448776554	0.472062897553109	0.763968551223446
20	0.187305799334444	0.374611598668888	0.812694200665556
21	0.151174015487692	0.302348030975384	0.848825984512308
22	0.113990647804410	0.227981295608820	0.88600935219559
23	0.198583095752682	0.397166191505364	0.801416904247318
24	0.159705287650408	0.319410575300815	0.840294712349593
25	0.113388983117610	0.226777966235220	0.88661101688239
26	0.105177652581467	0.210355305162934	0.894822347418533
27	0.0769462346036761	0.153892469207352	0.923053765396324
28	0.0553633046730464	0.110726609346093	0.944636695326954
29	0.0380159159246459	0.0760318318492917	0.961984084075354
30	0.0277570839664933	0.0555141679329866	0.972242916033507
31	0.0196627503197175	0.0393255006394349	0.980337249680282
32	0.0240029822852979	0.0480059645705958	0.975997017714702
33	0.0154650809415682	0.0309301618831364	0.984534919058432
34	0.010736817713614	0.021473635427228	0.989263182286386
35	0.0494832176992929	0.0989664353985858	0.950516782300707
36	0.092245672564766	0.184491345129532	0.907754327435234
37	0.659991608712508	0.680016782574985	0.340008391287493
38	0.938116116677975	0.123767766644049	0.0618838833220246
39	0.927453616026723	0.145092767946555	0.0725463839732774
40	0.882991167406407	0.234017665187186	0.117008832593593
41	0.956163381780883	0.087673236438233	0.0438366182191165
42	0.91865955185206	0.162680896295879	0.0813404481479394
43	0.903011464580366	0.193977070839269	0.0969885354196343
44	0.855042917075347	0.289914165849305	0.144957082924653
45	0.756375786452976	0.487248427094048	0.243624213547024
46	0.715817940718264	0.568364118563471	0.284182059281736
47	0.655616975448154	0.688766049103692	0.344383024551846

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	4	0.105263157894737	NOK
10% type I error level	8	0.210526315789474	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 & 4 & 0.105263157894737 & NOK \tabularnewline
10% type I error level & 8 & 0.210526315789474 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=59637&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]4[/C][C]0.105263157894737[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]8[/C][C]0.210526315789474[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=59637&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=59637&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	4	0.105263157894737	NOK
10% type I error level	8	0.210526315789474	NOK

Figure 1

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Figure 2

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Figure 3

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Figure 4

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Figure 5

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Figure 6

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Figure 7

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Figure 8

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Figure 9

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Figure 10

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Parameters (Session):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;

R code (references can be found in the software module):

library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,mysum$coefficients[i,1])
a<-table.element(a, round(mysum$coefficients[i,2],6))
a<-table.element(a, round(mysum$coefficients[i,3],4))
a<-table.element(a, round(mysum$coefficients[i,4],6))
a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, sqrt(mysum$r.squared))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, mysum$r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, mysum$adj.r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, mysum$fstatistic[1])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[3])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a, mysum$sigma)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, sum(myerror*myerror))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,x[i])
a<-table.element(a,x[i]-mysum$resid[i])
a<-table.element(a,mysum$resid[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
if (n > n25) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-values',header=TRUE)
a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'breakpoint index',header=TRUE)
a<-table.element(a,'greater',header=TRUE)
a<-table.element(a,'2-sided',header=TRUE)
a<-table.element(a,'less',header=TRUE)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
a<-table.element(a,mypoint,header=TRUE)
a<-table.element(a,gqarr[mypoint-kp3+1,1])
a<-table.element(a,gqarr[mypoint-kp3+1,2])
a<-table.element(a,gqarr[mypoint-kp3+1,3])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,numsignificant1)
a<-table.element(a,numsignificant1/numgqtests)
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,numsignificant5)
a<-table.element(a,numsignificant5/numgqtests)
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,numsignificant10)
a<-table.element(a,numsignificant10/numgqtests)
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code