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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Fri, 20 Nov 2009 03:56:15 -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/20/t1258715142g91xm14skmhwvun.htm/, Retrieved Thu, 25 Apr 2024 19:43:24 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=58032, Retrieved Thu, 25 Apr 2024 19:43:24 +0000

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IsPrivate?

No (this computation is public)

User-defined keywords

ETSHWW7(1)

Estimated Impact

116

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

-     [Multiple Regression] [Q1 The Seatbeltlaw] [2007-11-14 19:27:43] [8cd6641b921d30ebe00b648d1481bba0]
- RMPD  [Multiple Regression] [Seatbelt] [2009-11-12 13:54:52] [b98453cac15ba1066b407e146608df68]
-   PD      [Multiple Regression] [Workshop 7: Multi...] [2009-11-20 10:56:15] [af31b947d6acaef3c71f428c4bb503e9] [Current]

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Dataseries X:

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Histogram

Boxplots

1,43	0,51
1,43	0,51
1,43	0,51
1,43	0,51
1,43	0,52
1,43	0,52
1,44	0,52
1,48	0,53
1,48	0,53
1,48	0,52
1,48	0,52
1,48	0,52
1,48	0,52
1,48	0,52
1,48	0,52
1,48	0,52
1,48	0,52
1,48	0,52
1,48	0,52
1,48	0,53
1,48	0,53
1,48	0,53
1,48	0,54
1,48	0,54
1,48	0,54
1,48	0,54
1,48	0,54
1,48	0,54
1,48	0,54
1,48	0,54
1,48	0,54
1,48	0,54
1,48	0,53
1,48	0,53
1,48	0,53
1,48	0,53
1,48	0,53
1,57	0,54
1,58	0,55
1,58	0,55
1,58	0,55
1,58	0,55
1,59	0,55
1,6	0,55
1,6	0,55
1,61	0,55
1,61	0,56
1,61	0,56
1,62	0,56
1,63	0,56
1,63	0,56
1,64	0,55
1,64	0,56
1,64	0,55
1,64	0,55
1,64	0,56
1,65	0,55
1,65	0,55
1,65	0,55
1,65	0,55

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	3 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 & 3 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58032&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]3 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=58032&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58032&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	3 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

Multiple Linear Regression - Estimated Regression Equation
Broodprijs[t] = + 0.864897321038386 + 1.05488984057064Bakmeelprijs[t] + 0.00313560599612924t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Broodprijs[t] =  +  0.864897321038386 +  1.05488984057064Bakmeelprijs[t] +  0.00313560599612924t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58032&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Broodprijs[t] =  +  0.864897321038386 +  1.05488984057064Bakmeelprijs[t] +  0.00313560599612924t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58032&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58032&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
Broodprijs[t] = + 0.864897321038386 + 1.05488984057064Bakmeelprijs[t] + 0.00313560599612924t + 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)	0.864897321038386	0.300891	2.8744	0.00568	0.00284
Bakmeelprijs	1.05488984057064	0.586451	1.7988	0.077351	0.038675
t	0.00313560599612924	0.000508	6.1734	0	0

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Ordinary Least Squares \tabularnewline
Variable & Parameter & S.D. & T-STATH0: parameter = 0 & 2-tail p-value & 1-tail p-value \tabularnewline
(Intercept) & 0.864897321038386 & 0.300891 & 2.8744 & 0.00568 & 0.00284 \tabularnewline
Bakmeelprijs & 1.05488984057064 & 0.586451 & 1.7988 & 0.077351 & 0.038675 \tabularnewline
t & 0.00313560599612924 & 0.000508 & 6.1734 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58032&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]0.864897321038386[/C][C]0.300891[/C][C]2.8744[/C][C]0.00568[/C][C]0.00284[/C][/ROW]
[ROW][C]Bakmeelprijs[/C][C]1.05488984057064[/C][C]0.586451[/C][C]1.7988[/C][C]0.077351[/C][C]0.038675[/C][/ROW]
[ROW][C]t[/C][C]0.00313560599612924[/C][C]0.000508[/C][C]6.1734[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58032&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58032&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)	0.864897321038386	0.300891	2.8744	0.00568	0.00284
Bakmeelprijs	1.05488984057064	0.586451	1.7988	0.077351	0.038675
t	0.00313560599612924	0.000508	6.1734	0	0

Multiple Linear Regression - Regression Statistics
Multiple R	0.925793510774895
R-squared	0.857093624592906
Adjusted R-squared	0.852079365806692
F-TEST (value)	170.931270430117
F-TEST (DF numerator)	2
F-TEST (DF denominator)	57
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.0288897766764698
Sum Squared Residuals	0.0475732941957291

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.925793510774895 \tabularnewline
R-squared & 0.857093624592906 \tabularnewline
Adjusted R-squared & 0.852079365806692 \tabularnewline
F-TEST (value) & 170.931270430117 \tabularnewline
F-TEST (DF numerator) & 2 \tabularnewline
F-TEST (DF denominator) & 57 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.0288897766764698 \tabularnewline
Sum Squared Residuals & 0.0475732941957291 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58032&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.925793510774895[/C][/ROW]
[ROW][C]R-squared[/C][C]0.857093624592906[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.852079365806692[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]170.931270430117[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]2[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]57[/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]0.0288897766764698[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]0.0475732941957291[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58032&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58032&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	0.925793510774895
R-squared	0.857093624592906
Adjusted R-squared	0.852079365806692
F-TEST (value)	170.931270430117
F-TEST (DF numerator)	2
F-TEST (DF denominator)	57
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.0288897766764698
Sum Squared Residuals	0.0475732941957291

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	1.43	1.40602674572555	0.0239732542744507
2	1.43	1.40916235172167	0.0208376482783286
3	1.43	1.4122979577178	0.0177020422821997
4	1.43	1.41543356371393	0.0145664362860704
5	1.43	1.42911806811577	0.000881931884234765
6	1.43	1.43225367411189	-0.00225367411189447
7	1.44	1.43538928010802	0.0046107198919763
8	1.48	1.44907378450986	0.0309262154901407
9	1.48	1.45220939050599	0.0277906094940115
10	1.48	1.44479609809641	0.0352039019035886
11	1.48	1.44793170409254	0.0320682959074594
12	1.48	1.45106731008867	0.0289326899113301
13	1.48	1.4542029160848	0.0257970839152009
14	1.48	1.45733852208093	0.0226614779190717
15	1.48	1.46047412807706	0.0195258719229424
16	1.48	1.46360973407319	0.0163902659268132
17	1.48	1.46674534006932	0.0132546599306839
18	1.48	1.46988094606545	0.0101190539345547
19	1.48	1.47301655206157	0.00698344793842546
20	1.48	1.48670105646341	-0.00670105646341016
21	1.48	1.48983666245954	-0.0098366624595394
22	1.48	1.49297226845567	-0.0129722684556686
23	1.48	1.50665677285750	-0.0266567728575043
24	1.48	1.50979237885363	-0.0297923788536335
25	1.48	1.51292798484976	-0.0329279848497627
26	1.48	1.51606359084589	-0.036063590845892
27	1.48	1.51919919684202	-0.0391991968420212
28	1.48	1.52233480283815	-0.0423348028381505
29	1.48	1.52547040883428	-0.0454704088342797
30	1.48	1.52860601483041	-0.0486060148304089
31	1.48	1.53174162082654	-0.0517416208265382
32	1.48	1.53487722682267	-0.0548772268226674
33	1.48	1.52746393441309	-0.0474639344130903
34	1.48	1.53059954040922	-0.0505995404092195
35	1.48	1.53373514640535	-0.0537351464053487
36	1.48	1.53687075240148	-0.056870752401478
37	1.48	1.54000635839761	-0.0600063583976072
38	1.57	1.55369086279944	0.0163091372005572
39	1.58	1.56737536720128	0.0126246327987216
40	1.58	1.57051097319741	0.00948902680259236
41	1.58	1.57364657919354	0.00635342080646312
42	1.58	1.57678218518967	0.00321781481033388
43	1.59	1.57991779118580	0.0100822088142047
44	1.6	1.58305339718192	0.0169466028180754
45	1.6	1.58618900317805	0.0138109968219462
46	1.61	1.58932460917418	0.0206753908258170
47	1.61	1.60300911357602	0.00699088642398134
48	1.61	1.60614471957215	0.0038552804278521
49	1.62	1.60928032556828	0.0107196744317229
50	1.63	1.61241593156441	0.0175840684355934
51	1.63	1.61555153756054	0.0144484624394642
52	1.64	1.60813824515096	0.0318617548490413
53	1.64	1.62182274955279	0.0181772504472057
54	1.64	1.61440945714322	0.0255905428567828
55	1.64	1.61754506313935	0.0224549368606536
56	1.64	1.63122956754118	0.008770432458818
57	1.65	1.62381627513160	0.0261837248683951
58	1.65	1.62695188112773	0.0230481188722659
59	1.65	1.63008748712386	0.0199125128761366
60	1.65	1.63322309311999	0.0167769068800074

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 1.43 & 1.40602674572555 & 0.0239732542744507 \tabularnewline
2 & 1.43 & 1.40916235172167 & 0.0208376482783286 \tabularnewline
3 & 1.43 & 1.4122979577178 & 0.0177020422821997 \tabularnewline
4 & 1.43 & 1.41543356371393 & 0.0145664362860704 \tabularnewline
5 & 1.43 & 1.42911806811577 & 0.000881931884234765 \tabularnewline
6 & 1.43 & 1.43225367411189 & -0.00225367411189447 \tabularnewline
7 & 1.44 & 1.43538928010802 & 0.0046107198919763 \tabularnewline
8 & 1.48 & 1.44907378450986 & 0.0309262154901407 \tabularnewline
9 & 1.48 & 1.45220939050599 & 0.0277906094940115 \tabularnewline
10 & 1.48 & 1.44479609809641 & 0.0352039019035886 \tabularnewline
11 & 1.48 & 1.44793170409254 & 0.0320682959074594 \tabularnewline
12 & 1.48 & 1.45106731008867 & 0.0289326899113301 \tabularnewline
13 & 1.48 & 1.4542029160848 & 0.0257970839152009 \tabularnewline
14 & 1.48 & 1.45733852208093 & 0.0226614779190717 \tabularnewline
15 & 1.48 & 1.46047412807706 & 0.0195258719229424 \tabularnewline
16 & 1.48 & 1.46360973407319 & 0.0163902659268132 \tabularnewline
17 & 1.48 & 1.46674534006932 & 0.0132546599306839 \tabularnewline
18 & 1.48 & 1.46988094606545 & 0.0101190539345547 \tabularnewline
19 & 1.48 & 1.47301655206157 & 0.00698344793842546 \tabularnewline
20 & 1.48 & 1.48670105646341 & -0.00670105646341016 \tabularnewline
21 & 1.48 & 1.48983666245954 & -0.0098366624595394 \tabularnewline
22 & 1.48 & 1.49297226845567 & -0.0129722684556686 \tabularnewline
23 & 1.48 & 1.50665677285750 & -0.0266567728575043 \tabularnewline
24 & 1.48 & 1.50979237885363 & -0.0297923788536335 \tabularnewline
25 & 1.48 & 1.51292798484976 & -0.0329279848497627 \tabularnewline
26 & 1.48 & 1.51606359084589 & -0.036063590845892 \tabularnewline
27 & 1.48 & 1.51919919684202 & -0.0391991968420212 \tabularnewline
28 & 1.48 & 1.52233480283815 & -0.0423348028381505 \tabularnewline
29 & 1.48 & 1.52547040883428 & -0.0454704088342797 \tabularnewline
30 & 1.48 & 1.52860601483041 & -0.0486060148304089 \tabularnewline
31 & 1.48 & 1.53174162082654 & -0.0517416208265382 \tabularnewline
32 & 1.48 & 1.53487722682267 & -0.0548772268226674 \tabularnewline
33 & 1.48 & 1.52746393441309 & -0.0474639344130903 \tabularnewline
34 & 1.48 & 1.53059954040922 & -0.0505995404092195 \tabularnewline
35 & 1.48 & 1.53373514640535 & -0.0537351464053487 \tabularnewline
36 & 1.48 & 1.53687075240148 & -0.056870752401478 \tabularnewline
37 & 1.48 & 1.54000635839761 & -0.0600063583976072 \tabularnewline
38 & 1.57 & 1.55369086279944 & 0.0163091372005572 \tabularnewline
39 & 1.58 & 1.56737536720128 & 0.0126246327987216 \tabularnewline
40 & 1.58 & 1.57051097319741 & 0.00948902680259236 \tabularnewline
41 & 1.58 & 1.57364657919354 & 0.00635342080646312 \tabularnewline
42 & 1.58 & 1.57678218518967 & 0.00321781481033388 \tabularnewline
43 & 1.59 & 1.57991779118580 & 0.0100822088142047 \tabularnewline
44 & 1.6 & 1.58305339718192 & 0.0169466028180754 \tabularnewline
45 & 1.6 & 1.58618900317805 & 0.0138109968219462 \tabularnewline
46 & 1.61 & 1.58932460917418 & 0.0206753908258170 \tabularnewline
47 & 1.61 & 1.60300911357602 & 0.00699088642398134 \tabularnewline
48 & 1.61 & 1.60614471957215 & 0.0038552804278521 \tabularnewline
49 & 1.62 & 1.60928032556828 & 0.0107196744317229 \tabularnewline
50 & 1.63 & 1.61241593156441 & 0.0175840684355934 \tabularnewline
51 & 1.63 & 1.61555153756054 & 0.0144484624394642 \tabularnewline
52 & 1.64 & 1.60813824515096 & 0.0318617548490413 \tabularnewline
53 & 1.64 & 1.62182274955279 & 0.0181772504472057 \tabularnewline
54 & 1.64 & 1.61440945714322 & 0.0255905428567828 \tabularnewline
55 & 1.64 & 1.61754506313935 & 0.0224549368606536 \tabularnewline
56 & 1.64 & 1.63122956754118 & 0.008770432458818 \tabularnewline
57 & 1.65 & 1.62381627513160 & 0.0261837248683951 \tabularnewline
58 & 1.65 & 1.62695188112773 & 0.0230481188722659 \tabularnewline
59 & 1.65 & 1.63008748712386 & 0.0199125128761366 \tabularnewline
60 & 1.65 & 1.63322309311999 & 0.0167769068800074 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58032&T=4

[TABLE]
[ROW][C]Multiple Linear Regression - Actuals, Interpolation, and Residuals[/C][/ROW]
[ROW][C]Time or Index[/C][C]Actuals[/C][C]InterpolationForecast[/C][C]ResidualsPrediction Error[/C][/ROW]
[ROW][C]1[/C][C]1.43[/C][C]1.40602674572555[/C][C]0.0239732542744507[/C][/ROW]
[ROW][C]2[/C][C]1.43[/C][C]1.40916235172167[/C][C]0.0208376482783286[/C][/ROW]
[ROW][C]3[/C][C]1.43[/C][C]1.4122979577178[/C][C]0.0177020422821997[/C][/ROW]
[ROW][C]4[/C][C]1.43[/C][C]1.41543356371393[/C][C]0.0145664362860704[/C][/ROW]
[ROW][C]5[/C][C]1.43[/C][C]1.42911806811577[/C][C]0.000881931884234765[/C][/ROW]
[ROW][C]6[/C][C]1.43[/C][C]1.43225367411189[/C][C]-0.00225367411189447[/C][/ROW]
[ROW][C]7[/C][C]1.44[/C][C]1.43538928010802[/C][C]0.0046107198919763[/C][/ROW]
[ROW][C]8[/C][C]1.48[/C][C]1.44907378450986[/C][C]0.0309262154901407[/C][/ROW]
[ROW][C]9[/C][C]1.48[/C][C]1.45220939050599[/C][C]0.0277906094940115[/C][/ROW]
[ROW][C]10[/C][C]1.48[/C][C]1.44479609809641[/C][C]0.0352039019035886[/C][/ROW]
[ROW][C]11[/C][C]1.48[/C][C]1.44793170409254[/C][C]0.0320682959074594[/C][/ROW]
[ROW][C]12[/C][C]1.48[/C][C]1.45106731008867[/C][C]0.0289326899113301[/C][/ROW]
[ROW][C]13[/C][C]1.48[/C][C]1.4542029160848[/C][C]0.0257970839152009[/C][/ROW]
[ROW][C]14[/C][C]1.48[/C][C]1.45733852208093[/C][C]0.0226614779190717[/C][/ROW]
[ROW][C]15[/C][C]1.48[/C][C]1.46047412807706[/C][C]0.0195258719229424[/C][/ROW]
[ROW][C]16[/C][C]1.48[/C][C]1.46360973407319[/C][C]0.0163902659268132[/C][/ROW]
[ROW][C]17[/C][C]1.48[/C][C]1.46674534006932[/C][C]0.0132546599306839[/C][/ROW]
[ROW][C]18[/C][C]1.48[/C][C]1.46988094606545[/C][C]0.0101190539345547[/C][/ROW]
[ROW][C]19[/C][C]1.48[/C][C]1.47301655206157[/C][C]0.00698344793842546[/C][/ROW]
[ROW][C]20[/C][C]1.48[/C][C]1.48670105646341[/C][C]-0.00670105646341016[/C][/ROW]
[ROW][C]21[/C][C]1.48[/C][C]1.48983666245954[/C][C]-0.0098366624595394[/C][/ROW]
[ROW][C]22[/C][C]1.48[/C][C]1.49297226845567[/C][C]-0.0129722684556686[/C][/ROW]
[ROW][C]23[/C][C]1.48[/C][C]1.50665677285750[/C][C]-0.0266567728575043[/C][/ROW]
[ROW][C]24[/C][C]1.48[/C][C]1.50979237885363[/C][C]-0.0297923788536335[/C][/ROW]
[ROW][C]25[/C][C]1.48[/C][C]1.51292798484976[/C][C]-0.0329279848497627[/C][/ROW]
[ROW][C]26[/C][C]1.48[/C][C]1.51606359084589[/C][C]-0.036063590845892[/C][/ROW]
[ROW][C]27[/C][C]1.48[/C][C]1.51919919684202[/C][C]-0.0391991968420212[/C][/ROW]
[ROW][C]28[/C][C]1.48[/C][C]1.52233480283815[/C][C]-0.0423348028381505[/C][/ROW]
[ROW][C]29[/C][C]1.48[/C][C]1.52547040883428[/C][C]-0.0454704088342797[/C][/ROW]
[ROW][C]30[/C][C]1.48[/C][C]1.52860601483041[/C][C]-0.0486060148304089[/C][/ROW]
[ROW][C]31[/C][C]1.48[/C][C]1.53174162082654[/C][C]-0.0517416208265382[/C][/ROW]
[ROW][C]32[/C][C]1.48[/C][C]1.53487722682267[/C][C]-0.0548772268226674[/C][/ROW]
[ROW][C]33[/C][C]1.48[/C][C]1.52746393441309[/C][C]-0.0474639344130903[/C][/ROW]
[ROW][C]34[/C][C]1.48[/C][C]1.53059954040922[/C][C]-0.0505995404092195[/C][/ROW]
[ROW][C]35[/C][C]1.48[/C][C]1.53373514640535[/C][C]-0.0537351464053487[/C][/ROW]
[ROW][C]36[/C][C]1.48[/C][C]1.53687075240148[/C][C]-0.056870752401478[/C][/ROW]
[ROW][C]37[/C][C]1.48[/C][C]1.54000635839761[/C][C]-0.0600063583976072[/C][/ROW]
[ROW][C]38[/C][C]1.57[/C][C]1.55369086279944[/C][C]0.0163091372005572[/C][/ROW]
[ROW][C]39[/C][C]1.58[/C][C]1.56737536720128[/C][C]0.0126246327987216[/C][/ROW]
[ROW][C]40[/C][C]1.58[/C][C]1.57051097319741[/C][C]0.00948902680259236[/C][/ROW]
[ROW][C]41[/C][C]1.58[/C][C]1.57364657919354[/C][C]0.00635342080646312[/C][/ROW]
[ROW][C]42[/C][C]1.58[/C][C]1.57678218518967[/C][C]0.00321781481033388[/C][/ROW]
[ROW][C]43[/C][C]1.59[/C][C]1.57991779118580[/C][C]0.0100822088142047[/C][/ROW]
[ROW][C]44[/C][C]1.6[/C][C]1.58305339718192[/C][C]0.0169466028180754[/C][/ROW]
[ROW][C]45[/C][C]1.6[/C][C]1.58618900317805[/C][C]0.0138109968219462[/C][/ROW]
[ROW][C]46[/C][C]1.61[/C][C]1.58932460917418[/C][C]0.0206753908258170[/C][/ROW]
[ROW][C]47[/C][C]1.61[/C][C]1.60300911357602[/C][C]0.00699088642398134[/C][/ROW]
[ROW][C]48[/C][C]1.61[/C][C]1.60614471957215[/C][C]0.0038552804278521[/C][/ROW]
[ROW][C]49[/C][C]1.62[/C][C]1.60928032556828[/C][C]0.0107196744317229[/C][/ROW]
[ROW][C]50[/C][C]1.63[/C][C]1.61241593156441[/C][C]0.0175840684355934[/C][/ROW]
[ROW][C]51[/C][C]1.63[/C][C]1.61555153756054[/C][C]0.0144484624394642[/C][/ROW]
[ROW][C]52[/C][C]1.64[/C][C]1.60813824515096[/C][C]0.0318617548490413[/C][/ROW]
[ROW][C]53[/C][C]1.64[/C][C]1.62182274955279[/C][C]0.0181772504472057[/C][/ROW]
[ROW][C]54[/C][C]1.64[/C][C]1.61440945714322[/C][C]0.0255905428567828[/C][/ROW]
[ROW][C]55[/C][C]1.64[/C][C]1.61754506313935[/C][C]0.0224549368606536[/C][/ROW]
[ROW][C]56[/C][C]1.64[/C][C]1.63122956754118[/C][C]0.008770432458818[/C][/ROW]
[ROW][C]57[/C][C]1.65[/C][C]1.62381627513160[/C][C]0.0261837248683951[/C][/ROW]
[ROW][C]58[/C][C]1.65[/C][C]1.62695188112773[/C][C]0.0230481188722659[/C][/ROW]
[ROW][C]59[/C][C]1.65[/C][C]1.63008748712386[/C][C]0.0199125128761366[/C][/ROW]
[ROW][C]60[/C][C]1.65[/C][C]1.63322309311999[/C][C]0.0167769068800074[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58032&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58032&T=4

As an alternative you can also use a QR Code:

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	1.43	1.40602674572555	0.0239732542744507
2	1.43	1.40916235172167	0.0208376482783286
3	1.43	1.4122979577178	0.0177020422821997
4	1.43	1.41543356371393	0.0145664362860704
5	1.43	1.42911806811577	0.000881931884234765
6	1.43	1.43225367411189	-0.00225367411189447
7	1.44	1.43538928010802	0.0046107198919763
8	1.48	1.44907378450986	0.0309262154901407
9	1.48	1.45220939050599	0.0277906094940115
10	1.48	1.44479609809641	0.0352039019035886
11	1.48	1.44793170409254	0.0320682959074594
12	1.48	1.45106731008867	0.0289326899113301
13	1.48	1.4542029160848	0.0257970839152009
14	1.48	1.45733852208093	0.0226614779190717
15	1.48	1.46047412807706	0.0195258719229424
16	1.48	1.46360973407319	0.0163902659268132
17	1.48	1.46674534006932	0.0132546599306839
18	1.48	1.46988094606545	0.0101190539345547
19	1.48	1.47301655206157	0.00698344793842546
20	1.48	1.48670105646341	-0.00670105646341016
21	1.48	1.48983666245954	-0.0098366624595394
22	1.48	1.49297226845567	-0.0129722684556686
23	1.48	1.50665677285750	-0.0266567728575043
24	1.48	1.50979237885363	-0.0297923788536335
25	1.48	1.51292798484976	-0.0329279848497627
26	1.48	1.51606359084589	-0.036063590845892
27	1.48	1.51919919684202	-0.0391991968420212
28	1.48	1.52233480283815	-0.0423348028381505
29	1.48	1.52547040883428	-0.0454704088342797
30	1.48	1.52860601483041	-0.0486060148304089
31	1.48	1.53174162082654	-0.0517416208265382
32	1.48	1.53487722682267	-0.0548772268226674
33	1.48	1.52746393441309	-0.0474639344130903
34	1.48	1.53059954040922	-0.0505995404092195
35	1.48	1.53373514640535	-0.0537351464053487
36	1.48	1.53687075240148	-0.056870752401478
37	1.48	1.54000635839761	-0.0600063583976072
38	1.57	1.55369086279944	0.0163091372005572
39	1.58	1.56737536720128	0.0126246327987216
40	1.58	1.57051097319741	0.00948902680259236
41	1.58	1.57364657919354	0.00635342080646312
42	1.58	1.57678218518967	0.00321781481033388
43	1.59	1.57991779118580	0.0100822088142047
44	1.6	1.58305339718192	0.0169466028180754
45	1.6	1.58618900317805	0.0138109968219462
46	1.61	1.58932460917418	0.0206753908258170
47	1.61	1.60300911357602	0.00699088642398134
48	1.61	1.60614471957215	0.0038552804278521
49	1.62	1.60928032556828	0.0107196744317229
50	1.63	1.61241593156441	0.0175840684355934
51	1.63	1.61555153756054	0.0144484624394642
52	1.64	1.60813824515096	0.0318617548490413
53	1.64	1.62182274955279	0.0181772504472057
54	1.64	1.61440945714322	0.0255905428567828
55	1.64	1.61754506313935	0.0224549368606536
56	1.64	1.63122956754118	0.008770432458818
57	1.65	1.62381627513160	0.0261837248683951
58	1.65	1.62695188112773	0.0230481188722659
59	1.65	1.63008748712386	0.0199125128761366
60	1.65	1.63322309311999	0.0167769068800074

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
6	3.25334993633869e-43	6.50669987267737e-43	1
7	0.000425956702821977	0.000851913405643954	0.999574043297178
8	0.0278302241393856	0.0556604482787712	0.972169775860614
9	0.0190924313193106	0.0381848626386212	0.98090756868069
10	0.0249896512408090	0.0499793024816179	0.975010348759191
11	0.0135815303389378	0.0271630606778756	0.986418469661062
12	0.00723541537675337	0.0144708307535067	0.992764584623247
13	0.00425414365840690	0.00850828731681379	0.995745856341593
14	0.00289348064355410	0.00578696128710819	0.997106519356446
15	0.0023341891101826	0.0046683782203652	0.997665810889817
16	0.00228662463667483	0.00457324927334966	0.997713375363325
17	0.00283342635475085	0.0056668527095017	0.99716657364525
18	0.00483582020175803	0.00967164040351606	0.995164179798242
19	0.0137155385609565	0.027431077121913	0.986284461439044
20	0.0541010147206224	0.108202029441245	0.945898985279378
21	0.141398146712376	0.282796293424752	0.858601853287624
22	0.316334252659234	0.632668505318468	0.683665747340766
23	0.411894412483112	0.823788824966224	0.588105587516888
24	0.445237560409165	0.89047512081833	0.554762439590835
25	0.448441486904224	0.896882973808449	0.551558513095776
26	0.433266150978375	0.86653230195675	0.566733849021625
27	0.406642075520650	0.813284151041299	0.59335792447935
28	0.375062908760712	0.750125817521423	0.624937091239288
29	0.346038674725471	0.692077349450941	0.653961325274529
30	0.328963306286164	0.657926612572327	0.671036693713836
31	0.337681193956894	0.675362387913787	0.662318806043106
32	0.399572723745708	0.799145447491415	0.600427276254292
33	0.360618827627787	0.721237655255573	0.639381172372213
34	0.331233509551164	0.662467019102328	0.668766490448836
35	0.342540568405555	0.685081136811109	0.657459431594446
36	0.504438967038685	0.99112206592263	0.495561032961315
37	0.999981999858044	3.6000283911358e-05	1.8000141955679e-05
38	0.999999482860813	1.03427837411307e-06	5.17139187056537e-07
39	0.999999879249345	2.41501309885836e-07	1.20750654942918e-07
40	0.999999885475569	2.29048861906836e-07	1.14524430953418e-07
41	0.999999859437624	2.81124752655335e-07	1.40562376327668e-07
42	0.999999937496537	1.25006925936110e-07	6.25034629680549e-08
43	0.99999992780395	1.44392098260608e-07	7.21960491303039e-08
44	0.99999981323112	3.73537761345183e-07	1.86768880672591e-07
45	0.99999976155545	4.76889098146584e-07	2.38444549073292e-07
46	0.999999383808759	1.23238248281469e-06	6.16191241407344e-07
47	0.999998586362513	2.82727497441699e-06	1.41363748720849e-06
48	0.999999801403076	3.97193847041453e-07	1.98596923520727e-07
49	0.999999897809436	2.04381128806766e-07	1.02190564403383e-07
50	0.999999054194992	1.89161001556534e-06	9.45805007782672e-07
51	0.999995677448863	8.64510227360306e-06	4.32255113680153e-06
52	0.999966060781594	6.78784368113524e-05	3.39392184056762e-05
53	0.99985925144494	0.000281497110119654	0.000140748555059827
54	0.998472078233791	0.00305584353241759	0.00152792176620880

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
6 & 3.25334993633869e-43 & 6.50669987267737e-43 & 1 \tabularnewline
7 & 0.000425956702821977 & 0.000851913405643954 & 0.999574043297178 \tabularnewline
8 & 0.0278302241393856 & 0.0556604482787712 & 0.972169775860614 \tabularnewline
9 & 0.0190924313193106 & 0.0381848626386212 & 0.98090756868069 \tabularnewline
10 & 0.0249896512408090 & 0.0499793024816179 & 0.975010348759191 \tabularnewline
11 & 0.0135815303389378 & 0.0271630606778756 & 0.986418469661062 \tabularnewline
12 & 0.00723541537675337 & 0.0144708307535067 & 0.992764584623247 \tabularnewline
13 & 0.00425414365840690 & 0.00850828731681379 & 0.995745856341593 \tabularnewline
14 & 0.00289348064355410 & 0.00578696128710819 & 0.997106519356446 \tabularnewline
15 & 0.0023341891101826 & 0.0046683782203652 & 0.997665810889817 \tabularnewline
16 & 0.00228662463667483 & 0.00457324927334966 & 0.997713375363325 \tabularnewline
17 & 0.00283342635475085 & 0.0056668527095017 & 0.99716657364525 \tabularnewline
18 & 0.00483582020175803 & 0.00967164040351606 & 0.995164179798242 \tabularnewline
19 & 0.0137155385609565 & 0.027431077121913 & 0.986284461439044 \tabularnewline
20 & 0.0541010147206224 & 0.108202029441245 & 0.945898985279378 \tabularnewline
21 & 0.141398146712376 & 0.282796293424752 & 0.858601853287624 \tabularnewline
22 & 0.316334252659234 & 0.632668505318468 & 0.683665747340766 \tabularnewline
23 & 0.411894412483112 & 0.823788824966224 & 0.588105587516888 \tabularnewline
24 & 0.445237560409165 & 0.89047512081833 & 0.554762439590835 \tabularnewline
25 & 0.448441486904224 & 0.896882973808449 & 0.551558513095776 \tabularnewline
26 & 0.433266150978375 & 0.86653230195675 & 0.566733849021625 \tabularnewline
27 & 0.406642075520650 & 0.813284151041299 & 0.59335792447935 \tabularnewline
28 & 0.375062908760712 & 0.750125817521423 & 0.624937091239288 \tabularnewline
29 & 0.346038674725471 & 0.692077349450941 & 0.653961325274529 \tabularnewline
30 & 0.328963306286164 & 0.657926612572327 & 0.671036693713836 \tabularnewline
31 & 0.337681193956894 & 0.675362387913787 & 0.662318806043106 \tabularnewline
32 & 0.399572723745708 & 0.799145447491415 & 0.600427276254292 \tabularnewline
33 & 0.360618827627787 & 0.721237655255573 & 0.639381172372213 \tabularnewline
34 & 0.331233509551164 & 0.662467019102328 & 0.668766490448836 \tabularnewline
35 & 0.342540568405555 & 0.685081136811109 & 0.657459431594446 \tabularnewline
36 & 0.504438967038685 & 0.99112206592263 & 0.495561032961315 \tabularnewline
37 & 0.999981999858044 & 3.6000283911358e-05 & 1.8000141955679e-05 \tabularnewline
38 & 0.999999482860813 & 1.03427837411307e-06 & 5.17139187056537e-07 \tabularnewline
39 & 0.999999879249345 & 2.41501309885836e-07 & 1.20750654942918e-07 \tabularnewline
40 & 0.999999885475569 & 2.29048861906836e-07 & 1.14524430953418e-07 \tabularnewline
41 & 0.999999859437624 & 2.81124752655335e-07 & 1.40562376327668e-07 \tabularnewline
42 & 0.999999937496537 & 1.25006925936110e-07 & 6.25034629680549e-08 \tabularnewline
43 & 0.99999992780395 & 1.44392098260608e-07 & 7.21960491303039e-08 \tabularnewline
44 & 0.99999981323112 & 3.73537761345183e-07 & 1.86768880672591e-07 \tabularnewline
45 & 0.99999976155545 & 4.76889098146584e-07 & 2.38444549073292e-07 \tabularnewline
46 & 0.999999383808759 & 1.23238248281469e-06 & 6.16191241407344e-07 \tabularnewline
47 & 0.999998586362513 & 2.82727497441699e-06 & 1.41363748720849e-06 \tabularnewline
48 & 0.999999801403076 & 3.97193847041453e-07 & 1.98596923520727e-07 \tabularnewline
49 & 0.999999897809436 & 2.04381128806766e-07 & 1.02190564403383e-07 \tabularnewline
50 & 0.999999054194992 & 1.89161001556534e-06 & 9.45805007782672e-07 \tabularnewline
51 & 0.999995677448863 & 8.64510227360306e-06 & 4.32255113680153e-06 \tabularnewline
52 & 0.999966060781594 & 6.78784368113524e-05 & 3.39392184056762e-05 \tabularnewline
53 & 0.99985925144494 & 0.000281497110119654 & 0.000140748555059827 \tabularnewline
54 & 0.998472078233791 & 0.00305584353241759 & 0.00152792176620880 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58032&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]6[/C][C]3.25334993633869e-43[/C][C]6.50669987267737e-43[/C][C]1[/C][/ROW]
[ROW][C]7[/C][C]0.000425956702821977[/C][C]0.000851913405643954[/C][C]0.999574043297178[/C][/ROW]
[ROW][C]8[/C][C]0.0278302241393856[/C][C]0.0556604482787712[/C][C]0.972169775860614[/C][/ROW]
[ROW][C]9[/C][C]0.0190924313193106[/C][C]0.0381848626386212[/C][C]0.98090756868069[/C][/ROW]
[ROW][C]10[/C][C]0.0249896512408090[/C][C]0.0499793024816179[/C][C]0.975010348759191[/C][/ROW]
[ROW][C]11[/C][C]0.0135815303389378[/C][C]0.0271630606778756[/C][C]0.986418469661062[/C][/ROW]
[ROW][C]12[/C][C]0.00723541537675337[/C][C]0.0144708307535067[/C][C]0.992764584623247[/C][/ROW]
[ROW][C]13[/C][C]0.00425414365840690[/C][C]0.00850828731681379[/C][C]0.995745856341593[/C][/ROW]
[ROW][C]14[/C][C]0.00289348064355410[/C][C]0.00578696128710819[/C][C]0.997106519356446[/C][/ROW]
[ROW][C]15[/C][C]0.0023341891101826[/C][C]0.0046683782203652[/C][C]0.997665810889817[/C][/ROW]
[ROW][C]16[/C][C]0.00228662463667483[/C][C]0.00457324927334966[/C][C]0.997713375363325[/C][/ROW]
[ROW][C]17[/C][C]0.00283342635475085[/C][C]0.0056668527095017[/C][C]0.99716657364525[/C][/ROW]
[ROW][C]18[/C][C]0.00483582020175803[/C][C]0.00967164040351606[/C][C]0.995164179798242[/C][/ROW]
[ROW][C]19[/C][C]0.0137155385609565[/C][C]0.027431077121913[/C][C]0.986284461439044[/C][/ROW]
[ROW][C]20[/C][C]0.0541010147206224[/C][C]0.108202029441245[/C][C]0.945898985279378[/C][/ROW]
[ROW][C]21[/C][C]0.141398146712376[/C][C]0.282796293424752[/C][C]0.858601853287624[/C][/ROW]
[ROW][C]22[/C][C]0.316334252659234[/C][C]0.632668505318468[/C][C]0.683665747340766[/C][/ROW]
[ROW][C]23[/C][C]0.411894412483112[/C][C]0.823788824966224[/C][C]0.588105587516888[/C][/ROW]
[ROW][C]24[/C][C]0.445237560409165[/C][C]0.89047512081833[/C][C]0.554762439590835[/C][/ROW]
[ROW][C]25[/C][C]0.448441486904224[/C][C]0.896882973808449[/C][C]0.551558513095776[/C][/ROW]
[ROW][C]26[/C][C]0.433266150978375[/C][C]0.86653230195675[/C][C]0.566733849021625[/C][/ROW]
[ROW][C]27[/C][C]0.406642075520650[/C][C]0.813284151041299[/C][C]0.59335792447935[/C][/ROW]
[ROW][C]28[/C][C]0.375062908760712[/C][C]0.750125817521423[/C][C]0.624937091239288[/C][/ROW]
[ROW][C]29[/C][C]0.346038674725471[/C][C]0.692077349450941[/C][C]0.653961325274529[/C][/ROW]
[ROW][C]30[/C][C]0.328963306286164[/C][C]0.657926612572327[/C][C]0.671036693713836[/C][/ROW]
[ROW][C]31[/C][C]0.337681193956894[/C][C]0.675362387913787[/C][C]0.662318806043106[/C][/ROW]
[ROW][C]32[/C][C]0.399572723745708[/C][C]0.799145447491415[/C][C]0.600427276254292[/C][/ROW]
[ROW][C]33[/C][C]0.360618827627787[/C][C]0.721237655255573[/C][C]0.639381172372213[/C][/ROW]
[ROW][C]34[/C][C]0.331233509551164[/C][C]0.662467019102328[/C][C]0.668766490448836[/C][/ROW]
[ROW][C]35[/C][C]0.342540568405555[/C][C]0.685081136811109[/C][C]0.657459431594446[/C][/ROW]
[ROW][C]36[/C][C]0.504438967038685[/C][C]0.99112206592263[/C][C]0.495561032961315[/C][/ROW]
[ROW][C]37[/C][C]0.999981999858044[/C][C]3.6000283911358e-05[/C][C]1.8000141955679e-05[/C][/ROW]
[ROW][C]38[/C][C]0.999999482860813[/C][C]1.03427837411307e-06[/C][C]5.17139187056537e-07[/C][/ROW]
[ROW][C]39[/C][C]0.999999879249345[/C][C]2.41501309885836e-07[/C][C]1.20750654942918e-07[/C][/ROW]
[ROW][C]40[/C][C]0.999999885475569[/C][C]2.29048861906836e-07[/C][C]1.14524430953418e-07[/C][/ROW]
[ROW][C]41[/C][C]0.999999859437624[/C][C]2.81124752655335e-07[/C][C]1.40562376327668e-07[/C][/ROW]
[ROW][C]42[/C][C]0.999999937496537[/C][C]1.25006925936110e-07[/C][C]6.25034629680549e-08[/C][/ROW]
[ROW][C]43[/C][C]0.99999992780395[/C][C]1.44392098260608e-07[/C][C]7.21960491303039e-08[/C][/ROW]
[ROW][C]44[/C][C]0.99999981323112[/C][C]3.73537761345183e-07[/C][C]1.86768880672591e-07[/C][/ROW]
[ROW][C]45[/C][C]0.99999976155545[/C][C]4.76889098146584e-07[/C][C]2.38444549073292e-07[/C][/ROW]
[ROW][C]46[/C][C]0.999999383808759[/C][C]1.23238248281469e-06[/C][C]6.16191241407344e-07[/C][/ROW]
[ROW][C]47[/C][C]0.999998586362513[/C][C]2.82727497441699e-06[/C][C]1.41363748720849e-06[/C][/ROW]
[ROW][C]48[/C][C]0.999999801403076[/C][C]3.97193847041453e-07[/C][C]1.98596923520727e-07[/C][/ROW]
[ROW][C]49[/C][C]0.999999897809436[/C][C]2.04381128806766e-07[/C][C]1.02190564403383e-07[/C][/ROW]
[ROW][C]50[/C][C]0.999999054194992[/C][C]1.89161001556534e-06[/C][C]9.45805007782672e-07[/C][/ROW]
[ROW][C]51[/C][C]0.999995677448863[/C][C]8.64510227360306e-06[/C][C]4.32255113680153e-06[/C][/ROW]
[ROW][C]52[/C][C]0.999966060781594[/C][C]6.78784368113524e-05[/C][C]3.39392184056762e-05[/C][/ROW]
[ROW][C]53[/C][C]0.99985925144494[/C][C]0.000281497110119654[/C][C]0.000140748555059827[/C][/ROW]
[ROW][C]54[/C][C]0.998472078233791[/C][C]0.00305584353241759[/C][C]0.00152792176620880[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58032&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58032&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
6	3.25334993633869e-43	6.50669987267737e-43	1
7	0.000425956702821977	0.000851913405643954	0.999574043297178
8	0.0278302241393856	0.0556604482787712	0.972169775860614
9	0.0190924313193106	0.0381848626386212	0.98090756868069
10	0.0249896512408090	0.0499793024816179	0.975010348759191
11	0.0135815303389378	0.0271630606778756	0.986418469661062
12	0.00723541537675337	0.0144708307535067	0.992764584623247
13	0.00425414365840690	0.00850828731681379	0.995745856341593
14	0.00289348064355410	0.00578696128710819	0.997106519356446
15	0.0023341891101826	0.0046683782203652	0.997665810889817
16	0.00228662463667483	0.00457324927334966	0.997713375363325
17	0.00283342635475085	0.0056668527095017	0.99716657364525
18	0.00483582020175803	0.00967164040351606	0.995164179798242
19	0.0137155385609565	0.027431077121913	0.986284461439044
20	0.0541010147206224	0.108202029441245	0.945898985279378
21	0.141398146712376	0.282796293424752	0.858601853287624
22	0.316334252659234	0.632668505318468	0.683665747340766
23	0.411894412483112	0.823788824966224	0.588105587516888
24	0.445237560409165	0.89047512081833	0.554762439590835
25	0.448441486904224	0.896882973808449	0.551558513095776
26	0.433266150978375	0.86653230195675	0.566733849021625
27	0.406642075520650	0.813284151041299	0.59335792447935
28	0.375062908760712	0.750125817521423	0.624937091239288
29	0.346038674725471	0.692077349450941	0.653961325274529
30	0.328963306286164	0.657926612572327	0.671036693713836
31	0.337681193956894	0.675362387913787	0.662318806043106
32	0.399572723745708	0.799145447491415	0.600427276254292
33	0.360618827627787	0.721237655255573	0.639381172372213
34	0.331233509551164	0.662467019102328	0.668766490448836
35	0.342540568405555	0.685081136811109	0.657459431594446
36	0.504438967038685	0.99112206592263	0.495561032961315
37	0.999981999858044	3.6000283911358e-05	1.8000141955679e-05
38	0.999999482860813	1.03427837411307e-06	5.17139187056537e-07
39	0.999999879249345	2.41501309885836e-07	1.20750654942918e-07
40	0.999999885475569	2.29048861906836e-07	1.14524430953418e-07
41	0.999999859437624	2.81124752655335e-07	1.40562376327668e-07
42	0.999999937496537	1.25006925936110e-07	6.25034629680549e-08
43	0.99999992780395	1.44392098260608e-07	7.21960491303039e-08
44	0.99999981323112	3.73537761345183e-07	1.86768880672591e-07
45	0.99999976155545	4.76889098146584e-07	2.38444549073292e-07
46	0.999999383808759	1.23238248281469e-06	6.16191241407344e-07
47	0.999998586362513	2.82727497441699e-06	1.41363748720849e-06
48	0.999999801403076	3.97193847041453e-07	1.98596923520727e-07
49	0.999999897809436	2.04381128806766e-07	1.02190564403383e-07
50	0.999999054194992	1.89161001556534e-06	9.45805007782672e-07
51	0.999995677448863	8.64510227360306e-06	4.32255113680153e-06
52	0.999966060781594	6.78784368113524e-05	3.39392184056762e-05
53	0.99985925144494	0.000281497110119654	0.000140748555059827
54	0.998472078233791	0.00305584353241759	0.00152792176620880

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	26	0.530612244897959	NOK
5% type I error level	31	0.63265306122449	NOK
10% type I error level	32	0.653061224489796	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 & 26 & 0.530612244897959 & NOK \tabularnewline
5% type I error level & 31 & 0.63265306122449 & NOK \tabularnewline
10% type I error level & 32 & 0.653061224489796 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58032&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]26[/C][C]0.530612244897959[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]31[/C][C]0.63265306122449[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]32[/C][C]0.653061224489796[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58032&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58032&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	26	0.530612244897959	NOK
5% type I error level	31	0.63265306122449	NOK
10% type I error level	32	0.653061224489796	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