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Author

*The author of this computation has been verified*

R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Thu, 19 Nov 2009 13:32:12 -0700

Cite this page as follows

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2009/Nov/19/t1258662899a6dlsjc9dugmhjf.htm/, Retrieved Sat, 20 Apr 2024 10:08:19 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=57939, Retrieved Sat, 20 Apr 2024 10:08:19 +0000

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Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

173

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]
- RM D  [Multiple Regression] [Seatbelt] [2009-11-12 14:10:54] [b98453cac15ba1066b407e146608df68]
F R PD      [Multiple Regression] [Model 1] [2009-11-19 20:32:12] [d5837f25ec8937f9733a894c487f865c] [Current]
F    D        [Multiple Regression] [Model 2] [2009-11-19 20:38:54] [c0117c881d5fcd069841276db0c34efe] 
-               [Multiple Regression] [Model 3] [2009-11-19 20:41:09] [c0117c881d5fcd069841276db0c34efe] 
-    D            [Multiple Regression] [Model 4] [2009-11-19 20:47:35] [c0117c881d5fcd069841276db0c34efe] 
-   PD              [Multiple Regression] [Model 5] [2009-11-20 15:42:18] [c0117c881d5fcd069841276db0c34efe] 

Feedback Forum

2009-11-21 08:22:34 [] [reply] 
de adjusted R²square is 63%, wat betekent dat dit model 63% verklaart van de spreiding van de variabele, maw 63% wordt verklaard door de verandering van de rentevoet.

Post a new message

Dataseries X:

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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=57939&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=57939&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57939&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
Tprod[t] = + 104.057458254309 -0.954366625790823Rente[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Tprod[t] =  +  104.057458254309 -0.954366625790823Rente[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57939&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Tprod[t] =  +  104.057458254309 -0.954366625790823Rente[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57939&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57939&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
Tprod[t] = + 104.057458254309 -0.954366625790823Rente[t] + e[t]

Multiple Linear Regression - Ordinary Least Squares
Variable	Parameter	S.D.	T-STATH0: parameter = 0	2-tail p-value	1-tail p-value
(Intercept)	104.057458254309	0.308823	336.9486	0	0
Rente	-0.954366625790823	0.093353	-10.2232	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) & 104.057458254309 & 0.308823 & 336.9486 & 0 & 0 \tabularnewline
Rente & -0.954366625790823 & 0.093353 & -10.2232 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57939&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]104.057458254309[/C][C]0.308823[/C][C]336.9486[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Rente[/C][C]-0.954366625790823[/C][C]0.093353[/C][C]-10.2232[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57939&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57939&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)	104.057458254309	0.308823	336.9486	0	0
Rente	-0.954366625790823	0.093353	-10.2232	0	0

Multiple Linear Regression - Regression Statistics
Multiple R	0.801939374471285
R-squared	0.643106760327396
Adjusted R-squared	0.636953428608903
F-TEST (value)	104.513585444225
F-TEST (DF numerator)	1
F-TEST (DF denominator)	58
p-value	1.35447209004269e-14
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.715892288571204
Sum Squared Residuals	29.7251025924716

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.801939374471285 \tabularnewline
R-squared & 0.643106760327396 \tabularnewline
Adjusted R-squared & 0.636953428608903 \tabularnewline
F-TEST (value) & 104.513585444225 \tabularnewline
F-TEST (DF numerator) & 1 \tabularnewline
F-TEST (DF denominator) & 58 \tabularnewline
p-value & 1.35447209004269e-14 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.715892288571204 \tabularnewline
Sum Squared Residuals & 29.7251025924716 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57939&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.801939374471285[/C][/ROW]
[ROW][C]R-squared[/C][C]0.643106760327396[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.636953428608903[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]104.513585444225[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]1[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]58[/C][/ROW]
[ROW][C]p-value[/C][C]1.35447209004269e-14[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.715892288571204[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]29.7251025924716[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57939&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57939&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	0.801939374471285
R-squared	0.643106760327396
Adjusted R-squared	0.636953428608903
F-TEST (value)	104.513585444225
F-TEST (DF numerator)	1
F-TEST (DF denominator)	58
p-value	1.35447209004269e-14
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.715892288571204
Sum Squared Residuals	29.7251025924716

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	101.2	101.194358376937	0.00564162306336613
2	101.1	100.993941385520	0.106058614479800
3	100.7	100.841242725394	-0.14124272539365
4	100.1	100.707631397783	-0.607631397782944
5	99.9	100.478583407593	-0.578583407593135
6	99.7	100.135011422308	-0.435011422308442
7	99.5	100.001400094698	-0.50140009469773
8	99.2	100.001400094698	-0.801400094697727
9	99	99.76280843825	-0.762808438250024
10	99	99.5719351130919	-0.571935113091859
11	99.3	99.5242167818023	-0.224216781802321
12	99.5	99.5242167818023	-0.0242167818023186
13	99.7	99.5242167818023	0.175783218197684
14	100	99.5242167818023	0.475783218197681
15	100.4	99.5242167818023	0.875783218197687
16	100.6	99.5242167818023	1.07578321819768
17	100.7	99.6864591081868	1.01354089181324
18	100.7	99.76280843825	0.937191561749979
19	100.6	99.76280843825	0.83719156174997
20	100.5	99.772352104508	0.727647895492068
21	100.6	100.211360752372	0.388639247628284
22	100.5	100.478583407593	0.0214165924068594
23	100.4	100.822155392878	-0.422155392877831
24	100.3	100.955766720489	-0.655766720488555
25	100.4	100.955766720489	-0.555766720488546
26	100.4	100.955766720489	-0.555766720488546
27	100.4	100.955766720489	-0.555766720488546
28	100.4	100.955766720489	-0.555766720488546
29	100.4	100.955766720489	-0.555766720488546
30	100.5	100.955766720489	-0.455766720488552
31	100.6	100.955766720489	-0.355766720488557
32	100.6	100.955766720489	-0.355766720488557
33	100.5	100.955766720489	-0.455766720488552
34	100.5	100.955766720489	-0.455766720488552
35	100.7	100.955766720489	-0.255766720488549
36	101.1	101.337513370805	-0.237513370804886
37	101.5	101.432950033384	0.0670499666160373
38	101.9	101.432950033384	0.467049966616043
39	102.1	101.623823358542	0.476176641457867
40	102.1	101.671541689832	0.428458310168326
41	102.1	101.671541689832	0.428458310168326
42	102.4	102.053288340148	0.346711659852009
43	102.8	102.148725002727	0.651274997272918
44	103.1	102.148725002727	0.951274997272915
45	103.1	102.148725002727	0.951274997272915
46	102.9	102.148725002727	0.751274997272926
47	102.4	102.148725002727	0.251274997272926
48	101.9	102.148725002727	-0.248725002727074
49	101.3	102.148725002727	-0.848725002727082
50	100.7	102.148725002727	-1.44872500272708
51	100.6	102.148725002727	-1.54872500272709
52	101	102.148725002727	-1.14872500272708
53	101.5	102.148725002727	-0.648725002727079
54	101.9	102.148725002727	-0.248725002727074
55	102.1	102.148725002727	-0.048725002727085
56	102.3	102.148725002727	0.151274997272918
57	102.5	102.148725002727	0.351274997272921
58	102.9	102.148725002727	0.751274997272926
59	103.6	102.148725002727	1.45127499727291
60	104.3	102.148725002727	2.15127499727292

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 101.2 & 101.194358376937 & 0.00564162306336613 \tabularnewline
2 & 101.1 & 100.993941385520 & 0.106058614479800 \tabularnewline
3 & 100.7 & 100.841242725394 & -0.14124272539365 \tabularnewline
4 & 100.1 & 100.707631397783 & -0.607631397782944 \tabularnewline
5 & 99.9 & 100.478583407593 & -0.578583407593135 \tabularnewline
6 & 99.7 & 100.135011422308 & -0.435011422308442 \tabularnewline
7 & 99.5 & 100.001400094698 & -0.50140009469773 \tabularnewline
8 & 99.2 & 100.001400094698 & -0.801400094697727 \tabularnewline
9 & 99 & 99.76280843825 & -0.762808438250024 \tabularnewline
10 & 99 & 99.5719351130919 & -0.571935113091859 \tabularnewline
11 & 99.3 & 99.5242167818023 & -0.224216781802321 \tabularnewline
12 & 99.5 & 99.5242167818023 & -0.0242167818023186 \tabularnewline
13 & 99.7 & 99.5242167818023 & 0.175783218197684 \tabularnewline
14 & 100 & 99.5242167818023 & 0.475783218197681 \tabularnewline
15 & 100.4 & 99.5242167818023 & 0.875783218197687 \tabularnewline
16 & 100.6 & 99.5242167818023 & 1.07578321819768 \tabularnewline
17 & 100.7 & 99.6864591081868 & 1.01354089181324 \tabularnewline
18 & 100.7 & 99.76280843825 & 0.937191561749979 \tabularnewline
19 & 100.6 & 99.76280843825 & 0.83719156174997 \tabularnewline
20 & 100.5 & 99.772352104508 & 0.727647895492068 \tabularnewline
21 & 100.6 & 100.211360752372 & 0.388639247628284 \tabularnewline
22 & 100.5 & 100.478583407593 & 0.0214165924068594 \tabularnewline
23 & 100.4 & 100.822155392878 & -0.422155392877831 \tabularnewline
24 & 100.3 & 100.955766720489 & -0.655766720488555 \tabularnewline
25 & 100.4 & 100.955766720489 & -0.555766720488546 \tabularnewline
26 & 100.4 & 100.955766720489 & -0.555766720488546 \tabularnewline
27 & 100.4 & 100.955766720489 & -0.555766720488546 \tabularnewline
28 & 100.4 & 100.955766720489 & -0.555766720488546 \tabularnewline
29 & 100.4 & 100.955766720489 & -0.555766720488546 \tabularnewline
30 & 100.5 & 100.955766720489 & -0.455766720488552 \tabularnewline
31 & 100.6 & 100.955766720489 & -0.355766720488557 \tabularnewline
32 & 100.6 & 100.955766720489 & -0.355766720488557 \tabularnewline
33 & 100.5 & 100.955766720489 & -0.455766720488552 \tabularnewline
34 & 100.5 & 100.955766720489 & -0.455766720488552 \tabularnewline
35 & 100.7 & 100.955766720489 & -0.255766720488549 \tabularnewline
36 & 101.1 & 101.337513370805 & -0.237513370804886 \tabularnewline
37 & 101.5 & 101.432950033384 & 0.0670499666160373 \tabularnewline
38 & 101.9 & 101.432950033384 & 0.467049966616043 \tabularnewline
39 & 102.1 & 101.623823358542 & 0.476176641457867 \tabularnewline
40 & 102.1 & 101.671541689832 & 0.428458310168326 \tabularnewline
41 & 102.1 & 101.671541689832 & 0.428458310168326 \tabularnewline
42 & 102.4 & 102.053288340148 & 0.346711659852009 \tabularnewline
43 & 102.8 & 102.148725002727 & 0.651274997272918 \tabularnewline
44 & 103.1 & 102.148725002727 & 0.951274997272915 \tabularnewline
45 & 103.1 & 102.148725002727 & 0.951274997272915 \tabularnewline
46 & 102.9 & 102.148725002727 & 0.751274997272926 \tabularnewline
47 & 102.4 & 102.148725002727 & 0.251274997272926 \tabularnewline
48 & 101.9 & 102.148725002727 & -0.248725002727074 \tabularnewline
49 & 101.3 & 102.148725002727 & -0.848725002727082 \tabularnewline
50 & 100.7 & 102.148725002727 & -1.44872500272708 \tabularnewline
51 & 100.6 & 102.148725002727 & -1.54872500272709 \tabularnewline
52 & 101 & 102.148725002727 & -1.14872500272708 \tabularnewline
53 & 101.5 & 102.148725002727 & -0.648725002727079 \tabularnewline
54 & 101.9 & 102.148725002727 & -0.248725002727074 \tabularnewline
55 & 102.1 & 102.148725002727 & -0.048725002727085 \tabularnewline
56 & 102.3 & 102.148725002727 & 0.151274997272918 \tabularnewline
57 & 102.5 & 102.148725002727 & 0.351274997272921 \tabularnewline
58 & 102.9 & 102.148725002727 & 0.751274997272926 \tabularnewline
59 & 103.6 & 102.148725002727 & 1.45127499727291 \tabularnewline
60 & 104.3 & 102.148725002727 & 2.15127499727292 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57939&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]101.2[/C][C]101.194358376937[/C][C]0.00564162306336613[/C][/ROW]
[ROW][C]2[/C][C]101.1[/C][C]100.993941385520[/C][C]0.106058614479800[/C][/ROW]
[ROW][C]3[/C][C]100.7[/C][C]100.841242725394[/C][C]-0.14124272539365[/C][/ROW]
[ROW][C]4[/C][C]100.1[/C][C]100.707631397783[/C][C]-0.607631397782944[/C][/ROW]
[ROW][C]5[/C][C]99.9[/C][C]100.478583407593[/C][C]-0.578583407593135[/C][/ROW]
[ROW][C]6[/C][C]99.7[/C][C]100.135011422308[/C][C]-0.435011422308442[/C][/ROW]
[ROW][C]7[/C][C]99.5[/C][C]100.001400094698[/C][C]-0.50140009469773[/C][/ROW]
[ROW][C]8[/C][C]99.2[/C][C]100.001400094698[/C][C]-0.801400094697727[/C][/ROW]
[ROW][C]9[/C][C]99[/C][C]99.76280843825[/C][C]-0.762808438250024[/C][/ROW]
[ROW][C]10[/C][C]99[/C][C]99.5719351130919[/C][C]-0.571935113091859[/C][/ROW]
[ROW][C]11[/C][C]99.3[/C][C]99.5242167818023[/C][C]-0.224216781802321[/C][/ROW]
[ROW][C]12[/C][C]99.5[/C][C]99.5242167818023[/C][C]-0.0242167818023186[/C][/ROW]
[ROW][C]13[/C][C]99.7[/C][C]99.5242167818023[/C][C]0.175783218197684[/C][/ROW]
[ROW][C]14[/C][C]100[/C][C]99.5242167818023[/C][C]0.475783218197681[/C][/ROW]
[ROW][C]15[/C][C]100.4[/C][C]99.5242167818023[/C][C]0.875783218197687[/C][/ROW]
[ROW][C]16[/C][C]100.6[/C][C]99.5242167818023[/C][C]1.07578321819768[/C][/ROW]
[ROW][C]17[/C][C]100.7[/C][C]99.6864591081868[/C][C]1.01354089181324[/C][/ROW]
[ROW][C]18[/C][C]100.7[/C][C]99.76280843825[/C][C]0.937191561749979[/C][/ROW]
[ROW][C]19[/C][C]100.6[/C][C]99.76280843825[/C][C]0.83719156174997[/C][/ROW]
[ROW][C]20[/C][C]100.5[/C][C]99.772352104508[/C][C]0.727647895492068[/C][/ROW]
[ROW][C]21[/C][C]100.6[/C][C]100.211360752372[/C][C]0.388639247628284[/C][/ROW]
[ROW][C]22[/C][C]100.5[/C][C]100.478583407593[/C][C]0.0214165924068594[/C][/ROW]
[ROW][C]23[/C][C]100.4[/C][C]100.822155392878[/C][C]-0.422155392877831[/C][/ROW]
[ROW][C]24[/C][C]100.3[/C][C]100.955766720489[/C][C]-0.655766720488555[/C][/ROW]
[ROW][C]25[/C][C]100.4[/C][C]100.955766720489[/C][C]-0.555766720488546[/C][/ROW]
[ROW][C]26[/C][C]100.4[/C][C]100.955766720489[/C][C]-0.555766720488546[/C][/ROW]
[ROW][C]27[/C][C]100.4[/C][C]100.955766720489[/C][C]-0.555766720488546[/C][/ROW]
[ROW][C]28[/C][C]100.4[/C][C]100.955766720489[/C][C]-0.555766720488546[/C][/ROW]
[ROW][C]29[/C][C]100.4[/C][C]100.955766720489[/C][C]-0.555766720488546[/C][/ROW]
[ROW][C]30[/C][C]100.5[/C][C]100.955766720489[/C][C]-0.455766720488552[/C][/ROW]
[ROW][C]31[/C][C]100.6[/C][C]100.955766720489[/C][C]-0.355766720488557[/C][/ROW]
[ROW][C]32[/C][C]100.6[/C][C]100.955766720489[/C][C]-0.355766720488557[/C][/ROW]
[ROW][C]33[/C][C]100.5[/C][C]100.955766720489[/C][C]-0.455766720488552[/C][/ROW]
[ROW][C]34[/C][C]100.5[/C][C]100.955766720489[/C][C]-0.455766720488552[/C][/ROW]
[ROW][C]35[/C][C]100.7[/C][C]100.955766720489[/C][C]-0.255766720488549[/C][/ROW]
[ROW][C]36[/C][C]101.1[/C][C]101.337513370805[/C][C]-0.237513370804886[/C][/ROW]
[ROW][C]37[/C][C]101.5[/C][C]101.432950033384[/C][C]0.0670499666160373[/C][/ROW]
[ROW][C]38[/C][C]101.9[/C][C]101.432950033384[/C][C]0.467049966616043[/C][/ROW]
[ROW][C]39[/C][C]102.1[/C][C]101.623823358542[/C][C]0.476176641457867[/C][/ROW]
[ROW][C]40[/C][C]102.1[/C][C]101.671541689832[/C][C]0.428458310168326[/C][/ROW]
[ROW][C]41[/C][C]102.1[/C][C]101.671541689832[/C][C]0.428458310168326[/C][/ROW]
[ROW][C]42[/C][C]102.4[/C][C]102.053288340148[/C][C]0.346711659852009[/C][/ROW]
[ROW][C]43[/C][C]102.8[/C][C]102.148725002727[/C][C]0.651274997272918[/C][/ROW]
[ROW][C]44[/C][C]103.1[/C][C]102.148725002727[/C][C]0.951274997272915[/C][/ROW]
[ROW][C]45[/C][C]103.1[/C][C]102.148725002727[/C][C]0.951274997272915[/C][/ROW]
[ROW][C]46[/C][C]102.9[/C][C]102.148725002727[/C][C]0.751274997272926[/C][/ROW]
[ROW][C]47[/C][C]102.4[/C][C]102.148725002727[/C][C]0.251274997272926[/C][/ROW]
[ROW][C]48[/C][C]101.9[/C][C]102.148725002727[/C][C]-0.248725002727074[/C][/ROW]
[ROW][C]49[/C][C]101.3[/C][C]102.148725002727[/C][C]-0.848725002727082[/C][/ROW]
[ROW][C]50[/C][C]100.7[/C][C]102.148725002727[/C][C]-1.44872500272708[/C][/ROW]
[ROW][C]51[/C][C]100.6[/C][C]102.148725002727[/C][C]-1.54872500272709[/C][/ROW]
[ROW][C]52[/C][C]101[/C][C]102.148725002727[/C][C]-1.14872500272708[/C][/ROW]
[ROW][C]53[/C][C]101.5[/C][C]102.148725002727[/C][C]-0.648725002727079[/C][/ROW]
[ROW][C]54[/C][C]101.9[/C][C]102.148725002727[/C][C]-0.248725002727074[/C][/ROW]
[ROW][C]55[/C][C]102.1[/C][C]102.148725002727[/C][C]-0.048725002727085[/C][/ROW]
[ROW][C]56[/C][C]102.3[/C][C]102.148725002727[/C][C]0.151274997272918[/C][/ROW]
[ROW][C]57[/C][C]102.5[/C][C]102.148725002727[/C][C]0.351274997272921[/C][/ROW]
[ROW][C]58[/C][C]102.9[/C][C]102.148725002727[/C][C]0.751274997272926[/C][/ROW]
[ROW][C]59[/C][C]103.6[/C][C]102.148725002727[/C][C]1.45127499727291[/C][/ROW]
[ROW][C]60[/C][C]104.3[/C][C]102.148725002727[/C][C]2.15127499727292[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57939&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57939&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	101.2	101.194358376937	0.00564162306336613
2	101.1	100.993941385520	0.106058614479800
3	100.7	100.841242725394	-0.14124272539365
4	100.1	100.707631397783	-0.607631397782944
5	99.9	100.478583407593	-0.578583407593135
6	99.7	100.135011422308	-0.435011422308442
7	99.5	100.001400094698	-0.50140009469773
8	99.2	100.001400094698	-0.801400094697727
9	99	99.76280843825	-0.762808438250024
10	99	99.5719351130919	-0.571935113091859
11	99.3	99.5242167818023	-0.224216781802321
12	99.5	99.5242167818023	-0.0242167818023186
13	99.7	99.5242167818023	0.175783218197684
14	100	99.5242167818023	0.475783218197681
15	100.4	99.5242167818023	0.875783218197687
16	100.6	99.5242167818023	1.07578321819768
17	100.7	99.6864591081868	1.01354089181324
18	100.7	99.76280843825	0.937191561749979
19	100.6	99.76280843825	0.83719156174997
20	100.5	99.772352104508	0.727647895492068
21	100.6	100.211360752372	0.388639247628284
22	100.5	100.478583407593	0.0214165924068594
23	100.4	100.822155392878	-0.422155392877831
24	100.3	100.955766720489	-0.655766720488555
25	100.4	100.955766720489	-0.555766720488546
26	100.4	100.955766720489	-0.555766720488546
27	100.4	100.955766720489	-0.555766720488546
28	100.4	100.955766720489	-0.555766720488546
29	100.4	100.955766720489	-0.555766720488546
30	100.5	100.955766720489	-0.455766720488552
31	100.6	100.955766720489	-0.355766720488557
32	100.6	100.955766720489	-0.355766720488557
33	100.5	100.955766720489	-0.455766720488552
34	100.5	100.955766720489	-0.455766720488552
35	100.7	100.955766720489	-0.255766720488549
36	101.1	101.337513370805	-0.237513370804886
37	101.5	101.432950033384	0.0670499666160373
38	101.9	101.432950033384	0.467049966616043
39	102.1	101.623823358542	0.476176641457867
40	102.1	101.671541689832	0.428458310168326
41	102.1	101.671541689832	0.428458310168326
42	102.4	102.053288340148	0.346711659852009
43	102.8	102.148725002727	0.651274997272918
44	103.1	102.148725002727	0.951274997272915
45	103.1	102.148725002727	0.951274997272915
46	102.9	102.148725002727	0.751274997272926
47	102.4	102.148725002727	0.251274997272926
48	101.9	102.148725002727	-0.248725002727074
49	101.3	102.148725002727	-0.848725002727082
50	100.7	102.148725002727	-1.44872500272708
51	100.6	102.148725002727	-1.54872500272709
52	101	102.148725002727	-1.14872500272708
53	101.5	102.148725002727	-0.648725002727079
54	101.9	102.148725002727	-0.248725002727074
55	102.1	102.148725002727	-0.048725002727085
56	102.3	102.148725002727	0.151274997272918
57	102.5	102.148725002727	0.351274997272921
58	102.9	102.148725002727	0.751274997272926
59	103.6	102.148725002727	1.45127499727291
60	104.3	102.148725002727	2.15127499727292

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
5	0.0238414848985247	0.0476829697970493	0.976158515101475
6	0.0175742657329251	0.0351485314658503	0.982425734267075
7	0.00588102145143733	0.0117620429028747	0.994118978548563
8	0.00212059348618305	0.0042411869723661	0.997879406513817
9	0.000587320787481028	0.00117464157496206	0.999412679212519
10	0.000355771251568655	0.00071154250313731	0.999644228748431
11	0.000916860634170218	0.00183372126834044	0.99908313936583
12	0.00188229342758154	0.00376458685516307	0.998117706572418
13	0.00370584581143945	0.0074116916228789	0.99629415418856
14	0.0101284476909174	0.0202568953818349	0.989871552309083
15	0.0396372483011431	0.0792744966022861	0.960362751698857
16	0.101478894352433	0.202957788704866	0.898521105647567
17	0.158906136109628	0.317812272219257	0.841093863890372
18	0.202557896956443	0.405115793912886	0.797442103043557
19	0.229450237180475	0.45890047436095	0.770549762819525
20	0.251077598223512	0.502155196447023	0.748922401776488
21	0.236879486477723	0.473758972955446	0.763120513522277
22	0.191609557800884	0.383219115601767	0.808390442199116
23	0.143768948443547	0.287537896887095	0.856231051556453
24	0.111371536587479	0.222743073174958	0.888628463412521
25	0.0804102619006545	0.160820523801309	0.919589738099346
26	0.0564017844102695	0.112803568820539	0.94359821558973
27	0.0384875308259434	0.0769750616518868	0.961512469174057
28	0.0255948325572355	0.0511896651144709	0.974405167442765
29	0.0166304111824254	0.0332608223648507	0.983369588817575
30	0.0102533731673888	0.0205067463347776	0.989746626832611
31	0.00610286334121223	0.0122057266824245	0.993897136658788
32	0.0035329438724069	0.0070658877448138	0.996467056127593
33	0.00204770988652225	0.0040954197730445	0.997952290113478
34	0.00120545321976379	0.00241090643952759	0.998794546780236
35	0.000709218934285636	0.00141843786857127	0.999290781065714
36	0.00049634629441276	0.00099269258882552	0.999503653705587
37	0.000449264742047960	0.000898529484095919	0.999550735257952
38	0.000635528068441109	0.00127105613688222	0.999364471931559
39	0.000780352109471887	0.00156070421894377	0.999219647890528
40	0.000730067843889776	0.00146013568777955	0.99926993215611
41	0.000598886702834567	0.00119777340566913	0.999401113297165
42	0.000426411742353679	0.000852823484707357	0.999573588257646
43	0.000450595353112242	0.000901190706224483	0.999549404646888
44	0.000759443295558633	0.00151888659111727	0.999240556704441
45	0.00109893598646372	0.00219787197292743	0.998901064013536
46	0.00102634543867735	0.00205269087735470	0.998973654561323
47	0.00053025011919278	0.00106050023838556	0.999469749880807
48	0.000260090374649883	0.000520180749299765	0.99973990962535
49	0.000274141591900503	0.000548283183801007	0.9997258584081
50	0.00147642595810291	0.00295285191620582	0.998523574041897
51	0.0118934802413496	0.0237869604826992	0.98810651975865
52	0.0409522125086278	0.0819044250172556	0.959047787491372
53	0.068976932881847	0.137953865763694	0.931023067118153
54	0.0798764811657025	0.159752962331405	0.920123518834298
55	0.0897685256990436	0.179537051398087	0.910231474300956

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
5 & 0.0238414848985247 & 0.0476829697970493 & 0.976158515101475 \tabularnewline
6 & 0.0175742657329251 & 0.0351485314658503 & 0.982425734267075 \tabularnewline
7 & 0.00588102145143733 & 0.0117620429028747 & 0.994118978548563 \tabularnewline
8 & 0.00212059348618305 & 0.0042411869723661 & 0.997879406513817 \tabularnewline
9 & 0.000587320787481028 & 0.00117464157496206 & 0.999412679212519 \tabularnewline
10 & 0.000355771251568655 & 0.00071154250313731 & 0.999644228748431 \tabularnewline
11 & 0.000916860634170218 & 0.00183372126834044 & 0.99908313936583 \tabularnewline
12 & 0.00188229342758154 & 0.00376458685516307 & 0.998117706572418 \tabularnewline
13 & 0.00370584581143945 & 0.0074116916228789 & 0.99629415418856 \tabularnewline
14 & 0.0101284476909174 & 0.0202568953818349 & 0.989871552309083 \tabularnewline
15 & 0.0396372483011431 & 0.0792744966022861 & 0.960362751698857 \tabularnewline
16 & 0.101478894352433 & 0.202957788704866 & 0.898521105647567 \tabularnewline
17 & 0.158906136109628 & 0.317812272219257 & 0.841093863890372 \tabularnewline
18 & 0.202557896956443 & 0.405115793912886 & 0.797442103043557 \tabularnewline
19 & 0.229450237180475 & 0.45890047436095 & 0.770549762819525 \tabularnewline
20 & 0.251077598223512 & 0.502155196447023 & 0.748922401776488 \tabularnewline
21 & 0.236879486477723 & 0.473758972955446 & 0.763120513522277 \tabularnewline
22 & 0.191609557800884 & 0.383219115601767 & 0.808390442199116 \tabularnewline
23 & 0.143768948443547 & 0.287537896887095 & 0.856231051556453 \tabularnewline
24 & 0.111371536587479 & 0.222743073174958 & 0.888628463412521 \tabularnewline
25 & 0.0804102619006545 & 0.160820523801309 & 0.919589738099346 \tabularnewline
26 & 0.0564017844102695 & 0.112803568820539 & 0.94359821558973 \tabularnewline
27 & 0.0384875308259434 & 0.0769750616518868 & 0.961512469174057 \tabularnewline
28 & 0.0255948325572355 & 0.0511896651144709 & 0.974405167442765 \tabularnewline
29 & 0.0166304111824254 & 0.0332608223648507 & 0.983369588817575 \tabularnewline
30 & 0.0102533731673888 & 0.0205067463347776 & 0.989746626832611 \tabularnewline
31 & 0.00610286334121223 & 0.0122057266824245 & 0.993897136658788 \tabularnewline
32 & 0.0035329438724069 & 0.0070658877448138 & 0.996467056127593 \tabularnewline
33 & 0.00204770988652225 & 0.0040954197730445 & 0.997952290113478 \tabularnewline
34 & 0.00120545321976379 & 0.00241090643952759 & 0.998794546780236 \tabularnewline
35 & 0.000709218934285636 & 0.00141843786857127 & 0.999290781065714 \tabularnewline
36 & 0.00049634629441276 & 0.00099269258882552 & 0.999503653705587 \tabularnewline
37 & 0.000449264742047960 & 0.000898529484095919 & 0.999550735257952 \tabularnewline
38 & 0.000635528068441109 & 0.00127105613688222 & 0.999364471931559 \tabularnewline
39 & 0.000780352109471887 & 0.00156070421894377 & 0.999219647890528 \tabularnewline
40 & 0.000730067843889776 & 0.00146013568777955 & 0.99926993215611 \tabularnewline
41 & 0.000598886702834567 & 0.00119777340566913 & 0.999401113297165 \tabularnewline
42 & 0.000426411742353679 & 0.000852823484707357 & 0.999573588257646 \tabularnewline
43 & 0.000450595353112242 & 0.000901190706224483 & 0.999549404646888 \tabularnewline
44 & 0.000759443295558633 & 0.00151888659111727 & 0.999240556704441 \tabularnewline
45 & 0.00109893598646372 & 0.00219787197292743 & 0.998901064013536 \tabularnewline
46 & 0.00102634543867735 & 0.00205269087735470 & 0.998973654561323 \tabularnewline
47 & 0.00053025011919278 & 0.00106050023838556 & 0.999469749880807 \tabularnewline
48 & 0.000260090374649883 & 0.000520180749299765 & 0.99973990962535 \tabularnewline
49 & 0.000274141591900503 & 0.000548283183801007 & 0.9997258584081 \tabularnewline
50 & 0.00147642595810291 & 0.00295285191620582 & 0.998523574041897 \tabularnewline
51 & 0.0118934802413496 & 0.0237869604826992 & 0.98810651975865 \tabularnewline
52 & 0.0409522125086278 & 0.0819044250172556 & 0.959047787491372 \tabularnewline
53 & 0.068976932881847 & 0.137953865763694 & 0.931023067118153 \tabularnewline
54 & 0.0798764811657025 & 0.159752962331405 & 0.920123518834298 \tabularnewline
55 & 0.0897685256990436 & 0.179537051398087 & 0.910231474300956 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57939&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]5[/C][C]0.0238414848985247[/C][C]0.0476829697970493[/C][C]0.976158515101475[/C][/ROW]
[ROW][C]6[/C][C]0.0175742657329251[/C][C]0.0351485314658503[/C][C]0.982425734267075[/C][/ROW]
[ROW][C]7[/C][C]0.00588102145143733[/C][C]0.0117620429028747[/C][C]0.994118978548563[/C][/ROW]
[ROW][C]8[/C][C]0.00212059348618305[/C][C]0.0042411869723661[/C][C]0.997879406513817[/C][/ROW]
[ROW][C]9[/C][C]0.000587320787481028[/C][C]0.00117464157496206[/C][C]0.999412679212519[/C][/ROW]
[ROW][C]10[/C][C]0.000355771251568655[/C][C]0.00071154250313731[/C][C]0.999644228748431[/C][/ROW]
[ROW][C]11[/C][C]0.000916860634170218[/C][C]0.00183372126834044[/C][C]0.99908313936583[/C][/ROW]
[ROW][C]12[/C][C]0.00188229342758154[/C][C]0.00376458685516307[/C][C]0.998117706572418[/C][/ROW]
[ROW][C]13[/C][C]0.00370584581143945[/C][C]0.0074116916228789[/C][C]0.99629415418856[/C][/ROW]
[ROW][C]14[/C][C]0.0101284476909174[/C][C]0.0202568953818349[/C][C]0.989871552309083[/C][/ROW]
[ROW][C]15[/C][C]0.0396372483011431[/C][C]0.0792744966022861[/C][C]0.960362751698857[/C][/ROW]
[ROW][C]16[/C][C]0.101478894352433[/C][C]0.202957788704866[/C][C]0.898521105647567[/C][/ROW]
[ROW][C]17[/C][C]0.158906136109628[/C][C]0.317812272219257[/C][C]0.841093863890372[/C][/ROW]
[ROW][C]18[/C][C]0.202557896956443[/C][C]0.405115793912886[/C][C]0.797442103043557[/C][/ROW]
[ROW][C]19[/C][C]0.229450237180475[/C][C]0.45890047436095[/C][C]0.770549762819525[/C][/ROW]
[ROW][C]20[/C][C]0.251077598223512[/C][C]0.502155196447023[/C][C]0.748922401776488[/C][/ROW]
[ROW][C]21[/C][C]0.236879486477723[/C][C]0.473758972955446[/C][C]0.763120513522277[/C][/ROW]
[ROW][C]22[/C][C]0.191609557800884[/C][C]0.383219115601767[/C][C]0.808390442199116[/C][/ROW]
[ROW][C]23[/C][C]0.143768948443547[/C][C]0.287537896887095[/C][C]0.856231051556453[/C][/ROW]
[ROW][C]24[/C][C]0.111371536587479[/C][C]0.222743073174958[/C][C]0.888628463412521[/C][/ROW]
[ROW][C]25[/C][C]0.0804102619006545[/C][C]0.160820523801309[/C][C]0.919589738099346[/C][/ROW]
[ROW][C]26[/C][C]0.0564017844102695[/C][C]0.112803568820539[/C][C]0.94359821558973[/C][/ROW]
[ROW][C]27[/C][C]0.0384875308259434[/C][C]0.0769750616518868[/C][C]0.961512469174057[/C][/ROW]
[ROW][C]28[/C][C]0.0255948325572355[/C][C]0.0511896651144709[/C][C]0.974405167442765[/C][/ROW]
[ROW][C]29[/C][C]0.0166304111824254[/C][C]0.0332608223648507[/C][C]0.983369588817575[/C][/ROW]
[ROW][C]30[/C][C]0.0102533731673888[/C][C]0.0205067463347776[/C][C]0.989746626832611[/C][/ROW]
[ROW][C]31[/C][C]0.00610286334121223[/C][C]0.0122057266824245[/C][C]0.993897136658788[/C][/ROW]
[ROW][C]32[/C][C]0.0035329438724069[/C][C]0.0070658877448138[/C][C]0.996467056127593[/C][/ROW]
[ROW][C]33[/C][C]0.00204770988652225[/C][C]0.0040954197730445[/C][C]0.997952290113478[/C][/ROW]
[ROW][C]34[/C][C]0.00120545321976379[/C][C]0.00241090643952759[/C][C]0.998794546780236[/C][/ROW]
[ROW][C]35[/C][C]0.000709218934285636[/C][C]0.00141843786857127[/C][C]0.999290781065714[/C][/ROW]
[ROW][C]36[/C][C]0.00049634629441276[/C][C]0.00099269258882552[/C][C]0.999503653705587[/C][/ROW]
[ROW][C]37[/C][C]0.000449264742047960[/C][C]0.000898529484095919[/C][C]0.999550735257952[/C][/ROW]
[ROW][C]38[/C][C]0.000635528068441109[/C][C]0.00127105613688222[/C][C]0.999364471931559[/C][/ROW]
[ROW][C]39[/C][C]0.000780352109471887[/C][C]0.00156070421894377[/C][C]0.999219647890528[/C][/ROW]
[ROW][C]40[/C][C]0.000730067843889776[/C][C]0.00146013568777955[/C][C]0.99926993215611[/C][/ROW]
[ROW][C]41[/C][C]0.000598886702834567[/C][C]0.00119777340566913[/C][C]0.999401113297165[/C][/ROW]
[ROW][C]42[/C][C]0.000426411742353679[/C][C]0.000852823484707357[/C][C]0.999573588257646[/C][/ROW]
[ROW][C]43[/C][C]0.000450595353112242[/C][C]0.000901190706224483[/C][C]0.999549404646888[/C][/ROW]
[ROW][C]44[/C][C]0.000759443295558633[/C][C]0.00151888659111727[/C][C]0.999240556704441[/C][/ROW]
[ROW][C]45[/C][C]0.00109893598646372[/C][C]0.00219787197292743[/C][C]0.998901064013536[/C][/ROW]
[ROW][C]46[/C][C]0.00102634543867735[/C][C]0.00205269087735470[/C][C]0.998973654561323[/C][/ROW]
[ROW][C]47[/C][C]0.00053025011919278[/C][C]0.00106050023838556[/C][C]0.999469749880807[/C][/ROW]
[ROW][C]48[/C][C]0.000260090374649883[/C][C]0.000520180749299765[/C][C]0.99973990962535[/C][/ROW]
[ROW][C]49[/C][C]0.000274141591900503[/C][C]0.000548283183801007[/C][C]0.9997258584081[/C][/ROW]
[ROW][C]50[/C][C]0.00147642595810291[/C][C]0.00295285191620582[/C][C]0.998523574041897[/C][/ROW]
[ROW][C]51[/C][C]0.0118934802413496[/C][C]0.0237869604826992[/C][C]0.98810651975865[/C][/ROW]
[ROW][C]52[/C][C]0.0409522125086278[/C][C]0.0819044250172556[/C][C]0.959047787491372[/C][/ROW]
[ROW][C]53[/C][C]0.068976932881847[/C][C]0.137953865763694[/C][C]0.931023067118153[/C][/ROW]
[ROW][C]54[/C][C]0.0798764811657025[/C][C]0.159752962331405[/C][C]0.920123518834298[/C][/ROW]
[ROW][C]55[/C][C]0.0897685256990436[/C][C]0.179537051398087[/C][C]0.910231474300956[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57939&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57939&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
5	0.0238414848985247	0.0476829697970493	0.976158515101475
6	0.0175742657329251	0.0351485314658503	0.982425734267075
7	0.00588102145143733	0.0117620429028747	0.994118978548563
8	0.00212059348618305	0.0042411869723661	0.997879406513817
9	0.000587320787481028	0.00117464157496206	0.999412679212519
10	0.000355771251568655	0.00071154250313731	0.999644228748431
11	0.000916860634170218	0.00183372126834044	0.99908313936583
12	0.00188229342758154	0.00376458685516307	0.998117706572418
13	0.00370584581143945	0.0074116916228789	0.99629415418856
14	0.0101284476909174	0.0202568953818349	0.989871552309083
15	0.0396372483011431	0.0792744966022861	0.960362751698857
16	0.101478894352433	0.202957788704866	0.898521105647567
17	0.158906136109628	0.317812272219257	0.841093863890372
18	0.202557896956443	0.405115793912886	0.797442103043557
19	0.229450237180475	0.45890047436095	0.770549762819525
20	0.251077598223512	0.502155196447023	0.748922401776488
21	0.236879486477723	0.473758972955446	0.763120513522277
22	0.191609557800884	0.383219115601767	0.808390442199116
23	0.143768948443547	0.287537896887095	0.856231051556453
24	0.111371536587479	0.222743073174958	0.888628463412521
25	0.0804102619006545	0.160820523801309	0.919589738099346
26	0.0564017844102695	0.112803568820539	0.94359821558973
27	0.0384875308259434	0.0769750616518868	0.961512469174057
28	0.0255948325572355	0.0511896651144709	0.974405167442765
29	0.0166304111824254	0.0332608223648507	0.983369588817575
30	0.0102533731673888	0.0205067463347776	0.989746626832611
31	0.00610286334121223	0.0122057266824245	0.993897136658788
32	0.0035329438724069	0.0070658877448138	0.996467056127593
33	0.00204770988652225	0.0040954197730445	0.997952290113478
34	0.00120545321976379	0.00241090643952759	0.998794546780236
35	0.000709218934285636	0.00141843786857127	0.999290781065714
36	0.00049634629441276	0.00099269258882552	0.999503653705587
37	0.000449264742047960	0.000898529484095919	0.999550735257952
38	0.000635528068441109	0.00127105613688222	0.999364471931559
39	0.000780352109471887	0.00156070421894377	0.999219647890528
40	0.000730067843889776	0.00146013568777955	0.99926993215611
41	0.000598886702834567	0.00119777340566913	0.999401113297165
42	0.000426411742353679	0.000852823484707357	0.999573588257646
43	0.000450595353112242	0.000901190706224483	0.999549404646888
44	0.000759443295558633	0.00151888659111727	0.999240556704441
45	0.00109893598646372	0.00219787197292743	0.998901064013536
46	0.00102634543867735	0.00205269087735470	0.998973654561323
47	0.00053025011919278	0.00106050023838556	0.999469749880807
48	0.000260090374649883	0.000520180749299765	0.99973990962535
49	0.000274141591900503	0.000548283183801007	0.9997258584081
50	0.00147642595810291	0.00295285191620582	0.998523574041897
51	0.0118934802413496	0.0237869604826992	0.98810651975865
52	0.0409522125086278	0.0819044250172556	0.959047787491372
53	0.068976932881847	0.137953865763694	0.931023067118153
54	0.0798764811657025	0.159752962331405	0.920123518834298
55	0.0897685256990436	0.179537051398087	0.910231474300956

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	25	0.490196078431373	NOK
5% type I error level	33	0.647058823529412	NOK
10% type I error level	37	0.725490196078431	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 & 25 & 0.490196078431373 & NOK \tabularnewline
5% type I error level & 33 & 0.647058823529412 & NOK \tabularnewline
10% type I error level & 37 & 0.725490196078431 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57939&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]25[/C][C]0.490196078431373[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]33[/C][C]0.647058823529412[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]37[/C][C]0.725490196078431[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57939&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57939&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	25	0.490196078431373	NOK
5% type I error level	33	0.647058823529412	NOK
10% type I error level	37	0.725490196078431	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 = 2 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 2 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

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

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

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code