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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Sat, 18 Dec 2010 19:19:51 +0000

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/2010/Dec/18/t1292699881f3krt0n0f5flkto.htm/, Retrieved Tue, 30 Apr 2024 06:08:36 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=112165, Retrieved Tue, 30 Apr 2024 06:08:36 +0000

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IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

169

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

F     [Univariate Data Series] [HPC Retail Sales] [2008-03-02 15:42:48] [74be16979710d4c4e7c6647856088456]
-  MPD  [Univariate Data Series] [WS8 1] [2010-11-30 15:47:30] [07a238a5afc23eb944f8545182f29d5a]
- RMP     [Classical Decomposition] [WS8 2] [2010-11-30 15:54:02] [07a238a5afc23eb944f8545182f29d5a]
- RMPD      [Univariate Data Series] [Statistiek: Werkl...] [2010-12-12 15:20:09] [07a238a5afc23eb944f8545182f29d5a]
-    D        [Univariate Data Series] [Statistiek: Werkl...] [2010-12-14 09:08:05] [07a238a5afc23eb944f8545182f29d5a]
-               [Univariate Data Series] [Statistiek: Werkl...] [2010-12-14 09:12:36] [07a238a5afc23eb944f8545182f29d5a]
- RMPD            [Univariate Explorative Data Analysis] [Statistiek: U EDA...] [2010-12-17 19:07:44] [07a238a5afc23eb944f8545182f29d5a]
- RMP               [Central Tendency] [Statistiek: centr...] [2010-12-18 09:18:46] [07a238a5afc23eb944f8545182f29d5a]
- RMP                 [Harrell-Davis Quantiles] [Statistiek: betro...] [2010-12-18 10:06:35] [07a238a5afc23eb944f8545182f29d5a]
- RMPD                  [Bivariate Explorative Data Analysis] [statistiek: Bivar...] [2010-12-18 13:22:48] [07a238a5afc23eb944f8545182f29d5a]
- RMPD                    [Pearson Correlation] [statistiek: pears...] [2010-12-18 14:12:28] [07a238a5afc23eb944f8545182f29d5a]
-                           [Pearson Correlation] [statistiek: pears...] [2010-12-18 14:15:15] [07a238a5afc23eb944f8545182f29d5a]
- RMPD                        [Trivariate Scatterplots] [Statistiek trivar...] [2010-12-18 16:57:07] [07a238a5afc23eb944f8545182f29d5a]
- RMPD                          [Multiple Regression] [statistiek Multip...] [2010-12-18 19:03:25] [07a238a5afc23eb944f8545182f29d5a]
-    D                              [Multiple Regression] [statistiek Multip...] [2010-12-18 19:19:51] [67e3c2d70de1dbb070b545ca6c893d5e] [Current]
-    D                                [Multiple Regression] [statistiek Multip...] [2010-12-18 19:32:07] [07a238a5afc23eb944f8545182f29d5a] 

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Dataseries X:

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6,5	8,9	-0,6	9
6,3	8,4	1,1	11
5,9	8,1	1,4	13
5,5	8,3	1,4	12
5,2	8,1	1,3	13
4,9	8	1,4	15
5,4	8,7	-0,1	13
5,8	9,2	1,8	16
5,7	9	1,5	10
5,6	8,9	1,5	14
5,5	8,5	1,4	14
5,4	8,1	1,6	15
5,4	7,5	1,6	13
5,4	7,1	1,6	8
5,5	6,9	1,4	7
5,8	7,1	1,7	3
5,7	7	1,8	3
5,4	6,7	1,9	4
5,6	7	2,2	4
5,8	7,3	2,1	0
6,2	7,7	2,4	-4
6,8	8,4	2,6	-14
6,7	8,4	2,8	-18
6,7	8,8	2,7	-8
6,4	9,1	2,6	-1
6,3	9	2,9	1
6,3	8,6	2,8	2
6,4	7,9	2,2	0
6,3	7,7	2,2	1
6	7,8	2,2	0
6,3	9,2	2	-1
6,3	9,4	2	-3
6,6	9,2	1,7	-3
7,5	8,7	1,4	-3
7,8	8,4	1,3	-4
7,9	8,6	1,4	-8
7,8	9	1,3	-9
7,6	9,1	1,3	-13
7,5	8,7	1,4	-18
7,6	8,2	2	-11
7,5	7,9	1,7	-9
7,3	7,9	1,8	-10
7,6	9,1	1,7	-13
7,5	9,4	1,6	-11
7,6	9,4	1,7	-5
7,9	9,1	1,9	-15
7,9	9	1,8	-6
8,1	9,3	1,7	-6
8,2	9,9	1,6	-3
8	9,8	1,8	-1
7,5	9,3	1,6	-3
6,8	8,3	1,5	-4
6,5	8	1,5	-6
6,6	8,5	1,3	0
7,6	10,4	1,4	-4
8	11,1	1,4	-2
8,1	10,9	1,3	-2
7,7	10	1,3	-6
7,5	9,2	1,2	-7
7,6	9,2	1,1	-6
7,8	9,5	1,4	-6
7,8	9,6	1,2	-3
7,8	9,5	1,5	-2
7,5	9,1	1,1	-5
7,5	8,9	1,3	-11
7,1	9	1,5	-11
7,5	10,1	1,1	-11
7,5	10,3	1,4	-10
7,6	10,2	1,3	-14
7,7	9,6	1,5	-8
7,7	9,2	1,6	-9
7,9	9,3	1,7	-5
8,1	9,4	1,1	-1
8,2	9,4	1,6	-2
8,2	9,2	1,3	-5
8,2	9	1,7	-4
7,9	9	1,6	-6
7,3	9	1,7	-2
6,9	9,8	1,9	-2
6,6	10	1,8	-2
6,7	9,8	1,9	-2
6,9	9,3	1,6	2
7	9	1,5	1
7,1	9	1,6	-8
7,2	9,1	1,6	-1
7,1	9,1	1,7	1
6,9	9,1	2	-1
7	9,2	2	2
6,8	8,8	1,9	2
6,4	8,3	1,7	1
6,7	8,4	1,8	-1
6,6	8,1	1,9	-2
6,4	7,7	1,7	-2
6,3	7,9	2	-1
6,2	7,9	2,1	-8
6,5	8	2,4	-4
6,8	7,9	2,5	-6
6,8	7,6	2,5	-3
6,4	7,1	2,6	-3
6,1	6,8	2,2	-7
5,8	6,5	2,5	-9
6,1	6,9	2,8	-11
7,2	8,2	2,8	-13
7,3	8,7	2,9	-11
6,9	8,3	3	-9
6,1	7,9	3,1	-17
5,8	7,5	2,9	-22
6,2	7,8	2,7	-25
7,1	8,3	2,2	-20
7,7	8,4	2,5	-24
8	8,2	2,3	-24
7,8	7,6	2,6	-22
7,4	7,2	2,3	-19
7,4	7,5	2,2	-18
7,7	8,7	1,8	-17
7,8	9	1,8	-11
7,8	8,6	2	-11
8	7,9	1,6	-12
8,1	7,8	1,5	-10
8,4	8,2	1,4	-15

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	8 seconds
R Server	'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 & 8 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=112165&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]8 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=112165&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=112165&T=0

As an alternative you can also use a QR Code:

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

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	8 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

Multiple Linear Regression - Estimated Regression Equation
Mannen[t] = + 3.68452713026474 + 0.426921611066371Vrouwen[t] -0.393998879715656Inflatie[t] -0.0659007049273914Consumvertr[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Mannen[t] =  +  3.68452713026474 +  0.426921611066371Vrouwen[t] -0.393998879715656Inflatie[t] -0.0659007049273914Consumvertr[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=112165&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Mannen[t] =  +  3.68452713026474 +  0.426921611066371Vrouwen[t] -0.393998879715656Inflatie[t] -0.0659007049273914Consumvertr[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=112165&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=112165&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
Mannen[t] = + 3.68452713026474 + 0.426921611066371Vrouwen[t] -0.393998879715656Inflatie[t] -0.0659007049273914Consumvertr[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)	3.68452713026474	0.563644	6.537	0	0
Vrouwen	0.426921611066371	0.054908	7.7752	0	0
Inflatie	-0.393998879715656	0.096037	-4.1026	7.6e-05	3.8e-05
Consumvertr	-0.0659007049273914	0.005595	-11.7783	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) & 3.68452713026474 & 0.563644 & 6.537 & 0 & 0 \tabularnewline
Vrouwen & 0.426921611066371 & 0.054908 & 7.7752 & 0 & 0 \tabularnewline
Inflatie & -0.393998879715656 & 0.096037 & -4.1026 & 7.6e-05 & 3.8e-05 \tabularnewline
Consumvertr & -0.0659007049273914 & 0.005595 & -11.7783 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=112165&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]3.68452713026474[/C][C]0.563644[/C][C]6.537[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Vrouwen[/C][C]0.426921611066371[/C][C]0.054908[/C][C]7.7752[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Inflatie[/C][C]-0.393998879715656[/C][C]0.096037[/C][C]-4.1026[/C][C]7.6e-05[/C][C]3.8e-05[/C][/ROW]
[ROW][C]Consumvertr[/C][C]-0.0659007049273914[/C][C]0.005595[/C][C]-11.7783[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=112165&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=112165&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)	3.68452713026474	0.563644	6.537	0	0
Vrouwen	0.426921611066371	0.054908	7.7752	0	0
Inflatie	-0.393998879715656	0.096037	-4.1026	7.6e-05	3.8e-05
Consumvertr	-0.0659007049273914	0.005595	-11.7783	0	0

Multiple Linear Regression - Regression Statistics
Multiple R	0.827745529807599
R-squared	0.685162662116463
Adjusted R-squared	0.677020317171199
F-TEST (value)	84.148076103678
F-TEST (DF numerator)	3
F-TEST (DF denominator)	116
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.49112439590422
Sum Squared Residuals	27.9795679812651

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.827745529807599 \tabularnewline
R-squared & 0.685162662116463 \tabularnewline
Adjusted R-squared & 0.677020317171199 \tabularnewline
F-TEST (value) & 84.148076103678 \tabularnewline
F-TEST (DF numerator) & 3 \tabularnewline
F-TEST (DF denominator) & 116 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.49112439590422 \tabularnewline
Sum Squared Residuals & 27.9795679812651 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=112165&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.827745529807599[/C][/ROW]
[ROW][C]R-squared[/C][C]0.685162662116463[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.677020317171199[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]84.148076103678[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]3[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]116[/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]0.49112439590422[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]27.9795679812651[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=112165&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=112165&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	0.827745529807599
R-squared	0.685162662116463
Adjusted R-squared	0.677020317171199
F-TEST (value)	84.148076103678
F-TEST (DF numerator)	3
F-TEST (DF denominator)	116
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.49112439590422
Sum Squared Residuals	27.9795679812651

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	6.5	7.12742245223832	-0.627422452238318
2	6.3	6.11236214133374	0.187637858666263
3	5.9	5.73428458424434	0.165715415755657
4	5.5	5.88556961138501	-0.385569611385008
5	5.2	5.77368447221591	-0.573684472215908
6	4.9	5.55979101328292	-0.659791013282922
7	5.4	6.58143587045765	-1.18143587045765
8	5.8	5.84859668974891	-0.0485966897489136
9	5.7	6.27681626101469	-0.576816261014685
10	5.6	5.97052128019848	-0.370521280198483
11	5.5	5.8391525237435	-0.339152523743499
12	5.4	5.52368339844643	-0.123683398446428
13	5.4	5.39933184166139	0.000668158338611988
14	5.4	5.5580667218718	-0.158066721871797
15	5.5	5.61738288052905	-0.117382880529046
16	5.8	5.84817035853719	-0.0481703585371886
17	5.7	5.76607830945899	-0.0660783094589857
18	5.4	5.53270123324012	-0.132701233240117
19	5.6	5.54257805264533	0.0574219473546677
20	5.8	5.97365724364637	-0.173657243646375
21	6.2	6.28982904386779	-0.089829043867792
22	6.8	7.16888144494504	-0.368881444945035
23	6.7	7.35368448871147	-0.653684488711469
24	6.7	6.90484597183567	-0.204845971835669
25	6.4	6.6110174086354	-0.211017408635406
26	6.3	6.31832417375929	-0.0183241737592898
27	6.3	6.12105471237692	0.178945287623085
28	6.4	6.19041032231463	0.209589677685368
29	6.3	6.03912529517397	0.260874704826034
30	6	6.147718161208	-0.147718161207995
31	6.3	6.89010889757144	-0.590108897571437
32	6.3	7.1072946296395	-0.807294629639494
33	6.6	7.14010997134092	-0.540109971340917
34	7.5	7.04484882972243	0.455151170277573
35	7.8	7.02207293930147	0.777927060698526
36	7.9	7.33166019325275	0.568339806747253
37	7.8	7.60772943057825	0.192270569421747
38	7.6	7.91402441139446	-0.314024411394456
39	7.5	8.0333594036333	-0.533359403633299
40	7.6	7.12219433577898	0.47780566422102
41	7.5	6.98051610651898	0.519483893481017
42	7.3	7.00701692347481	0.292983076525191
43	7.6	7.7564248595082	-0.156424859508194
44	7.5	7.79209982094489	-0.292099820944888
45	7.6	7.35729570340897	0.242704296591026
46	7.9	7.80942649341984	0.0905735065801552
47	7.9	7.21302787593825	0.686972124061749
48	8.1	7.38050424722973	0.719495752770271
49	8.2	7.47835498705894	0.721645012941057
50	8	7.22506164015439	0.774938359845609
51	7.5	7.22220202041912	0.277797979580880
52	6.8	6.90058100225171	-0.100581002251706
53	6.5	6.90430592878658	-0.404305928786577
54	6.6	6.80116228069855	-0.201162280698545
55	7.6	7.83651627346265	-0.236516273462651
56	8	8.00355999135433	-0.00355999135432682
57	8.1	7.95757555711262	0.142424442887381
58	7.7	7.83694892686245	-0.13694892686245
59	7.5	7.60071223090831	-0.10071223090831
60	7.6	7.57421141395248	0.0257885860475155
61	7.8	7.5840882333577	0.215911766642301
62	7.8	7.5078780556253	0.292121944374707
63	7.8	7.28108552567657	0.518914474323432
64	7.5	7.46561854791846	0.0343814520815443
65	7.5	7.6968386793264	-0.196838679326399
66	7.1	7.6607310644899	-0.560731064489905
67	7.5	8.28794438854917	-0.787944388549175
68	7.5	8.18922834192036	-0.689228341920362
69	7.6	8.44953888849486	-0.849538888494856
70	7.7	7.71918191634755	-0.0191819163475530
71	7.7	7.57491408887683	0.12508591112317
72	7.9	7.31460354230234	0.585396457697663
73	8.1	7.3300922115288	0.769907788471198
74	8.2	7.19899347659836	1.00100652340163
75	8.2	7.42951093308196	0.770489066918038
76	8.2	7.12062635405503	1.07937364594497
77	7.9	7.29182765188138	0.608172348118618
78	7.3	6.98882494420025	0.311175055799749
79	6.9	7.25156245711022	-0.351562457110217
80	6.6	7.37634666729506	-0.776346667295057
81	6.7	7.25156245711022	-0.551562457110217
82	6.9	6.89269849578216	0.00730150421783795
83	7	6.8699226053612	0.130077394638792
84	7.1	7.42362906173616	-0.323629061736165
85	7.2	7.00501628835106	0.194983711648938
86	7.1	6.83381499052471	0.266185009475286
87	6.9	6.8474167364648	0.0525832635352007
88	7	6.69240678278926	0.307593217210738
89	6.8	6.56103802633428	0.238961973665720
90	6.4	6.49227770167162	-0.0922777016716169
91	6.7	6.62737138466147	0.0726286153385288
92	6.6	6.52579571829739	0.074204281702614
93	6.4	6.43382684981397	-0.0338268498139682
94	6.3	6.33511080318515	-0.0351108031851547
95	6.2	6.75701584970533	-0.557015849705329
96	6.5	6.4179055271877	0.0820944728122966
97	6.8	6.46761488796428	0.332385112035716
98	6.8	6.1418362898622	0.658163710137802
99	6.4	5.88897559635745	0.511024403642554
100	6.1	6.18210148463336	-0.0821014846333637
101	5.8	6.06762674725354	-0.267626747253538
102	6.1	6.25199713762017	-0.151997137620173
103	7.2	6.93879664186124	0.261203358138762
104	7.3	6.98105614956807	0.318943850431925
105	6.9	6.63908620731518	0.260913792684822
106	6.1	6.9561233143362	-0.856123314336196
107	5.8	7.19365797048974	-1.39365797048974
108	6.2	7.59823634453495	-1.39823634453495
109	7.1	7.67919306528901	-0.579193065289009
110	7.7	7.86728838219052	-0.167288382190515
111	8	7.86070383592037	0.139296164079629
112	7.8	7.35454979551107	0.445450204488931
113	7.4	7.10427870021704	0.295721299782957
114	7.4	7.20585436658113	0.194145633418871
115	7.7	7.80985914681964	-0.109859146819645
116	7.8	7.54253140057521	0.257468599424792
117	7.8	7.29296298020553	0.507037019794471
118	8	7.21761810927272	0.782381890727277
119	8.1	7.08252442628287	1.01747557371713
120	8.4	7.62219648331794	0.777803516682061

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 6.5 & 7.12742245223832 & -0.627422452238318 \tabularnewline
2 & 6.3 & 6.11236214133374 & 0.187637858666263 \tabularnewline
3 & 5.9 & 5.73428458424434 & 0.165715415755657 \tabularnewline
4 & 5.5 & 5.88556961138501 & -0.385569611385008 \tabularnewline
5 & 5.2 & 5.77368447221591 & -0.573684472215908 \tabularnewline
6 & 4.9 & 5.55979101328292 & -0.659791013282922 \tabularnewline
7 & 5.4 & 6.58143587045765 & -1.18143587045765 \tabularnewline
8 & 5.8 & 5.84859668974891 & -0.0485966897489136 \tabularnewline
9 & 5.7 & 6.27681626101469 & -0.576816261014685 \tabularnewline
10 & 5.6 & 5.97052128019848 & -0.370521280198483 \tabularnewline
11 & 5.5 & 5.8391525237435 & -0.339152523743499 \tabularnewline
12 & 5.4 & 5.52368339844643 & -0.123683398446428 \tabularnewline
13 & 5.4 & 5.39933184166139 & 0.000668158338611988 \tabularnewline
14 & 5.4 & 5.5580667218718 & -0.158066721871797 \tabularnewline
15 & 5.5 & 5.61738288052905 & -0.117382880529046 \tabularnewline
16 & 5.8 & 5.84817035853719 & -0.0481703585371886 \tabularnewline
17 & 5.7 & 5.76607830945899 & -0.0660783094589857 \tabularnewline
18 & 5.4 & 5.53270123324012 & -0.132701233240117 \tabularnewline
19 & 5.6 & 5.54257805264533 & 0.0574219473546677 \tabularnewline
20 & 5.8 & 5.97365724364637 & -0.173657243646375 \tabularnewline
21 & 6.2 & 6.28982904386779 & -0.089829043867792 \tabularnewline
22 & 6.8 & 7.16888144494504 & -0.368881444945035 \tabularnewline
23 & 6.7 & 7.35368448871147 & -0.653684488711469 \tabularnewline
24 & 6.7 & 6.90484597183567 & -0.204845971835669 \tabularnewline
25 & 6.4 & 6.6110174086354 & -0.211017408635406 \tabularnewline
26 & 6.3 & 6.31832417375929 & -0.0183241737592898 \tabularnewline
27 & 6.3 & 6.12105471237692 & 0.178945287623085 \tabularnewline
28 & 6.4 & 6.19041032231463 & 0.209589677685368 \tabularnewline
29 & 6.3 & 6.03912529517397 & 0.260874704826034 \tabularnewline
30 & 6 & 6.147718161208 & -0.147718161207995 \tabularnewline
31 & 6.3 & 6.89010889757144 & -0.590108897571437 \tabularnewline
32 & 6.3 & 7.1072946296395 & -0.807294629639494 \tabularnewline
33 & 6.6 & 7.14010997134092 & -0.540109971340917 \tabularnewline
34 & 7.5 & 7.04484882972243 & 0.455151170277573 \tabularnewline
35 & 7.8 & 7.02207293930147 & 0.777927060698526 \tabularnewline
36 & 7.9 & 7.33166019325275 & 0.568339806747253 \tabularnewline
37 & 7.8 & 7.60772943057825 & 0.192270569421747 \tabularnewline
38 & 7.6 & 7.91402441139446 & -0.314024411394456 \tabularnewline
39 & 7.5 & 8.0333594036333 & -0.533359403633299 \tabularnewline
40 & 7.6 & 7.12219433577898 & 0.47780566422102 \tabularnewline
41 & 7.5 & 6.98051610651898 & 0.519483893481017 \tabularnewline
42 & 7.3 & 7.00701692347481 & 0.292983076525191 \tabularnewline
43 & 7.6 & 7.7564248595082 & -0.156424859508194 \tabularnewline
44 & 7.5 & 7.79209982094489 & -0.292099820944888 \tabularnewline
45 & 7.6 & 7.35729570340897 & 0.242704296591026 \tabularnewline
46 & 7.9 & 7.80942649341984 & 0.0905735065801552 \tabularnewline
47 & 7.9 & 7.21302787593825 & 0.686972124061749 \tabularnewline
48 & 8.1 & 7.38050424722973 & 0.719495752770271 \tabularnewline
49 & 8.2 & 7.47835498705894 & 0.721645012941057 \tabularnewline
50 & 8 & 7.22506164015439 & 0.774938359845609 \tabularnewline
51 & 7.5 & 7.22220202041912 & 0.277797979580880 \tabularnewline
52 & 6.8 & 6.90058100225171 & -0.100581002251706 \tabularnewline
53 & 6.5 & 6.90430592878658 & -0.404305928786577 \tabularnewline
54 & 6.6 & 6.80116228069855 & -0.201162280698545 \tabularnewline
55 & 7.6 & 7.83651627346265 & -0.236516273462651 \tabularnewline
56 & 8 & 8.00355999135433 & -0.00355999135432682 \tabularnewline
57 & 8.1 & 7.95757555711262 & 0.142424442887381 \tabularnewline
58 & 7.7 & 7.83694892686245 & -0.13694892686245 \tabularnewline
59 & 7.5 & 7.60071223090831 & -0.10071223090831 \tabularnewline
60 & 7.6 & 7.57421141395248 & 0.0257885860475155 \tabularnewline
61 & 7.8 & 7.5840882333577 & 0.215911766642301 \tabularnewline
62 & 7.8 & 7.5078780556253 & 0.292121944374707 \tabularnewline
63 & 7.8 & 7.28108552567657 & 0.518914474323432 \tabularnewline
64 & 7.5 & 7.46561854791846 & 0.0343814520815443 \tabularnewline
65 & 7.5 & 7.6968386793264 & -0.196838679326399 \tabularnewline
66 & 7.1 & 7.6607310644899 & -0.560731064489905 \tabularnewline
67 & 7.5 & 8.28794438854917 & -0.787944388549175 \tabularnewline
68 & 7.5 & 8.18922834192036 & -0.689228341920362 \tabularnewline
69 & 7.6 & 8.44953888849486 & -0.849538888494856 \tabularnewline
70 & 7.7 & 7.71918191634755 & -0.0191819163475530 \tabularnewline
71 & 7.7 & 7.57491408887683 & 0.12508591112317 \tabularnewline
72 & 7.9 & 7.31460354230234 & 0.585396457697663 \tabularnewline
73 & 8.1 & 7.3300922115288 & 0.769907788471198 \tabularnewline
74 & 8.2 & 7.19899347659836 & 1.00100652340163 \tabularnewline
75 & 8.2 & 7.42951093308196 & 0.770489066918038 \tabularnewline
76 & 8.2 & 7.12062635405503 & 1.07937364594497 \tabularnewline
77 & 7.9 & 7.29182765188138 & 0.608172348118618 \tabularnewline
78 & 7.3 & 6.98882494420025 & 0.311175055799749 \tabularnewline
79 & 6.9 & 7.25156245711022 & -0.351562457110217 \tabularnewline
80 & 6.6 & 7.37634666729506 & -0.776346667295057 \tabularnewline
81 & 6.7 & 7.25156245711022 & -0.551562457110217 \tabularnewline
82 & 6.9 & 6.89269849578216 & 0.00730150421783795 \tabularnewline
83 & 7 & 6.8699226053612 & 0.130077394638792 \tabularnewline
84 & 7.1 & 7.42362906173616 & -0.323629061736165 \tabularnewline
85 & 7.2 & 7.00501628835106 & 0.194983711648938 \tabularnewline
86 & 7.1 & 6.83381499052471 & 0.266185009475286 \tabularnewline
87 & 6.9 & 6.8474167364648 & 0.0525832635352007 \tabularnewline
88 & 7 & 6.69240678278926 & 0.307593217210738 \tabularnewline
89 & 6.8 & 6.56103802633428 & 0.238961973665720 \tabularnewline
90 & 6.4 & 6.49227770167162 & -0.0922777016716169 \tabularnewline
91 & 6.7 & 6.62737138466147 & 0.0726286153385288 \tabularnewline
92 & 6.6 & 6.52579571829739 & 0.074204281702614 \tabularnewline
93 & 6.4 & 6.43382684981397 & -0.0338268498139682 \tabularnewline
94 & 6.3 & 6.33511080318515 & -0.0351108031851547 \tabularnewline
95 & 6.2 & 6.75701584970533 & -0.557015849705329 \tabularnewline
96 & 6.5 & 6.4179055271877 & 0.0820944728122966 \tabularnewline
97 & 6.8 & 6.46761488796428 & 0.332385112035716 \tabularnewline
98 & 6.8 & 6.1418362898622 & 0.658163710137802 \tabularnewline
99 & 6.4 & 5.88897559635745 & 0.511024403642554 \tabularnewline
100 & 6.1 & 6.18210148463336 & -0.0821014846333637 \tabularnewline
101 & 5.8 & 6.06762674725354 & -0.267626747253538 \tabularnewline
102 & 6.1 & 6.25199713762017 & -0.151997137620173 \tabularnewline
103 & 7.2 & 6.93879664186124 & 0.261203358138762 \tabularnewline
104 & 7.3 & 6.98105614956807 & 0.318943850431925 \tabularnewline
105 & 6.9 & 6.63908620731518 & 0.260913792684822 \tabularnewline
106 & 6.1 & 6.9561233143362 & -0.856123314336196 \tabularnewline
107 & 5.8 & 7.19365797048974 & -1.39365797048974 \tabularnewline
108 & 6.2 & 7.59823634453495 & -1.39823634453495 \tabularnewline
109 & 7.1 & 7.67919306528901 & -0.579193065289009 \tabularnewline
110 & 7.7 & 7.86728838219052 & -0.167288382190515 \tabularnewline
111 & 8 & 7.86070383592037 & 0.139296164079629 \tabularnewline
112 & 7.8 & 7.35454979551107 & 0.445450204488931 \tabularnewline
113 & 7.4 & 7.10427870021704 & 0.295721299782957 \tabularnewline
114 & 7.4 & 7.20585436658113 & 0.194145633418871 \tabularnewline
115 & 7.7 & 7.80985914681964 & -0.109859146819645 \tabularnewline
116 & 7.8 & 7.54253140057521 & 0.257468599424792 \tabularnewline
117 & 7.8 & 7.29296298020553 & 0.507037019794471 \tabularnewline
118 & 8 & 7.21761810927272 & 0.782381890727277 \tabularnewline
119 & 8.1 & 7.08252442628287 & 1.01747557371713 \tabularnewline
120 & 8.4 & 7.62219648331794 & 0.777803516682061 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=112165&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]6.5[/C][C]7.12742245223832[/C][C]-0.627422452238318[/C][/ROW]
[ROW][C]2[/C][C]6.3[/C][C]6.11236214133374[/C][C]0.187637858666263[/C][/ROW]
[ROW][C]3[/C][C]5.9[/C][C]5.73428458424434[/C][C]0.165715415755657[/C][/ROW]
[ROW][C]4[/C][C]5.5[/C][C]5.88556961138501[/C][C]-0.385569611385008[/C][/ROW]
[ROW][C]5[/C][C]5.2[/C][C]5.77368447221591[/C][C]-0.573684472215908[/C][/ROW]
[ROW][C]6[/C][C]4.9[/C][C]5.55979101328292[/C][C]-0.659791013282922[/C][/ROW]
[ROW][C]7[/C][C]5.4[/C][C]6.58143587045765[/C][C]-1.18143587045765[/C][/ROW]
[ROW][C]8[/C][C]5.8[/C][C]5.84859668974891[/C][C]-0.0485966897489136[/C][/ROW]
[ROW][C]9[/C][C]5.7[/C][C]6.27681626101469[/C][C]-0.576816261014685[/C][/ROW]
[ROW][C]10[/C][C]5.6[/C][C]5.97052128019848[/C][C]-0.370521280198483[/C][/ROW]
[ROW][C]11[/C][C]5.5[/C][C]5.8391525237435[/C][C]-0.339152523743499[/C][/ROW]
[ROW][C]12[/C][C]5.4[/C][C]5.52368339844643[/C][C]-0.123683398446428[/C][/ROW]
[ROW][C]13[/C][C]5.4[/C][C]5.39933184166139[/C][C]0.000668158338611988[/C][/ROW]
[ROW][C]14[/C][C]5.4[/C][C]5.5580667218718[/C][C]-0.158066721871797[/C][/ROW]
[ROW][C]15[/C][C]5.5[/C][C]5.61738288052905[/C][C]-0.117382880529046[/C][/ROW]
[ROW][C]16[/C][C]5.8[/C][C]5.84817035853719[/C][C]-0.0481703585371886[/C][/ROW]
[ROW][C]17[/C][C]5.7[/C][C]5.76607830945899[/C][C]-0.0660783094589857[/C][/ROW]
[ROW][C]18[/C][C]5.4[/C][C]5.53270123324012[/C][C]-0.132701233240117[/C][/ROW]
[ROW][C]19[/C][C]5.6[/C][C]5.54257805264533[/C][C]0.0574219473546677[/C][/ROW]
[ROW][C]20[/C][C]5.8[/C][C]5.97365724364637[/C][C]-0.173657243646375[/C][/ROW]
[ROW][C]21[/C][C]6.2[/C][C]6.28982904386779[/C][C]-0.089829043867792[/C][/ROW]
[ROW][C]22[/C][C]6.8[/C][C]7.16888144494504[/C][C]-0.368881444945035[/C][/ROW]
[ROW][C]23[/C][C]6.7[/C][C]7.35368448871147[/C][C]-0.653684488711469[/C][/ROW]
[ROW][C]24[/C][C]6.7[/C][C]6.90484597183567[/C][C]-0.204845971835669[/C][/ROW]
[ROW][C]25[/C][C]6.4[/C][C]6.6110174086354[/C][C]-0.211017408635406[/C][/ROW]
[ROW][C]26[/C][C]6.3[/C][C]6.31832417375929[/C][C]-0.0183241737592898[/C][/ROW]
[ROW][C]27[/C][C]6.3[/C][C]6.12105471237692[/C][C]0.178945287623085[/C][/ROW]
[ROW][C]28[/C][C]6.4[/C][C]6.19041032231463[/C][C]0.209589677685368[/C][/ROW]
[ROW][C]29[/C][C]6.3[/C][C]6.03912529517397[/C][C]0.260874704826034[/C][/ROW]
[ROW][C]30[/C][C]6[/C][C]6.147718161208[/C][C]-0.147718161207995[/C][/ROW]
[ROW][C]31[/C][C]6.3[/C][C]6.89010889757144[/C][C]-0.590108897571437[/C][/ROW]
[ROW][C]32[/C][C]6.3[/C][C]7.1072946296395[/C][C]-0.807294629639494[/C][/ROW]
[ROW][C]33[/C][C]6.6[/C][C]7.14010997134092[/C][C]-0.540109971340917[/C][/ROW]
[ROW][C]34[/C][C]7.5[/C][C]7.04484882972243[/C][C]0.455151170277573[/C][/ROW]
[ROW][C]35[/C][C]7.8[/C][C]7.02207293930147[/C][C]0.777927060698526[/C][/ROW]
[ROW][C]36[/C][C]7.9[/C][C]7.33166019325275[/C][C]0.568339806747253[/C][/ROW]
[ROW][C]37[/C][C]7.8[/C][C]7.60772943057825[/C][C]0.192270569421747[/C][/ROW]
[ROW][C]38[/C][C]7.6[/C][C]7.91402441139446[/C][C]-0.314024411394456[/C][/ROW]
[ROW][C]39[/C][C]7.5[/C][C]8.0333594036333[/C][C]-0.533359403633299[/C][/ROW]
[ROW][C]40[/C][C]7.6[/C][C]7.12219433577898[/C][C]0.47780566422102[/C][/ROW]
[ROW][C]41[/C][C]7.5[/C][C]6.98051610651898[/C][C]0.519483893481017[/C][/ROW]
[ROW][C]42[/C][C]7.3[/C][C]7.00701692347481[/C][C]0.292983076525191[/C][/ROW]
[ROW][C]43[/C][C]7.6[/C][C]7.7564248595082[/C][C]-0.156424859508194[/C][/ROW]
[ROW][C]44[/C][C]7.5[/C][C]7.79209982094489[/C][C]-0.292099820944888[/C][/ROW]
[ROW][C]45[/C][C]7.6[/C][C]7.35729570340897[/C][C]0.242704296591026[/C][/ROW]
[ROW][C]46[/C][C]7.9[/C][C]7.80942649341984[/C][C]0.0905735065801552[/C][/ROW]
[ROW][C]47[/C][C]7.9[/C][C]7.21302787593825[/C][C]0.686972124061749[/C][/ROW]
[ROW][C]48[/C][C]8.1[/C][C]7.38050424722973[/C][C]0.719495752770271[/C][/ROW]
[ROW][C]49[/C][C]8.2[/C][C]7.47835498705894[/C][C]0.721645012941057[/C][/ROW]
[ROW][C]50[/C][C]8[/C][C]7.22506164015439[/C][C]0.774938359845609[/C][/ROW]
[ROW][C]51[/C][C]7.5[/C][C]7.22220202041912[/C][C]0.277797979580880[/C][/ROW]
[ROW][C]52[/C][C]6.8[/C][C]6.90058100225171[/C][C]-0.100581002251706[/C][/ROW]
[ROW][C]53[/C][C]6.5[/C][C]6.90430592878658[/C][C]-0.404305928786577[/C][/ROW]
[ROW][C]54[/C][C]6.6[/C][C]6.80116228069855[/C][C]-0.201162280698545[/C][/ROW]
[ROW][C]55[/C][C]7.6[/C][C]7.83651627346265[/C][C]-0.236516273462651[/C][/ROW]
[ROW][C]56[/C][C]8[/C][C]8.00355999135433[/C][C]-0.00355999135432682[/C][/ROW]
[ROW][C]57[/C][C]8.1[/C][C]7.95757555711262[/C][C]0.142424442887381[/C][/ROW]
[ROW][C]58[/C][C]7.7[/C][C]7.83694892686245[/C][C]-0.13694892686245[/C][/ROW]
[ROW][C]59[/C][C]7.5[/C][C]7.60071223090831[/C][C]-0.10071223090831[/C][/ROW]
[ROW][C]60[/C][C]7.6[/C][C]7.57421141395248[/C][C]0.0257885860475155[/C][/ROW]
[ROW][C]61[/C][C]7.8[/C][C]7.5840882333577[/C][C]0.215911766642301[/C][/ROW]
[ROW][C]62[/C][C]7.8[/C][C]7.5078780556253[/C][C]0.292121944374707[/C][/ROW]
[ROW][C]63[/C][C]7.8[/C][C]7.28108552567657[/C][C]0.518914474323432[/C][/ROW]
[ROW][C]64[/C][C]7.5[/C][C]7.46561854791846[/C][C]0.0343814520815443[/C][/ROW]
[ROW][C]65[/C][C]7.5[/C][C]7.6968386793264[/C][C]-0.196838679326399[/C][/ROW]
[ROW][C]66[/C][C]7.1[/C][C]7.6607310644899[/C][C]-0.560731064489905[/C][/ROW]
[ROW][C]67[/C][C]7.5[/C][C]8.28794438854917[/C][C]-0.787944388549175[/C][/ROW]
[ROW][C]68[/C][C]7.5[/C][C]8.18922834192036[/C][C]-0.689228341920362[/C][/ROW]
[ROW][C]69[/C][C]7.6[/C][C]8.44953888849486[/C][C]-0.849538888494856[/C][/ROW]
[ROW][C]70[/C][C]7.7[/C][C]7.71918191634755[/C][C]-0.0191819163475530[/C][/ROW]
[ROW][C]71[/C][C]7.7[/C][C]7.57491408887683[/C][C]0.12508591112317[/C][/ROW]
[ROW][C]72[/C][C]7.9[/C][C]7.31460354230234[/C][C]0.585396457697663[/C][/ROW]
[ROW][C]73[/C][C]8.1[/C][C]7.3300922115288[/C][C]0.769907788471198[/C][/ROW]
[ROW][C]74[/C][C]8.2[/C][C]7.19899347659836[/C][C]1.00100652340163[/C][/ROW]
[ROW][C]75[/C][C]8.2[/C][C]7.42951093308196[/C][C]0.770489066918038[/C][/ROW]
[ROW][C]76[/C][C]8.2[/C][C]7.12062635405503[/C][C]1.07937364594497[/C][/ROW]
[ROW][C]77[/C][C]7.9[/C][C]7.29182765188138[/C][C]0.608172348118618[/C][/ROW]
[ROW][C]78[/C][C]7.3[/C][C]6.98882494420025[/C][C]0.311175055799749[/C][/ROW]
[ROW][C]79[/C][C]6.9[/C][C]7.25156245711022[/C][C]-0.351562457110217[/C][/ROW]
[ROW][C]80[/C][C]6.6[/C][C]7.37634666729506[/C][C]-0.776346667295057[/C][/ROW]
[ROW][C]81[/C][C]6.7[/C][C]7.25156245711022[/C][C]-0.551562457110217[/C][/ROW]
[ROW][C]82[/C][C]6.9[/C][C]6.89269849578216[/C][C]0.00730150421783795[/C][/ROW]
[ROW][C]83[/C][C]7[/C][C]6.8699226053612[/C][C]0.130077394638792[/C][/ROW]
[ROW][C]84[/C][C]7.1[/C][C]7.42362906173616[/C][C]-0.323629061736165[/C][/ROW]
[ROW][C]85[/C][C]7.2[/C][C]7.00501628835106[/C][C]0.194983711648938[/C][/ROW]
[ROW][C]86[/C][C]7.1[/C][C]6.83381499052471[/C][C]0.266185009475286[/C][/ROW]
[ROW][C]87[/C][C]6.9[/C][C]6.8474167364648[/C][C]0.0525832635352007[/C][/ROW]
[ROW][C]88[/C][C]7[/C][C]6.69240678278926[/C][C]0.307593217210738[/C][/ROW]
[ROW][C]89[/C][C]6.8[/C][C]6.56103802633428[/C][C]0.238961973665720[/C][/ROW]
[ROW][C]90[/C][C]6.4[/C][C]6.49227770167162[/C][C]-0.0922777016716169[/C][/ROW]
[ROW][C]91[/C][C]6.7[/C][C]6.62737138466147[/C][C]0.0726286153385288[/C][/ROW]
[ROW][C]92[/C][C]6.6[/C][C]6.52579571829739[/C][C]0.074204281702614[/C][/ROW]
[ROW][C]93[/C][C]6.4[/C][C]6.43382684981397[/C][C]-0.0338268498139682[/C][/ROW]
[ROW][C]94[/C][C]6.3[/C][C]6.33511080318515[/C][C]-0.0351108031851547[/C][/ROW]
[ROW][C]95[/C][C]6.2[/C][C]6.75701584970533[/C][C]-0.557015849705329[/C][/ROW]
[ROW][C]96[/C][C]6.5[/C][C]6.4179055271877[/C][C]0.0820944728122966[/C][/ROW]
[ROW][C]97[/C][C]6.8[/C][C]6.46761488796428[/C][C]0.332385112035716[/C][/ROW]
[ROW][C]98[/C][C]6.8[/C][C]6.1418362898622[/C][C]0.658163710137802[/C][/ROW]
[ROW][C]99[/C][C]6.4[/C][C]5.88897559635745[/C][C]0.511024403642554[/C][/ROW]
[ROW][C]100[/C][C]6.1[/C][C]6.18210148463336[/C][C]-0.0821014846333637[/C][/ROW]
[ROW][C]101[/C][C]5.8[/C][C]6.06762674725354[/C][C]-0.267626747253538[/C][/ROW]
[ROW][C]102[/C][C]6.1[/C][C]6.25199713762017[/C][C]-0.151997137620173[/C][/ROW]
[ROW][C]103[/C][C]7.2[/C][C]6.93879664186124[/C][C]0.261203358138762[/C][/ROW]
[ROW][C]104[/C][C]7.3[/C][C]6.98105614956807[/C][C]0.318943850431925[/C][/ROW]
[ROW][C]105[/C][C]6.9[/C][C]6.63908620731518[/C][C]0.260913792684822[/C][/ROW]
[ROW][C]106[/C][C]6.1[/C][C]6.9561233143362[/C][C]-0.856123314336196[/C][/ROW]
[ROW][C]107[/C][C]5.8[/C][C]7.19365797048974[/C][C]-1.39365797048974[/C][/ROW]
[ROW][C]108[/C][C]6.2[/C][C]7.59823634453495[/C][C]-1.39823634453495[/C][/ROW]
[ROW][C]109[/C][C]7.1[/C][C]7.67919306528901[/C][C]-0.579193065289009[/C][/ROW]
[ROW][C]110[/C][C]7.7[/C][C]7.86728838219052[/C][C]-0.167288382190515[/C][/ROW]
[ROW][C]111[/C][C]8[/C][C]7.86070383592037[/C][C]0.139296164079629[/C][/ROW]
[ROW][C]112[/C][C]7.8[/C][C]7.35454979551107[/C][C]0.445450204488931[/C][/ROW]
[ROW][C]113[/C][C]7.4[/C][C]7.10427870021704[/C][C]0.295721299782957[/C][/ROW]
[ROW][C]114[/C][C]7.4[/C][C]7.20585436658113[/C][C]0.194145633418871[/C][/ROW]
[ROW][C]115[/C][C]7.7[/C][C]7.80985914681964[/C][C]-0.109859146819645[/C][/ROW]
[ROW][C]116[/C][C]7.8[/C][C]7.54253140057521[/C][C]0.257468599424792[/C][/ROW]
[ROW][C]117[/C][C]7.8[/C][C]7.29296298020553[/C][C]0.507037019794471[/C][/ROW]
[ROW][C]118[/C][C]8[/C][C]7.21761810927272[/C][C]0.782381890727277[/C][/ROW]
[ROW][C]119[/C][C]8.1[/C][C]7.08252442628287[/C][C]1.01747557371713[/C][/ROW]
[ROW][C]120[/C][C]8.4[/C][C]7.62219648331794[/C][C]0.777803516682061[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=112165&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=112165&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	6.5	7.12742245223832	-0.627422452238318
2	6.3	6.11236214133374	0.187637858666263
3	5.9	5.73428458424434	0.165715415755657
4	5.5	5.88556961138501	-0.385569611385008
5	5.2	5.77368447221591	-0.573684472215908
6	4.9	5.55979101328292	-0.659791013282922
7	5.4	6.58143587045765	-1.18143587045765
8	5.8	5.84859668974891	-0.0485966897489136
9	5.7	6.27681626101469	-0.576816261014685
10	5.6	5.97052128019848	-0.370521280198483
11	5.5	5.8391525237435	-0.339152523743499
12	5.4	5.52368339844643	-0.123683398446428
13	5.4	5.39933184166139	0.000668158338611988
14	5.4	5.5580667218718	-0.158066721871797
15	5.5	5.61738288052905	-0.117382880529046
16	5.8	5.84817035853719	-0.0481703585371886
17	5.7	5.76607830945899	-0.0660783094589857
18	5.4	5.53270123324012	-0.132701233240117
19	5.6	5.54257805264533	0.0574219473546677
20	5.8	5.97365724364637	-0.173657243646375
21	6.2	6.28982904386779	-0.089829043867792
22	6.8	7.16888144494504	-0.368881444945035
23	6.7	7.35368448871147	-0.653684488711469
24	6.7	6.90484597183567	-0.204845971835669
25	6.4	6.6110174086354	-0.211017408635406
26	6.3	6.31832417375929	-0.0183241737592898
27	6.3	6.12105471237692	0.178945287623085
28	6.4	6.19041032231463	0.209589677685368
29	6.3	6.03912529517397	0.260874704826034
30	6	6.147718161208	-0.147718161207995
31	6.3	6.89010889757144	-0.590108897571437
32	6.3	7.1072946296395	-0.807294629639494
33	6.6	7.14010997134092	-0.540109971340917
34	7.5	7.04484882972243	0.455151170277573
35	7.8	7.02207293930147	0.777927060698526
36	7.9	7.33166019325275	0.568339806747253
37	7.8	7.60772943057825	0.192270569421747
38	7.6	7.91402441139446	-0.314024411394456
39	7.5	8.0333594036333	-0.533359403633299
40	7.6	7.12219433577898	0.47780566422102
41	7.5	6.98051610651898	0.519483893481017
42	7.3	7.00701692347481	0.292983076525191
43	7.6	7.7564248595082	-0.156424859508194
44	7.5	7.79209982094489	-0.292099820944888
45	7.6	7.35729570340897	0.242704296591026
46	7.9	7.80942649341984	0.0905735065801552
47	7.9	7.21302787593825	0.686972124061749
48	8.1	7.38050424722973	0.719495752770271
49	8.2	7.47835498705894	0.721645012941057
50	8	7.22506164015439	0.774938359845609
51	7.5	7.22220202041912	0.277797979580880
52	6.8	6.90058100225171	-0.100581002251706
53	6.5	6.90430592878658	-0.404305928786577
54	6.6	6.80116228069855	-0.201162280698545
55	7.6	7.83651627346265	-0.236516273462651
56	8	8.00355999135433	-0.00355999135432682
57	8.1	7.95757555711262	0.142424442887381
58	7.7	7.83694892686245	-0.13694892686245
59	7.5	7.60071223090831	-0.10071223090831
60	7.6	7.57421141395248	0.0257885860475155
61	7.8	7.5840882333577	0.215911766642301
62	7.8	7.5078780556253	0.292121944374707
63	7.8	7.28108552567657	0.518914474323432
64	7.5	7.46561854791846	0.0343814520815443
65	7.5	7.6968386793264	-0.196838679326399
66	7.1	7.6607310644899	-0.560731064489905
67	7.5	8.28794438854917	-0.787944388549175
68	7.5	8.18922834192036	-0.689228341920362
69	7.6	8.44953888849486	-0.849538888494856
70	7.7	7.71918191634755	-0.0191819163475530
71	7.7	7.57491408887683	0.12508591112317
72	7.9	7.31460354230234	0.585396457697663
73	8.1	7.3300922115288	0.769907788471198
74	8.2	7.19899347659836	1.00100652340163
75	8.2	7.42951093308196	0.770489066918038
76	8.2	7.12062635405503	1.07937364594497
77	7.9	7.29182765188138	0.608172348118618
78	7.3	6.98882494420025	0.311175055799749
79	6.9	7.25156245711022	-0.351562457110217
80	6.6	7.37634666729506	-0.776346667295057
81	6.7	7.25156245711022	-0.551562457110217
82	6.9	6.89269849578216	0.00730150421783795
83	7	6.8699226053612	0.130077394638792
84	7.1	7.42362906173616	-0.323629061736165
85	7.2	7.00501628835106	0.194983711648938
86	7.1	6.83381499052471	0.266185009475286
87	6.9	6.8474167364648	0.0525832635352007
88	7	6.69240678278926	0.307593217210738
89	6.8	6.56103802633428	0.238961973665720
90	6.4	6.49227770167162	-0.0922777016716169
91	6.7	6.62737138466147	0.0726286153385288
92	6.6	6.52579571829739	0.074204281702614
93	6.4	6.43382684981397	-0.0338268498139682
94	6.3	6.33511080318515	-0.0351108031851547
95	6.2	6.75701584970533	-0.557015849705329
96	6.5	6.4179055271877	0.0820944728122966
97	6.8	6.46761488796428	0.332385112035716
98	6.8	6.1418362898622	0.658163710137802
99	6.4	5.88897559635745	0.511024403642554
100	6.1	6.18210148463336	-0.0821014846333637
101	5.8	6.06762674725354	-0.267626747253538
102	6.1	6.25199713762017	-0.151997137620173
103	7.2	6.93879664186124	0.261203358138762
104	7.3	6.98105614956807	0.318943850431925
105	6.9	6.63908620731518	0.260913792684822
106	6.1	6.9561233143362	-0.856123314336196
107	5.8	7.19365797048974	-1.39365797048974
108	6.2	7.59823634453495	-1.39823634453495
109	7.1	7.67919306528901	-0.579193065289009
110	7.7	7.86728838219052	-0.167288382190515
111	8	7.86070383592037	0.139296164079629
112	7.8	7.35454979551107	0.445450204488931
113	7.4	7.10427870021704	0.295721299782957
114	7.4	7.20585436658113	0.194145633418871
115	7.7	7.80985914681964	-0.109859146819645
116	7.8	7.54253140057521	0.257468599424792
117	7.8	7.29296298020553	0.507037019794471
118	8	7.21761810927272	0.782381890727277
119	8.1	7.08252442628287	1.01747557371713
120	8.4	7.62219648331794	0.777803516682061

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
7	0.371898082465908	0.743796164931816	0.628101917534092
8	0.251730794010919	0.503461588021838	0.74826920598908
9	0.54574599183176	0.908508016336479	0.454254008168239
10	0.424140127407054	0.848280254814108	0.575859872592946
11	0.317981380002062	0.635962760004124	0.682018619997938
12	0.240967300826959	0.481934601653918	0.759032699173041
13	0.170695156938326	0.341390313876652	0.829304843061674
14	0.131505607981956	0.263011215963913	0.868494392018044
15	0.0896824488235724	0.179364897647145	0.910317551176428
16	0.0596897723697587	0.119379544739517	0.94031022763024
17	0.0391063850781463	0.0782127701562926	0.960893614921854
18	0.0276751360232245	0.055350272046449	0.972324863976776
19	0.0166286529226134	0.0332573058452267	0.983371347077387
20	0.0118625034961129	0.0237250069922257	0.988137496503887
21	0.0069156487538201	0.0138312975076402	0.99308435124618
22	0.00428522596993973	0.00857045193987947	0.99571477403006
23	0.00349238548498683	0.00698477096997365	0.996507614515013
24	0.00207861037962961	0.00415722075925922	0.99792138962037
25	0.00112939554302189	0.00225879108604377	0.998870604456978
26	0.000624265169103494	0.00124853033820699	0.999375734830896
27	0.000442842376913901	0.000885684753827803	0.999557157623086
28	0.000483971612896719	0.000967943225793438	0.999516028387103
29	0.000492597178990225	0.00098519435798045	0.99950740282101
30	0.000279917472457482	0.000559834944914965	0.999720082527543
31	0.000216914075621245	0.00043382815124249	0.999783085924379
32	0.000263443901212857	0.000526887802425713	0.999736556098787
33	0.000180419942309369	0.000360839884618738	0.99981958005769
34	0.00491646648024632	0.00983293296049264	0.995083533519754
35	0.0608641858213145	0.121728371642629	0.939135814178686
36	0.117123153872368	0.234246307744736	0.882876846127632
37	0.109844158114402	0.219688316228804	0.890155841885598
38	0.0877741576511475	0.175548315302295	0.912225842348853
39	0.0841244609337334	0.168248921867467	0.915875539066267
40	0.095556883551824	0.191113767103648	0.904443116448176
41	0.103858183698499	0.207716367396998	0.8961418163015
42	0.0869645852597672	0.173929170519534	0.913035414740233
43	0.0662831782903852	0.132566356580770	0.933716821709615
44	0.0510967101069132	0.102193420213826	0.948903289893087
45	0.0511992767540234	0.102398553508047	0.948800723245977
46	0.040264401770251	0.080528803540502	0.959735598229749
47	0.0727526648952477	0.145505329790495	0.927247335104752
48	0.126746027388126	0.253492054776252	0.873253972611874
49	0.204474880557329	0.408949761114658	0.795525119442671
50	0.296903459349063	0.593806918698127	0.703096540650937
51	0.266210114915575	0.53242022983115	0.733789885084425
52	0.233819783767987	0.467639567535974	0.766180216232013
53	0.247496610474995	0.49499322094999	0.752503389525005
54	0.239748473359731	0.479496946719461	0.76025152664027
55	0.205815443615118	0.411630887230235	0.794184556384882
56	0.171522663643838	0.343045327287677	0.828477336356162
57	0.146845641288626	0.293691282577253	0.853154358711374
58	0.119524722758276	0.239049445516552	0.880475277241724
59	0.0983302381795049	0.196660476359010	0.901669761820495
60	0.0792557843624406	0.158511568724881	0.92074421563756
61	0.064528210232103	0.129056420464206	0.935471789767897
62	0.054394298511313	0.108788597022626	0.945605701488687
63	0.0564336107773984	0.112867221554797	0.943566389222602
64	0.0444788650633094	0.0889577301266188	0.95552113493669
65	0.0370605526644852	0.0741211053289704	0.962939447335515
66	0.0461963928858566	0.0923927857717132	0.953803607114143
67	0.078383661124857	0.156767322249714	0.921616338875143
68	0.100063384474804	0.200126768949609	0.899936615525196
69	0.170898377458503	0.341796754917007	0.829101622541497
70	0.143240610258524	0.286481220517048	0.856759389741476
71	0.117435498124612	0.234870996249224	0.882564501875388
72	0.125104616022163	0.250209232044325	0.874895383977837
73	0.151166041979441	0.302332083958881	0.84883395802056
74	0.253185098505182	0.506370197010364	0.746814901494818
75	0.287370917941285	0.574741835882571	0.712629082058715
76	0.459110648124279	0.918221296248559	0.54088935187572
77	0.470259450421639	0.940518900843278	0.529740549578361
78	0.427860109719806	0.855720219439612	0.572139890280194
79	0.394178837104038	0.788357674208076	0.605821162895962
80	0.474670127291805	0.94934025458361	0.525329872708195
81	0.504055442247718	0.991889115504564	0.495944557752282
82	0.463595113800152	0.927190227600303	0.536404886199848
83	0.420411675365874	0.840823350731749	0.579588324634126
84	0.433647619399842	0.867295238799684	0.566352380600158
85	0.387860399337857	0.775720798675714	0.612139600662143
86	0.341990235438495	0.68398047087699	0.658009764561505
87	0.298289951910688	0.596579903821377	0.701710048089312
88	0.254097581364017	0.508195162728034	0.745902418635983
89	0.215729730396148	0.431459460792297	0.784270269603851
90	0.226669813213679	0.453339626427358	0.773330186786321
91	0.214405536968969	0.428811073937937	0.785594463031031
92	0.198698739745807	0.397397479491615	0.801301260254192
93	0.225916375361554	0.451832750723109	0.774083624638446
94	0.265576071476616	0.531152142953231	0.734423928523384
95	0.460719615407099	0.921439230814199	0.5392803845929
96	0.449243234380078	0.898486468760156	0.550756765619922
97	0.385528312306976	0.771056624613952	0.614471687693024
98	0.34196934864505	0.6839386972901	0.65803065135495
99	0.291469910778403	0.582939821556807	0.708530089221597
100	0.283820813001128	0.567641626002256	0.716179186998872
101	0.305959546492602	0.611919092985204	0.694040453507398
102	0.281509511652742	0.563019023305484	0.718490488347258
103	0.237896466104520	0.475792932209041	0.76210353389548
104	0.234107832569596	0.468215665139191	0.765892167430404
105	0.273402804741132	0.546805609482263	0.726597195258868
106	0.24174247611224	0.48348495222448	0.75825752388776
107	0.455952003393071	0.911904006786143	0.544047996606929
108	0.891798041393471	0.216403917213057	0.108201958606529
109	0.97145406124368	0.05709187751264	0.02854593875632
110	0.93893247055608	0.122135058887840	0.0610675294439202
111	0.90439058994209	0.191218820115818	0.095609410057909
112	0.987042880709796	0.0259142385804083	0.0129571192902042
113	0.956813610501623	0.0863727789967539	0.0431863894983769

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
7 & 0.371898082465908 & 0.743796164931816 & 0.628101917534092 \tabularnewline
8 & 0.251730794010919 & 0.503461588021838 & 0.74826920598908 \tabularnewline
9 & 0.54574599183176 & 0.908508016336479 & 0.454254008168239 \tabularnewline
10 & 0.424140127407054 & 0.848280254814108 & 0.575859872592946 \tabularnewline
11 & 0.317981380002062 & 0.635962760004124 & 0.682018619997938 \tabularnewline
12 & 0.240967300826959 & 0.481934601653918 & 0.759032699173041 \tabularnewline
13 & 0.170695156938326 & 0.341390313876652 & 0.829304843061674 \tabularnewline
14 & 0.131505607981956 & 0.263011215963913 & 0.868494392018044 \tabularnewline
15 & 0.0896824488235724 & 0.179364897647145 & 0.910317551176428 \tabularnewline
16 & 0.0596897723697587 & 0.119379544739517 & 0.94031022763024 \tabularnewline
17 & 0.0391063850781463 & 0.0782127701562926 & 0.960893614921854 \tabularnewline
18 & 0.0276751360232245 & 0.055350272046449 & 0.972324863976776 \tabularnewline
19 & 0.0166286529226134 & 0.0332573058452267 & 0.983371347077387 \tabularnewline
20 & 0.0118625034961129 & 0.0237250069922257 & 0.988137496503887 \tabularnewline
21 & 0.0069156487538201 & 0.0138312975076402 & 0.99308435124618 \tabularnewline
22 & 0.00428522596993973 & 0.00857045193987947 & 0.99571477403006 \tabularnewline
23 & 0.00349238548498683 & 0.00698477096997365 & 0.996507614515013 \tabularnewline
24 & 0.00207861037962961 & 0.00415722075925922 & 0.99792138962037 \tabularnewline
25 & 0.00112939554302189 & 0.00225879108604377 & 0.998870604456978 \tabularnewline
26 & 0.000624265169103494 & 0.00124853033820699 & 0.999375734830896 \tabularnewline
27 & 0.000442842376913901 & 0.000885684753827803 & 0.999557157623086 \tabularnewline
28 & 0.000483971612896719 & 0.000967943225793438 & 0.999516028387103 \tabularnewline
29 & 0.000492597178990225 & 0.00098519435798045 & 0.99950740282101 \tabularnewline
30 & 0.000279917472457482 & 0.000559834944914965 & 0.999720082527543 \tabularnewline
31 & 0.000216914075621245 & 0.00043382815124249 & 0.999783085924379 \tabularnewline
32 & 0.000263443901212857 & 0.000526887802425713 & 0.999736556098787 \tabularnewline
33 & 0.000180419942309369 & 0.000360839884618738 & 0.99981958005769 \tabularnewline
34 & 0.00491646648024632 & 0.00983293296049264 & 0.995083533519754 \tabularnewline
35 & 0.0608641858213145 & 0.121728371642629 & 0.939135814178686 \tabularnewline
36 & 0.117123153872368 & 0.234246307744736 & 0.882876846127632 \tabularnewline
37 & 0.109844158114402 & 0.219688316228804 & 0.890155841885598 \tabularnewline
38 & 0.0877741576511475 & 0.175548315302295 & 0.912225842348853 \tabularnewline
39 & 0.0841244609337334 & 0.168248921867467 & 0.915875539066267 \tabularnewline
40 & 0.095556883551824 & 0.191113767103648 & 0.904443116448176 \tabularnewline
41 & 0.103858183698499 & 0.207716367396998 & 0.8961418163015 \tabularnewline
42 & 0.0869645852597672 & 0.173929170519534 & 0.913035414740233 \tabularnewline
43 & 0.0662831782903852 & 0.132566356580770 & 0.933716821709615 \tabularnewline
44 & 0.0510967101069132 & 0.102193420213826 & 0.948903289893087 \tabularnewline
45 & 0.0511992767540234 & 0.102398553508047 & 0.948800723245977 \tabularnewline
46 & 0.040264401770251 & 0.080528803540502 & 0.959735598229749 \tabularnewline
47 & 0.0727526648952477 & 0.145505329790495 & 0.927247335104752 \tabularnewline
48 & 0.126746027388126 & 0.253492054776252 & 0.873253972611874 \tabularnewline
49 & 0.204474880557329 & 0.408949761114658 & 0.795525119442671 \tabularnewline
50 & 0.296903459349063 & 0.593806918698127 & 0.703096540650937 \tabularnewline
51 & 0.266210114915575 & 0.53242022983115 & 0.733789885084425 \tabularnewline
52 & 0.233819783767987 & 0.467639567535974 & 0.766180216232013 \tabularnewline
53 & 0.247496610474995 & 0.49499322094999 & 0.752503389525005 \tabularnewline
54 & 0.239748473359731 & 0.479496946719461 & 0.76025152664027 \tabularnewline
55 & 0.205815443615118 & 0.411630887230235 & 0.794184556384882 \tabularnewline
56 & 0.171522663643838 & 0.343045327287677 & 0.828477336356162 \tabularnewline
57 & 0.146845641288626 & 0.293691282577253 & 0.853154358711374 \tabularnewline
58 & 0.119524722758276 & 0.239049445516552 & 0.880475277241724 \tabularnewline
59 & 0.0983302381795049 & 0.196660476359010 & 0.901669761820495 \tabularnewline
60 & 0.0792557843624406 & 0.158511568724881 & 0.92074421563756 \tabularnewline
61 & 0.064528210232103 & 0.129056420464206 & 0.935471789767897 \tabularnewline
62 & 0.054394298511313 & 0.108788597022626 & 0.945605701488687 \tabularnewline
63 & 0.0564336107773984 & 0.112867221554797 & 0.943566389222602 \tabularnewline
64 & 0.0444788650633094 & 0.0889577301266188 & 0.95552113493669 \tabularnewline
65 & 0.0370605526644852 & 0.0741211053289704 & 0.962939447335515 \tabularnewline
66 & 0.0461963928858566 & 0.0923927857717132 & 0.953803607114143 \tabularnewline
67 & 0.078383661124857 & 0.156767322249714 & 0.921616338875143 \tabularnewline
68 & 0.100063384474804 & 0.200126768949609 & 0.899936615525196 \tabularnewline
69 & 0.170898377458503 & 0.341796754917007 & 0.829101622541497 \tabularnewline
70 & 0.143240610258524 & 0.286481220517048 & 0.856759389741476 \tabularnewline
71 & 0.117435498124612 & 0.234870996249224 & 0.882564501875388 \tabularnewline
72 & 0.125104616022163 & 0.250209232044325 & 0.874895383977837 \tabularnewline
73 & 0.151166041979441 & 0.302332083958881 & 0.84883395802056 \tabularnewline
74 & 0.253185098505182 & 0.506370197010364 & 0.746814901494818 \tabularnewline
75 & 0.287370917941285 & 0.574741835882571 & 0.712629082058715 \tabularnewline
76 & 0.459110648124279 & 0.918221296248559 & 0.54088935187572 \tabularnewline
77 & 0.470259450421639 & 0.940518900843278 & 0.529740549578361 \tabularnewline
78 & 0.427860109719806 & 0.855720219439612 & 0.572139890280194 \tabularnewline
79 & 0.394178837104038 & 0.788357674208076 & 0.605821162895962 \tabularnewline
80 & 0.474670127291805 & 0.94934025458361 & 0.525329872708195 \tabularnewline
81 & 0.504055442247718 & 0.991889115504564 & 0.495944557752282 \tabularnewline
82 & 0.463595113800152 & 0.927190227600303 & 0.536404886199848 \tabularnewline
83 & 0.420411675365874 & 0.840823350731749 & 0.579588324634126 \tabularnewline
84 & 0.433647619399842 & 0.867295238799684 & 0.566352380600158 \tabularnewline
85 & 0.387860399337857 & 0.775720798675714 & 0.612139600662143 \tabularnewline
86 & 0.341990235438495 & 0.68398047087699 & 0.658009764561505 \tabularnewline
87 & 0.298289951910688 & 0.596579903821377 & 0.701710048089312 \tabularnewline
88 & 0.254097581364017 & 0.508195162728034 & 0.745902418635983 \tabularnewline
89 & 0.215729730396148 & 0.431459460792297 & 0.784270269603851 \tabularnewline
90 & 0.226669813213679 & 0.453339626427358 & 0.773330186786321 \tabularnewline
91 & 0.214405536968969 & 0.428811073937937 & 0.785594463031031 \tabularnewline
92 & 0.198698739745807 & 0.397397479491615 & 0.801301260254192 \tabularnewline
93 & 0.225916375361554 & 0.451832750723109 & 0.774083624638446 \tabularnewline
94 & 0.265576071476616 & 0.531152142953231 & 0.734423928523384 \tabularnewline
95 & 0.460719615407099 & 0.921439230814199 & 0.5392803845929 \tabularnewline
96 & 0.449243234380078 & 0.898486468760156 & 0.550756765619922 \tabularnewline
97 & 0.385528312306976 & 0.771056624613952 & 0.614471687693024 \tabularnewline
98 & 0.34196934864505 & 0.6839386972901 & 0.65803065135495 \tabularnewline
99 & 0.291469910778403 & 0.582939821556807 & 0.708530089221597 \tabularnewline
100 & 0.283820813001128 & 0.567641626002256 & 0.716179186998872 \tabularnewline
101 & 0.305959546492602 & 0.611919092985204 & 0.694040453507398 \tabularnewline
102 & 0.281509511652742 & 0.563019023305484 & 0.718490488347258 \tabularnewline
103 & 0.237896466104520 & 0.475792932209041 & 0.76210353389548 \tabularnewline
104 & 0.234107832569596 & 0.468215665139191 & 0.765892167430404 \tabularnewline
105 & 0.273402804741132 & 0.546805609482263 & 0.726597195258868 \tabularnewline
106 & 0.24174247611224 & 0.48348495222448 & 0.75825752388776 \tabularnewline
107 & 0.455952003393071 & 0.911904006786143 & 0.544047996606929 \tabularnewline
108 & 0.891798041393471 & 0.216403917213057 & 0.108201958606529 \tabularnewline
109 & 0.97145406124368 & 0.05709187751264 & 0.02854593875632 \tabularnewline
110 & 0.93893247055608 & 0.122135058887840 & 0.0610675294439202 \tabularnewline
111 & 0.90439058994209 & 0.191218820115818 & 0.095609410057909 \tabularnewline
112 & 0.987042880709796 & 0.0259142385804083 & 0.0129571192902042 \tabularnewline
113 & 0.956813610501623 & 0.0863727789967539 & 0.0431863894983769 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=112165&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]7[/C][C]0.371898082465908[/C][C]0.743796164931816[/C][C]0.628101917534092[/C][/ROW]
[ROW][C]8[/C][C]0.251730794010919[/C][C]0.503461588021838[/C][C]0.74826920598908[/C][/ROW]
[ROW][C]9[/C][C]0.54574599183176[/C][C]0.908508016336479[/C][C]0.454254008168239[/C][/ROW]
[ROW][C]10[/C][C]0.424140127407054[/C][C]0.848280254814108[/C][C]0.575859872592946[/C][/ROW]
[ROW][C]11[/C][C]0.317981380002062[/C][C]0.635962760004124[/C][C]0.682018619997938[/C][/ROW]
[ROW][C]12[/C][C]0.240967300826959[/C][C]0.481934601653918[/C][C]0.759032699173041[/C][/ROW]
[ROW][C]13[/C][C]0.170695156938326[/C][C]0.341390313876652[/C][C]0.829304843061674[/C][/ROW]
[ROW][C]14[/C][C]0.131505607981956[/C][C]0.263011215963913[/C][C]0.868494392018044[/C][/ROW]
[ROW][C]15[/C][C]0.0896824488235724[/C][C]0.179364897647145[/C][C]0.910317551176428[/C][/ROW]
[ROW][C]16[/C][C]0.0596897723697587[/C][C]0.119379544739517[/C][C]0.94031022763024[/C][/ROW]
[ROW][C]17[/C][C]0.0391063850781463[/C][C]0.0782127701562926[/C][C]0.960893614921854[/C][/ROW]
[ROW][C]18[/C][C]0.0276751360232245[/C][C]0.055350272046449[/C][C]0.972324863976776[/C][/ROW]
[ROW][C]19[/C][C]0.0166286529226134[/C][C]0.0332573058452267[/C][C]0.983371347077387[/C][/ROW]
[ROW][C]20[/C][C]0.0118625034961129[/C][C]0.0237250069922257[/C][C]0.988137496503887[/C][/ROW]
[ROW][C]21[/C][C]0.0069156487538201[/C][C]0.0138312975076402[/C][C]0.99308435124618[/C][/ROW]
[ROW][C]22[/C][C]0.00428522596993973[/C][C]0.00857045193987947[/C][C]0.99571477403006[/C][/ROW]
[ROW][C]23[/C][C]0.00349238548498683[/C][C]0.00698477096997365[/C][C]0.996507614515013[/C][/ROW]
[ROW][C]24[/C][C]0.00207861037962961[/C][C]0.00415722075925922[/C][C]0.99792138962037[/C][/ROW]
[ROW][C]25[/C][C]0.00112939554302189[/C][C]0.00225879108604377[/C][C]0.998870604456978[/C][/ROW]
[ROW][C]26[/C][C]0.000624265169103494[/C][C]0.00124853033820699[/C][C]0.999375734830896[/C][/ROW]
[ROW][C]27[/C][C]0.000442842376913901[/C][C]0.000885684753827803[/C][C]0.999557157623086[/C][/ROW]
[ROW][C]28[/C][C]0.000483971612896719[/C][C]0.000967943225793438[/C][C]0.999516028387103[/C][/ROW]
[ROW][C]29[/C][C]0.000492597178990225[/C][C]0.00098519435798045[/C][C]0.99950740282101[/C][/ROW]
[ROW][C]30[/C][C]0.000279917472457482[/C][C]0.000559834944914965[/C][C]0.999720082527543[/C][/ROW]
[ROW][C]31[/C][C]0.000216914075621245[/C][C]0.00043382815124249[/C][C]0.999783085924379[/C][/ROW]
[ROW][C]32[/C][C]0.000263443901212857[/C][C]0.000526887802425713[/C][C]0.999736556098787[/C][/ROW]
[ROW][C]33[/C][C]0.000180419942309369[/C][C]0.000360839884618738[/C][C]0.99981958005769[/C][/ROW]
[ROW][C]34[/C][C]0.00491646648024632[/C][C]0.00983293296049264[/C][C]0.995083533519754[/C][/ROW]
[ROW][C]35[/C][C]0.0608641858213145[/C][C]0.121728371642629[/C][C]0.939135814178686[/C][/ROW]
[ROW][C]36[/C][C]0.117123153872368[/C][C]0.234246307744736[/C][C]0.882876846127632[/C][/ROW]
[ROW][C]37[/C][C]0.109844158114402[/C][C]0.219688316228804[/C][C]0.890155841885598[/C][/ROW]
[ROW][C]38[/C][C]0.0877741576511475[/C][C]0.175548315302295[/C][C]0.912225842348853[/C][/ROW]
[ROW][C]39[/C][C]0.0841244609337334[/C][C]0.168248921867467[/C][C]0.915875539066267[/C][/ROW]
[ROW][C]40[/C][C]0.095556883551824[/C][C]0.191113767103648[/C][C]0.904443116448176[/C][/ROW]
[ROW][C]41[/C][C]0.103858183698499[/C][C]0.207716367396998[/C][C]0.8961418163015[/C][/ROW]
[ROW][C]42[/C][C]0.0869645852597672[/C][C]0.173929170519534[/C][C]0.913035414740233[/C][/ROW]
[ROW][C]43[/C][C]0.0662831782903852[/C][C]0.132566356580770[/C][C]0.933716821709615[/C][/ROW]
[ROW][C]44[/C][C]0.0510967101069132[/C][C]0.102193420213826[/C][C]0.948903289893087[/C][/ROW]
[ROW][C]45[/C][C]0.0511992767540234[/C][C]0.102398553508047[/C][C]0.948800723245977[/C][/ROW]
[ROW][C]46[/C][C]0.040264401770251[/C][C]0.080528803540502[/C][C]0.959735598229749[/C][/ROW]
[ROW][C]47[/C][C]0.0727526648952477[/C][C]0.145505329790495[/C][C]0.927247335104752[/C][/ROW]
[ROW][C]48[/C][C]0.126746027388126[/C][C]0.253492054776252[/C][C]0.873253972611874[/C][/ROW]
[ROW][C]49[/C][C]0.204474880557329[/C][C]0.408949761114658[/C][C]0.795525119442671[/C][/ROW]
[ROW][C]50[/C][C]0.296903459349063[/C][C]0.593806918698127[/C][C]0.703096540650937[/C][/ROW]
[ROW][C]51[/C][C]0.266210114915575[/C][C]0.53242022983115[/C][C]0.733789885084425[/C][/ROW]
[ROW][C]52[/C][C]0.233819783767987[/C][C]0.467639567535974[/C][C]0.766180216232013[/C][/ROW]
[ROW][C]53[/C][C]0.247496610474995[/C][C]0.49499322094999[/C][C]0.752503389525005[/C][/ROW]
[ROW][C]54[/C][C]0.239748473359731[/C][C]0.479496946719461[/C][C]0.76025152664027[/C][/ROW]
[ROW][C]55[/C][C]0.205815443615118[/C][C]0.411630887230235[/C][C]0.794184556384882[/C][/ROW]
[ROW][C]56[/C][C]0.171522663643838[/C][C]0.343045327287677[/C][C]0.828477336356162[/C][/ROW]
[ROW][C]57[/C][C]0.146845641288626[/C][C]0.293691282577253[/C][C]0.853154358711374[/C][/ROW]
[ROW][C]58[/C][C]0.119524722758276[/C][C]0.239049445516552[/C][C]0.880475277241724[/C][/ROW]
[ROW][C]59[/C][C]0.0983302381795049[/C][C]0.196660476359010[/C][C]0.901669761820495[/C][/ROW]
[ROW][C]60[/C][C]0.0792557843624406[/C][C]0.158511568724881[/C][C]0.92074421563756[/C][/ROW]
[ROW][C]61[/C][C]0.064528210232103[/C][C]0.129056420464206[/C][C]0.935471789767897[/C][/ROW]
[ROW][C]62[/C][C]0.054394298511313[/C][C]0.108788597022626[/C][C]0.945605701488687[/C][/ROW]
[ROW][C]63[/C][C]0.0564336107773984[/C][C]0.112867221554797[/C][C]0.943566389222602[/C][/ROW]
[ROW][C]64[/C][C]0.0444788650633094[/C][C]0.0889577301266188[/C][C]0.95552113493669[/C][/ROW]
[ROW][C]65[/C][C]0.0370605526644852[/C][C]0.0741211053289704[/C][C]0.962939447335515[/C][/ROW]
[ROW][C]66[/C][C]0.0461963928858566[/C][C]0.0923927857717132[/C][C]0.953803607114143[/C][/ROW]
[ROW][C]67[/C][C]0.078383661124857[/C][C]0.156767322249714[/C][C]0.921616338875143[/C][/ROW]
[ROW][C]68[/C][C]0.100063384474804[/C][C]0.200126768949609[/C][C]0.899936615525196[/C][/ROW]
[ROW][C]69[/C][C]0.170898377458503[/C][C]0.341796754917007[/C][C]0.829101622541497[/C][/ROW]
[ROW][C]70[/C][C]0.143240610258524[/C][C]0.286481220517048[/C][C]0.856759389741476[/C][/ROW]
[ROW][C]71[/C][C]0.117435498124612[/C][C]0.234870996249224[/C][C]0.882564501875388[/C][/ROW]
[ROW][C]72[/C][C]0.125104616022163[/C][C]0.250209232044325[/C][C]0.874895383977837[/C][/ROW]
[ROW][C]73[/C][C]0.151166041979441[/C][C]0.302332083958881[/C][C]0.84883395802056[/C][/ROW]
[ROW][C]74[/C][C]0.253185098505182[/C][C]0.506370197010364[/C][C]0.746814901494818[/C][/ROW]
[ROW][C]75[/C][C]0.287370917941285[/C][C]0.574741835882571[/C][C]0.712629082058715[/C][/ROW]
[ROW][C]76[/C][C]0.459110648124279[/C][C]0.918221296248559[/C][C]0.54088935187572[/C][/ROW]
[ROW][C]77[/C][C]0.470259450421639[/C][C]0.940518900843278[/C][C]0.529740549578361[/C][/ROW]
[ROW][C]78[/C][C]0.427860109719806[/C][C]0.855720219439612[/C][C]0.572139890280194[/C][/ROW]
[ROW][C]79[/C][C]0.394178837104038[/C][C]0.788357674208076[/C][C]0.605821162895962[/C][/ROW]
[ROW][C]80[/C][C]0.474670127291805[/C][C]0.94934025458361[/C][C]0.525329872708195[/C][/ROW]
[ROW][C]81[/C][C]0.504055442247718[/C][C]0.991889115504564[/C][C]0.495944557752282[/C][/ROW]
[ROW][C]82[/C][C]0.463595113800152[/C][C]0.927190227600303[/C][C]0.536404886199848[/C][/ROW]
[ROW][C]83[/C][C]0.420411675365874[/C][C]0.840823350731749[/C][C]0.579588324634126[/C][/ROW]
[ROW][C]84[/C][C]0.433647619399842[/C][C]0.867295238799684[/C][C]0.566352380600158[/C][/ROW]
[ROW][C]85[/C][C]0.387860399337857[/C][C]0.775720798675714[/C][C]0.612139600662143[/C][/ROW]
[ROW][C]86[/C][C]0.341990235438495[/C][C]0.68398047087699[/C][C]0.658009764561505[/C][/ROW]
[ROW][C]87[/C][C]0.298289951910688[/C][C]0.596579903821377[/C][C]0.701710048089312[/C][/ROW]
[ROW][C]88[/C][C]0.254097581364017[/C][C]0.508195162728034[/C][C]0.745902418635983[/C][/ROW]
[ROW][C]89[/C][C]0.215729730396148[/C][C]0.431459460792297[/C][C]0.784270269603851[/C][/ROW]
[ROW][C]90[/C][C]0.226669813213679[/C][C]0.453339626427358[/C][C]0.773330186786321[/C][/ROW]
[ROW][C]91[/C][C]0.214405536968969[/C][C]0.428811073937937[/C][C]0.785594463031031[/C][/ROW]
[ROW][C]92[/C][C]0.198698739745807[/C][C]0.397397479491615[/C][C]0.801301260254192[/C][/ROW]
[ROW][C]93[/C][C]0.225916375361554[/C][C]0.451832750723109[/C][C]0.774083624638446[/C][/ROW]
[ROW][C]94[/C][C]0.265576071476616[/C][C]0.531152142953231[/C][C]0.734423928523384[/C][/ROW]
[ROW][C]95[/C][C]0.460719615407099[/C][C]0.921439230814199[/C][C]0.5392803845929[/C][/ROW]
[ROW][C]96[/C][C]0.449243234380078[/C][C]0.898486468760156[/C][C]0.550756765619922[/C][/ROW]
[ROW][C]97[/C][C]0.385528312306976[/C][C]0.771056624613952[/C][C]0.614471687693024[/C][/ROW]
[ROW][C]98[/C][C]0.34196934864505[/C][C]0.6839386972901[/C][C]0.65803065135495[/C][/ROW]
[ROW][C]99[/C][C]0.291469910778403[/C][C]0.582939821556807[/C][C]0.708530089221597[/C][/ROW]
[ROW][C]100[/C][C]0.283820813001128[/C][C]0.567641626002256[/C][C]0.716179186998872[/C][/ROW]
[ROW][C]101[/C][C]0.305959546492602[/C][C]0.611919092985204[/C][C]0.694040453507398[/C][/ROW]
[ROW][C]102[/C][C]0.281509511652742[/C][C]0.563019023305484[/C][C]0.718490488347258[/C][/ROW]
[ROW][C]103[/C][C]0.237896466104520[/C][C]0.475792932209041[/C][C]0.76210353389548[/C][/ROW]
[ROW][C]104[/C][C]0.234107832569596[/C][C]0.468215665139191[/C][C]0.765892167430404[/C][/ROW]
[ROW][C]105[/C][C]0.273402804741132[/C][C]0.546805609482263[/C][C]0.726597195258868[/C][/ROW]
[ROW][C]106[/C][C]0.24174247611224[/C][C]0.48348495222448[/C][C]0.75825752388776[/C][/ROW]
[ROW][C]107[/C][C]0.455952003393071[/C][C]0.911904006786143[/C][C]0.544047996606929[/C][/ROW]
[ROW][C]108[/C][C]0.891798041393471[/C][C]0.216403917213057[/C][C]0.108201958606529[/C][/ROW]
[ROW][C]109[/C][C]0.97145406124368[/C][C]0.05709187751264[/C][C]0.02854593875632[/C][/ROW]
[ROW][C]110[/C][C]0.93893247055608[/C][C]0.122135058887840[/C][C]0.0610675294439202[/C][/ROW]
[ROW][C]111[/C][C]0.90439058994209[/C][C]0.191218820115818[/C][C]0.095609410057909[/C][/ROW]
[ROW][C]112[/C][C]0.987042880709796[/C][C]0.0259142385804083[/C][C]0.0129571192902042[/C][/ROW]
[ROW][C]113[/C][C]0.956813610501623[/C][C]0.0863727789967539[/C][C]0.0431863894983769[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=112165&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=112165&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
7	0.371898082465908	0.743796164931816	0.628101917534092
8	0.251730794010919	0.503461588021838	0.74826920598908
9	0.54574599183176	0.908508016336479	0.454254008168239
10	0.424140127407054	0.848280254814108	0.575859872592946
11	0.317981380002062	0.635962760004124	0.682018619997938
12	0.240967300826959	0.481934601653918	0.759032699173041
13	0.170695156938326	0.341390313876652	0.829304843061674
14	0.131505607981956	0.263011215963913	0.868494392018044
15	0.0896824488235724	0.179364897647145	0.910317551176428
16	0.0596897723697587	0.119379544739517	0.94031022763024
17	0.0391063850781463	0.0782127701562926	0.960893614921854
18	0.0276751360232245	0.055350272046449	0.972324863976776
19	0.0166286529226134	0.0332573058452267	0.983371347077387
20	0.0118625034961129	0.0237250069922257	0.988137496503887
21	0.0069156487538201	0.0138312975076402	0.99308435124618
22	0.00428522596993973	0.00857045193987947	0.99571477403006
23	0.00349238548498683	0.00698477096997365	0.996507614515013
24	0.00207861037962961	0.00415722075925922	0.99792138962037
25	0.00112939554302189	0.00225879108604377	0.998870604456978
26	0.000624265169103494	0.00124853033820699	0.999375734830896
27	0.000442842376913901	0.000885684753827803	0.999557157623086
28	0.000483971612896719	0.000967943225793438	0.999516028387103
29	0.000492597178990225	0.00098519435798045	0.99950740282101
30	0.000279917472457482	0.000559834944914965	0.999720082527543
31	0.000216914075621245	0.00043382815124249	0.999783085924379
32	0.000263443901212857	0.000526887802425713	0.999736556098787
33	0.000180419942309369	0.000360839884618738	0.99981958005769
34	0.00491646648024632	0.00983293296049264	0.995083533519754
35	0.0608641858213145	0.121728371642629	0.939135814178686
36	0.117123153872368	0.234246307744736	0.882876846127632
37	0.109844158114402	0.219688316228804	0.890155841885598
38	0.0877741576511475	0.175548315302295	0.912225842348853
39	0.0841244609337334	0.168248921867467	0.915875539066267
40	0.095556883551824	0.191113767103648	0.904443116448176
41	0.103858183698499	0.207716367396998	0.8961418163015
42	0.0869645852597672	0.173929170519534	0.913035414740233
43	0.0662831782903852	0.132566356580770	0.933716821709615
44	0.0510967101069132	0.102193420213826	0.948903289893087
45	0.0511992767540234	0.102398553508047	0.948800723245977
46	0.040264401770251	0.080528803540502	0.959735598229749
47	0.0727526648952477	0.145505329790495	0.927247335104752
48	0.126746027388126	0.253492054776252	0.873253972611874
49	0.204474880557329	0.408949761114658	0.795525119442671
50	0.296903459349063	0.593806918698127	0.703096540650937
51	0.266210114915575	0.53242022983115	0.733789885084425
52	0.233819783767987	0.467639567535974	0.766180216232013
53	0.247496610474995	0.49499322094999	0.752503389525005
54	0.239748473359731	0.479496946719461	0.76025152664027
55	0.205815443615118	0.411630887230235	0.794184556384882
56	0.171522663643838	0.343045327287677	0.828477336356162
57	0.146845641288626	0.293691282577253	0.853154358711374
58	0.119524722758276	0.239049445516552	0.880475277241724
59	0.0983302381795049	0.196660476359010	0.901669761820495
60	0.0792557843624406	0.158511568724881	0.92074421563756
61	0.064528210232103	0.129056420464206	0.935471789767897
62	0.054394298511313	0.108788597022626	0.945605701488687
63	0.0564336107773984	0.112867221554797	0.943566389222602
64	0.0444788650633094	0.0889577301266188	0.95552113493669
65	0.0370605526644852	0.0741211053289704	0.962939447335515
66	0.0461963928858566	0.0923927857717132	0.953803607114143
67	0.078383661124857	0.156767322249714	0.921616338875143
68	0.100063384474804	0.200126768949609	0.899936615525196
69	0.170898377458503	0.341796754917007	0.829101622541497
70	0.143240610258524	0.286481220517048	0.856759389741476
71	0.117435498124612	0.234870996249224	0.882564501875388
72	0.125104616022163	0.250209232044325	0.874895383977837
73	0.151166041979441	0.302332083958881	0.84883395802056
74	0.253185098505182	0.506370197010364	0.746814901494818
75	0.287370917941285	0.574741835882571	0.712629082058715
76	0.459110648124279	0.918221296248559	0.54088935187572
77	0.470259450421639	0.940518900843278	0.529740549578361
78	0.427860109719806	0.855720219439612	0.572139890280194
79	0.394178837104038	0.788357674208076	0.605821162895962
80	0.474670127291805	0.94934025458361	0.525329872708195
81	0.504055442247718	0.991889115504564	0.495944557752282
82	0.463595113800152	0.927190227600303	0.536404886199848
83	0.420411675365874	0.840823350731749	0.579588324634126
84	0.433647619399842	0.867295238799684	0.566352380600158
85	0.387860399337857	0.775720798675714	0.612139600662143
86	0.341990235438495	0.68398047087699	0.658009764561505
87	0.298289951910688	0.596579903821377	0.701710048089312
88	0.254097581364017	0.508195162728034	0.745902418635983
89	0.215729730396148	0.431459460792297	0.784270269603851
90	0.226669813213679	0.453339626427358	0.773330186786321
91	0.214405536968969	0.428811073937937	0.785594463031031
92	0.198698739745807	0.397397479491615	0.801301260254192
93	0.225916375361554	0.451832750723109	0.774083624638446
94	0.265576071476616	0.531152142953231	0.734423928523384
95	0.460719615407099	0.921439230814199	0.5392803845929
96	0.449243234380078	0.898486468760156	0.550756765619922
97	0.385528312306976	0.771056624613952	0.614471687693024
98	0.34196934864505	0.6839386972901	0.65803065135495
99	0.291469910778403	0.582939821556807	0.708530089221597
100	0.283820813001128	0.567641626002256	0.716179186998872
101	0.305959546492602	0.611919092985204	0.694040453507398
102	0.281509511652742	0.563019023305484	0.718490488347258
103	0.237896466104520	0.475792932209041	0.76210353389548
104	0.234107832569596	0.468215665139191	0.765892167430404
105	0.273402804741132	0.546805609482263	0.726597195258868
106	0.24174247611224	0.48348495222448	0.75825752388776
107	0.455952003393071	0.911904006786143	0.544047996606929
108	0.891798041393471	0.216403917213057	0.108201958606529
109	0.97145406124368	0.05709187751264	0.02854593875632
110	0.93893247055608	0.122135058887840	0.0610675294439202
111	0.90439058994209	0.191218820115818	0.095609410057909
112	0.987042880709796	0.0259142385804083	0.0129571192902042
113	0.956813610501623	0.0863727789967539	0.0431863894983769

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	13	0.121495327102804	NOK
5% type I error level	17	0.158878504672897	NOK
10% type I error level	25	0.233644859813084	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 & 13 & 0.121495327102804 & NOK \tabularnewline
5% type I error level & 17 & 0.158878504672897 & NOK \tabularnewline
10% type I error level & 25 & 0.233644859813084 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=112165&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]13[/C][C]0.121495327102804[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]17[/C][C]0.158878504672897[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]25[/C][C]0.233644859813084[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=112165&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=112165&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	13	0.121495327102804	NOK
5% type I error level	17	0.158878504672897	NOK
10% type I error level	25	0.233644859813084	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):

Parameters (R input):

par1 = 1 ; 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