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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Tue, 17 Nov 2009 11:48:27 -0700

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2009/Nov/17/t1258483827wlitpc2g7t810k5.htm/, Retrieved Fri, 07 Nov 2025 06:02:35 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=57401, Retrieved Fri, 07 Nov 2025 06:02:35 +0000

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IsPrivate?

No (this computation is public)

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Estimated Impact

490

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

-     [Multiple Regression] [Q1 The Seatbeltlaw] [2007-11-14 19:27:43] [8cd6641b921d30ebe00b648d1481bba0]
- RMPD  [Multiple Regression] [Seatbelt] [2009-11-12 13:54:52] [b98453cac15ba1066b407e146608df68]
-    D      [Multiple Regression] [workshop 7 bereke...] [2009-11-17 18:48:27] [78d370e6d5f4594e9982a5085e7604c6] [Current]

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Dataseries X:

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6.70	2.04
6.40	2.16
6.30	2.75
6.80	2.79
7.30	2.88
7.10	3.36
7.00	2.97
6.80	3.10
6.60	2.49
6.30	2.20
6.10	2.25
6.10	2.09
6.30	2.79
6.30	3.14
6.00	2.93
6.20	2.65
6.40	2.67
6.80	2.26
7.50	2.35
7.50	2.13
7.60	2.18
7.60	2.90
7.40	2.63
7.30	2.67
7.10	1.81
6.90	1.33
6.80	0.88
7.50	1.28
7.60	1.26
7.80	1.26
8.00	1.29
8.10	1.10
8.20	1.37
8.30	1.21
8.20	1.74
8.00	1.76
7.90	1.48
7.60	1.04
7.60	1.62
8.30	1.49
8.40	1.79
8.40	1.80
8.40	1.58
8.40	1.86
8.60	1.74
8.90	1.59
8.80	1.26
8.30	1.13
7.50	1.92
7.20	2.61
7.40	2.26
8.80	2.41
9.30	2.26
9.30	2.03
8.70	2.86
8.20	2.55
8.30	2.27
8.50	2.26
8.60	2.57
8.50	3.07
8.20	2.76
8.10	2.51
7.90	2.87
8.60	3.14
8.70	3.11
8.70	3.16
8.50	2.47
8.40	2.57
8.50	2.89
8.70	2.63
8.70	2.38
8.60	1.69
8.50	1.96
8.30	2.19
8.00	1.87
8.20	1.60
8.10	1.63
8.10	1.22
8.00	1.21
7.90	1.49
7.90	1.64
8.00	1.66
8.00	1.77
7.90	1.82
8.00	1.78
7.70	1.28
7.20	1.29
7.50	1.37
7.30	1.12
7.00	1.51
7.00	2.24
7.00	2.94
7.20	3.09
7.30	3.46
7.10	3.64
6.80	4.39
6.40	4.15
6.10	5.21
6.50	5.80
7.70	5.91
7.90	5.39
7.50	5.46
6.90	4.72
6.60	3.14
6.90	2.63
7.70	2.32
8.00	1.93
8.00	0.62

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	4 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 & 4 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57401&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]4 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=57401&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57401&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	4 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

Multiple Linear Regression - Estimated Regression Equation
Y[t] = + 8.19716164420583 -0.23130426630238X[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Y[t] =  +  8.19716164420583 -0.23130426630238X[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57401&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Y[t] =  +  8.19716164420583 -0.23130426630238X[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57401&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57401&T=1

As an alternative you can also use a QR Code:

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

Multiple Linear Regression - Estimated Regression Equation
Y[t] = + 8.19716164420583 -0.23130426630238X[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)	8.19716164420583	0.183843	44.5879	0	0
X	-0.23130426630238	0.071629	-3.2292	0.001653	0.000826

\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) & 8.19716164420583 & 0.183843 & 44.5879 & 0 & 0 \tabularnewline
X & -0.23130426630238 & 0.071629 & -3.2292 & 0.001653 & 0.000826 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57401&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]8.19716164420583[/C][C]0.183843[/C][C]44.5879[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]X[/C][C]-0.23130426630238[/C][C]0.071629[/C][C]-3.2292[/C][C]0.001653[/C][C]0.000826[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57401&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57401&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)	8.19716164420583	0.183843	44.5879	0	0
X	-0.23130426630238	0.071629	-3.2292	0.001653	0.000826

Multiple Linear Regression - Regression Statistics
Multiple R	0.299274017301092
R-squared	0.089564937431534
Adjusted R-squared	0.080975927407303
F-TEST (value)	10.4278534055565
F-TEST (DF numerator)	1
F-TEST (DF denominator)	106
p-value	0.00165294208368527
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.7689867461205
Sum Squared Residuals	62.6821052651534

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.299274017301092 \tabularnewline
R-squared & 0.089564937431534 \tabularnewline
Adjusted R-squared & 0.080975927407303 \tabularnewline
F-TEST (value) & 10.4278534055565 \tabularnewline
F-TEST (DF numerator) & 1 \tabularnewline
F-TEST (DF denominator) & 106 \tabularnewline
p-value & 0.00165294208368527 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.7689867461205 \tabularnewline
Sum Squared Residuals & 62.6821052651534 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57401&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.299274017301092[/C][/ROW]
[ROW][C]R-squared[/C][C]0.089564937431534[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.080975927407303[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]10.4278534055565[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]1[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]106[/C][/ROW]
[ROW][C]p-value[/C][C]0.00165294208368527[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.7689867461205[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]62.6821052651534[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57401&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57401&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	0.299274017301092
R-squared	0.089564937431534
Adjusted R-squared	0.080975927407303
F-TEST (value)	10.4278534055565
F-TEST (DF numerator)	1
F-TEST (DF denominator)	106
p-value	0.00165294208368527
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.7689867461205
Sum Squared Residuals	62.6821052651534

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	6.7	7.72530094094887	-1.02530094094887
2	6.4	7.69754442899268	-1.29754442899268
3	6.3	7.56107491187428	-1.26107491187428
4	6.8	7.55182274122218	-0.751822741222184
5	7.3	7.53100535725497	-0.23100535725497
6	7.1	7.41997930942983	-0.319979309429828
7	7	7.51018797328776	-0.510187973287755
8	6.8	7.48011841866845	-0.680118418668446
9	6.6	7.6212140211129	-1.02121402111290
10	6.3	7.68829225834059	-1.38829225834059
11	6.1	7.67672704502547	-1.57672704502547
12	6.1	7.71373572763385	-1.61373572763385
13	6.3	7.55182274122218	-1.25182274122218
14	6.3	7.47086624801635	-1.17086624801635
15	6	7.51944014393985	-1.51944014393985
16	6.2	7.58420533850452	-1.38420533850452
17	6.4	7.57957925317847	-1.17957925317847
18	6.8	7.67441400236244	-0.874414002362446
19	7.5	7.65359661839523	-0.153596618395231
20	7.5	7.70448355698175	-0.204483556981755
21	7.6	7.69291834366664	-0.092918343666636
22	7.6	7.52637927192892	0.0736207280710776
23	7.4	7.58883142383056	-0.188831423830564
24	7.3	7.57957925317847	-0.279579253178470
25	7.1	7.77850092219852	-0.678500922198517
26	6.9	7.88952697002366	-0.989526970023658
27	6.8	7.99361388985973	-1.19361388985973
28	7.5	7.90109218333878	-0.401092183338778
29	7.6	7.90571826866483	-0.305718268664826
30	7.8	7.90571826866483	-0.105718268664826
31	8	7.89877914067575	0.101220859324246
32	8.1	7.9427269512732	0.157273048726793
33	8.2	7.88027479937156	0.319725200628436
34	8.3	7.91728348197994	0.382716518020056
35	8.2	7.79469222083968	0.405307779160316
36	8	7.79006613551364	0.209933864486365
37	7.9	7.8548313300783	0.0451686699216985
38	7.6	7.95660520725135	-0.356605207251349
39	7.6	7.82244873279597	-0.222448732795969
40	8.3	7.85251828741528	0.447481712584723
41	8.4	7.78312700752456	0.616872992475436
42	8.4	7.78081396486154	0.61918603513846
43	8.4	7.83170090344806	0.568299096551937
44	8.4	7.7669357088834	0.633064291116603
45	8.6	7.79469222083968	0.805307779160317
46	8.9	7.82938786078504	1.07061213921496
47	8.8	7.90571826866483	0.894281731335175
48	8.3	7.93578782328413	0.364212176715866
49	7.5	7.75305745290525	-0.253057452905255
50	7.2	7.59345750915661	-0.393457509156612
51	7.4	7.67441400236244	-0.274414002362445
52	8.8	7.63971836241709	1.16028163758291
53	9.3	7.67441400236244	1.62558599763756
54	9.3	7.727613983612	1.57238601638801
55	8.7	7.53563144258102	1.16436855741898
56	8.2	7.60733576513475	0.592664234865244
57	8.3	7.67210095969942	0.627899040300579
58	8.5	7.67441400236244	0.825585997637555
59	8.6	7.60270967980871	0.997290320191292
60	8.5	7.48705754665752	1.01294245334248
61	8.2	7.55876186921126	0.641238130788744
62	8.1	7.61658793578685	0.483412064213149
63	7.9	7.533318399918	0.366681600082007
64	8.6	7.47086624801635	1.12913375198365
65	8.7	7.47780537600542	1.22219462399458
66	8.7	7.4662401626903	1.23375983730970
67	8.5	7.62584010643895	0.874159893561054
68	8.4	7.60270967980871	0.797290320191293
69	8.5	7.52869231459195	0.971307685408054
70	8.7	7.58883142383056	1.11116857616943
71	8.7	7.64665749040616	1.05334250959384
72	8.6	7.8062574341548	0.793742565845198
73	8.5	7.74380528225316	0.75619471774684
74	8.3	7.69060530100361	0.609394698996389
75	8	7.76462266622037	0.235377333779626
76	8.2	7.82707481812202	0.372925181877983
77	8.1	7.82013569013295	0.279864309867055
78	8.1	7.91497043931692	0.185029560683079
79	8	7.91728348197994	0.0827165180200555
80	7.9	7.85251828741528	0.0474817125847223
81	7.9	7.81782264746992	0.0821773525300793
82	8	7.81319656214387	0.186803437856127
83	8	7.78775309285061	0.212246907149388
84	7.9	7.7761878795355	0.123812120464508
85	8	7.78544005018759	0.214559949812412
86	7.7	7.90109218333878	-0.201092183338778
87	7.2	7.89877914067575	-0.698779140675754
88	7.5	7.88027479937156	-0.380274799371564
89	7.3	7.93810086594716	-0.638100865947159
90	7	7.84789220208923	-0.84789220208923
91	7	7.6790400876885	-0.679040087688493
92	7	7.51712710127683	-0.517127101276827
93	7.2	7.48243146133147	-0.28243146133147
94	7.3	7.39684888279959	-0.0968488827995894
95	7.1	7.35521411486516	-0.255214114865161
96	6.8	7.18173591513838	-0.381735915138376
97	6.4	7.23724893905095	-0.837248939050947
98	6.1	6.99206641677042	-0.892066416770424
99	6.5	6.85559689965202	-0.35559689965202
100	7.7	6.83015343035876	0.869846569641242
101	7.9	6.950431648836	0.949568351164005
102	7.5	6.93424035019483	0.565759649805171
103	6.9	7.10540550725859	-0.20540550725859
104	6.6	7.47086624801635	-0.870866248016351
105	6.9	7.58883142383056	-0.688831423830564
106	7.7	7.6605357463843	0.0394642536156976
107	8	7.75074441024223	0.249255589757769
108	8	8.05375299909835	-0.0537529990983487

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 6.7 & 7.72530094094887 & -1.02530094094887 \tabularnewline
2 & 6.4 & 7.69754442899268 & -1.29754442899268 \tabularnewline
3 & 6.3 & 7.56107491187428 & -1.26107491187428 \tabularnewline
4 & 6.8 & 7.55182274122218 & -0.751822741222184 \tabularnewline
5 & 7.3 & 7.53100535725497 & -0.23100535725497 \tabularnewline
6 & 7.1 & 7.41997930942983 & -0.319979309429828 \tabularnewline
7 & 7 & 7.51018797328776 & -0.510187973287755 \tabularnewline
8 & 6.8 & 7.48011841866845 & -0.680118418668446 \tabularnewline
9 & 6.6 & 7.6212140211129 & -1.02121402111290 \tabularnewline
10 & 6.3 & 7.68829225834059 & -1.38829225834059 \tabularnewline
11 & 6.1 & 7.67672704502547 & -1.57672704502547 \tabularnewline
12 & 6.1 & 7.71373572763385 & -1.61373572763385 \tabularnewline
13 & 6.3 & 7.55182274122218 & -1.25182274122218 \tabularnewline
14 & 6.3 & 7.47086624801635 & -1.17086624801635 \tabularnewline
15 & 6 & 7.51944014393985 & -1.51944014393985 \tabularnewline
16 & 6.2 & 7.58420533850452 & -1.38420533850452 \tabularnewline
17 & 6.4 & 7.57957925317847 & -1.17957925317847 \tabularnewline
18 & 6.8 & 7.67441400236244 & -0.874414002362446 \tabularnewline
19 & 7.5 & 7.65359661839523 & -0.153596618395231 \tabularnewline
20 & 7.5 & 7.70448355698175 & -0.204483556981755 \tabularnewline
21 & 7.6 & 7.69291834366664 & -0.092918343666636 \tabularnewline
22 & 7.6 & 7.52637927192892 & 0.0736207280710776 \tabularnewline
23 & 7.4 & 7.58883142383056 & -0.188831423830564 \tabularnewline
24 & 7.3 & 7.57957925317847 & -0.279579253178470 \tabularnewline
25 & 7.1 & 7.77850092219852 & -0.678500922198517 \tabularnewline
26 & 6.9 & 7.88952697002366 & -0.989526970023658 \tabularnewline
27 & 6.8 & 7.99361388985973 & -1.19361388985973 \tabularnewline
28 & 7.5 & 7.90109218333878 & -0.401092183338778 \tabularnewline
29 & 7.6 & 7.90571826866483 & -0.305718268664826 \tabularnewline
30 & 7.8 & 7.90571826866483 & -0.105718268664826 \tabularnewline
31 & 8 & 7.89877914067575 & 0.101220859324246 \tabularnewline
32 & 8.1 & 7.9427269512732 & 0.157273048726793 \tabularnewline
33 & 8.2 & 7.88027479937156 & 0.319725200628436 \tabularnewline
34 & 8.3 & 7.91728348197994 & 0.382716518020056 \tabularnewline
35 & 8.2 & 7.79469222083968 & 0.405307779160316 \tabularnewline
36 & 8 & 7.79006613551364 & 0.209933864486365 \tabularnewline
37 & 7.9 & 7.8548313300783 & 0.0451686699216985 \tabularnewline
38 & 7.6 & 7.95660520725135 & -0.356605207251349 \tabularnewline
39 & 7.6 & 7.82244873279597 & -0.222448732795969 \tabularnewline
40 & 8.3 & 7.85251828741528 & 0.447481712584723 \tabularnewline
41 & 8.4 & 7.78312700752456 & 0.616872992475436 \tabularnewline
42 & 8.4 & 7.78081396486154 & 0.61918603513846 \tabularnewline
43 & 8.4 & 7.83170090344806 & 0.568299096551937 \tabularnewline
44 & 8.4 & 7.7669357088834 & 0.633064291116603 \tabularnewline
45 & 8.6 & 7.79469222083968 & 0.805307779160317 \tabularnewline
46 & 8.9 & 7.82938786078504 & 1.07061213921496 \tabularnewline
47 & 8.8 & 7.90571826866483 & 0.894281731335175 \tabularnewline
48 & 8.3 & 7.93578782328413 & 0.364212176715866 \tabularnewline
49 & 7.5 & 7.75305745290525 & -0.253057452905255 \tabularnewline
50 & 7.2 & 7.59345750915661 & -0.393457509156612 \tabularnewline
51 & 7.4 & 7.67441400236244 & -0.274414002362445 \tabularnewline
52 & 8.8 & 7.63971836241709 & 1.16028163758291 \tabularnewline
53 & 9.3 & 7.67441400236244 & 1.62558599763756 \tabularnewline
54 & 9.3 & 7.727613983612 & 1.57238601638801 \tabularnewline
55 & 8.7 & 7.53563144258102 & 1.16436855741898 \tabularnewline
56 & 8.2 & 7.60733576513475 & 0.592664234865244 \tabularnewline
57 & 8.3 & 7.67210095969942 & 0.627899040300579 \tabularnewline
58 & 8.5 & 7.67441400236244 & 0.825585997637555 \tabularnewline
59 & 8.6 & 7.60270967980871 & 0.997290320191292 \tabularnewline
60 & 8.5 & 7.48705754665752 & 1.01294245334248 \tabularnewline
61 & 8.2 & 7.55876186921126 & 0.641238130788744 \tabularnewline
62 & 8.1 & 7.61658793578685 & 0.483412064213149 \tabularnewline
63 & 7.9 & 7.533318399918 & 0.366681600082007 \tabularnewline
64 & 8.6 & 7.47086624801635 & 1.12913375198365 \tabularnewline
65 & 8.7 & 7.47780537600542 & 1.22219462399458 \tabularnewline
66 & 8.7 & 7.4662401626903 & 1.23375983730970 \tabularnewline
67 & 8.5 & 7.62584010643895 & 0.874159893561054 \tabularnewline
68 & 8.4 & 7.60270967980871 & 0.797290320191293 \tabularnewline
69 & 8.5 & 7.52869231459195 & 0.971307685408054 \tabularnewline
70 & 8.7 & 7.58883142383056 & 1.11116857616943 \tabularnewline
71 & 8.7 & 7.64665749040616 & 1.05334250959384 \tabularnewline
72 & 8.6 & 7.8062574341548 & 0.793742565845198 \tabularnewline
73 & 8.5 & 7.74380528225316 & 0.75619471774684 \tabularnewline
74 & 8.3 & 7.69060530100361 & 0.609394698996389 \tabularnewline
75 & 8 & 7.76462266622037 & 0.235377333779626 \tabularnewline
76 & 8.2 & 7.82707481812202 & 0.372925181877983 \tabularnewline
77 & 8.1 & 7.82013569013295 & 0.279864309867055 \tabularnewline
78 & 8.1 & 7.91497043931692 & 0.185029560683079 \tabularnewline
79 & 8 & 7.91728348197994 & 0.0827165180200555 \tabularnewline
80 & 7.9 & 7.85251828741528 & 0.0474817125847223 \tabularnewline
81 & 7.9 & 7.81782264746992 & 0.0821773525300793 \tabularnewline
82 & 8 & 7.81319656214387 & 0.186803437856127 \tabularnewline
83 & 8 & 7.78775309285061 & 0.212246907149388 \tabularnewline
84 & 7.9 & 7.7761878795355 & 0.123812120464508 \tabularnewline
85 & 8 & 7.78544005018759 & 0.214559949812412 \tabularnewline
86 & 7.7 & 7.90109218333878 & -0.201092183338778 \tabularnewline
87 & 7.2 & 7.89877914067575 & -0.698779140675754 \tabularnewline
88 & 7.5 & 7.88027479937156 & -0.380274799371564 \tabularnewline
89 & 7.3 & 7.93810086594716 & -0.638100865947159 \tabularnewline
90 & 7 & 7.84789220208923 & -0.84789220208923 \tabularnewline
91 & 7 & 7.6790400876885 & -0.679040087688493 \tabularnewline
92 & 7 & 7.51712710127683 & -0.517127101276827 \tabularnewline
93 & 7.2 & 7.48243146133147 & -0.28243146133147 \tabularnewline
94 & 7.3 & 7.39684888279959 & -0.0968488827995894 \tabularnewline
95 & 7.1 & 7.35521411486516 & -0.255214114865161 \tabularnewline
96 & 6.8 & 7.18173591513838 & -0.381735915138376 \tabularnewline
97 & 6.4 & 7.23724893905095 & -0.837248939050947 \tabularnewline
98 & 6.1 & 6.99206641677042 & -0.892066416770424 \tabularnewline
99 & 6.5 & 6.85559689965202 & -0.35559689965202 \tabularnewline
100 & 7.7 & 6.83015343035876 & 0.869846569641242 \tabularnewline
101 & 7.9 & 6.950431648836 & 0.949568351164005 \tabularnewline
102 & 7.5 & 6.93424035019483 & 0.565759649805171 \tabularnewline
103 & 6.9 & 7.10540550725859 & -0.20540550725859 \tabularnewline
104 & 6.6 & 7.47086624801635 & -0.870866248016351 \tabularnewline
105 & 6.9 & 7.58883142383056 & -0.688831423830564 \tabularnewline
106 & 7.7 & 7.6605357463843 & 0.0394642536156976 \tabularnewline
107 & 8 & 7.75074441024223 & 0.249255589757769 \tabularnewline
108 & 8 & 8.05375299909835 & -0.0537529990983487 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57401&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]6.7[/C][C]7.72530094094887[/C][C]-1.02530094094887[/C][/ROW]
[ROW][C]2[/C][C]6.4[/C][C]7.69754442899268[/C][C]-1.29754442899268[/C][/ROW]
[ROW][C]3[/C][C]6.3[/C][C]7.56107491187428[/C][C]-1.26107491187428[/C][/ROW]
[ROW][C]4[/C][C]6.8[/C][C]7.55182274122218[/C][C]-0.751822741222184[/C][/ROW]
[ROW][C]5[/C][C]7.3[/C][C]7.53100535725497[/C][C]-0.23100535725497[/C][/ROW]
[ROW][C]6[/C][C]7.1[/C][C]7.41997930942983[/C][C]-0.319979309429828[/C][/ROW]
[ROW][C]7[/C][C]7[/C][C]7.51018797328776[/C][C]-0.510187973287755[/C][/ROW]
[ROW][C]8[/C][C]6.8[/C][C]7.48011841866845[/C][C]-0.680118418668446[/C][/ROW]
[ROW][C]9[/C][C]6.6[/C][C]7.6212140211129[/C][C]-1.02121402111290[/C][/ROW]
[ROW][C]10[/C][C]6.3[/C][C]7.68829225834059[/C][C]-1.38829225834059[/C][/ROW]
[ROW][C]11[/C][C]6.1[/C][C]7.67672704502547[/C][C]-1.57672704502547[/C][/ROW]
[ROW][C]12[/C][C]6.1[/C][C]7.71373572763385[/C][C]-1.61373572763385[/C][/ROW]
[ROW][C]13[/C][C]6.3[/C][C]7.55182274122218[/C][C]-1.25182274122218[/C][/ROW]
[ROW][C]14[/C][C]6.3[/C][C]7.47086624801635[/C][C]-1.17086624801635[/C][/ROW]
[ROW][C]15[/C][C]6[/C][C]7.51944014393985[/C][C]-1.51944014393985[/C][/ROW]
[ROW][C]16[/C][C]6.2[/C][C]7.58420533850452[/C][C]-1.38420533850452[/C][/ROW]
[ROW][C]17[/C][C]6.4[/C][C]7.57957925317847[/C][C]-1.17957925317847[/C][/ROW]
[ROW][C]18[/C][C]6.8[/C][C]7.67441400236244[/C][C]-0.874414002362446[/C][/ROW]
[ROW][C]19[/C][C]7.5[/C][C]7.65359661839523[/C][C]-0.153596618395231[/C][/ROW]
[ROW][C]20[/C][C]7.5[/C][C]7.70448355698175[/C][C]-0.204483556981755[/C][/ROW]
[ROW][C]21[/C][C]7.6[/C][C]7.69291834366664[/C][C]-0.092918343666636[/C][/ROW]
[ROW][C]22[/C][C]7.6[/C][C]7.52637927192892[/C][C]0.0736207280710776[/C][/ROW]
[ROW][C]23[/C][C]7.4[/C][C]7.58883142383056[/C][C]-0.188831423830564[/C][/ROW]
[ROW][C]24[/C][C]7.3[/C][C]7.57957925317847[/C][C]-0.279579253178470[/C][/ROW]
[ROW][C]25[/C][C]7.1[/C][C]7.77850092219852[/C][C]-0.678500922198517[/C][/ROW]
[ROW][C]26[/C][C]6.9[/C][C]7.88952697002366[/C][C]-0.989526970023658[/C][/ROW]
[ROW][C]27[/C][C]6.8[/C][C]7.99361388985973[/C][C]-1.19361388985973[/C][/ROW]
[ROW][C]28[/C][C]7.5[/C][C]7.90109218333878[/C][C]-0.401092183338778[/C][/ROW]
[ROW][C]29[/C][C]7.6[/C][C]7.90571826866483[/C][C]-0.305718268664826[/C][/ROW]
[ROW][C]30[/C][C]7.8[/C][C]7.90571826866483[/C][C]-0.105718268664826[/C][/ROW]
[ROW][C]31[/C][C]8[/C][C]7.89877914067575[/C][C]0.101220859324246[/C][/ROW]
[ROW][C]32[/C][C]8.1[/C][C]7.9427269512732[/C][C]0.157273048726793[/C][/ROW]
[ROW][C]33[/C][C]8.2[/C][C]7.88027479937156[/C][C]0.319725200628436[/C][/ROW]
[ROW][C]34[/C][C]8.3[/C][C]7.91728348197994[/C][C]0.382716518020056[/C][/ROW]
[ROW][C]35[/C][C]8.2[/C][C]7.79469222083968[/C][C]0.405307779160316[/C][/ROW]
[ROW][C]36[/C][C]8[/C][C]7.79006613551364[/C][C]0.209933864486365[/C][/ROW]
[ROW][C]37[/C][C]7.9[/C][C]7.8548313300783[/C][C]0.0451686699216985[/C][/ROW]
[ROW][C]38[/C][C]7.6[/C][C]7.95660520725135[/C][C]-0.356605207251349[/C][/ROW]
[ROW][C]39[/C][C]7.6[/C][C]7.82244873279597[/C][C]-0.222448732795969[/C][/ROW]
[ROW][C]40[/C][C]8.3[/C][C]7.85251828741528[/C][C]0.447481712584723[/C][/ROW]
[ROW][C]41[/C][C]8.4[/C][C]7.78312700752456[/C][C]0.616872992475436[/C][/ROW]
[ROW][C]42[/C][C]8.4[/C][C]7.78081396486154[/C][C]0.61918603513846[/C][/ROW]
[ROW][C]43[/C][C]8.4[/C][C]7.83170090344806[/C][C]0.568299096551937[/C][/ROW]
[ROW][C]44[/C][C]8.4[/C][C]7.7669357088834[/C][C]0.633064291116603[/C][/ROW]
[ROW][C]45[/C][C]8.6[/C][C]7.79469222083968[/C][C]0.805307779160317[/C][/ROW]
[ROW][C]46[/C][C]8.9[/C][C]7.82938786078504[/C][C]1.07061213921496[/C][/ROW]
[ROW][C]47[/C][C]8.8[/C][C]7.90571826866483[/C][C]0.894281731335175[/C][/ROW]
[ROW][C]48[/C][C]8.3[/C][C]7.93578782328413[/C][C]0.364212176715866[/C][/ROW]
[ROW][C]49[/C][C]7.5[/C][C]7.75305745290525[/C][C]-0.253057452905255[/C][/ROW]
[ROW][C]50[/C][C]7.2[/C][C]7.59345750915661[/C][C]-0.393457509156612[/C][/ROW]
[ROW][C]51[/C][C]7.4[/C][C]7.67441400236244[/C][C]-0.274414002362445[/C][/ROW]
[ROW][C]52[/C][C]8.8[/C][C]7.63971836241709[/C][C]1.16028163758291[/C][/ROW]
[ROW][C]53[/C][C]9.3[/C][C]7.67441400236244[/C][C]1.62558599763756[/C][/ROW]
[ROW][C]54[/C][C]9.3[/C][C]7.727613983612[/C][C]1.57238601638801[/C][/ROW]
[ROW][C]55[/C][C]8.7[/C][C]7.53563144258102[/C][C]1.16436855741898[/C][/ROW]
[ROW][C]56[/C][C]8.2[/C][C]7.60733576513475[/C][C]0.592664234865244[/C][/ROW]
[ROW][C]57[/C][C]8.3[/C][C]7.67210095969942[/C][C]0.627899040300579[/C][/ROW]
[ROW][C]58[/C][C]8.5[/C][C]7.67441400236244[/C][C]0.825585997637555[/C][/ROW]
[ROW][C]59[/C][C]8.6[/C][C]7.60270967980871[/C][C]0.997290320191292[/C][/ROW]
[ROW][C]60[/C][C]8.5[/C][C]7.48705754665752[/C][C]1.01294245334248[/C][/ROW]
[ROW][C]61[/C][C]8.2[/C][C]7.55876186921126[/C][C]0.641238130788744[/C][/ROW]
[ROW][C]62[/C][C]8.1[/C][C]7.61658793578685[/C][C]0.483412064213149[/C][/ROW]
[ROW][C]63[/C][C]7.9[/C][C]7.533318399918[/C][C]0.366681600082007[/C][/ROW]
[ROW][C]64[/C][C]8.6[/C][C]7.47086624801635[/C][C]1.12913375198365[/C][/ROW]
[ROW][C]65[/C][C]8.7[/C][C]7.47780537600542[/C][C]1.22219462399458[/C][/ROW]
[ROW][C]66[/C][C]8.7[/C][C]7.4662401626903[/C][C]1.23375983730970[/C][/ROW]
[ROW][C]67[/C][C]8.5[/C][C]7.62584010643895[/C][C]0.874159893561054[/C][/ROW]
[ROW][C]68[/C][C]8.4[/C][C]7.60270967980871[/C][C]0.797290320191293[/C][/ROW]
[ROW][C]69[/C][C]8.5[/C][C]7.52869231459195[/C][C]0.971307685408054[/C][/ROW]
[ROW][C]70[/C][C]8.7[/C][C]7.58883142383056[/C][C]1.11116857616943[/C][/ROW]
[ROW][C]71[/C][C]8.7[/C][C]7.64665749040616[/C][C]1.05334250959384[/C][/ROW]
[ROW][C]72[/C][C]8.6[/C][C]7.8062574341548[/C][C]0.793742565845198[/C][/ROW]
[ROW][C]73[/C][C]8.5[/C][C]7.74380528225316[/C][C]0.75619471774684[/C][/ROW]
[ROW][C]74[/C][C]8.3[/C][C]7.69060530100361[/C][C]0.609394698996389[/C][/ROW]
[ROW][C]75[/C][C]8[/C][C]7.76462266622037[/C][C]0.235377333779626[/C][/ROW]
[ROW][C]76[/C][C]8.2[/C][C]7.82707481812202[/C][C]0.372925181877983[/C][/ROW]
[ROW][C]77[/C][C]8.1[/C][C]7.82013569013295[/C][C]0.279864309867055[/C][/ROW]
[ROW][C]78[/C][C]8.1[/C][C]7.91497043931692[/C][C]0.185029560683079[/C][/ROW]
[ROW][C]79[/C][C]8[/C][C]7.91728348197994[/C][C]0.0827165180200555[/C][/ROW]
[ROW][C]80[/C][C]7.9[/C][C]7.85251828741528[/C][C]0.0474817125847223[/C][/ROW]
[ROW][C]81[/C][C]7.9[/C][C]7.81782264746992[/C][C]0.0821773525300793[/C][/ROW]
[ROW][C]82[/C][C]8[/C][C]7.81319656214387[/C][C]0.186803437856127[/C][/ROW]
[ROW][C]83[/C][C]8[/C][C]7.78775309285061[/C][C]0.212246907149388[/C][/ROW]
[ROW][C]84[/C][C]7.9[/C][C]7.7761878795355[/C][C]0.123812120464508[/C][/ROW]
[ROW][C]85[/C][C]8[/C][C]7.78544005018759[/C][C]0.214559949812412[/C][/ROW]
[ROW][C]86[/C][C]7.7[/C][C]7.90109218333878[/C][C]-0.201092183338778[/C][/ROW]
[ROW][C]87[/C][C]7.2[/C][C]7.89877914067575[/C][C]-0.698779140675754[/C][/ROW]
[ROW][C]88[/C][C]7.5[/C][C]7.88027479937156[/C][C]-0.380274799371564[/C][/ROW]
[ROW][C]89[/C][C]7.3[/C][C]7.93810086594716[/C][C]-0.638100865947159[/C][/ROW]
[ROW][C]90[/C][C]7[/C][C]7.84789220208923[/C][C]-0.84789220208923[/C][/ROW]
[ROW][C]91[/C][C]7[/C][C]7.6790400876885[/C][C]-0.679040087688493[/C][/ROW]
[ROW][C]92[/C][C]7[/C][C]7.51712710127683[/C][C]-0.517127101276827[/C][/ROW]
[ROW][C]93[/C][C]7.2[/C][C]7.48243146133147[/C][C]-0.28243146133147[/C][/ROW]
[ROW][C]94[/C][C]7.3[/C][C]7.39684888279959[/C][C]-0.0968488827995894[/C][/ROW]
[ROW][C]95[/C][C]7.1[/C][C]7.35521411486516[/C][C]-0.255214114865161[/C][/ROW]
[ROW][C]96[/C][C]6.8[/C][C]7.18173591513838[/C][C]-0.381735915138376[/C][/ROW]
[ROW][C]97[/C][C]6.4[/C][C]7.23724893905095[/C][C]-0.837248939050947[/C][/ROW]
[ROW][C]98[/C][C]6.1[/C][C]6.99206641677042[/C][C]-0.892066416770424[/C][/ROW]
[ROW][C]99[/C][C]6.5[/C][C]6.85559689965202[/C][C]-0.35559689965202[/C][/ROW]
[ROW][C]100[/C][C]7.7[/C][C]6.83015343035876[/C][C]0.869846569641242[/C][/ROW]
[ROW][C]101[/C][C]7.9[/C][C]6.950431648836[/C][C]0.949568351164005[/C][/ROW]
[ROW][C]102[/C][C]7.5[/C][C]6.93424035019483[/C][C]0.565759649805171[/C][/ROW]
[ROW][C]103[/C][C]6.9[/C][C]7.10540550725859[/C][C]-0.20540550725859[/C][/ROW]
[ROW][C]104[/C][C]6.6[/C][C]7.47086624801635[/C][C]-0.870866248016351[/C][/ROW]
[ROW][C]105[/C][C]6.9[/C][C]7.58883142383056[/C][C]-0.688831423830564[/C][/ROW]
[ROW][C]106[/C][C]7.7[/C][C]7.6605357463843[/C][C]0.0394642536156976[/C][/ROW]
[ROW][C]107[/C][C]8[/C][C]7.75074441024223[/C][C]0.249255589757769[/C][/ROW]
[ROW][C]108[/C][C]8[/C][C]8.05375299909835[/C][C]-0.0537529990983487[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57401&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57401&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	6.7	7.72530094094887	-1.02530094094887
2	6.4	7.69754442899268	-1.29754442899268
3	6.3	7.56107491187428	-1.26107491187428
4	6.8	7.55182274122218	-0.751822741222184
5	7.3	7.53100535725497	-0.23100535725497
6	7.1	7.41997930942983	-0.319979309429828
7	7	7.51018797328776	-0.510187973287755
8	6.8	7.48011841866845	-0.680118418668446
9	6.6	7.6212140211129	-1.02121402111290
10	6.3	7.68829225834059	-1.38829225834059
11	6.1	7.67672704502547	-1.57672704502547
12	6.1	7.71373572763385	-1.61373572763385
13	6.3	7.55182274122218	-1.25182274122218
14	6.3	7.47086624801635	-1.17086624801635
15	6	7.51944014393985	-1.51944014393985
16	6.2	7.58420533850452	-1.38420533850452
17	6.4	7.57957925317847	-1.17957925317847
18	6.8	7.67441400236244	-0.874414002362446
19	7.5	7.65359661839523	-0.153596618395231
20	7.5	7.70448355698175	-0.204483556981755
21	7.6	7.69291834366664	-0.092918343666636
22	7.6	7.52637927192892	0.0736207280710776
23	7.4	7.58883142383056	-0.188831423830564
24	7.3	7.57957925317847	-0.279579253178470
25	7.1	7.77850092219852	-0.678500922198517
26	6.9	7.88952697002366	-0.989526970023658
27	6.8	7.99361388985973	-1.19361388985973
28	7.5	7.90109218333878	-0.401092183338778
29	7.6	7.90571826866483	-0.305718268664826
30	7.8	7.90571826866483	-0.105718268664826
31	8	7.89877914067575	0.101220859324246
32	8.1	7.9427269512732	0.157273048726793
33	8.2	7.88027479937156	0.319725200628436
34	8.3	7.91728348197994	0.382716518020056
35	8.2	7.79469222083968	0.405307779160316
36	8	7.79006613551364	0.209933864486365
37	7.9	7.8548313300783	0.0451686699216985
38	7.6	7.95660520725135	-0.356605207251349
39	7.6	7.82244873279597	-0.222448732795969
40	8.3	7.85251828741528	0.447481712584723
41	8.4	7.78312700752456	0.616872992475436
42	8.4	7.78081396486154	0.61918603513846
43	8.4	7.83170090344806	0.568299096551937
44	8.4	7.7669357088834	0.633064291116603
45	8.6	7.79469222083968	0.805307779160317
46	8.9	7.82938786078504	1.07061213921496
47	8.8	7.90571826866483	0.894281731335175
48	8.3	7.93578782328413	0.364212176715866
49	7.5	7.75305745290525	-0.253057452905255
50	7.2	7.59345750915661	-0.393457509156612
51	7.4	7.67441400236244	-0.274414002362445
52	8.8	7.63971836241709	1.16028163758291
53	9.3	7.67441400236244	1.62558599763756
54	9.3	7.727613983612	1.57238601638801
55	8.7	7.53563144258102	1.16436855741898
56	8.2	7.60733576513475	0.592664234865244
57	8.3	7.67210095969942	0.627899040300579
58	8.5	7.67441400236244	0.825585997637555
59	8.6	7.60270967980871	0.997290320191292
60	8.5	7.48705754665752	1.01294245334248
61	8.2	7.55876186921126	0.641238130788744
62	8.1	7.61658793578685	0.483412064213149
63	7.9	7.533318399918	0.366681600082007
64	8.6	7.47086624801635	1.12913375198365
65	8.7	7.47780537600542	1.22219462399458
66	8.7	7.4662401626903	1.23375983730970
67	8.5	7.62584010643895	0.874159893561054
68	8.4	7.60270967980871	0.797290320191293
69	8.5	7.52869231459195	0.971307685408054
70	8.7	7.58883142383056	1.11116857616943
71	8.7	7.64665749040616	1.05334250959384
72	8.6	7.8062574341548	0.793742565845198
73	8.5	7.74380528225316	0.75619471774684
74	8.3	7.69060530100361	0.609394698996389
75	8	7.76462266622037	0.235377333779626
76	8.2	7.82707481812202	0.372925181877983
77	8.1	7.82013569013295	0.279864309867055
78	8.1	7.91497043931692	0.185029560683079
79	8	7.91728348197994	0.0827165180200555
80	7.9	7.85251828741528	0.0474817125847223
81	7.9	7.81782264746992	0.0821773525300793
82	8	7.81319656214387	0.186803437856127
83	8	7.78775309285061	0.212246907149388
84	7.9	7.7761878795355	0.123812120464508
85	8	7.78544005018759	0.214559949812412
86	7.7	7.90109218333878	-0.201092183338778
87	7.2	7.89877914067575	-0.698779140675754
88	7.5	7.88027479937156	-0.380274799371564
89	7.3	7.93810086594716	-0.638100865947159
90	7	7.84789220208923	-0.84789220208923
91	7	7.6790400876885	-0.679040087688493
92	7	7.51712710127683	-0.517127101276827
93	7.2	7.48243146133147	-0.28243146133147
94	7.3	7.39684888279959	-0.0968488827995894
95	7.1	7.35521411486516	-0.255214114865161
96	6.8	7.18173591513838	-0.381735915138376
97	6.4	7.23724893905095	-0.837248939050947
98	6.1	6.99206641677042	-0.892066416770424
99	6.5	6.85559689965202	-0.35559689965202
100	7.7	6.83015343035876	0.869846569641242
101	7.9	6.950431648836	0.949568351164005
102	7.5	6.93424035019483	0.565759649805171
103	6.9	7.10540550725859	-0.20540550725859
104	6.6	7.47086624801635	-0.870866248016351
105	6.9	7.58883142383056	-0.688831423830564
106	7.7	7.6605357463843	0.0394642536156976
107	8	7.75074441024223	0.249255589757769
108	8	8.05375299909835	-0.0537529990983487

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
5	0.173958538695498	0.347917077390996	0.826041461304502
6	0.0749913074962567	0.149982614992513	0.925008692503743
7	0.0305225682271213	0.0610451364542426	0.969477431772879
8	0.0129721222841324	0.0259442445682647	0.987027877715868
9	0.00499043027402638	0.00998086054805277	0.995009569725974
10	0.00254464321418543	0.00508928642837086	0.997455356785815
11	0.0024417918887141	0.0048835837774282	0.997558208111286
12	0.00151137856220320	0.00302275712440641	0.998488621437797
13	0.0018013630433953	0.0036027260867906	0.998198636956605
14	0.00350468496070329	0.00700936992140657	0.996495315039297
15	0.0098575672911791	0.0197151345823582	0.99014243270882
16	0.00916582512972444	0.0183316502594489	0.990834174870276
17	0.00650159805270711	0.0130031961054142	0.993498401947293
18	0.00676208433825451	0.0135241686765090	0.993237915661745
19	0.0346682017660394	0.0693364035320789	0.96533179823396
20	0.0802066961053498	0.160413392210700	0.91979330389465
21	0.136213068328418	0.272426136656835	0.863786931671582
22	0.186545734342992	0.373091468685985	0.813454265657008
23	0.194163944246571	0.388327888493143	0.805836055753429
24	0.185042360729269	0.370084721458539	0.81495763927073
25	0.168810397222794	0.337620794445588	0.831189602777206
26	0.157279821958286	0.314559643916571	0.842720178041715
27	0.161348128729680	0.322696257459361	0.83865187127032
28	0.170546479828528	0.341092959657056	0.829453520171472
29	0.175516556297864	0.351033112595728	0.824483443702136
30	0.188373212034584	0.376746424069168	0.811626787965416
31	0.213494773488633	0.426989546977266	0.786505226511367
32	0.226223565310899	0.452447130621798	0.773776434689101
33	0.25569252352688	0.51138504705376	0.74430747647312
34	0.274747886353549	0.549495772707098	0.725252113646451
35	0.309535337953206	0.619070675906412	0.690464662046794
36	0.305604554019976	0.611209108039952	0.694395445980024
37	0.274173854766594	0.548347709533188	0.725826145233406
38	0.244863326132868	0.489726652265737	0.755136673867131
39	0.214379305959134	0.428758611918269	0.785620694040866
40	0.222194568815796	0.444389137631591	0.777805431184204
41	0.265651703095328	0.531303406190655	0.734348296904672
42	0.30331906537413	0.60663813074826	0.69668093462587
43	0.312747660068185	0.625495320136371	0.687252339931815
44	0.343376100308725	0.686752200617451	0.656623899691275
45	0.393037424757472	0.786074849514944	0.606962575242528
46	0.485763953347577	0.971527906695155	0.514236046652423
47	0.505595557640991	0.988808884718017	0.494404442359009
48	0.454748676249809	0.909497352499619	0.545251323750191
49	0.414136029553497	0.828272059106995	0.585863970446503
50	0.388640497711726	0.777280995423451	0.611359502288274
51	0.355061000598861	0.710122001197723	0.644938999401139
52	0.520665537811266	0.958668924377468	0.479334462188734
53	0.778543335853324	0.442913328293351	0.221456664146676
54	0.90733160485352	0.185336790292960	0.0926683951464799
55	0.951862211182744	0.096275577634513	0.0481377888172565
56	0.950247063980332	0.099505872039335	0.0497529360196675
57	0.947157127415851	0.105685745168298	0.0528428725841489
58	0.951391314424137	0.097217371151725	0.0486086855758625
59	0.963891642690464	0.0722167146190717	0.0361083573095358
60	0.97527478662677	0.0494504267464588	0.0247252133732294
61	0.973641819673195	0.0527163606536107	0.0263581803268053
62	0.968107864349304	0.0637842713013921	0.0318921356506961
63	0.960006204846994	0.0799875903060123	0.0399937951530061
64	0.974297307788222	0.0514053844235551	0.0257026922117775
65	0.986059905678784	0.0278801886424313	0.0139400943212156
66	0.993155258993591	0.0136894820128172	0.00684474100640858
67	0.994204220257868	0.0115915594842643	0.00579577974213215
68	0.994675447441758	0.0106491051164841	0.00532455255824205
69	0.996392094594465	0.00721581081107028	0.00360790540553514
70	0.998337045405397	0.0033259091892064	0.0016629545946032
71	0.999268491638083	0.00146301672383397	0.000731508361916986
72	0.999494682246213	0.00101063550757494	0.000505317753787469
73	0.999656605996087	0.000686788007826346	0.000343394003913173
74	0.999701880779954	0.000596238440091527	0.000298119220045764
75	0.99955339250518	0.000893214989638366	0.000446607494819183
76	0.999463255244162	0.00107348951167546	0.000536744755837729
77	0.999288527296104	0.00142294540779285	0.000711472703896423
78	0.998998731910946	0.00200253617810876	0.00100126808905438
79	0.99848761696171	0.00302476607658110	0.00151238303829055
80	0.997679368841317	0.00464126231736545	0.00232063115868273
81	0.996610981947727	0.00677803610454609	0.00338901805227305
82	0.99572328864161	0.00855342271678187	0.00427671135839093
83	0.994966840177136	0.0100663196457271	0.00503315982286356
84	0.993636295985729	0.0127274080285413	0.00636370401427064
85	0.993383866190169	0.0132322676196616	0.0066161338098308
86	0.990155584196286	0.0196888316074277	0.00984441580371384
87	0.985019130835986	0.0299617383280278	0.0149808691640139
88	0.97637897901529	0.047242041969420	0.023621020984710
89	0.963982206977695	0.0720355860446097	0.0360177930223048
90	0.95376547131709	0.0924690573658181	0.0462345286829090
91	0.938176761115546	0.123646477768907	0.0618232388844537
92	0.91404925613587	0.171901487728261	0.0859507438641305
93	0.873986329793627	0.252027340412745	0.126013670206372
94	0.820047392630186	0.359905214739628	0.179952607369814
95	0.752667982400279	0.494664035199442	0.247332017599721
96	0.684523704101643	0.630952591796713	0.315476295898357
97	0.697659617188729	0.604680765622543	0.302340382811271
98	0.796224620280132	0.407550759439735	0.203775379719868
99	0.81547686167772	0.369046276644559	0.184523138322280
100	0.759599407802273	0.480801184395455	0.240400592197727
101	0.792512838322796	0.414974323354409	0.207487161677204
102	0.851458114016506	0.297083771966988	0.148541885983494
103	0.820319291693959	0.359361416612083	0.179680708306041

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
5 & 0.173958538695498 & 0.347917077390996 & 0.826041461304502 \tabularnewline
6 & 0.0749913074962567 & 0.149982614992513 & 0.925008692503743 \tabularnewline
7 & 0.0305225682271213 & 0.0610451364542426 & 0.969477431772879 \tabularnewline
8 & 0.0129721222841324 & 0.0259442445682647 & 0.987027877715868 \tabularnewline
9 & 0.00499043027402638 & 0.00998086054805277 & 0.995009569725974 \tabularnewline
10 & 0.00254464321418543 & 0.00508928642837086 & 0.997455356785815 \tabularnewline
11 & 0.0024417918887141 & 0.0048835837774282 & 0.997558208111286 \tabularnewline
12 & 0.00151137856220320 & 0.00302275712440641 & 0.998488621437797 \tabularnewline
13 & 0.0018013630433953 & 0.0036027260867906 & 0.998198636956605 \tabularnewline
14 & 0.00350468496070329 & 0.00700936992140657 & 0.996495315039297 \tabularnewline
15 & 0.0098575672911791 & 0.0197151345823582 & 0.99014243270882 \tabularnewline
16 & 0.00916582512972444 & 0.0183316502594489 & 0.990834174870276 \tabularnewline
17 & 0.00650159805270711 & 0.0130031961054142 & 0.993498401947293 \tabularnewline
18 & 0.00676208433825451 & 0.0135241686765090 & 0.993237915661745 \tabularnewline
19 & 0.0346682017660394 & 0.0693364035320789 & 0.96533179823396 \tabularnewline
20 & 0.0802066961053498 & 0.160413392210700 & 0.91979330389465 \tabularnewline
21 & 0.136213068328418 & 0.272426136656835 & 0.863786931671582 \tabularnewline
22 & 0.186545734342992 & 0.373091468685985 & 0.813454265657008 \tabularnewline
23 & 0.194163944246571 & 0.388327888493143 & 0.805836055753429 \tabularnewline
24 & 0.185042360729269 & 0.370084721458539 & 0.81495763927073 \tabularnewline
25 & 0.168810397222794 & 0.337620794445588 & 0.831189602777206 \tabularnewline
26 & 0.157279821958286 & 0.314559643916571 & 0.842720178041715 \tabularnewline
27 & 0.161348128729680 & 0.322696257459361 & 0.83865187127032 \tabularnewline
28 & 0.170546479828528 & 0.341092959657056 & 0.829453520171472 \tabularnewline
29 & 0.175516556297864 & 0.351033112595728 & 0.824483443702136 \tabularnewline
30 & 0.188373212034584 & 0.376746424069168 & 0.811626787965416 \tabularnewline
31 & 0.213494773488633 & 0.426989546977266 & 0.786505226511367 \tabularnewline
32 & 0.226223565310899 & 0.452447130621798 & 0.773776434689101 \tabularnewline
33 & 0.25569252352688 & 0.51138504705376 & 0.74430747647312 \tabularnewline
34 & 0.274747886353549 & 0.549495772707098 & 0.725252113646451 \tabularnewline
35 & 0.309535337953206 & 0.619070675906412 & 0.690464662046794 \tabularnewline
36 & 0.305604554019976 & 0.611209108039952 & 0.694395445980024 \tabularnewline
37 & 0.274173854766594 & 0.548347709533188 & 0.725826145233406 \tabularnewline
38 & 0.244863326132868 & 0.489726652265737 & 0.755136673867131 \tabularnewline
39 & 0.214379305959134 & 0.428758611918269 & 0.785620694040866 \tabularnewline
40 & 0.222194568815796 & 0.444389137631591 & 0.777805431184204 \tabularnewline
41 & 0.265651703095328 & 0.531303406190655 & 0.734348296904672 \tabularnewline
42 & 0.30331906537413 & 0.60663813074826 & 0.69668093462587 \tabularnewline
43 & 0.312747660068185 & 0.625495320136371 & 0.687252339931815 \tabularnewline
44 & 0.343376100308725 & 0.686752200617451 & 0.656623899691275 \tabularnewline
45 & 0.393037424757472 & 0.786074849514944 & 0.606962575242528 \tabularnewline
46 & 0.485763953347577 & 0.971527906695155 & 0.514236046652423 \tabularnewline
47 & 0.505595557640991 & 0.988808884718017 & 0.494404442359009 \tabularnewline
48 & 0.454748676249809 & 0.909497352499619 & 0.545251323750191 \tabularnewline
49 & 0.414136029553497 & 0.828272059106995 & 0.585863970446503 \tabularnewline
50 & 0.388640497711726 & 0.777280995423451 & 0.611359502288274 \tabularnewline
51 & 0.355061000598861 & 0.710122001197723 & 0.644938999401139 \tabularnewline
52 & 0.520665537811266 & 0.958668924377468 & 0.479334462188734 \tabularnewline
53 & 0.778543335853324 & 0.442913328293351 & 0.221456664146676 \tabularnewline
54 & 0.90733160485352 & 0.185336790292960 & 0.0926683951464799 \tabularnewline
55 & 0.951862211182744 & 0.096275577634513 & 0.0481377888172565 \tabularnewline
56 & 0.950247063980332 & 0.099505872039335 & 0.0497529360196675 \tabularnewline
57 & 0.947157127415851 & 0.105685745168298 & 0.0528428725841489 \tabularnewline
58 & 0.951391314424137 & 0.097217371151725 & 0.0486086855758625 \tabularnewline
59 & 0.963891642690464 & 0.0722167146190717 & 0.0361083573095358 \tabularnewline
60 & 0.97527478662677 & 0.0494504267464588 & 0.0247252133732294 \tabularnewline
61 & 0.973641819673195 & 0.0527163606536107 & 0.0263581803268053 \tabularnewline
62 & 0.968107864349304 & 0.0637842713013921 & 0.0318921356506961 \tabularnewline
63 & 0.960006204846994 & 0.0799875903060123 & 0.0399937951530061 \tabularnewline
64 & 0.974297307788222 & 0.0514053844235551 & 0.0257026922117775 \tabularnewline
65 & 0.986059905678784 & 0.0278801886424313 & 0.0139400943212156 \tabularnewline
66 & 0.993155258993591 & 0.0136894820128172 & 0.00684474100640858 \tabularnewline
67 & 0.994204220257868 & 0.0115915594842643 & 0.00579577974213215 \tabularnewline
68 & 0.994675447441758 & 0.0106491051164841 & 0.00532455255824205 \tabularnewline
69 & 0.996392094594465 & 0.00721581081107028 & 0.00360790540553514 \tabularnewline
70 & 0.998337045405397 & 0.0033259091892064 & 0.0016629545946032 \tabularnewline
71 & 0.999268491638083 & 0.00146301672383397 & 0.000731508361916986 \tabularnewline
72 & 0.999494682246213 & 0.00101063550757494 & 0.000505317753787469 \tabularnewline
73 & 0.999656605996087 & 0.000686788007826346 & 0.000343394003913173 \tabularnewline
74 & 0.999701880779954 & 0.000596238440091527 & 0.000298119220045764 \tabularnewline
75 & 0.99955339250518 & 0.000893214989638366 & 0.000446607494819183 \tabularnewline
76 & 0.999463255244162 & 0.00107348951167546 & 0.000536744755837729 \tabularnewline
77 & 0.999288527296104 & 0.00142294540779285 & 0.000711472703896423 \tabularnewline
78 & 0.998998731910946 & 0.00200253617810876 & 0.00100126808905438 \tabularnewline
79 & 0.99848761696171 & 0.00302476607658110 & 0.00151238303829055 \tabularnewline
80 & 0.997679368841317 & 0.00464126231736545 & 0.00232063115868273 \tabularnewline
81 & 0.996610981947727 & 0.00677803610454609 & 0.00338901805227305 \tabularnewline
82 & 0.99572328864161 & 0.00855342271678187 & 0.00427671135839093 \tabularnewline
83 & 0.994966840177136 & 0.0100663196457271 & 0.00503315982286356 \tabularnewline
84 & 0.993636295985729 & 0.0127274080285413 & 0.00636370401427064 \tabularnewline
85 & 0.993383866190169 & 0.0132322676196616 & 0.0066161338098308 \tabularnewline
86 & 0.990155584196286 & 0.0196888316074277 & 0.00984441580371384 \tabularnewline
87 & 0.985019130835986 & 0.0299617383280278 & 0.0149808691640139 \tabularnewline
88 & 0.97637897901529 & 0.047242041969420 & 0.023621020984710 \tabularnewline
89 & 0.963982206977695 & 0.0720355860446097 & 0.0360177930223048 \tabularnewline
90 & 0.95376547131709 & 0.0924690573658181 & 0.0462345286829090 \tabularnewline
91 & 0.938176761115546 & 0.123646477768907 & 0.0618232388844537 \tabularnewline
92 & 0.91404925613587 & 0.171901487728261 & 0.0859507438641305 \tabularnewline
93 & 0.873986329793627 & 0.252027340412745 & 0.126013670206372 \tabularnewline
94 & 0.820047392630186 & 0.359905214739628 & 0.179952607369814 \tabularnewline
95 & 0.752667982400279 & 0.494664035199442 & 0.247332017599721 \tabularnewline
96 & 0.684523704101643 & 0.630952591796713 & 0.315476295898357 \tabularnewline
97 & 0.697659617188729 & 0.604680765622543 & 0.302340382811271 \tabularnewline
98 & 0.796224620280132 & 0.407550759439735 & 0.203775379719868 \tabularnewline
99 & 0.81547686167772 & 0.369046276644559 & 0.184523138322280 \tabularnewline
100 & 0.759599407802273 & 0.480801184395455 & 0.240400592197727 \tabularnewline
101 & 0.792512838322796 & 0.414974323354409 & 0.207487161677204 \tabularnewline
102 & 0.851458114016506 & 0.297083771966988 & 0.148541885983494 \tabularnewline
103 & 0.820319291693959 & 0.359361416612083 & 0.179680708306041 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57401&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]0.173958538695498[/C][C]0.347917077390996[/C][C]0.826041461304502[/C][/ROW]
[ROW][C]6[/C][C]0.0749913074962567[/C][C]0.149982614992513[/C][C]0.925008692503743[/C][/ROW]
[ROW][C]7[/C][C]0.0305225682271213[/C][C]0.0610451364542426[/C][C]0.969477431772879[/C][/ROW]
[ROW][C]8[/C][C]0.0129721222841324[/C][C]0.0259442445682647[/C][C]0.987027877715868[/C][/ROW]
[ROW][C]9[/C][C]0.00499043027402638[/C][C]0.00998086054805277[/C][C]0.995009569725974[/C][/ROW]
[ROW][C]10[/C][C]0.00254464321418543[/C][C]0.00508928642837086[/C][C]0.997455356785815[/C][/ROW]
[ROW][C]11[/C][C]0.0024417918887141[/C][C]0.0048835837774282[/C][C]0.997558208111286[/C][/ROW]
[ROW][C]12[/C][C]0.00151137856220320[/C][C]0.00302275712440641[/C][C]0.998488621437797[/C][/ROW]
[ROW][C]13[/C][C]0.0018013630433953[/C][C]0.0036027260867906[/C][C]0.998198636956605[/C][/ROW]
[ROW][C]14[/C][C]0.00350468496070329[/C][C]0.00700936992140657[/C][C]0.996495315039297[/C][/ROW]
[ROW][C]15[/C][C]0.0098575672911791[/C][C]0.0197151345823582[/C][C]0.99014243270882[/C][/ROW]
[ROW][C]16[/C][C]0.00916582512972444[/C][C]0.0183316502594489[/C][C]0.990834174870276[/C][/ROW]
[ROW][C]17[/C][C]0.00650159805270711[/C][C]0.0130031961054142[/C][C]0.993498401947293[/C][/ROW]
[ROW][C]18[/C][C]0.00676208433825451[/C][C]0.0135241686765090[/C][C]0.993237915661745[/C][/ROW]
[ROW][C]19[/C][C]0.0346682017660394[/C][C]0.0693364035320789[/C][C]0.96533179823396[/C][/ROW]
[ROW][C]20[/C][C]0.0802066961053498[/C][C]0.160413392210700[/C][C]0.91979330389465[/C][/ROW]
[ROW][C]21[/C][C]0.136213068328418[/C][C]0.272426136656835[/C][C]0.863786931671582[/C][/ROW]
[ROW][C]22[/C][C]0.186545734342992[/C][C]0.373091468685985[/C][C]0.813454265657008[/C][/ROW]
[ROW][C]23[/C][C]0.194163944246571[/C][C]0.388327888493143[/C][C]0.805836055753429[/C][/ROW]
[ROW][C]24[/C][C]0.185042360729269[/C][C]0.370084721458539[/C][C]0.81495763927073[/C][/ROW]
[ROW][C]25[/C][C]0.168810397222794[/C][C]0.337620794445588[/C][C]0.831189602777206[/C][/ROW]
[ROW][C]26[/C][C]0.157279821958286[/C][C]0.314559643916571[/C][C]0.842720178041715[/C][/ROW]
[ROW][C]27[/C][C]0.161348128729680[/C][C]0.322696257459361[/C][C]0.83865187127032[/C][/ROW]
[ROW][C]28[/C][C]0.170546479828528[/C][C]0.341092959657056[/C][C]0.829453520171472[/C][/ROW]
[ROW][C]29[/C][C]0.175516556297864[/C][C]0.351033112595728[/C][C]0.824483443702136[/C][/ROW]
[ROW][C]30[/C][C]0.188373212034584[/C][C]0.376746424069168[/C][C]0.811626787965416[/C][/ROW]
[ROW][C]31[/C][C]0.213494773488633[/C][C]0.426989546977266[/C][C]0.786505226511367[/C][/ROW]
[ROW][C]32[/C][C]0.226223565310899[/C][C]0.452447130621798[/C][C]0.773776434689101[/C][/ROW]
[ROW][C]33[/C][C]0.25569252352688[/C][C]0.51138504705376[/C][C]0.74430747647312[/C][/ROW]
[ROW][C]34[/C][C]0.274747886353549[/C][C]0.549495772707098[/C][C]0.725252113646451[/C][/ROW]
[ROW][C]35[/C][C]0.309535337953206[/C][C]0.619070675906412[/C][C]0.690464662046794[/C][/ROW]
[ROW][C]36[/C][C]0.305604554019976[/C][C]0.611209108039952[/C][C]0.694395445980024[/C][/ROW]
[ROW][C]37[/C][C]0.274173854766594[/C][C]0.548347709533188[/C][C]0.725826145233406[/C][/ROW]
[ROW][C]38[/C][C]0.244863326132868[/C][C]0.489726652265737[/C][C]0.755136673867131[/C][/ROW]
[ROW][C]39[/C][C]0.214379305959134[/C][C]0.428758611918269[/C][C]0.785620694040866[/C][/ROW]
[ROW][C]40[/C][C]0.222194568815796[/C][C]0.444389137631591[/C][C]0.777805431184204[/C][/ROW]
[ROW][C]41[/C][C]0.265651703095328[/C][C]0.531303406190655[/C][C]0.734348296904672[/C][/ROW]
[ROW][C]42[/C][C]0.30331906537413[/C][C]0.60663813074826[/C][C]0.69668093462587[/C][/ROW]
[ROW][C]43[/C][C]0.312747660068185[/C][C]0.625495320136371[/C][C]0.687252339931815[/C][/ROW]
[ROW][C]44[/C][C]0.343376100308725[/C][C]0.686752200617451[/C][C]0.656623899691275[/C][/ROW]
[ROW][C]45[/C][C]0.393037424757472[/C][C]0.786074849514944[/C][C]0.606962575242528[/C][/ROW]
[ROW][C]46[/C][C]0.485763953347577[/C][C]0.971527906695155[/C][C]0.514236046652423[/C][/ROW]
[ROW][C]47[/C][C]0.505595557640991[/C][C]0.988808884718017[/C][C]0.494404442359009[/C][/ROW]
[ROW][C]48[/C][C]0.454748676249809[/C][C]0.909497352499619[/C][C]0.545251323750191[/C][/ROW]
[ROW][C]49[/C][C]0.414136029553497[/C][C]0.828272059106995[/C][C]0.585863970446503[/C][/ROW]
[ROW][C]50[/C][C]0.388640497711726[/C][C]0.777280995423451[/C][C]0.611359502288274[/C][/ROW]
[ROW][C]51[/C][C]0.355061000598861[/C][C]0.710122001197723[/C][C]0.644938999401139[/C][/ROW]
[ROW][C]52[/C][C]0.520665537811266[/C][C]0.958668924377468[/C][C]0.479334462188734[/C][/ROW]
[ROW][C]53[/C][C]0.778543335853324[/C][C]0.442913328293351[/C][C]0.221456664146676[/C][/ROW]
[ROW][C]54[/C][C]0.90733160485352[/C][C]0.185336790292960[/C][C]0.0926683951464799[/C][/ROW]
[ROW][C]55[/C][C]0.951862211182744[/C][C]0.096275577634513[/C][C]0.0481377888172565[/C][/ROW]
[ROW][C]56[/C][C]0.950247063980332[/C][C]0.099505872039335[/C][C]0.0497529360196675[/C][/ROW]
[ROW][C]57[/C][C]0.947157127415851[/C][C]0.105685745168298[/C][C]0.0528428725841489[/C][/ROW]
[ROW][C]58[/C][C]0.951391314424137[/C][C]0.097217371151725[/C][C]0.0486086855758625[/C][/ROW]
[ROW][C]59[/C][C]0.963891642690464[/C][C]0.0722167146190717[/C][C]0.0361083573095358[/C][/ROW]
[ROW][C]60[/C][C]0.97527478662677[/C][C]0.0494504267464588[/C][C]0.0247252133732294[/C][/ROW]
[ROW][C]61[/C][C]0.973641819673195[/C][C]0.0527163606536107[/C][C]0.0263581803268053[/C][/ROW]
[ROW][C]62[/C][C]0.968107864349304[/C][C]0.0637842713013921[/C][C]0.0318921356506961[/C][/ROW]
[ROW][C]63[/C][C]0.960006204846994[/C][C]0.0799875903060123[/C][C]0.0399937951530061[/C][/ROW]
[ROW][C]64[/C][C]0.974297307788222[/C][C]0.0514053844235551[/C][C]0.0257026922117775[/C][/ROW]
[ROW][C]65[/C][C]0.986059905678784[/C][C]0.0278801886424313[/C][C]0.0139400943212156[/C][/ROW]
[ROW][C]66[/C][C]0.993155258993591[/C][C]0.0136894820128172[/C][C]0.00684474100640858[/C][/ROW]
[ROW][C]67[/C][C]0.994204220257868[/C][C]0.0115915594842643[/C][C]0.00579577974213215[/C][/ROW]
[ROW][C]68[/C][C]0.994675447441758[/C][C]0.0106491051164841[/C][C]0.00532455255824205[/C][/ROW]
[ROW][C]69[/C][C]0.996392094594465[/C][C]0.00721581081107028[/C][C]0.00360790540553514[/C][/ROW]
[ROW][C]70[/C][C]0.998337045405397[/C][C]0.0033259091892064[/C][C]0.0016629545946032[/C][/ROW]
[ROW][C]71[/C][C]0.999268491638083[/C][C]0.00146301672383397[/C][C]0.000731508361916986[/C][/ROW]
[ROW][C]72[/C][C]0.999494682246213[/C][C]0.00101063550757494[/C][C]0.000505317753787469[/C][/ROW]
[ROW][C]73[/C][C]0.999656605996087[/C][C]0.000686788007826346[/C][C]0.000343394003913173[/C][/ROW]
[ROW][C]74[/C][C]0.999701880779954[/C][C]0.000596238440091527[/C][C]0.000298119220045764[/C][/ROW]
[ROW][C]75[/C][C]0.99955339250518[/C][C]0.000893214989638366[/C][C]0.000446607494819183[/C][/ROW]
[ROW][C]76[/C][C]0.999463255244162[/C][C]0.00107348951167546[/C][C]0.000536744755837729[/C][/ROW]
[ROW][C]77[/C][C]0.999288527296104[/C][C]0.00142294540779285[/C][C]0.000711472703896423[/C][/ROW]
[ROW][C]78[/C][C]0.998998731910946[/C][C]0.00200253617810876[/C][C]0.00100126808905438[/C][/ROW]
[ROW][C]79[/C][C]0.99848761696171[/C][C]0.00302476607658110[/C][C]0.00151238303829055[/C][/ROW]
[ROW][C]80[/C][C]0.997679368841317[/C][C]0.00464126231736545[/C][C]0.00232063115868273[/C][/ROW]
[ROW][C]81[/C][C]0.996610981947727[/C][C]0.00677803610454609[/C][C]0.00338901805227305[/C][/ROW]
[ROW][C]82[/C][C]0.99572328864161[/C][C]0.00855342271678187[/C][C]0.00427671135839093[/C][/ROW]
[ROW][C]83[/C][C]0.994966840177136[/C][C]0.0100663196457271[/C][C]0.00503315982286356[/C][/ROW]
[ROW][C]84[/C][C]0.993636295985729[/C][C]0.0127274080285413[/C][C]0.00636370401427064[/C][/ROW]
[ROW][C]85[/C][C]0.993383866190169[/C][C]0.0132322676196616[/C][C]0.0066161338098308[/C][/ROW]
[ROW][C]86[/C][C]0.990155584196286[/C][C]0.0196888316074277[/C][C]0.00984441580371384[/C][/ROW]
[ROW][C]87[/C][C]0.985019130835986[/C][C]0.0299617383280278[/C][C]0.0149808691640139[/C][/ROW]
[ROW][C]88[/C][C]0.97637897901529[/C][C]0.047242041969420[/C][C]0.023621020984710[/C][/ROW]
[ROW][C]89[/C][C]0.963982206977695[/C][C]0.0720355860446097[/C][C]0.0360177930223048[/C][/ROW]
[ROW][C]90[/C][C]0.95376547131709[/C][C]0.0924690573658181[/C][C]0.0462345286829090[/C][/ROW]
[ROW][C]91[/C][C]0.938176761115546[/C][C]0.123646477768907[/C][C]0.0618232388844537[/C][/ROW]
[ROW][C]92[/C][C]0.91404925613587[/C][C]0.171901487728261[/C][C]0.0859507438641305[/C][/ROW]
[ROW][C]93[/C][C]0.873986329793627[/C][C]0.252027340412745[/C][C]0.126013670206372[/C][/ROW]
[ROW][C]94[/C][C]0.820047392630186[/C][C]0.359905214739628[/C][C]0.179952607369814[/C][/ROW]
[ROW][C]95[/C][C]0.752667982400279[/C][C]0.494664035199442[/C][C]0.247332017599721[/C][/ROW]
[ROW][C]96[/C][C]0.684523704101643[/C][C]0.630952591796713[/C][C]0.315476295898357[/C][/ROW]
[ROW][C]97[/C][C]0.697659617188729[/C][C]0.604680765622543[/C][C]0.302340382811271[/C][/ROW]
[ROW][C]98[/C][C]0.796224620280132[/C][C]0.407550759439735[/C][C]0.203775379719868[/C][/ROW]
[ROW][C]99[/C][C]0.81547686167772[/C][C]0.369046276644559[/C][C]0.184523138322280[/C][/ROW]
[ROW][C]100[/C][C]0.759599407802273[/C][C]0.480801184395455[/C][C]0.240400592197727[/C][/ROW]
[ROW][C]101[/C][C]0.792512838322796[/C][C]0.414974323354409[/C][C]0.207487161677204[/C][/ROW]
[ROW][C]102[/C][C]0.851458114016506[/C][C]0.297083771966988[/C][C]0.148541885983494[/C][/ROW]
[ROW][C]103[/C][C]0.820319291693959[/C][C]0.359361416612083[/C][C]0.179680708306041[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57401&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57401&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	0.173958538695498	0.347917077390996	0.826041461304502
6	0.0749913074962567	0.149982614992513	0.925008692503743
7	0.0305225682271213	0.0610451364542426	0.969477431772879
8	0.0129721222841324	0.0259442445682647	0.987027877715868
9	0.00499043027402638	0.00998086054805277	0.995009569725974
10	0.00254464321418543	0.00508928642837086	0.997455356785815
11	0.0024417918887141	0.0048835837774282	0.997558208111286
12	0.00151137856220320	0.00302275712440641	0.998488621437797
13	0.0018013630433953	0.0036027260867906	0.998198636956605
14	0.00350468496070329	0.00700936992140657	0.996495315039297
15	0.0098575672911791	0.0197151345823582	0.99014243270882
16	0.00916582512972444	0.0183316502594489	0.990834174870276
17	0.00650159805270711	0.0130031961054142	0.993498401947293
18	0.00676208433825451	0.0135241686765090	0.993237915661745
19	0.0346682017660394	0.0693364035320789	0.96533179823396
20	0.0802066961053498	0.160413392210700	0.91979330389465
21	0.136213068328418	0.272426136656835	0.863786931671582
22	0.186545734342992	0.373091468685985	0.813454265657008
23	0.194163944246571	0.388327888493143	0.805836055753429
24	0.185042360729269	0.370084721458539	0.81495763927073
25	0.168810397222794	0.337620794445588	0.831189602777206
26	0.157279821958286	0.314559643916571	0.842720178041715
27	0.161348128729680	0.322696257459361	0.83865187127032
28	0.170546479828528	0.341092959657056	0.829453520171472
29	0.175516556297864	0.351033112595728	0.824483443702136
30	0.188373212034584	0.376746424069168	0.811626787965416
31	0.213494773488633	0.426989546977266	0.786505226511367
32	0.226223565310899	0.452447130621798	0.773776434689101
33	0.25569252352688	0.51138504705376	0.74430747647312
34	0.274747886353549	0.549495772707098	0.725252113646451
35	0.309535337953206	0.619070675906412	0.690464662046794
36	0.305604554019976	0.611209108039952	0.694395445980024
37	0.274173854766594	0.548347709533188	0.725826145233406
38	0.244863326132868	0.489726652265737	0.755136673867131
39	0.214379305959134	0.428758611918269	0.785620694040866
40	0.222194568815796	0.444389137631591	0.777805431184204
41	0.265651703095328	0.531303406190655	0.734348296904672
42	0.30331906537413	0.60663813074826	0.69668093462587
43	0.312747660068185	0.625495320136371	0.687252339931815
44	0.343376100308725	0.686752200617451	0.656623899691275
45	0.393037424757472	0.786074849514944	0.606962575242528
46	0.485763953347577	0.971527906695155	0.514236046652423
47	0.505595557640991	0.988808884718017	0.494404442359009
48	0.454748676249809	0.909497352499619	0.545251323750191
49	0.414136029553497	0.828272059106995	0.585863970446503
50	0.388640497711726	0.777280995423451	0.611359502288274
51	0.355061000598861	0.710122001197723	0.644938999401139
52	0.520665537811266	0.958668924377468	0.479334462188734
53	0.778543335853324	0.442913328293351	0.221456664146676
54	0.90733160485352	0.185336790292960	0.0926683951464799
55	0.951862211182744	0.096275577634513	0.0481377888172565
56	0.950247063980332	0.099505872039335	0.0497529360196675
57	0.947157127415851	0.105685745168298	0.0528428725841489
58	0.951391314424137	0.097217371151725	0.0486086855758625
59	0.963891642690464	0.0722167146190717	0.0361083573095358
60	0.97527478662677	0.0494504267464588	0.0247252133732294
61	0.973641819673195	0.0527163606536107	0.0263581803268053
62	0.968107864349304	0.0637842713013921	0.0318921356506961
63	0.960006204846994	0.0799875903060123	0.0399937951530061
64	0.974297307788222	0.0514053844235551	0.0257026922117775
65	0.986059905678784	0.0278801886424313	0.0139400943212156
66	0.993155258993591	0.0136894820128172	0.00684474100640858
67	0.994204220257868	0.0115915594842643	0.00579577974213215
68	0.994675447441758	0.0106491051164841	0.00532455255824205
69	0.996392094594465	0.00721581081107028	0.00360790540553514
70	0.998337045405397	0.0033259091892064	0.0016629545946032
71	0.999268491638083	0.00146301672383397	0.000731508361916986
72	0.999494682246213	0.00101063550757494	0.000505317753787469
73	0.999656605996087	0.000686788007826346	0.000343394003913173
74	0.999701880779954	0.000596238440091527	0.000298119220045764
75	0.99955339250518	0.000893214989638366	0.000446607494819183
76	0.999463255244162	0.00107348951167546	0.000536744755837729
77	0.999288527296104	0.00142294540779285	0.000711472703896423
78	0.998998731910946	0.00200253617810876	0.00100126808905438
79	0.99848761696171	0.00302476607658110	0.00151238303829055
80	0.997679368841317	0.00464126231736545	0.00232063115868273
81	0.996610981947727	0.00677803610454609	0.00338901805227305
82	0.99572328864161	0.00855342271678187	0.00427671135839093
83	0.994966840177136	0.0100663196457271	0.00503315982286356
84	0.993636295985729	0.0127274080285413	0.00636370401427064
85	0.993383866190169	0.0132322676196616	0.0066161338098308
86	0.990155584196286	0.0196888316074277	0.00984441580371384
87	0.985019130835986	0.0299617383280278	0.0149808691640139
88	0.97637897901529	0.047242041969420	0.023621020984710
89	0.963982206977695	0.0720355860446097	0.0360177930223048
90	0.95376547131709	0.0924690573658181	0.0462345286829090
91	0.938176761115546	0.123646477768907	0.0618232388844537
92	0.91404925613587	0.171901487728261	0.0859507438641305
93	0.873986329793627	0.252027340412745	0.126013670206372
94	0.820047392630186	0.359905214739628	0.179952607369814
95	0.752667982400279	0.494664035199442	0.247332017599721
96	0.684523704101643	0.630952591796713	0.315476295898357
97	0.697659617188729	0.604680765622543	0.302340382811271
98	0.796224620280132	0.407550759439735	0.203775379719868
99	0.81547686167772	0.369046276644559	0.184523138322280
100	0.759599407802273	0.480801184395455	0.240400592197727
101	0.792512838322796	0.414974323354409	0.207487161677204
102	0.851458114016506	0.297083771966988	0.148541885983494
103	0.820319291693959	0.359361416612083	0.179680708306041

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	20	0.202020202020202	NOK
5% type I error level	36	0.363636363636364	NOK
10% type I error level	48	0.484848484848485	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 & 20 & 0.202020202020202 & NOK \tabularnewline
5% type I error level & 36 & 0.363636363636364 & NOK \tabularnewline
10% type I error level & 48 & 0.484848484848485 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57401&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]20[/C][C]0.202020202020202[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]36[/C][C]0.363636363636364[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]48[/C][C]0.484848484848485[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57401&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57401&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	20	0.202020202020202	NOK
5% type I error level	36	0.363636363636364	NOK
10% type I error level	48	0.484848484848485	NOK

Figure 1

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Figure 6

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Figure 7

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Figure 8

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Figure 9

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Figure 10

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Parameters (Session):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

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