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rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Tue, 22 Nov 2011 14:01:15 -0500

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Nov/22/t1321988495d5nxmegomm565a7.htm/, Retrieved Fri, 01 Nov 2024 00:30:37 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=146367, Retrieved Fri, 01 Nov 2024 00:30:37 +0000

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169

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-     [Multiple Regression] [Competence to learn] [2010-11-17 07:43:53] [b98453cac15ba1066b407e146608df68]
-   PD  [Multiple Regression] [Nyrstar] [2011-11-19 10:08:04] [25b6caf3839c2bdc14961e5bff2d6373]
-   PD    [Multiple Regression] [test] [2011-11-22 18:44:21] [25b6caf3839c2bdc14961e5bff2d6373]
-   PD        [Multiple Regression] [jill] [2011-11-22 19:01:15] [2adf2d2c11e011c12275478b9efd18e5] [Current]

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6654000	5712000	3,3	38,6	645	3	5	3
1000	6600	8,3	4,5	42	3	1	3
3385	44500	12,5	14	60	1	1	1
0,92	5700	16,5	0	25	5	2	3
2547000	4603000	3,9	69	624	3	5	4
10550	179500	9,8	27	180	4	4	4
0,023	0,3	19,7	19	35	1	1	1
160000	169000	6,2	30,4	392	4	5	4
3300	25600	14,5	28	63	1	2	1
52160	440000	9,7	50	230	1	1	1
0,425	6400	12,5	7	112	5	4	4
465000	423000	3,9	30	281	5	5	5
0,55	2400	10,3	0	0	2	1	2
187100	419000	3,1	40	365	5	5	5
0,075	1200	8,4	3,5	42	1	1	1
3000	25000	8,6	50	28	2	2	2
0,785	3500	10,7	6	42	2	2	2
0,2	5000	10,7	10,4	120	2	2	2
1410	17500	6,1	34	0	1	2	1
60000	81000	18,1	7	0	1	1	1
27660	115000	3,8	20	148	5	5	5
0,12	1000	14,4	3,9	16	3	1	2
207000	406000	12	39,3	252	1	4	1
85000	325000	6,2	41	310	1	3	1
36330	119500	13	16,2	63	1	1	1
0,101	4000	13,8	9	28	5	1	3
1040	5500	8,2	7,6	68	5	3	4
521000	655000	2,9	46	336	5	5	5
100000	157000	10,8	22,4	100	1	1	1
0,005	0,14	9,1	2,6	21,5	5	2	4
0,01	0,25	19,9	24	50	1	1	1
62000	1320000	8	100	267	1	1	1
0,122	3000	10,6	0	30	2	1	1
1350	8100	11,2	0	45	3	1	3
0,023	0,4	13,2	3,2	19	4	1	3
0,048	0,33	12,8	2	30	4	1	3
1700	6300	19,4	5	12	2	1	1
3500	10800	17,4	6,5	120	2	1	1
0,48	15500	17	12	140	2	2	2
10000	115000	10,9	20,2	170	4	4	4
1620	11400	13,7	13	17	2	1	2
192000	180000	8,4	27	115	4	4	4
2500	12100	8,4	18	31	5	5	5
4288	39200	12,5	13,7	63	2	2	2
0,28	1900	13,2	4,7	21	3	1	3
4235	50400	9,8	9,8	52	1	1	1
6800	179000	9,6	29	164	2	3	2
0,75	12300	6,6	7	225	2	2	2
3600	21000	5,4	6	225	3	2	3
14830	98200	2,6	17	150	5	5	5
55500	175000	3,8	20	151	5	5	5
1400	12500	11	12,7	90	2	2	2
0,06	1000	10,3	3,5	0	3	1	2
0,9	2600	13,3	4,5	60	2	1	2
2000	12300	5,4	7,5	200	3	1	3
0,104	2500	15,8	2,3	46	3	2	2
4190	58000	10,3	24	210	4	3	4
3500	3900	19,4	3	14	2	1	1

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	'Gertrude Mary Cox' @ cox.wessa.net

\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 & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146367&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]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146367&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146367&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	'Gertrude Mary Cox' @ cox.wessa.net

Multiple Linear Regression - Estimated Regression Equation
totaleslaap[t] = + 15.9045939067489 -1.6117594600388e-06gewicht[t] + 2.07321499875609e-06brein[t] -0.0477119610443166levensduur[t] -0.0119939929006805drachttijd[t] + 2.07493347685895`jager?`[t] + 0.313113835483447blootgesteldheidslaap[t] -3.84823801074653algemeengevaar[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
totaleslaap[t] =  +  15.9045939067489 -1.6117594600388e-06gewicht[t] +  2.07321499875609e-06brein[t] -0.0477119610443166levensduur[t] -0.0119939929006805drachttijd[t] +  2.07493347685895`jager?`[t] +  0.313113835483447blootgesteldheidslaap[t] -3.84823801074653algemeengevaar[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146367&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]totaleslaap[t] =  +  15.9045939067489 -1.6117594600388e-06gewicht[t] +  2.07321499875609e-06brein[t] -0.0477119610443166levensduur[t] -0.0119939929006805drachttijd[t] +  2.07493347685895`jager?`[t] +  0.313113835483447blootgesteldheidslaap[t] -3.84823801074653algemeengevaar[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146367&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146367&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
totaleslaap[t] = + 15.9045939067489 -1.6117594600388e-06gewicht[t] + 2.07321499875609e-06brein[t] -0.0477119610443166levensduur[t] -0.0119939929006805drachttijd[t] + 2.07493347685895`jager?`[t] + 0.313113835483447blootgesteldheidslaap[t] -3.84823801074653algemeengevaar[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)	15.9045939067489	1.068495	14.885	0	0
gewicht	-1.6117594600388e-06	2e-06	-1.0257	0.309993	0.154996
brein	2.07321499875609e-06	2e-06	1.2004	0.235647	0.117823
levensduur	-0.0477119610443166	0.037131	-1.285	0.204727	0.102363
drachttijd	-0.0119939929006805	0.006433	-1.8643	0.06815	0.034075
`jager?`	2.07493347685895	0.891892	2.3264	0.024088	0.012044
blootgesteldheidslaap	0.313113835483447	0.580145	0.5397	0.591788	0.295894
algemeengevaar	-3.84823801074653	1.107553	-3.4745	0.001067	0.000533

\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) & 15.9045939067489 & 1.068495 & 14.885 & 0 & 0 \tabularnewline
gewicht & -1.6117594600388e-06 & 2e-06 & -1.0257 & 0.309993 & 0.154996 \tabularnewline
brein & 2.07321499875609e-06 & 2e-06 & 1.2004 & 0.235647 & 0.117823 \tabularnewline
levensduur & -0.0477119610443166 & 0.037131 & -1.285 & 0.204727 & 0.102363 \tabularnewline
drachttijd & -0.0119939929006805 & 0.006433 & -1.8643 & 0.06815 & 0.034075 \tabularnewline
`jager?` & 2.07493347685895 & 0.891892 & 2.3264 & 0.024088 & 0.012044 \tabularnewline
blootgesteldheidslaap & 0.313113835483447 & 0.580145 & 0.5397 & 0.591788 & 0.295894 \tabularnewline
algemeengevaar & -3.84823801074653 & 1.107553 & -3.4745 & 0.001067 & 0.000533 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146367&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]15.9045939067489[/C][C]1.068495[/C][C]14.885[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]gewicht[/C][C]-1.6117594600388e-06[/C][C]2e-06[/C][C]-1.0257[/C][C]0.309993[/C][C]0.154996[/C][/ROW]
[ROW][C]brein[/C][C]2.07321499875609e-06[/C][C]2e-06[/C][C]1.2004[/C][C]0.235647[/C][C]0.117823[/C][/ROW]
[ROW][C]levensduur[/C][C]-0.0477119610443166[/C][C]0.037131[/C][C]-1.285[/C][C]0.204727[/C][C]0.102363[/C][/ROW]
[ROW][C]drachttijd[/C][C]-0.0119939929006805[/C][C]0.006433[/C][C]-1.8643[/C][C]0.06815[/C][C]0.034075[/C][/ROW]
[ROW][C]`jager?`[/C][C]2.07493347685895[/C][C]0.891892[/C][C]2.3264[/C][C]0.024088[/C][C]0.012044[/C][/ROW]
[ROW][C]blootgesteldheidslaap[/C][C]0.313113835483447[/C][C]0.580145[/C][C]0.5397[/C][C]0.591788[/C][C]0.295894[/C][/ROW]
[ROW][C]algemeengevaar[/C][C]-3.84823801074653[/C][C]1.107553[/C][C]-3.4745[/C][C]0.001067[/C][C]0.000533[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146367&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146367&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)	15.9045939067489	1.068495	14.885	0	0
gewicht	-1.6117594600388e-06	2e-06	-1.0257	0.309993	0.154996
brein	2.07321499875609e-06	2e-06	1.2004	0.235647	0.117823
levensduur	-0.0477119610443166	0.037131	-1.285	0.204727	0.102363
drachttijd	-0.0119939929006805	0.006433	-1.8643	0.06815	0.034075
`jager?`	2.07493347685895	0.891892	2.3264	0.024088	0.012044
blootgesteldheidslaap	0.313113835483447	0.580145	0.5397	0.591788	0.295894
algemeengevaar	-3.84823801074653	1.107553	-3.4745	0.001067	0.000533

Multiple Linear Regression - Regression Statistics
Multiple R	0.7741492855136
R-squared	0.599307116261218
Adjusted R-squared	0.543210112537789
F-TEST (value)	10.6834068931013
F-TEST (DF numerator)	7
F-TEST (DF denominator)	50
p-value	3.88024755659799e-08
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	3.11353581179839
Sum Squared Residuals	484.705262567553

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.7741492855136 \tabularnewline
R-squared & 0.599307116261218 \tabularnewline
Adjusted R-squared & 0.543210112537789 \tabularnewline
F-TEST (value) & 10.6834068931013 \tabularnewline
F-TEST (DF numerator) & 7 \tabularnewline
F-TEST (DF denominator) & 50 \tabularnewline
p-value & 3.88024755659799e-08 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 3.11353581179839 \tabularnewline
Sum Squared Residuals & 484.705262567553 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146367&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.7741492855136[/C][/ROW]
[ROW][C]R-squared[/C][C]0.599307116261218[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.543210112537789[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]10.6834068931013[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]7[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]50[/C][/ROW]
[ROW][C]p-value[/C][C]3.88024755659799e-08[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]3.11353581179839[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]484.705262567553[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146367&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146367&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	0.7741492855136
R-squared	0.599307116261218
Adjusted R-squared	0.543210112537789
F-TEST (value)	10.6834068931013
F-TEST (DF numerator)	7
F-TEST (DF denominator)	50
p-value	3.88024755659799e-08
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	3.11353581179839
Sum Squared Residuals	484.705262567553

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	3.3	3.68999899105046	-0.389998991050456
2	8.3	10.1914140735733	-1.89141407357333
3	12.5	13.1435984413559	-0.643598441355881
4	16.5	15.0727409499281	1.42725905007187
5	3.9	2.96349188422981	0.93650811577019
6	9.8	6.97182747278661	2.82817252721339
7	19.7	13.1180867818729	6.58191321812707
8	6.2	4.31734793698538	1.88265206301462
9	14.5	12.7137160795945	1.78628392040553
10	9.7	10.1283320149894	-0.428332014989438
11	12.5	10.4747215487991	2.02527845120087
12	3.9	3.92947137386328	-0.0294713738632782
13	10.3	12.6760735039865	-2.37607350398647
14	3.1	2.8844714537127	0.215528546287296
15	8.4	13.7761513799776	-5.37615137997759
16	8.6	10.3097777530945	-1.70977775309451
17	10.7	12.2014480291106	-1.50144802911059
18	10.7	11.0590947196399	-0.359094719639937
19	6.1	13.169319049961	-7.06931904996099
20	18.1	14.1816443283314	3.91835567166857
21	3.8	6.06812870273344	-2.26812870273344
22	14.4	14.37012463842	0.0298753615799922
23	12	10.9942695160489	1.00573048395112
24	6.2	9.93309799777611	-3.73309799777611
25	13	13.1050418578521	-0.105041857852073
26	13.8	14.2907143408769	-0.490714340876903
27	8.2	10.6571747859795	-2.45717478597949
28	2.9	2.89713773756602	0.00286226243397486
29	10.8	12.3405747996848	-1.54057479968481
30	9.1	11.1306152551359	-2.03061525513585
31	19.9	12.6996170004333	7.20038299956674
32	8	9.10752571126701	-1.107525711267
33	10.6	16.1657363465449	-5.56573634654488
34	11.2	10.3726816262578	0.827318373742169
35	13.2	12.5921642691893	0.607835730810676
36	12.8	12.517484515116	0.282515484884019
37	19.4	16.147170228584	3.25282977141597
38	17.4	14.7866793542104	2.61332064578961
39	17	10.7646440301497	6.23535596985029
40	10.9	7.28337283717803	3.61662716282197
41	13.7	11.86796890223	1.83203109776997
42	8.4	7.46001986480618	0.939980135193815
43	8.4	7.39406783884428	1.00593216115572
44	12.5	11.6492958942772	0.850704105722814
45	13.2	10.425612729952	2.77438727004801
46	9.8	13.4508025938991	-3.65080259389908
47	9.6	10.3068101598763	-0.706810159876341
48	6.6	9.97707971564237	-3.37707971564237
49	5.4	8.26372298805175	-2.86372298805175
50	2.6	6.17312546195823	-3.57312546195823
51	3.8	6.11166824058929	-2.31166824058929
52	11	11.3224699678569	-0.322469967856933
53	10.3	14.5811134059542	-4.28111340595419
54	13.3	11.7421441841302	1.55785581586985
55	5.4	8.16343287816573	-2.76343287816572
56	15.8	14.4028676728402	1.39713232715977
57	10.3	6.20038490123268	4.09961509876732
58	19.4	16.2107292818462	3.18927071815378

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 3.3 & 3.68999899105046 & -0.389998991050456 \tabularnewline
2 & 8.3 & 10.1914140735733 & -1.89141407357333 \tabularnewline
3 & 12.5 & 13.1435984413559 & -0.643598441355881 \tabularnewline
4 & 16.5 & 15.0727409499281 & 1.42725905007187 \tabularnewline
5 & 3.9 & 2.96349188422981 & 0.93650811577019 \tabularnewline
6 & 9.8 & 6.97182747278661 & 2.82817252721339 \tabularnewline
7 & 19.7 & 13.1180867818729 & 6.58191321812707 \tabularnewline
8 & 6.2 & 4.31734793698538 & 1.88265206301462 \tabularnewline
9 & 14.5 & 12.7137160795945 & 1.78628392040553 \tabularnewline
10 & 9.7 & 10.1283320149894 & -0.428332014989438 \tabularnewline
11 & 12.5 & 10.4747215487991 & 2.02527845120087 \tabularnewline
12 & 3.9 & 3.92947137386328 & -0.0294713738632782 \tabularnewline
13 & 10.3 & 12.6760735039865 & -2.37607350398647 \tabularnewline
14 & 3.1 & 2.8844714537127 & 0.215528546287296 \tabularnewline
15 & 8.4 & 13.7761513799776 & -5.37615137997759 \tabularnewline
16 & 8.6 & 10.3097777530945 & -1.70977775309451 \tabularnewline
17 & 10.7 & 12.2014480291106 & -1.50144802911059 \tabularnewline
18 & 10.7 & 11.0590947196399 & -0.359094719639937 \tabularnewline
19 & 6.1 & 13.169319049961 & -7.06931904996099 \tabularnewline
20 & 18.1 & 14.1816443283314 & 3.91835567166857 \tabularnewline
21 & 3.8 & 6.06812870273344 & -2.26812870273344 \tabularnewline
22 & 14.4 & 14.37012463842 & 0.0298753615799922 \tabularnewline
23 & 12 & 10.9942695160489 & 1.00573048395112 \tabularnewline
24 & 6.2 & 9.93309799777611 & -3.73309799777611 \tabularnewline
25 & 13 & 13.1050418578521 & -0.105041857852073 \tabularnewline
26 & 13.8 & 14.2907143408769 & -0.490714340876903 \tabularnewline
27 & 8.2 & 10.6571747859795 & -2.45717478597949 \tabularnewline
28 & 2.9 & 2.89713773756602 & 0.00286226243397486 \tabularnewline
29 & 10.8 & 12.3405747996848 & -1.54057479968481 \tabularnewline
30 & 9.1 & 11.1306152551359 & -2.03061525513585 \tabularnewline
31 & 19.9 & 12.6996170004333 & 7.20038299956674 \tabularnewline
32 & 8 & 9.10752571126701 & -1.107525711267 \tabularnewline
33 & 10.6 & 16.1657363465449 & -5.56573634654488 \tabularnewline
34 & 11.2 & 10.3726816262578 & 0.827318373742169 \tabularnewline
35 & 13.2 & 12.5921642691893 & 0.607835730810676 \tabularnewline
36 & 12.8 & 12.517484515116 & 0.282515484884019 \tabularnewline
37 & 19.4 & 16.147170228584 & 3.25282977141597 \tabularnewline
38 & 17.4 & 14.7866793542104 & 2.61332064578961 \tabularnewline
39 & 17 & 10.7646440301497 & 6.23535596985029 \tabularnewline
40 & 10.9 & 7.28337283717803 & 3.61662716282197 \tabularnewline
41 & 13.7 & 11.86796890223 & 1.83203109776997 \tabularnewline
42 & 8.4 & 7.46001986480618 & 0.939980135193815 \tabularnewline
43 & 8.4 & 7.39406783884428 & 1.00593216115572 \tabularnewline
44 & 12.5 & 11.6492958942772 & 0.850704105722814 \tabularnewline
45 & 13.2 & 10.425612729952 & 2.77438727004801 \tabularnewline
46 & 9.8 & 13.4508025938991 & -3.65080259389908 \tabularnewline
47 & 9.6 & 10.3068101598763 & -0.706810159876341 \tabularnewline
48 & 6.6 & 9.97707971564237 & -3.37707971564237 \tabularnewline
49 & 5.4 & 8.26372298805175 & -2.86372298805175 \tabularnewline
50 & 2.6 & 6.17312546195823 & -3.57312546195823 \tabularnewline
51 & 3.8 & 6.11166824058929 & -2.31166824058929 \tabularnewline
52 & 11 & 11.3224699678569 & -0.322469967856933 \tabularnewline
53 & 10.3 & 14.5811134059542 & -4.28111340595419 \tabularnewline
54 & 13.3 & 11.7421441841302 & 1.55785581586985 \tabularnewline
55 & 5.4 & 8.16343287816573 & -2.76343287816572 \tabularnewline
56 & 15.8 & 14.4028676728402 & 1.39713232715977 \tabularnewline
57 & 10.3 & 6.20038490123268 & 4.09961509876732 \tabularnewline
58 & 19.4 & 16.2107292818462 & 3.18927071815378 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146367&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]3.3[/C][C]3.68999899105046[/C][C]-0.389998991050456[/C][/ROW]
[ROW][C]2[/C][C]8.3[/C][C]10.1914140735733[/C][C]-1.89141407357333[/C][/ROW]
[ROW][C]3[/C][C]12.5[/C][C]13.1435984413559[/C][C]-0.643598441355881[/C][/ROW]
[ROW][C]4[/C][C]16.5[/C][C]15.0727409499281[/C][C]1.42725905007187[/C][/ROW]
[ROW][C]5[/C][C]3.9[/C][C]2.96349188422981[/C][C]0.93650811577019[/C][/ROW]
[ROW][C]6[/C][C]9.8[/C][C]6.97182747278661[/C][C]2.82817252721339[/C][/ROW]
[ROW][C]7[/C][C]19.7[/C][C]13.1180867818729[/C][C]6.58191321812707[/C][/ROW]
[ROW][C]8[/C][C]6.2[/C][C]4.31734793698538[/C][C]1.88265206301462[/C][/ROW]
[ROW][C]9[/C][C]14.5[/C][C]12.7137160795945[/C][C]1.78628392040553[/C][/ROW]
[ROW][C]10[/C][C]9.7[/C][C]10.1283320149894[/C][C]-0.428332014989438[/C][/ROW]
[ROW][C]11[/C][C]12.5[/C][C]10.4747215487991[/C][C]2.02527845120087[/C][/ROW]
[ROW][C]12[/C][C]3.9[/C][C]3.92947137386328[/C][C]-0.0294713738632782[/C][/ROW]
[ROW][C]13[/C][C]10.3[/C][C]12.6760735039865[/C][C]-2.37607350398647[/C][/ROW]
[ROW][C]14[/C][C]3.1[/C][C]2.8844714537127[/C][C]0.215528546287296[/C][/ROW]
[ROW][C]15[/C][C]8.4[/C][C]13.7761513799776[/C][C]-5.37615137997759[/C][/ROW]
[ROW][C]16[/C][C]8.6[/C][C]10.3097777530945[/C][C]-1.70977775309451[/C][/ROW]
[ROW][C]17[/C][C]10.7[/C][C]12.2014480291106[/C][C]-1.50144802911059[/C][/ROW]
[ROW][C]18[/C][C]10.7[/C][C]11.0590947196399[/C][C]-0.359094719639937[/C][/ROW]
[ROW][C]19[/C][C]6.1[/C][C]13.169319049961[/C][C]-7.06931904996099[/C][/ROW]
[ROW][C]20[/C][C]18.1[/C][C]14.1816443283314[/C][C]3.91835567166857[/C][/ROW]
[ROW][C]21[/C][C]3.8[/C][C]6.06812870273344[/C][C]-2.26812870273344[/C][/ROW]
[ROW][C]22[/C][C]14.4[/C][C]14.37012463842[/C][C]0.0298753615799922[/C][/ROW]
[ROW][C]23[/C][C]12[/C][C]10.9942695160489[/C][C]1.00573048395112[/C][/ROW]
[ROW][C]24[/C][C]6.2[/C][C]9.93309799777611[/C][C]-3.73309799777611[/C][/ROW]
[ROW][C]25[/C][C]13[/C][C]13.1050418578521[/C][C]-0.105041857852073[/C][/ROW]
[ROW][C]26[/C][C]13.8[/C][C]14.2907143408769[/C][C]-0.490714340876903[/C][/ROW]
[ROW][C]27[/C][C]8.2[/C][C]10.6571747859795[/C][C]-2.45717478597949[/C][/ROW]
[ROW][C]28[/C][C]2.9[/C][C]2.89713773756602[/C][C]0.00286226243397486[/C][/ROW]
[ROW][C]29[/C][C]10.8[/C][C]12.3405747996848[/C][C]-1.54057479968481[/C][/ROW]
[ROW][C]30[/C][C]9.1[/C][C]11.1306152551359[/C][C]-2.03061525513585[/C][/ROW]
[ROW][C]31[/C][C]19.9[/C][C]12.6996170004333[/C][C]7.20038299956674[/C][/ROW]
[ROW][C]32[/C][C]8[/C][C]9.10752571126701[/C][C]-1.107525711267[/C][/ROW]
[ROW][C]33[/C][C]10.6[/C][C]16.1657363465449[/C][C]-5.56573634654488[/C][/ROW]
[ROW][C]34[/C][C]11.2[/C][C]10.3726816262578[/C][C]0.827318373742169[/C][/ROW]
[ROW][C]35[/C][C]13.2[/C][C]12.5921642691893[/C][C]0.607835730810676[/C][/ROW]
[ROW][C]36[/C][C]12.8[/C][C]12.517484515116[/C][C]0.282515484884019[/C][/ROW]
[ROW][C]37[/C][C]19.4[/C][C]16.147170228584[/C][C]3.25282977141597[/C][/ROW]
[ROW][C]38[/C][C]17.4[/C][C]14.7866793542104[/C][C]2.61332064578961[/C][/ROW]
[ROW][C]39[/C][C]17[/C][C]10.7646440301497[/C][C]6.23535596985029[/C][/ROW]
[ROW][C]40[/C][C]10.9[/C][C]7.28337283717803[/C][C]3.61662716282197[/C][/ROW]
[ROW][C]41[/C][C]13.7[/C][C]11.86796890223[/C][C]1.83203109776997[/C][/ROW]
[ROW][C]42[/C][C]8.4[/C][C]7.46001986480618[/C][C]0.939980135193815[/C][/ROW]
[ROW][C]43[/C][C]8.4[/C][C]7.39406783884428[/C][C]1.00593216115572[/C][/ROW]
[ROW][C]44[/C][C]12.5[/C][C]11.6492958942772[/C][C]0.850704105722814[/C][/ROW]
[ROW][C]45[/C][C]13.2[/C][C]10.425612729952[/C][C]2.77438727004801[/C][/ROW]
[ROW][C]46[/C][C]9.8[/C][C]13.4508025938991[/C][C]-3.65080259389908[/C][/ROW]
[ROW][C]47[/C][C]9.6[/C][C]10.3068101598763[/C][C]-0.706810159876341[/C][/ROW]
[ROW][C]48[/C][C]6.6[/C][C]9.97707971564237[/C][C]-3.37707971564237[/C][/ROW]
[ROW][C]49[/C][C]5.4[/C][C]8.26372298805175[/C][C]-2.86372298805175[/C][/ROW]
[ROW][C]50[/C][C]2.6[/C][C]6.17312546195823[/C][C]-3.57312546195823[/C][/ROW]
[ROW][C]51[/C][C]3.8[/C][C]6.11166824058929[/C][C]-2.31166824058929[/C][/ROW]
[ROW][C]52[/C][C]11[/C][C]11.3224699678569[/C][C]-0.322469967856933[/C][/ROW]
[ROW][C]53[/C][C]10.3[/C][C]14.5811134059542[/C][C]-4.28111340595419[/C][/ROW]
[ROW][C]54[/C][C]13.3[/C][C]11.7421441841302[/C][C]1.55785581586985[/C][/ROW]
[ROW][C]55[/C][C]5.4[/C][C]8.16343287816573[/C][C]-2.76343287816572[/C][/ROW]
[ROW][C]56[/C][C]15.8[/C][C]14.4028676728402[/C][C]1.39713232715977[/C][/ROW]
[ROW][C]57[/C][C]10.3[/C][C]6.20038490123268[/C][C]4.09961509876732[/C][/ROW]
[ROW][C]58[/C][C]19.4[/C][C]16.2107292818462[/C][C]3.18927071815378[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146367&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146367&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	3.3	3.68999899105046	-0.389998991050456
2	8.3	10.1914140735733	-1.89141407357333
3	12.5	13.1435984413559	-0.643598441355881
4	16.5	15.0727409499281	1.42725905007187
5	3.9	2.96349188422981	0.93650811577019
6	9.8	6.97182747278661	2.82817252721339
7	19.7	13.1180867818729	6.58191321812707
8	6.2	4.31734793698538	1.88265206301462
9	14.5	12.7137160795945	1.78628392040553
10	9.7	10.1283320149894	-0.428332014989438
11	12.5	10.4747215487991	2.02527845120087
12	3.9	3.92947137386328	-0.0294713738632782
13	10.3	12.6760735039865	-2.37607350398647
14	3.1	2.8844714537127	0.215528546287296
15	8.4	13.7761513799776	-5.37615137997759
16	8.6	10.3097777530945	-1.70977775309451
17	10.7	12.2014480291106	-1.50144802911059
18	10.7	11.0590947196399	-0.359094719639937
19	6.1	13.169319049961	-7.06931904996099
20	18.1	14.1816443283314	3.91835567166857
21	3.8	6.06812870273344	-2.26812870273344
22	14.4	14.37012463842	0.0298753615799922
23	12	10.9942695160489	1.00573048395112
24	6.2	9.93309799777611	-3.73309799777611
25	13	13.1050418578521	-0.105041857852073
26	13.8	14.2907143408769	-0.490714340876903
27	8.2	10.6571747859795	-2.45717478597949
28	2.9	2.89713773756602	0.00286226243397486
29	10.8	12.3405747996848	-1.54057479968481
30	9.1	11.1306152551359	-2.03061525513585
31	19.9	12.6996170004333	7.20038299956674
32	8	9.10752571126701	-1.107525711267
33	10.6	16.1657363465449	-5.56573634654488
34	11.2	10.3726816262578	0.827318373742169
35	13.2	12.5921642691893	0.607835730810676
36	12.8	12.517484515116	0.282515484884019
37	19.4	16.147170228584	3.25282977141597
38	17.4	14.7866793542104	2.61332064578961
39	17	10.7646440301497	6.23535596985029
40	10.9	7.28337283717803	3.61662716282197
41	13.7	11.86796890223	1.83203109776997
42	8.4	7.46001986480618	0.939980135193815
43	8.4	7.39406783884428	1.00593216115572
44	12.5	11.6492958942772	0.850704105722814
45	13.2	10.425612729952	2.77438727004801
46	9.8	13.4508025938991	-3.65080259389908
47	9.6	10.3068101598763	-0.706810159876341
48	6.6	9.97707971564237	-3.37707971564237
49	5.4	8.26372298805175	-2.86372298805175
50	2.6	6.17312546195823	-3.57312546195823
51	3.8	6.11166824058929	-2.31166824058929
52	11	11.3224699678569	-0.322469967856933
53	10.3	14.5811134059542	-4.28111340595419
54	13.3	11.7421441841302	1.55785581586985
55	5.4	8.16343287816573	-2.76343287816572
56	15.8	14.4028676728402	1.39713232715977
57	10.3	6.20038490123268	4.09961509876732
58	19.4	16.2107292818462	3.18927071815378

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
11	0.614755894616575	0.77048821076685	0.385244105383425
12	0.467499827809316	0.934999655618633	0.532500172190684
13	0.406286505951506	0.812573011903012	0.593713494048494
14	0.27811602639083	0.556232052781661	0.721883973609169
15	0.449518550907453	0.899037101814907	0.550481449092547
16	0.534928178312483	0.930143643375035	0.465071821687517
17	0.443930081100837	0.887860162201674	0.556069918899163
18	0.339257387133052	0.678514774266105	0.660742612866948
19	0.818319886595128	0.363360226809745	0.181680113404872
20	0.872710592446513	0.254578815106974	0.127289407553487
21	0.830516082951198	0.338967834097604	0.169483917048802
22	0.765825156522158	0.468349686955683	0.234174843477842
23	0.705057012631802	0.589885974736396	0.294942987368198
24	0.777205910143931	0.445588179712139	0.222794089856069
25	0.709507208465846	0.580985583068307	0.290492791534154
26	0.659156007087861	0.681687985824277	0.340843992912139
27	0.634622024567474	0.730755950865053	0.365377975432526
28	0.604399894248257	0.791200211503485	0.395600105751743
29	0.561794044746233	0.876411910507534	0.438205955253767
30	0.50571791865637	0.988564162687261	0.49428208134363
31	0.662409543354339	0.675180913291323	0.337590456645661
32	0.591028994923513	0.817942010152974	0.408971005076487
33	0.718863860665085	0.562272278669829	0.281136139334915
34	0.685861936661564	0.628276126676873	0.314138063338436
35	0.602393244815577	0.795213510368846	0.397606755184423
36	0.5104330656574	0.9791338686852	0.4895669343426
37	0.474198179459268	0.948396358918536	0.525801820540732
38	0.406345607518114	0.812691215036228	0.593654392481886
39	0.631368385176618	0.737263229646765	0.368631614823382
40	0.73114269182318	0.537714616353641	0.26885730817682
41	0.640105879225258	0.719788241549483	0.359894120774742
42	0.545403131730019	0.909193736539963	0.454596868269981
43	0.452924006374488	0.905848012748976	0.547075993625512
44	0.333769871765024	0.667539743530047	0.666230128234977
45	0.352458939626752	0.704917879253504	0.647541060373248
46	0.302685810843434	0.605371621686868	0.697314189156566
47	0.193976446469197	0.387952892938394	0.806023553530803

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
11 & 0.614755894616575 & 0.77048821076685 & 0.385244105383425 \tabularnewline
12 & 0.467499827809316 & 0.934999655618633 & 0.532500172190684 \tabularnewline
13 & 0.406286505951506 & 0.812573011903012 & 0.593713494048494 \tabularnewline
14 & 0.27811602639083 & 0.556232052781661 & 0.721883973609169 \tabularnewline
15 & 0.449518550907453 & 0.899037101814907 & 0.550481449092547 \tabularnewline
16 & 0.534928178312483 & 0.930143643375035 & 0.465071821687517 \tabularnewline
17 & 0.443930081100837 & 0.887860162201674 & 0.556069918899163 \tabularnewline
18 & 0.339257387133052 & 0.678514774266105 & 0.660742612866948 \tabularnewline
19 & 0.818319886595128 & 0.363360226809745 & 0.181680113404872 \tabularnewline
20 & 0.872710592446513 & 0.254578815106974 & 0.127289407553487 \tabularnewline
21 & 0.830516082951198 & 0.338967834097604 & 0.169483917048802 \tabularnewline
22 & 0.765825156522158 & 0.468349686955683 & 0.234174843477842 \tabularnewline
23 & 0.705057012631802 & 0.589885974736396 & 0.294942987368198 \tabularnewline
24 & 0.777205910143931 & 0.445588179712139 & 0.222794089856069 \tabularnewline
25 & 0.709507208465846 & 0.580985583068307 & 0.290492791534154 \tabularnewline
26 & 0.659156007087861 & 0.681687985824277 & 0.340843992912139 \tabularnewline
27 & 0.634622024567474 & 0.730755950865053 & 0.365377975432526 \tabularnewline
28 & 0.604399894248257 & 0.791200211503485 & 0.395600105751743 \tabularnewline
29 & 0.561794044746233 & 0.876411910507534 & 0.438205955253767 \tabularnewline
30 & 0.50571791865637 & 0.988564162687261 & 0.49428208134363 \tabularnewline
31 & 0.662409543354339 & 0.675180913291323 & 0.337590456645661 \tabularnewline
32 & 0.591028994923513 & 0.817942010152974 & 0.408971005076487 \tabularnewline
33 & 0.718863860665085 & 0.562272278669829 & 0.281136139334915 \tabularnewline
34 & 0.685861936661564 & 0.628276126676873 & 0.314138063338436 \tabularnewline
35 & 0.602393244815577 & 0.795213510368846 & 0.397606755184423 \tabularnewline
36 & 0.5104330656574 & 0.9791338686852 & 0.4895669343426 \tabularnewline
37 & 0.474198179459268 & 0.948396358918536 & 0.525801820540732 \tabularnewline
38 & 0.406345607518114 & 0.812691215036228 & 0.593654392481886 \tabularnewline
39 & 0.631368385176618 & 0.737263229646765 & 0.368631614823382 \tabularnewline
40 & 0.73114269182318 & 0.537714616353641 & 0.26885730817682 \tabularnewline
41 & 0.640105879225258 & 0.719788241549483 & 0.359894120774742 \tabularnewline
42 & 0.545403131730019 & 0.909193736539963 & 0.454596868269981 \tabularnewline
43 & 0.452924006374488 & 0.905848012748976 & 0.547075993625512 \tabularnewline
44 & 0.333769871765024 & 0.667539743530047 & 0.666230128234977 \tabularnewline
45 & 0.352458939626752 & 0.704917879253504 & 0.647541060373248 \tabularnewline
46 & 0.302685810843434 & 0.605371621686868 & 0.697314189156566 \tabularnewline
47 & 0.193976446469197 & 0.387952892938394 & 0.806023553530803 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146367&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]11[/C][C]0.614755894616575[/C][C]0.77048821076685[/C][C]0.385244105383425[/C][/ROW]
[ROW][C]12[/C][C]0.467499827809316[/C][C]0.934999655618633[/C][C]0.532500172190684[/C][/ROW]
[ROW][C]13[/C][C]0.406286505951506[/C][C]0.812573011903012[/C][C]0.593713494048494[/C][/ROW]
[ROW][C]14[/C][C]0.27811602639083[/C][C]0.556232052781661[/C][C]0.721883973609169[/C][/ROW]
[ROW][C]15[/C][C]0.449518550907453[/C][C]0.899037101814907[/C][C]0.550481449092547[/C][/ROW]
[ROW][C]16[/C][C]0.534928178312483[/C][C]0.930143643375035[/C][C]0.465071821687517[/C][/ROW]
[ROW][C]17[/C][C]0.443930081100837[/C][C]0.887860162201674[/C][C]0.556069918899163[/C][/ROW]
[ROW][C]18[/C][C]0.339257387133052[/C][C]0.678514774266105[/C][C]0.660742612866948[/C][/ROW]
[ROW][C]19[/C][C]0.818319886595128[/C][C]0.363360226809745[/C][C]0.181680113404872[/C][/ROW]
[ROW][C]20[/C][C]0.872710592446513[/C][C]0.254578815106974[/C][C]0.127289407553487[/C][/ROW]
[ROW][C]21[/C][C]0.830516082951198[/C][C]0.338967834097604[/C][C]0.169483917048802[/C][/ROW]
[ROW][C]22[/C][C]0.765825156522158[/C][C]0.468349686955683[/C][C]0.234174843477842[/C][/ROW]
[ROW][C]23[/C][C]0.705057012631802[/C][C]0.589885974736396[/C][C]0.294942987368198[/C][/ROW]
[ROW][C]24[/C][C]0.777205910143931[/C][C]0.445588179712139[/C][C]0.222794089856069[/C][/ROW]
[ROW][C]25[/C][C]0.709507208465846[/C][C]0.580985583068307[/C][C]0.290492791534154[/C][/ROW]
[ROW][C]26[/C][C]0.659156007087861[/C][C]0.681687985824277[/C][C]0.340843992912139[/C][/ROW]
[ROW][C]27[/C][C]0.634622024567474[/C][C]0.730755950865053[/C][C]0.365377975432526[/C][/ROW]
[ROW][C]28[/C][C]0.604399894248257[/C][C]0.791200211503485[/C][C]0.395600105751743[/C][/ROW]
[ROW][C]29[/C][C]0.561794044746233[/C][C]0.876411910507534[/C][C]0.438205955253767[/C][/ROW]
[ROW][C]30[/C][C]0.50571791865637[/C][C]0.988564162687261[/C][C]0.49428208134363[/C][/ROW]
[ROW][C]31[/C][C]0.662409543354339[/C][C]0.675180913291323[/C][C]0.337590456645661[/C][/ROW]
[ROW][C]32[/C][C]0.591028994923513[/C][C]0.817942010152974[/C][C]0.408971005076487[/C][/ROW]
[ROW][C]33[/C][C]0.718863860665085[/C][C]0.562272278669829[/C][C]0.281136139334915[/C][/ROW]
[ROW][C]34[/C][C]0.685861936661564[/C][C]0.628276126676873[/C][C]0.314138063338436[/C][/ROW]
[ROW][C]35[/C][C]0.602393244815577[/C][C]0.795213510368846[/C][C]0.397606755184423[/C][/ROW]
[ROW][C]36[/C][C]0.5104330656574[/C][C]0.9791338686852[/C][C]0.4895669343426[/C][/ROW]
[ROW][C]37[/C][C]0.474198179459268[/C][C]0.948396358918536[/C][C]0.525801820540732[/C][/ROW]
[ROW][C]38[/C][C]0.406345607518114[/C][C]0.812691215036228[/C][C]0.593654392481886[/C][/ROW]
[ROW][C]39[/C][C]0.631368385176618[/C][C]0.737263229646765[/C][C]0.368631614823382[/C][/ROW]
[ROW][C]40[/C][C]0.73114269182318[/C][C]0.537714616353641[/C][C]0.26885730817682[/C][/ROW]
[ROW][C]41[/C][C]0.640105879225258[/C][C]0.719788241549483[/C][C]0.359894120774742[/C][/ROW]
[ROW][C]42[/C][C]0.545403131730019[/C][C]0.909193736539963[/C][C]0.454596868269981[/C][/ROW]
[ROW][C]43[/C][C]0.452924006374488[/C][C]0.905848012748976[/C][C]0.547075993625512[/C][/ROW]
[ROW][C]44[/C][C]0.333769871765024[/C][C]0.667539743530047[/C][C]0.666230128234977[/C][/ROW]
[ROW][C]45[/C][C]0.352458939626752[/C][C]0.704917879253504[/C][C]0.647541060373248[/C][/ROW]
[ROW][C]46[/C][C]0.302685810843434[/C][C]0.605371621686868[/C][C]0.697314189156566[/C][/ROW]
[ROW][C]47[/C][C]0.193976446469197[/C][C]0.387952892938394[/C][C]0.806023553530803[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146367&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146367&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
11	0.614755894616575	0.77048821076685	0.385244105383425
12	0.467499827809316	0.934999655618633	0.532500172190684
13	0.406286505951506	0.812573011903012	0.593713494048494
14	0.27811602639083	0.556232052781661	0.721883973609169
15	0.449518550907453	0.899037101814907	0.550481449092547
16	0.534928178312483	0.930143643375035	0.465071821687517
17	0.443930081100837	0.887860162201674	0.556069918899163
18	0.339257387133052	0.678514774266105	0.660742612866948
19	0.818319886595128	0.363360226809745	0.181680113404872
20	0.872710592446513	0.254578815106974	0.127289407553487
21	0.830516082951198	0.338967834097604	0.169483917048802
22	0.765825156522158	0.468349686955683	0.234174843477842
23	0.705057012631802	0.589885974736396	0.294942987368198
24	0.777205910143931	0.445588179712139	0.222794089856069
25	0.709507208465846	0.580985583068307	0.290492791534154
26	0.659156007087861	0.681687985824277	0.340843992912139
27	0.634622024567474	0.730755950865053	0.365377975432526
28	0.604399894248257	0.791200211503485	0.395600105751743
29	0.561794044746233	0.876411910507534	0.438205955253767
30	0.50571791865637	0.988564162687261	0.49428208134363
31	0.662409543354339	0.675180913291323	0.337590456645661
32	0.591028994923513	0.817942010152974	0.408971005076487
33	0.718863860665085	0.562272278669829	0.281136139334915
34	0.685861936661564	0.628276126676873	0.314138063338436
35	0.602393244815577	0.795213510368846	0.397606755184423
36	0.5104330656574	0.9791338686852	0.4895669343426
37	0.474198179459268	0.948396358918536	0.525801820540732
38	0.406345607518114	0.812691215036228	0.593654392481886
39	0.631368385176618	0.737263229646765	0.368631614823382
40	0.73114269182318	0.537714616353641	0.26885730817682
41	0.640105879225258	0.719788241549483	0.359894120774742
42	0.545403131730019	0.909193736539963	0.454596868269981
43	0.452924006374488	0.905848012748976	0.547075993625512
44	0.333769871765024	0.667539743530047	0.666230128234977
45	0.352458939626752	0.704917879253504	0.647541060373248
46	0.302685810843434	0.605371621686868	0.697314189156566
47	0.193976446469197	0.387952892938394	0.806023553530803

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	0	0	OK
5% type I error level	0	0	OK
10% type I error level	0	0	OK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 0 & 0 & OK \tabularnewline
10% type I error level & 0 & 0 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146367&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146367&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146367&T=6

As an alternative you can also use a QR Code:

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	0	0	OK
5% type I error level	0	0	OK
10% type I error level	0	0	OK

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 = 3 ; 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