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R Software Module

rwasp_multipleregression.wasp

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Multiple Regression

Date of computation

Fri, 28 Jan 2011 13:33:30 +0000

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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/Jan/28/t1296221504twv37l2ic02wxjm.htm/, Retrieved Thu, 16 May 2024 15:26:48 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=117882, Retrieved Thu, 16 May 2024 15:26:48 +0000

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Estimated Impact

182

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-     [Multiple Regression] [Competence to learn] [2010-11-17 07:43:53] [b98453cac15ba1066b407e146608df68]
- R  D    [Multiple Regression] [ws7TimDamen] [2011-01-28 13:33:30] [d41d8cd98f00b204e9800998ecf8427e] [Current]

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6654,00	5712,00	3,30	38,60	645,00	3,00
1,00	6,60	8,30	4,50	42,00	3,00
3,39	44,50	12,50	14,00	60,00	1,00
0,92	5,70	16,50		25,00	5,00
2547,00	4603,00	3,90	69,00	624,00	3,00
10,55	179,50	9,80	27,00	180,00	4,00
0,02	0,30	19,70	19,00	35,00	1,00
160,00	169,00	6,20	30,40	392,00	4,00
3,30	25,60	14,50	28,00	63,00	1,00
52,16	440,00	9,70	50,00	230,00	1,00
0,43	6,40	12,50	7,00	112,00	5,00
465,00	423,00	3,90	30,00	281,00	5,00
0,55	2,40	10,30			2,00
187,10	419,00	3,10	40,00	365,00	5,00
0,08	1,20	8,40	3,50	42,00	1,00
3,00	25,00	8,60	50,00	28,00	2,00
0,79	3,50	10,70	6,00	42,00	2,00
0,20	5,00	10,70	10,40	120,00	2,00
1,41	17,50	6,10	34,00		1,00
60,00	81,00	18,10	7,00		1,00
529,00	680,00		28,00	400,00	5,00
27,66	115,00	3,80	20,00	148,00	5,00
0,12	1,00	14,40	3,90	16,00	3,00
207,00	406,00	12,00	39,30	252,00	1,00
85,00	325,00	6,20	41,00	310,00	1,00
36,33	119,50	13,00	16,20	63,00	1,00
0,10	4,00	13,80	9,00	28,00	5,00
1,04	5,50	8,20	7,60	68,00	5,00
521,00	655,00	2,90	46,00	336,00	5,00
100,00	157,00	10,80	22,40	100,00	1,00
35,00	56,00		16,30	33,00	3,00
0,01	0,14	9,10	2,60	21,50	5,00
0,01	0,25	19,90	24,00	50,00	1,00
62,00	1320,00	8,00	100,00	267,00	1,00
0,12	3,00	10,60		30,00	2,00
1,35	8,10	11,20		45,00	3,00
0,02	0,40	13,20	3,20	19,00	4,00
0,05	0,33	12,80	2,00	30,00	4,00
1,70	6,30	19,40	5,00	12,00	2,00
3,50	10,80	17,40	6,50	120,00	2,00
250,00	490,00		23,60	440,00	5,00
0,48	15,50	17,00	12,00	140,00	2,00
10,00	115,00	10,90	20,20	170,00	4,00
1,62	11,40	13,70	13,00	17,00	2,00
192,00	180,00	8,40	27,00	115,00	4,00
2,50	12,10	8,40	18,00	31,00	5,00
4,29	39,20	12,50	13,70	63,00	2,00
0,28	1,90	13,20	4,70	21,00	3,00
4,24	50,40	9,80	9,80	52,00	1,00
6,80	179,00	9,60	29,00	164,00	2,00
0,75	12,30	6,60	7,00	225,00	2,00
3,60	21,00	5,40	6,00	225,00	3,00
14,83	98,20	2,60	17,00	150,00	5,00
55,50	175,00	3,80	20,00	151,00	5,00
1,40	12,50	11,00	12,70	90,00	2,00
0,06	1,00	10,30	3,50		3,00
0,90	2,60	13,30	4,50	60,00	2,00
2,00	12,30	5,40	7,50	200,00	3,00
0,10	2,50	15,80	2,30	46,00	3,00
4,19	58,00	10,30	24,00	210,00	4,00
3,50	3,90	19,40	3,00	14,00	2,00
4,05	17,00		13,00	38,00	3,00

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	5 seconds
R Server	'Sir Ronald Aylmer Fisher' @ 193.190.124.24
R Framework error message	Warning: there are blank lines in the 'Data X' field. Please, use NA for missing data - blank lines are simply deleted and are NOT treated as missing values.

\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 & 5 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
R Framework error message & Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values. \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=117882&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]5 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Ronald Aylmer Fisher' @ 193.190.124.24[/C][/ROW]
[ROW][C]R Framework error message[/C][C]Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=117882&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=117882&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	5 seconds
R Server	'Sir Ronald Aylmer Fisher' @ 193.190.124.24
R Framework error message	Warning: there are blank lines in the 'Data X' field. Please, use NA for missing data - blank lines are simply deleted and are NOT treated as missing values.

Multiple Linear Regression - Estimated Regression Equation
Y[t] = + 10.1204183249928 -0.0120503930556895G[t] -0.0130609895244657H[t] + 0.119711702613191J[t] + 0.245246671582954Z[t] + 0.000989834348735287P[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Y[t] =  +  10.1204183249928 -0.0120503930556895G[t] -0.0130609895244657H[t] +  0.119711702613191J[t] +  0.245246671582954Z[t] +  0.000989834348735287P[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=117882&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Y[t] =  +  10.1204183249928 -0.0120503930556895G[t] -0.0130609895244657H[t] +  0.119711702613191J[t] +  0.245246671582954Z[t] +  0.000989834348735287P[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=117882&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=117882&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] = + 10.1204183249928 -0.0120503930556895G[t] -0.0130609895244657H[t] + 0.119711702613191J[t] + 0.245246671582954Z[t] + 0.000989834348735287P[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)	10.1204183249928	14.745474	0.6863	0.49533	0.247665
G	-0.0120503930556895	0.028689	-0.42	0.676071	0.338036
H	-0.0130609895244657	0.042102	-0.3102	0.757545	0.378773
J	0.119711702613191	0.158877	0.7535	0.454315	0.227157
Z	0.245246671582954	0.122129	2.0081	0.049467	0.024733
P	0.000989834348735287	0.034605	0.0286	0.977282	0.488641

\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) & 10.1204183249928 & 14.745474 & 0.6863 & 0.49533 & 0.247665 \tabularnewline
G & -0.0120503930556895 & 0.028689 & -0.42 & 0.676071 & 0.338036 \tabularnewline
H & -0.0130609895244657 & 0.042102 & -0.3102 & 0.757545 & 0.378773 \tabularnewline
J & 0.119711702613191 & 0.158877 & 0.7535 & 0.454315 & 0.227157 \tabularnewline
Z & 0.245246671582954 & 0.122129 & 2.0081 & 0.049467 & 0.024733 \tabularnewline
P & 0.000989834348735287 & 0.034605 & 0.0286 & 0.977282 & 0.488641 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=117882&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]10.1204183249928[/C][C]14.745474[/C][C]0.6863[/C][C]0.49533[/C][C]0.247665[/C][/ROW]
[ROW][C]G[/C][C]-0.0120503930556895[/C][C]0.028689[/C][C]-0.42[/C][C]0.676071[/C][C]0.338036[/C][/ROW]
[ROW][C]H[/C][C]-0.0130609895244657[/C][C]0.042102[/C][C]-0.3102[/C][C]0.757545[/C][C]0.378773[/C][/ROW]
[ROW][C]J[/C][C]0.119711702613191[/C][C]0.158877[/C][C]0.7535[/C][C]0.454315[/C][C]0.227157[/C][/ROW]
[ROW][C]Z[/C][C]0.245246671582954[/C][C]0.122129[/C][C]2.0081[/C][C]0.049467[/C][C]0.024733[/C][/ROW]
[ROW][C]P[/C][C]0.000989834348735287[/C][C]0.034605[/C][C]0.0286[/C][C]0.977282[/C][C]0.488641[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=117882&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=117882&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)	10.1204183249928	14.745474	0.6863	0.49533	0.247665
G	-0.0120503930556895	0.028689	-0.42	0.676071	0.338036
H	-0.0130609895244657	0.042102	-0.3102	0.757545	0.378773
J	0.119711702613191	0.158877	0.7535	0.454315	0.227157
Z	0.245246671582954	0.122129	2.0081	0.049467	0.024733
P	0.000989834348735287	0.034605	0.0286	0.977282	0.488641

Multiple Linear Regression - Regression Statistics
Multiple R	0.326715892536400
R-squared	0.106743274435857
Adjusted R-squared	0.0269882096533440
F-TEST (value)	1.33838866192309
F-TEST (DF numerator)	5
F-TEST (DF denominator)	56
p-value	0.261640216229769
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	88.054285716776
Sum Squared Residuals	434199.205053131

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.326715892536400 \tabularnewline
R-squared & 0.106743274435857 \tabularnewline
Adjusted R-squared & 0.0269882096533440 \tabularnewline
F-TEST (value) & 1.33838866192309 \tabularnewline
F-TEST (DF numerator) & 5 \tabularnewline
F-TEST (DF denominator) & 56 \tabularnewline
p-value & 0.261640216229769 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 88.054285716776 \tabularnewline
Sum Squared Residuals & 434199.205053131 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=117882&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.326715892536400[/C][/ROW]
[ROW][C]R-squared[/C][C]0.106743274435857[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.0269882096533440[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]1.33838866192309[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]5[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]56[/C][/ROW]
[ROW][C]p-value[/C][C]0.261640216229769[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]88.054285716776[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]434199.205053131[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=117882&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=117882&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	0.326715892536400
R-squared	0.106743274435857
Adjusted R-squared	0.0269882096533440
F-TEST (value)	1.33838866192309
F-TEST (DF numerator)	5
F-TEST (DF denominator)	56
p-value	0.261640216229769
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	88.054285716776
Sum Squared Residuals	434199.205053131

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	3.3	18.1406751636078	-14.8406751636078
2	8.3	20.8641977723653	-12.5641977723653
3	12.5	25.8901074246059	-13.3901074246059
4	16.5	16.7750183325654	-0.275018332565432
5	69	30.0478064282679	38.9521935717321
6	27	30.3584880275500	-3.35848802754995
7	19	14.4530314722864	4.54696852771358
8	30.4	55.9141643275831	-25.5141643275831
9	28	17.4612576105064	10.5387423894936
10	50	32.4709176834889	17.5290823165110
11	7	24.9742504631347	-17.9742504631347
12	30	39.837930404404	-9.83793040440398
13	2	135.116452628224	-133.116452628224
14	5	5.18332897697325	-0.183328976973246
15	1	16.0284948620829	-15.0284948620829
16	2	10.1157177886595	-8.11571778865953
17	2	10.7603213326	-8.7603213326
18	2	12.8944237371913	-10.8944237371913
19	60	23.840185479336	36.159814520664
20	680	104.654649951589	575.34535004841
21	3.8	46.9807812760436	-43.1807812760436
22	14.4	14.4997031768666	-0.0997031768665824
23	12	68.8310462014836	-56.8310462014836
24	6.2	85.7869511520132	-79.5869511520132
25	13	25.5126990235145	-12.5126990235145
26	13.8	18.0162306271745	-4.21623062717449
27	8.2	27.6275822530751	-19.4275822530751
28	2.9	83.2017845482766	-80.3017845482766
29	10.8	34.0720027952624	-23.2720027952624
30	16.3	13.6534752540013	2.64652474599874
31	2.6	13.7999211277342	-11.1999211277342
32	24	16.1496935670561	7.85030643294388
33	100	26.3178016247137	73.6821983752863
34	30	10.5243447269545	19.4756552730455
35	3	9.51126811025689	-6.51126811025689
36	4	9.9332851326666	-5.93328513266661
37	4	11.4722545649280	-7.47225456492796
38	2	12.9883126153308	-10.9883126153308
39	2	158.596926846770	-156.596926846770
40	0.48	10.7895432524666	-10.3095432524666
41	10	24.8672704927630	-14.8672704927630
42	1.62	12.7570682044381	-11.1370682044381
43	192	33.5243437030842	158.475656296916
44	2.5	12.2087798266843	-9.70877982668432
45	4.29	17.4533940604458	-13.1633940604458
46	0.28	12.8044821047346	-12.5244821047346
47	4.24	18.2747646720853	-14.0347646720853
48	6.8	33.2922049076434	-26.4922049076434
49	0.75	11.2160426998417	-10.4660426998417
50	3.6	11.2271746959311	-7.62717469593112
51	14.83	19.7800546455488	-4.95005464554882
52	55.5	30.1488364153154	25.3511635846846
53	1.4	12.4421845922677	-11.0421845922677
54	0.06	11.6589778110700	-11.5989778110700
55	2.6	12.8276779830566	-10.2276779830566
56	12.3	12.7539556605630	-0.453955660562951
57	2.5	12.5840056728443	-10.0840056728443
58	58	17.3563574678594	40.6436425321406
59	3.9	13.0985080157615	-9.19850801576149
60	17	20.9220156884773	-3.92201568847729
61	3.3	18.1406751636078	-14.8406751636078
62	8.3	20.8641977723653	-12.5641977723653

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 3.3 & 18.1406751636078 & -14.8406751636078 \tabularnewline
2 & 8.3 & 20.8641977723653 & -12.5641977723653 \tabularnewline
3 & 12.5 & 25.8901074246059 & -13.3901074246059 \tabularnewline
4 & 16.5 & 16.7750183325654 & -0.275018332565432 \tabularnewline
5 & 69 & 30.0478064282679 & 38.9521935717321 \tabularnewline
6 & 27 & 30.3584880275500 & -3.35848802754995 \tabularnewline
7 & 19 & 14.4530314722864 & 4.54696852771358 \tabularnewline
8 & 30.4 & 55.9141643275831 & -25.5141643275831 \tabularnewline
9 & 28 & 17.4612576105064 & 10.5387423894936 \tabularnewline
10 & 50 & 32.4709176834889 & 17.5290823165110 \tabularnewline
11 & 7 & 24.9742504631347 & -17.9742504631347 \tabularnewline
12 & 30 & 39.837930404404 & -9.83793040440398 \tabularnewline
13 & 2 & 135.116452628224 & -133.116452628224 \tabularnewline
14 & 5 & 5.18332897697325 & -0.183328976973246 \tabularnewline
15 & 1 & 16.0284948620829 & -15.0284948620829 \tabularnewline
16 & 2 & 10.1157177886595 & -8.11571778865953 \tabularnewline
17 & 2 & 10.7603213326 & -8.7603213326 \tabularnewline
18 & 2 & 12.8944237371913 & -10.8944237371913 \tabularnewline
19 & 60 & 23.840185479336 & 36.159814520664 \tabularnewline
20 & 680 & 104.654649951589 & 575.34535004841 \tabularnewline
21 & 3.8 & 46.9807812760436 & -43.1807812760436 \tabularnewline
22 & 14.4 & 14.4997031768666 & -0.0997031768665824 \tabularnewline
23 & 12 & 68.8310462014836 & -56.8310462014836 \tabularnewline
24 & 6.2 & 85.7869511520132 & -79.5869511520132 \tabularnewline
25 & 13 & 25.5126990235145 & -12.5126990235145 \tabularnewline
26 & 13.8 & 18.0162306271745 & -4.21623062717449 \tabularnewline
27 & 8.2 & 27.6275822530751 & -19.4275822530751 \tabularnewline
28 & 2.9 & 83.2017845482766 & -80.3017845482766 \tabularnewline
29 & 10.8 & 34.0720027952624 & -23.2720027952624 \tabularnewline
30 & 16.3 & 13.6534752540013 & 2.64652474599874 \tabularnewline
31 & 2.6 & 13.7999211277342 & -11.1999211277342 \tabularnewline
32 & 24 & 16.1496935670561 & 7.85030643294388 \tabularnewline
33 & 100 & 26.3178016247137 & 73.6821983752863 \tabularnewline
34 & 30 & 10.5243447269545 & 19.4756552730455 \tabularnewline
35 & 3 & 9.51126811025689 & -6.51126811025689 \tabularnewline
36 & 4 & 9.9332851326666 & -5.93328513266661 \tabularnewline
37 & 4 & 11.4722545649280 & -7.47225456492796 \tabularnewline
38 & 2 & 12.9883126153308 & -10.9883126153308 \tabularnewline
39 & 2 & 158.596926846770 & -156.596926846770 \tabularnewline
40 & 0.48 & 10.7895432524666 & -10.3095432524666 \tabularnewline
41 & 10 & 24.8672704927630 & -14.8672704927630 \tabularnewline
42 & 1.62 & 12.7570682044381 & -11.1370682044381 \tabularnewline
43 & 192 & 33.5243437030842 & 158.475656296916 \tabularnewline
44 & 2.5 & 12.2087798266843 & -9.70877982668432 \tabularnewline
45 & 4.29 & 17.4533940604458 & -13.1633940604458 \tabularnewline
46 & 0.28 & 12.8044821047346 & -12.5244821047346 \tabularnewline
47 & 4.24 & 18.2747646720853 & -14.0347646720853 \tabularnewline
48 & 6.8 & 33.2922049076434 & -26.4922049076434 \tabularnewline
49 & 0.75 & 11.2160426998417 & -10.4660426998417 \tabularnewline
50 & 3.6 & 11.2271746959311 & -7.62717469593112 \tabularnewline
51 & 14.83 & 19.7800546455488 & -4.95005464554882 \tabularnewline
52 & 55.5 & 30.1488364153154 & 25.3511635846846 \tabularnewline
53 & 1.4 & 12.4421845922677 & -11.0421845922677 \tabularnewline
54 & 0.06 & 11.6589778110700 & -11.5989778110700 \tabularnewline
55 & 2.6 & 12.8276779830566 & -10.2276779830566 \tabularnewline
56 & 12.3 & 12.7539556605630 & -0.453955660562951 \tabularnewline
57 & 2.5 & 12.5840056728443 & -10.0840056728443 \tabularnewline
58 & 58 & 17.3563574678594 & 40.6436425321406 \tabularnewline
59 & 3.9 & 13.0985080157615 & -9.19850801576149 \tabularnewline
60 & 17 & 20.9220156884773 & -3.92201568847729 \tabularnewline
61 & 3.3 & 18.1406751636078 & -14.8406751636078 \tabularnewline
62 & 8.3 & 20.8641977723653 & -12.5641977723653 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=117882&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]18.1406751636078[/C][C]-14.8406751636078[/C][/ROW]
[ROW][C]2[/C][C]8.3[/C][C]20.8641977723653[/C][C]-12.5641977723653[/C][/ROW]
[ROW][C]3[/C][C]12.5[/C][C]25.8901074246059[/C][C]-13.3901074246059[/C][/ROW]
[ROW][C]4[/C][C]16.5[/C][C]16.7750183325654[/C][C]-0.275018332565432[/C][/ROW]
[ROW][C]5[/C][C]69[/C][C]30.0478064282679[/C][C]38.9521935717321[/C][/ROW]
[ROW][C]6[/C][C]27[/C][C]30.3584880275500[/C][C]-3.35848802754995[/C][/ROW]
[ROW][C]7[/C][C]19[/C][C]14.4530314722864[/C][C]4.54696852771358[/C][/ROW]
[ROW][C]8[/C][C]30.4[/C][C]55.9141643275831[/C][C]-25.5141643275831[/C][/ROW]
[ROW][C]9[/C][C]28[/C][C]17.4612576105064[/C][C]10.5387423894936[/C][/ROW]
[ROW][C]10[/C][C]50[/C][C]32.4709176834889[/C][C]17.5290823165110[/C][/ROW]
[ROW][C]11[/C][C]7[/C][C]24.9742504631347[/C][C]-17.9742504631347[/C][/ROW]
[ROW][C]12[/C][C]30[/C][C]39.837930404404[/C][C]-9.83793040440398[/C][/ROW]
[ROW][C]13[/C][C]2[/C][C]135.116452628224[/C][C]-133.116452628224[/C][/ROW]
[ROW][C]14[/C][C]5[/C][C]5.18332897697325[/C][C]-0.183328976973246[/C][/ROW]
[ROW][C]15[/C][C]1[/C][C]16.0284948620829[/C][C]-15.0284948620829[/C][/ROW]
[ROW][C]16[/C][C]2[/C][C]10.1157177886595[/C][C]-8.11571778865953[/C][/ROW]
[ROW][C]17[/C][C]2[/C][C]10.7603213326[/C][C]-8.7603213326[/C][/ROW]
[ROW][C]18[/C][C]2[/C][C]12.8944237371913[/C][C]-10.8944237371913[/C][/ROW]
[ROW][C]19[/C][C]60[/C][C]23.840185479336[/C][C]36.159814520664[/C][/ROW]
[ROW][C]20[/C][C]680[/C][C]104.654649951589[/C][C]575.34535004841[/C][/ROW]
[ROW][C]21[/C][C]3.8[/C][C]46.9807812760436[/C][C]-43.1807812760436[/C][/ROW]
[ROW][C]22[/C][C]14.4[/C][C]14.4997031768666[/C][C]-0.0997031768665824[/C][/ROW]
[ROW][C]23[/C][C]12[/C][C]68.8310462014836[/C][C]-56.8310462014836[/C][/ROW]
[ROW][C]24[/C][C]6.2[/C][C]85.7869511520132[/C][C]-79.5869511520132[/C][/ROW]
[ROW][C]25[/C][C]13[/C][C]25.5126990235145[/C][C]-12.5126990235145[/C][/ROW]
[ROW][C]26[/C][C]13.8[/C][C]18.0162306271745[/C][C]-4.21623062717449[/C][/ROW]
[ROW][C]27[/C][C]8.2[/C][C]27.6275822530751[/C][C]-19.4275822530751[/C][/ROW]
[ROW][C]28[/C][C]2.9[/C][C]83.2017845482766[/C][C]-80.3017845482766[/C][/ROW]
[ROW][C]29[/C][C]10.8[/C][C]34.0720027952624[/C][C]-23.2720027952624[/C][/ROW]
[ROW][C]30[/C][C]16.3[/C][C]13.6534752540013[/C][C]2.64652474599874[/C][/ROW]
[ROW][C]31[/C][C]2.6[/C][C]13.7999211277342[/C][C]-11.1999211277342[/C][/ROW]
[ROW][C]32[/C][C]24[/C][C]16.1496935670561[/C][C]7.85030643294388[/C][/ROW]
[ROW][C]33[/C][C]100[/C][C]26.3178016247137[/C][C]73.6821983752863[/C][/ROW]
[ROW][C]34[/C][C]30[/C][C]10.5243447269545[/C][C]19.4756552730455[/C][/ROW]
[ROW][C]35[/C][C]3[/C][C]9.51126811025689[/C][C]-6.51126811025689[/C][/ROW]
[ROW][C]36[/C][C]4[/C][C]9.9332851326666[/C][C]-5.93328513266661[/C][/ROW]
[ROW][C]37[/C][C]4[/C][C]11.4722545649280[/C][C]-7.47225456492796[/C][/ROW]
[ROW][C]38[/C][C]2[/C][C]12.9883126153308[/C][C]-10.9883126153308[/C][/ROW]
[ROW][C]39[/C][C]2[/C][C]158.596926846770[/C][C]-156.596926846770[/C][/ROW]
[ROW][C]40[/C][C]0.48[/C][C]10.7895432524666[/C][C]-10.3095432524666[/C][/ROW]
[ROW][C]41[/C][C]10[/C][C]24.8672704927630[/C][C]-14.8672704927630[/C][/ROW]
[ROW][C]42[/C][C]1.62[/C][C]12.7570682044381[/C][C]-11.1370682044381[/C][/ROW]
[ROW][C]43[/C][C]192[/C][C]33.5243437030842[/C][C]158.475656296916[/C][/ROW]
[ROW][C]44[/C][C]2.5[/C][C]12.2087798266843[/C][C]-9.70877982668432[/C][/ROW]
[ROW][C]45[/C][C]4.29[/C][C]17.4533940604458[/C][C]-13.1633940604458[/C][/ROW]
[ROW][C]46[/C][C]0.28[/C][C]12.8044821047346[/C][C]-12.5244821047346[/C][/ROW]
[ROW][C]47[/C][C]4.24[/C][C]18.2747646720853[/C][C]-14.0347646720853[/C][/ROW]
[ROW][C]48[/C][C]6.8[/C][C]33.2922049076434[/C][C]-26.4922049076434[/C][/ROW]
[ROW][C]49[/C][C]0.75[/C][C]11.2160426998417[/C][C]-10.4660426998417[/C][/ROW]
[ROW][C]50[/C][C]3.6[/C][C]11.2271746959311[/C][C]-7.62717469593112[/C][/ROW]
[ROW][C]51[/C][C]14.83[/C][C]19.7800546455488[/C][C]-4.95005464554882[/C][/ROW]
[ROW][C]52[/C][C]55.5[/C][C]30.1488364153154[/C][C]25.3511635846846[/C][/ROW]
[ROW][C]53[/C][C]1.4[/C][C]12.4421845922677[/C][C]-11.0421845922677[/C][/ROW]
[ROW][C]54[/C][C]0.06[/C][C]11.6589778110700[/C][C]-11.5989778110700[/C][/ROW]
[ROW][C]55[/C][C]2.6[/C][C]12.8276779830566[/C][C]-10.2276779830566[/C][/ROW]
[ROW][C]56[/C][C]12.3[/C][C]12.7539556605630[/C][C]-0.453955660562951[/C][/ROW]
[ROW][C]57[/C][C]2.5[/C][C]12.5840056728443[/C][C]-10.0840056728443[/C][/ROW]
[ROW][C]58[/C][C]58[/C][C]17.3563574678594[/C][C]40.6436425321406[/C][/ROW]
[ROW][C]59[/C][C]3.9[/C][C]13.0985080157615[/C][C]-9.19850801576149[/C][/ROW]
[ROW][C]60[/C][C]17[/C][C]20.9220156884773[/C][C]-3.92201568847729[/C][/ROW]
[ROW][C]61[/C][C]3.3[/C][C]18.1406751636078[/C][C]-14.8406751636078[/C][/ROW]
[ROW][C]62[/C][C]8.3[/C][C]20.8641977723653[/C][C]-12.5641977723653[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=117882&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=117882&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	18.1406751636078	-14.8406751636078
2	8.3	20.8641977723653	-12.5641977723653
3	12.5	25.8901074246059	-13.3901074246059
4	16.5	16.7750183325654	-0.275018332565432
5	69	30.0478064282679	38.9521935717321
6	27	30.3584880275500	-3.35848802754995
7	19	14.4530314722864	4.54696852771358
8	30.4	55.9141643275831	-25.5141643275831
9	28	17.4612576105064	10.5387423894936
10	50	32.4709176834889	17.5290823165110
11	7	24.9742504631347	-17.9742504631347
12	30	39.837930404404	-9.83793040440398
13	2	135.116452628224	-133.116452628224
14	5	5.18332897697325	-0.183328976973246
15	1	16.0284948620829	-15.0284948620829
16	2	10.1157177886595	-8.11571778865953
17	2	10.7603213326	-8.7603213326
18	2	12.8944237371913	-10.8944237371913
19	60	23.840185479336	36.159814520664
20	680	104.654649951589	575.34535004841
21	3.8	46.9807812760436	-43.1807812760436
22	14.4	14.4997031768666	-0.0997031768665824
23	12	68.8310462014836	-56.8310462014836
24	6.2	85.7869511520132	-79.5869511520132
25	13	25.5126990235145	-12.5126990235145
26	13.8	18.0162306271745	-4.21623062717449
27	8.2	27.6275822530751	-19.4275822530751
28	2.9	83.2017845482766	-80.3017845482766
29	10.8	34.0720027952624	-23.2720027952624
30	16.3	13.6534752540013	2.64652474599874
31	2.6	13.7999211277342	-11.1999211277342
32	24	16.1496935670561	7.85030643294388
33	100	26.3178016247137	73.6821983752863
34	30	10.5243447269545	19.4756552730455
35	3	9.51126811025689	-6.51126811025689
36	4	9.9332851326666	-5.93328513266661
37	4	11.4722545649280	-7.47225456492796
38	2	12.9883126153308	-10.9883126153308
39	2	158.596926846770	-156.596926846770
40	0.48	10.7895432524666	-10.3095432524666
41	10	24.8672704927630	-14.8672704927630
42	1.62	12.7570682044381	-11.1370682044381
43	192	33.5243437030842	158.475656296916
44	2.5	12.2087798266843	-9.70877982668432
45	4.29	17.4533940604458	-13.1633940604458
46	0.28	12.8044821047346	-12.5244821047346
47	4.24	18.2747646720853	-14.0347646720853
48	6.8	33.2922049076434	-26.4922049076434
49	0.75	11.2160426998417	-10.4660426998417
50	3.6	11.2271746959311	-7.62717469593112
51	14.83	19.7800546455488	-4.95005464554882
52	55.5	30.1488364153154	25.3511635846846
53	1.4	12.4421845922677	-11.0421845922677
54	0.06	11.6589778110700	-11.5989778110700
55	2.6	12.8276779830566	-10.2276779830566
56	12.3	12.7539556605630	-0.453955660562951
57	2.5	12.5840056728443	-10.0840056728443
58	58	17.3563574678594	40.6436425321406
59	3.9	13.0985080157615	-9.19850801576149
60	17	20.9220156884773	-3.92201568847729
61	3.3	18.1406751636078	-14.8406751636078
62	8.3	20.8641977723653	-12.5641977723653

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
9	0.000160336155675089	0.000320672311350177	0.999839663844325
10	0.00022248001421323	0.00044496002842646	0.999777519985787
11	7.99151160019593e-05	0.000159830232003919	0.999920084883998
12	8.44196857805806e-06	1.68839371561161e-05	0.999991558031422
13	1.40727334770573e-06	2.81454669541147e-06	0.999998592726652
14	2.31487646231095e-07	4.62975292462189e-07	0.999999768512354
15	4.2147455410618e-08	8.4294910821236e-08	0.999999957852545
16	6.4050421309236e-09	1.28100842618472e-08	0.999999993594958
17	8.3819728849268e-10	1.67639457698536e-09	0.999999999161803
18	9.78181548896698e-11	1.95636309779340e-10	0.999999999902182
19	4.2468584887981e-09	8.4937169775962e-09	0.999999995753141
20	0.999999999999995	9.26654733059169e-15	4.63327366529585e-15
21	0.999999999999988	2.47278857144052e-14	1.23639428572026e-14
22	0.99999999999994	1.21299238416744e-13	6.0649619208372e-14
23	0.9999999999999	2.01615437212864e-13	1.00807718606432e-13
24	0.999999999999886	2.29001437464186e-13	1.14500718732093e-13
25	0.99999999999947	1.05939286752355e-12	5.29696433761775e-13
26	0.999999999997638	4.72323329484307e-12	2.36161664742153e-12
27	0.999999999990501	1.89978294405391e-11	9.49891472026956e-12
28	0.999999999985583	2.88336352562653e-11	1.44168176281327e-11
29	0.999999999946926	1.06148335675553e-10	5.30741678377764e-11
30	0.999999999771467	4.57066256754119e-10	2.28533128377059e-10
31	0.999999999057708	1.88458329152106e-09	9.42291645760532e-10
32	0.999999996326044	7.3479127510349e-09	3.67395637551745e-09
33	0.999999991458285	1.70834302136221e-08	8.54171510681105e-09
34	0.999999975543737	4.89125259417864e-08	2.44562629708932e-08
35	0.99999990984157	1.80316860999706e-07	9.01584304998531e-08
36	0.999999678788439	6.42423122309911e-07	3.21211561154955e-07
37	0.999998903838847	2.19232230661293e-06	1.09616115330647e-06
38	0.999996411437892	7.17712421688893e-06	3.58856210844447e-06
39	0.999996184553976	7.63089204846793e-06	3.81544602423397e-06
40	0.999989086167063	2.18276658743064e-05	1.09138329371532e-05
41	0.999976569036477	4.68619270452844e-05	2.34309635226422e-05
42	0.999929088723335	0.000141822553329095	7.09112766645475e-05
43	0.999999999861562	2.76876847352376e-10	1.38438423676188e-10
44	0.999999998768155	2.46368995675849e-09	1.23184497837924e-09
45	0.99999998959382	2.08123586073642e-08	1.04061793036821e-08
46	0.999999916751143	1.66497714071765e-07	8.32488570358824e-08
47	0.99999937035813	1.25928374080833e-06	6.29641870404163e-07
48	0.999999888268832	2.23462335916333e-07	1.11731167958166e-07
49	0.999998867864728	2.26427054480413e-06	1.13213527240206e-06
50	0.999990437390183	1.91252196339293e-05	9.56260981696467e-06
51	0.999940229013657	0.000119541972686882	5.97709863434412e-05
52	0.999619468326631	0.000761063346737145	0.000380531673368572
53	0.999535310496333	0.000929379007333708	0.000464689503666854

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
9 & 0.000160336155675089 & 0.000320672311350177 & 0.999839663844325 \tabularnewline
10 & 0.00022248001421323 & 0.00044496002842646 & 0.999777519985787 \tabularnewline
11 & 7.99151160019593e-05 & 0.000159830232003919 & 0.999920084883998 \tabularnewline
12 & 8.44196857805806e-06 & 1.68839371561161e-05 & 0.999991558031422 \tabularnewline
13 & 1.40727334770573e-06 & 2.81454669541147e-06 & 0.999998592726652 \tabularnewline
14 & 2.31487646231095e-07 & 4.62975292462189e-07 & 0.999999768512354 \tabularnewline
15 & 4.2147455410618e-08 & 8.4294910821236e-08 & 0.999999957852545 \tabularnewline
16 & 6.4050421309236e-09 & 1.28100842618472e-08 & 0.999999993594958 \tabularnewline
17 & 8.3819728849268e-10 & 1.67639457698536e-09 & 0.999999999161803 \tabularnewline
18 & 9.78181548896698e-11 & 1.95636309779340e-10 & 0.999999999902182 \tabularnewline
19 & 4.2468584887981e-09 & 8.4937169775962e-09 & 0.999999995753141 \tabularnewline
20 & 0.999999999999995 & 9.26654733059169e-15 & 4.63327366529585e-15 \tabularnewline
21 & 0.999999999999988 & 2.47278857144052e-14 & 1.23639428572026e-14 \tabularnewline
22 & 0.99999999999994 & 1.21299238416744e-13 & 6.0649619208372e-14 \tabularnewline
23 & 0.9999999999999 & 2.01615437212864e-13 & 1.00807718606432e-13 \tabularnewline
24 & 0.999999999999886 & 2.29001437464186e-13 & 1.14500718732093e-13 \tabularnewline
25 & 0.99999999999947 & 1.05939286752355e-12 & 5.29696433761775e-13 \tabularnewline
26 & 0.999999999997638 & 4.72323329484307e-12 & 2.36161664742153e-12 \tabularnewline
27 & 0.999999999990501 & 1.89978294405391e-11 & 9.49891472026956e-12 \tabularnewline
28 & 0.999999999985583 & 2.88336352562653e-11 & 1.44168176281327e-11 \tabularnewline
29 & 0.999999999946926 & 1.06148335675553e-10 & 5.30741678377764e-11 \tabularnewline
30 & 0.999999999771467 & 4.57066256754119e-10 & 2.28533128377059e-10 \tabularnewline
31 & 0.999999999057708 & 1.88458329152106e-09 & 9.42291645760532e-10 \tabularnewline
32 & 0.999999996326044 & 7.3479127510349e-09 & 3.67395637551745e-09 \tabularnewline
33 & 0.999999991458285 & 1.70834302136221e-08 & 8.54171510681105e-09 \tabularnewline
34 & 0.999999975543737 & 4.89125259417864e-08 & 2.44562629708932e-08 \tabularnewline
35 & 0.99999990984157 & 1.80316860999706e-07 & 9.01584304998531e-08 \tabularnewline
36 & 0.999999678788439 & 6.42423122309911e-07 & 3.21211561154955e-07 \tabularnewline
37 & 0.999998903838847 & 2.19232230661293e-06 & 1.09616115330647e-06 \tabularnewline
38 & 0.999996411437892 & 7.17712421688893e-06 & 3.58856210844447e-06 \tabularnewline
39 & 0.999996184553976 & 7.63089204846793e-06 & 3.81544602423397e-06 \tabularnewline
40 & 0.999989086167063 & 2.18276658743064e-05 & 1.09138329371532e-05 \tabularnewline
41 & 0.999976569036477 & 4.68619270452844e-05 & 2.34309635226422e-05 \tabularnewline
42 & 0.999929088723335 & 0.000141822553329095 & 7.09112766645475e-05 \tabularnewline
43 & 0.999999999861562 & 2.76876847352376e-10 & 1.38438423676188e-10 \tabularnewline
44 & 0.999999998768155 & 2.46368995675849e-09 & 1.23184497837924e-09 \tabularnewline
45 & 0.99999998959382 & 2.08123586073642e-08 & 1.04061793036821e-08 \tabularnewline
46 & 0.999999916751143 & 1.66497714071765e-07 & 8.32488570358824e-08 \tabularnewline
47 & 0.99999937035813 & 1.25928374080833e-06 & 6.29641870404163e-07 \tabularnewline
48 & 0.999999888268832 & 2.23462335916333e-07 & 1.11731167958166e-07 \tabularnewline
49 & 0.999998867864728 & 2.26427054480413e-06 & 1.13213527240206e-06 \tabularnewline
50 & 0.999990437390183 & 1.91252196339293e-05 & 9.56260981696467e-06 \tabularnewline
51 & 0.999940229013657 & 0.000119541972686882 & 5.97709863434412e-05 \tabularnewline
52 & 0.999619468326631 & 0.000761063346737145 & 0.000380531673368572 \tabularnewline
53 & 0.999535310496333 & 0.000929379007333708 & 0.000464689503666854 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=117882&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]9[/C][C]0.000160336155675089[/C][C]0.000320672311350177[/C][C]0.999839663844325[/C][/ROW]
[ROW][C]10[/C][C]0.00022248001421323[/C][C]0.00044496002842646[/C][C]0.999777519985787[/C][/ROW]
[ROW][C]11[/C][C]7.99151160019593e-05[/C][C]0.000159830232003919[/C][C]0.999920084883998[/C][/ROW]
[ROW][C]12[/C][C]8.44196857805806e-06[/C][C]1.68839371561161e-05[/C][C]0.999991558031422[/C][/ROW]
[ROW][C]13[/C][C]1.40727334770573e-06[/C][C]2.81454669541147e-06[/C][C]0.999998592726652[/C][/ROW]
[ROW][C]14[/C][C]2.31487646231095e-07[/C][C]4.62975292462189e-07[/C][C]0.999999768512354[/C][/ROW]
[ROW][C]15[/C][C]4.2147455410618e-08[/C][C]8.4294910821236e-08[/C][C]0.999999957852545[/C][/ROW]
[ROW][C]16[/C][C]6.4050421309236e-09[/C][C]1.28100842618472e-08[/C][C]0.999999993594958[/C][/ROW]
[ROW][C]17[/C][C]8.3819728849268e-10[/C][C]1.67639457698536e-09[/C][C]0.999999999161803[/C][/ROW]
[ROW][C]18[/C][C]9.78181548896698e-11[/C][C]1.95636309779340e-10[/C][C]0.999999999902182[/C][/ROW]
[ROW][C]19[/C][C]4.2468584887981e-09[/C][C]8.4937169775962e-09[/C][C]0.999999995753141[/C][/ROW]
[ROW][C]20[/C][C]0.999999999999995[/C][C]9.26654733059169e-15[/C][C]4.63327366529585e-15[/C][/ROW]
[ROW][C]21[/C][C]0.999999999999988[/C][C]2.47278857144052e-14[/C][C]1.23639428572026e-14[/C][/ROW]
[ROW][C]22[/C][C]0.99999999999994[/C][C]1.21299238416744e-13[/C][C]6.0649619208372e-14[/C][/ROW]
[ROW][C]23[/C][C]0.9999999999999[/C][C]2.01615437212864e-13[/C][C]1.00807718606432e-13[/C][/ROW]
[ROW][C]24[/C][C]0.999999999999886[/C][C]2.29001437464186e-13[/C][C]1.14500718732093e-13[/C][/ROW]
[ROW][C]25[/C][C]0.99999999999947[/C][C]1.05939286752355e-12[/C][C]5.29696433761775e-13[/C][/ROW]
[ROW][C]26[/C][C]0.999999999997638[/C][C]4.72323329484307e-12[/C][C]2.36161664742153e-12[/C][/ROW]
[ROW][C]27[/C][C]0.999999999990501[/C][C]1.89978294405391e-11[/C][C]9.49891472026956e-12[/C][/ROW]
[ROW][C]28[/C][C]0.999999999985583[/C][C]2.88336352562653e-11[/C][C]1.44168176281327e-11[/C][/ROW]
[ROW][C]29[/C][C]0.999999999946926[/C][C]1.06148335675553e-10[/C][C]5.30741678377764e-11[/C][/ROW]
[ROW][C]30[/C][C]0.999999999771467[/C][C]4.57066256754119e-10[/C][C]2.28533128377059e-10[/C][/ROW]
[ROW][C]31[/C][C]0.999999999057708[/C][C]1.88458329152106e-09[/C][C]9.42291645760532e-10[/C][/ROW]
[ROW][C]32[/C][C]0.999999996326044[/C][C]7.3479127510349e-09[/C][C]3.67395637551745e-09[/C][/ROW]
[ROW][C]33[/C][C]0.999999991458285[/C][C]1.70834302136221e-08[/C][C]8.54171510681105e-09[/C][/ROW]
[ROW][C]34[/C][C]0.999999975543737[/C][C]4.89125259417864e-08[/C][C]2.44562629708932e-08[/C][/ROW]
[ROW][C]35[/C][C]0.99999990984157[/C][C]1.80316860999706e-07[/C][C]9.01584304998531e-08[/C][/ROW]
[ROW][C]36[/C][C]0.999999678788439[/C][C]6.42423122309911e-07[/C][C]3.21211561154955e-07[/C][/ROW]
[ROW][C]37[/C][C]0.999998903838847[/C][C]2.19232230661293e-06[/C][C]1.09616115330647e-06[/C][/ROW]
[ROW][C]38[/C][C]0.999996411437892[/C][C]7.17712421688893e-06[/C][C]3.58856210844447e-06[/C][/ROW]
[ROW][C]39[/C][C]0.999996184553976[/C][C]7.63089204846793e-06[/C][C]3.81544602423397e-06[/C][/ROW]
[ROW][C]40[/C][C]0.999989086167063[/C][C]2.18276658743064e-05[/C][C]1.09138329371532e-05[/C][/ROW]
[ROW][C]41[/C][C]0.999976569036477[/C][C]4.68619270452844e-05[/C][C]2.34309635226422e-05[/C][/ROW]
[ROW][C]42[/C][C]0.999929088723335[/C][C]0.000141822553329095[/C][C]7.09112766645475e-05[/C][/ROW]
[ROW][C]43[/C][C]0.999999999861562[/C][C]2.76876847352376e-10[/C][C]1.38438423676188e-10[/C][/ROW]
[ROW][C]44[/C][C]0.999999998768155[/C][C]2.46368995675849e-09[/C][C]1.23184497837924e-09[/C][/ROW]
[ROW][C]45[/C][C]0.99999998959382[/C][C]2.08123586073642e-08[/C][C]1.04061793036821e-08[/C][/ROW]
[ROW][C]46[/C][C]0.999999916751143[/C][C]1.66497714071765e-07[/C][C]8.32488570358824e-08[/C][/ROW]
[ROW][C]47[/C][C]0.99999937035813[/C][C]1.25928374080833e-06[/C][C]6.29641870404163e-07[/C][/ROW]
[ROW][C]48[/C][C]0.999999888268832[/C][C]2.23462335916333e-07[/C][C]1.11731167958166e-07[/C][/ROW]
[ROW][C]49[/C][C]0.999998867864728[/C][C]2.26427054480413e-06[/C][C]1.13213527240206e-06[/C][/ROW]
[ROW][C]50[/C][C]0.999990437390183[/C][C]1.91252196339293e-05[/C][C]9.56260981696467e-06[/C][/ROW]
[ROW][C]51[/C][C]0.999940229013657[/C][C]0.000119541972686882[/C][C]5.97709863434412e-05[/C][/ROW]
[ROW][C]52[/C][C]0.999619468326631[/C][C]0.000761063346737145[/C][C]0.000380531673368572[/C][/ROW]
[ROW][C]53[/C][C]0.999535310496333[/C][C]0.000929379007333708[/C][C]0.000464689503666854[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=117882&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=117882&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
9	0.000160336155675089	0.000320672311350177	0.999839663844325
10	0.00022248001421323	0.00044496002842646	0.999777519985787
11	7.99151160019593e-05	0.000159830232003919	0.999920084883998
12	8.44196857805806e-06	1.68839371561161e-05	0.999991558031422
13	1.40727334770573e-06	2.81454669541147e-06	0.999998592726652
14	2.31487646231095e-07	4.62975292462189e-07	0.999999768512354
15	4.2147455410618e-08	8.4294910821236e-08	0.999999957852545
16	6.4050421309236e-09	1.28100842618472e-08	0.999999993594958
17	8.3819728849268e-10	1.67639457698536e-09	0.999999999161803
18	9.78181548896698e-11	1.95636309779340e-10	0.999999999902182
19	4.2468584887981e-09	8.4937169775962e-09	0.999999995753141
20	0.999999999999995	9.26654733059169e-15	4.63327366529585e-15
21	0.999999999999988	2.47278857144052e-14	1.23639428572026e-14
22	0.99999999999994	1.21299238416744e-13	6.0649619208372e-14
23	0.9999999999999	2.01615437212864e-13	1.00807718606432e-13
24	0.999999999999886	2.29001437464186e-13	1.14500718732093e-13
25	0.99999999999947	1.05939286752355e-12	5.29696433761775e-13
26	0.999999999997638	4.72323329484307e-12	2.36161664742153e-12
27	0.999999999990501	1.89978294405391e-11	9.49891472026956e-12
28	0.999999999985583	2.88336352562653e-11	1.44168176281327e-11
29	0.999999999946926	1.06148335675553e-10	5.30741678377764e-11
30	0.999999999771467	4.57066256754119e-10	2.28533128377059e-10
31	0.999999999057708	1.88458329152106e-09	9.42291645760532e-10
32	0.999999996326044	7.3479127510349e-09	3.67395637551745e-09
33	0.999999991458285	1.70834302136221e-08	8.54171510681105e-09
34	0.999999975543737	4.89125259417864e-08	2.44562629708932e-08
35	0.99999990984157	1.80316860999706e-07	9.01584304998531e-08
36	0.999999678788439	6.42423122309911e-07	3.21211561154955e-07
37	0.999998903838847	2.19232230661293e-06	1.09616115330647e-06
38	0.999996411437892	7.17712421688893e-06	3.58856210844447e-06
39	0.999996184553976	7.63089204846793e-06	3.81544602423397e-06
40	0.999989086167063	2.18276658743064e-05	1.09138329371532e-05
41	0.999976569036477	4.68619270452844e-05	2.34309635226422e-05
42	0.999929088723335	0.000141822553329095	7.09112766645475e-05
43	0.999999999861562	2.76876847352376e-10	1.38438423676188e-10
44	0.999999998768155	2.46368995675849e-09	1.23184497837924e-09
45	0.99999998959382	2.08123586073642e-08	1.04061793036821e-08
46	0.999999916751143	1.66497714071765e-07	8.32488570358824e-08
47	0.99999937035813	1.25928374080833e-06	6.29641870404163e-07
48	0.999999888268832	2.23462335916333e-07	1.11731167958166e-07
49	0.999998867864728	2.26427054480413e-06	1.13213527240206e-06
50	0.999990437390183	1.91252196339293e-05	9.56260981696467e-06
51	0.999940229013657	0.000119541972686882	5.97709863434412e-05
52	0.999619468326631	0.000761063346737145	0.000380531673368572
53	0.999535310496333	0.000929379007333708	0.000464689503666854

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	45	1	NOK
5% type I error level	45	1	NOK
10% type I error level	45	1	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 & 45 & 1 & NOK \tabularnewline
5% type I error level & 45 & 1 & NOK \tabularnewline
10% type I error level & 45 & 1 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=117882&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]45[/C][C]1[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]45[/C][C]1[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]45[/C][C]1[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=117882&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=117882&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	45	1	NOK
5% type I error level	45	1	NOK
10% type I error level	45	1	NOK

Figure 1

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Figure 3

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Figure 4

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Figure 5

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Figure 6

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Figure 7

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Figure 8

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Figure 9

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Figure 10

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Parameters (Session):

par1 = 3 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

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