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rwasp_multipleregression.wasp

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Multiple Regression

Date of computation

Sat, 18 Dec 2010 21:27:24 +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/2010/Dec/18/t1292707543jlviyzf5dezsgof.htm/, Retrieved Tue, 30 Apr 2024 02:35:31 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=112202, Retrieved Tue, 30 Apr 2024 02:35:31 +0000

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Estimated Impact

282

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

-     [Multiple Regression] [] [2010-12-05 18:56:24] [b98453cac15ba1066b407e146608df68]
-   PD  [Multiple Regression] [Multiple regression ] [2010-12-10 10:42:21] [74deae64b71f9d77c839af86f7c687b5]
-   PD      [Multiple Regression] [multiple regression ] [2010-12-18 21:27:24] [e665313c9926a9f4bdf6ad1ee5aefad6] [Current]

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101,82	107,34	93,63	99,85	101,76
101,68	107,34	93,63	99,91	102,37
101,68	107,34	93,63	99,87	102,38
102,45	107,34	96,13	99,86	102,86
102,45	107,34	96,13	100,10	102,87
102,45	107,34	96,13	100,10	102,92
102,45	107,34	96,13	100,12	102,95
102,45	107,34	96,13	99,95	103,02
102,45	112,60	96,13	99,94	104,08
102,52	112,60	96,13	100,18	104,16
102,52	112,60	96,13	100,31	104,24
102,85	112,60	96,13	100,65	104,33
102,85	112,61	96,13	100,65	104,73
102,85	112,61	96,13	100,69	104,86
103,25	112,61	96,13	101,26	105,03
103,25	112,61	98,73	101,26	105,62
103,25	112,61	98,73	101,38	105,63
103,25	112,61	98,73	101,38	105,63
104,45	112,61	98,73	101,38	105,94
104,45	112,61	98,73	101,44	106,61
104,45	118,65	98,73	101,40	107,69
104,80	118,65	98,73	101,40	107,78
104,80	118,65	98,73	100,58	107,93
105,29	118,65	98,73	100,58	108,48
105,29	114,29	98,73	100,58	108,14
105,29	114,29	98,73	100,59	108,48
105,29	114,29	98,73	100,81	108,48
106,04	114,29	101,67	100,75	108,89
105,94	114,29	101,67	100,75	108,93
105,94	114,29	101,67	100,96	109,21
105,94	114,29	101,67	101,31	109,47
106,28	114,29	101,67	101,64	109,80
106,48	123,33	101,67	101,46	111,73
107,19	123,33	101,67	101,73	111,85
108,14	123,33	101,67	101,73	112,12
108,22	123,33	101,67	101,64	112,15
108,22	123,33	101,67	101,77	112,17
108,61	123,33	101,67	101,74	112,67
108,61	123,33	101,67	101,89	112,80
108,61	123,33	107,94	101,89	113,44
108,61	123,33	107,94	101,93	113,53
109,06	123,33	107,94	101,93	114,53
109,06	123,33	107,94	102,32	114,51
112,93	123,33	107,94	102,41	115,05
115,84	129,03	107,94	103,58	116,67
118,57	128,76	107,94	104,12	117,07
118,57	128,76	107,94	104,10	116,92
118,86	128,76	107,94	104,15	117,00
118,98	128,76	107,94	104,15	117,02
119,27	128,76	107,94	104,16	117,35
119,39	128,76	107,94	102,94	117,36
119,49	128,76	110,30	103,07	117,82
119,59	128,76	110,30	103,04	117,88
120,12	128,76	110,30	103,06	118,24
120,14	128,76	110,30	103,05	118,50
120,14	128,76	110,30	102,95	118,80
120,14	132,63	110,30	102,95	119,76
120,14	132,63	110,30	103,05	120,09
120,62	132,63	110,30	102,65	120,16

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	6 seconds
R Server	'George Udny Yule' @ 72.249.76.132

\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 & 6 seconds \tabularnewline
R Server & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=112202&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]6 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ 72.249.76.132[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=112202&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=112202&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	6 seconds
R Server	'George Udny Yule' @ 72.249.76.132

Multiple Linear Regression - Estimated Regression Equation
vrijetijdsbesteding[t] = + 30.5494145341633 + 0.119037724082992bios[t] + 0.352061816366981schouwburg[t] + 0.441421037945499eedagsacttractie[t] -0.197117207935265huurDVD[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
vrijetijdsbesteding[t] =  +  30.5494145341633 +  0.119037724082992bios[t] +  0.352061816366981schouwburg[t] +  0.441421037945499eedagsacttractie[t] -0.197117207935265huurDVD[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=112202&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]vrijetijdsbesteding[t] =  +  30.5494145341633 +  0.119037724082992bios[t] +  0.352061816366981schouwburg[t] +  0.441421037945499eedagsacttractie[t] -0.197117207935265huurDVD[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=112202&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=112202&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
vrijetijdsbesteding[t] = + 30.5494145341633 + 0.119037724082992bios[t] + 0.352061816366981schouwburg[t] + 0.441421037945499eedagsacttractie[t] -0.197117207935265huurDVD[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)	30.5494145341633	15.039442	2.0313	0.04716	0.02358
bios	0.119037724082992	0.041677	2.8562	0.006074	0.003037
schouwburg	0.352061816366981	0.032113	10.9631	0	0
eedagsacttractie	0.441421037945499	0.047899	9.2156	0	0
huurDVD	-0.197117207935265	0.181806	-1.0842	0.283085	0.141542

\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) & 30.5494145341633 & 15.039442 & 2.0313 & 0.04716 & 0.02358 \tabularnewline
bios & 0.119037724082992 & 0.041677 & 2.8562 & 0.006074 & 0.003037 \tabularnewline
schouwburg & 0.352061816366981 & 0.032113 & 10.9631 & 0 & 0 \tabularnewline
eedagsacttractie & 0.441421037945499 & 0.047899 & 9.2156 & 0 & 0 \tabularnewline
huurDVD & -0.197117207935265 & 0.181806 & -1.0842 & 0.283085 & 0.141542 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=112202&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]30.5494145341633[/C][C]15.039442[/C][C]2.0313[/C][C]0.04716[/C][C]0.02358[/C][/ROW]
[ROW][C]bios[/C][C]0.119037724082992[/C][C]0.041677[/C][C]2.8562[/C][C]0.006074[/C][C]0.003037[/C][/ROW]
[ROW][C]schouwburg[/C][C]0.352061816366981[/C][C]0.032113[/C][C]10.9631[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]eedagsacttractie[/C][C]0.441421037945499[/C][C]0.047899[/C][C]9.2156[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]huurDVD[/C][C]-0.197117207935265[/C][C]0.181806[/C][C]-1.0842[/C][C]0.283085[/C][C]0.141542[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=112202&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=112202&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)	30.5494145341633	15.039442	2.0313	0.04716	0.02358
bios	0.119037724082992	0.041677	2.8562	0.006074	0.003037
schouwburg	0.352061816366981	0.032113	10.9631	0	0
eedagsacttractie	0.441421037945499	0.047899	9.2156	0	0
huurDVD	-0.197117207935265	0.181806	-1.0842	0.283085	0.141542

Multiple Linear Regression - Regression Statistics
Multiple R	0.99401619029679
R-squared	0.988068186572143
Adjusted R-squared	0.98718434854045
F-TEST (value)	1117.92902222071
F-TEST (DF numerator)	4
F-TEST (DF denominator)	54
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.64259719517157
Sum Squared Residuals	22.2982823830879

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.99401619029679 \tabularnewline
R-squared & 0.988068186572143 \tabularnewline
Adjusted R-squared & 0.98718434854045 \tabularnewline
F-TEST (value) & 1117.92902222071 \tabularnewline
F-TEST (DF numerator) & 4 \tabularnewline
F-TEST (DF denominator) & 54 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.64259719517157 \tabularnewline
Sum Squared Residuals & 22.2982823830879 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=112202&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.99401619029679[/C][/ROW]
[ROW][C]R-squared[/C][C]0.988068186572143[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.98718434854045[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]1117.92902222071[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]4[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]54[/C][/ROW]
[ROW][C]p-value[/C][C]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.64259719517157[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]22.2982823830879[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=112202&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=112202&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	0.99401619029679
R-squared	0.988068186572143
Adjusted R-squared	0.98718434854045
F-TEST (value)	1117.92902222071
F-TEST (DF numerator)	4
F-TEST (DF denominator)	54
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.64259719517157
Sum Squared Residuals	22.2982823830879

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	101.76	102.108249539626	-0.348249539625742
2	102.37	102.079757225778	0.290242774221512
3	102.38	102.087641914096	0.292358085904087
4	102.86	103.284824728583	-0.424824728582917
5	102.87	103.237516598678	-0.36751659867845
6	102.92	103.237516598678	-0.317516598678453
7	102.95	103.233574254520	-0.283574254519745
8	103.02	103.267084179869	-0.247084179868747
9	104.08	105.120900506038	-1.04090050603842
10	104.16	105.081925016820	-0.921925016819762
11	104.24	105.056299779788	-0.81629977978818
12	104.33	105.028562378038	-0.698562378037574
13	104.73	105.032082996201	-0.30208299620124
14	104.86	105.024198307884	-0.164198307883835
15	105.03	104.959456588994	0.0705434110060713
16	105.62	106.107151287652	-0.487151287652227
17	105.63	106.0834972227	-0.453497222700007
18	105.63	106.0834972227	-0.453497222700007
19	105.94	106.226342491600	-0.286342491599596
20	106.61	106.214515459123	0.395484540876522
21	107.69	108.348853518297	-0.658853518297459
22	107.78	108.390516721726	-0.610516721726502
23	107.93	108.552152832233	-0.622152832233414
24	108.48	108.610481317034	-0.130481317034084
25	108.14	107.075491797674	1.06450820232595
26	108.48	107.073520625595	1.40647937440531
27	108.48	107.030154839849	1.44984516015107
28	108.89	108.429038016947	0.460961983052936
29	108.93	108.417134244539	0.512865755461242
30	109.21	108.375739630872	0.834260369127633
31	109.47	108.306748608095	1.16325139190498
32	109.8	108.282172755665	1.5178272443354
33	111.73	111.524100217867	0.205899782132949
34	111.85	111.555395355823	0.294604644176539
35	112.12	111.668481193702	0.451518806297707
36	112.15	111.695744760343	0.454255239656895
37	112.17	111.670119523312	0.499880476688474
38	112.67	111.722457751942	0.94754224805805
39	112.8	111.692890170752	1.10710982924834
40	113.44	114.46060007867	-1.02060007866994
41	113.53	114.452715390353	-0.922715390352529
42	114.53	114.506282366190	0.0237176338101247
43	114.51	114.429406655095	0.0805933449048791
44	115.05	114.872342098582	0.177657901417867
45	116.67	116.994867095671	-0.324867095671171
46	117.07	117.118340099714	-0.0483400997136127
47	116.92	117.122282443872	-0.202282443872311
48	117	117.146947523460	-0.146947523459616
49	117.02	117.161232050350	-0.141232050349579
50	117.35	117.193781818254	0.156218181745704
51	117.36	117.448549338825	-0.0885493388252743
52	117.82	118.476581523753	-0.656581523753373
53	117.88	118.494398812400	-0.614398812399727
54	118.24	118.553546462005	-0.31354646200501
55	118.5	118.557898388566	-0.0578983885660175
56	118.8	118.577610109360	0.222389890640454
57	119.76	119.940089338700	-0.180089338699758
58	120.09	119.920377617906	0.169622382093766
59	120.16	120.056362608640	0.103637391359820

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 101.76 & 102.108249539626 & -0.348249539625742 \tabularnewline
2 & 102.37 & 102.079757225778 & 0.290242774221512 \tabularnewline
3 & 102.38 & 102.087641914096 & 0.292358085904087 \tabularnewline
4 & 102.86 & 103.284824728583 & -0.424824728582917 \tabularnewline
5 & 102.87 & 103.237516598678 & -0.36751659867845 \tabularnewline
6 & 102.92 & 103.237516598678 & -0.317516598678453 \tabularnewline
7 & 102.95 & 103.233574254520 & -0.283574254519745 \tabularnewline
8 & 103.02 & 103.267084179869 & -0.247084179868747 \tabularnewline
9 & 104.08 & 105.120900506038 & -1.04090050603842 \tabularnewline
10 & 104.16 & 105.081925016820 & -0.921925016819762 \tabularnewline
11 & 104.24 & 105.056299779788 & -0.81629977978818 \tabularnewline
12 & 104.33 & 105.028562378038 & -0.698562378037574 \tabularnewline
13 & 104.73 & 105.032082996201 & -0.30208299620124 \tabularnewline
14 & 104.86 & 105.024198307884 & -0.164198307883835 \tabularnewline
15 & 105.03 & 104.959456588994 & 0.0705434110060713 \tabularnewline
16 & 105.62 & 106.107151287652 & -0.487151287652227 \tabularnewline
17 & 105.63 & 106.0834972227 & -0.453497222700007 \tabularnewline
18 & 105.63 & 106.0834972227 & -0.453497222700007 \tabularnewline
19 & 105.94 & 106.226342491600 & -0.286342491599596 \tabularnewline
20 & 106.61 & 106.214515459123 & 0.395484540876522 \tabularnewline
21 & 107.69 & 108.348853518297 & -0.658853518297459 \tabularnewline
22 & 107.78 & 108.390516721726 & -0.610516721726502 \tabularnewline
23 & 107.93 & 108.552152832233 & -0.622152832233414 \tabularnewline
24 & 108.48 & 108.610481317034 & -0.130481317034084 \tabularnewline
25 & 108.14 & 107.075491797674 & 1.06450820232595 \tabularnewline
26 & 108.48 & 107.073520625595 & 1.40647937440531 \tabularnewline
27 & 108.48 & 107.030154839849 & 1.44984516015107 \tabularnewline
28 & 108.89 & 108.429038016947 & 0.460961983052936 \tabularnewline
29 & 108.93 & 108.417134244539 & 0.512865755461242 \tabularnewline
30 & 109.21 & 108.375739630872 & 0.834260369127633 \tabularnewline
31 & 109.47 & 108.306748608095 & 1.16325139190498 \tabularnewline
32 & 109.8 & 108.282172755665 & 1.5178272443354 \tabularnewline
33 & 111.73 & 111.524100217867 & 0.205899782132949 \tabularnewline
34 & 111.85 & 111.555395355823 & 0.294604644176539 \tabularnewline
35 & 112.12 & 111.668481193702 & 0.451518806297707 \tabularnewline
36 & 112.15 & 111.695744760343 & 0.454255239656895 \tabularnewline
37 & 112.17 & 111.670119523312 & 0.499880476688474 \tabularnewline
38 & 112.67 & 111.722457751942 & 0.94754224805805 \tabularnewline
39 & 112.8 & 111.692890170752 & 1.10710982924834 \tabularnewline
40 & 113.44 & 114.46060007867 & -1.02060007866994 \tabularnewline
41 & 113.53 & 114.452715390353 & -0.922715390352529 \tabularnewline
42 & 114.53 & 114.506282366190 & 0.0237176338101247 \tabularnewline
43 & 114.51 & 114.429406655095 & 0.0805933449048791 \tabularnewline
44 & 115.05 & 114.872342098582 & 0.177657901417867 \tabularnewline
45 & 116.67 & 116.994867095671 & -0.324867095671171 \tabularnewline
46 & 117.07 & 117.118340099714 & -0.0483400997136127 \tabularnewline
47 & 116.92 & 117.122282443872 & -0.202282443872311 \tabularnewline
48 & 117 & 117.146947523460 & -0.146947523459616 \tabularnewline
49 & 117.02 & 117.161232050350 & -0.141232050349579 \tabularnewline
50 & 117.35 & 117.193781818254 & 0.156218181745704 \tabularnewline
51 & 117.36 & 117.448549338825 & -0.0885493388252743 \tabularnewline
52 & 117.82 & 118.476581523753 & -0.656581523753373 \tabularnewline
53 & 117.88 & 118.494398812400 & -0.614398812399727 \tabularnewline
54 & 118.24 & 118.553546462005 & -0.31354646200501 \tabularnewline
55 & 118.5 & 118.557898388566 & -0.0578983885660175 \tabularnewline
56 & 118.8 & 118.577610109360 & 0.222389890640454 \tabularnewline
57 & 119.76 & 119.940089338700 & -0.180089338699758 \tabularnewline
58 & 120.09 & 119.920377617906 & 0.169622382093766 \tabularnewline
59 & 120.16 & 120.056362608640 & 0.103637391359820 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=112202&T=4

[TABLE]
[ROW][C]Multiple Linear Regression - Actuals, Interpolation, and Residuals[/C][/ROW]
[ROW][C]Time or Index[/C][C]Actuals[/C][C]InterpolationForecast[/C][C]ResidualsPrediction Error[/C][/ROW]
[ROW][C]1[/C][C]101.76[/C][C]102.108249539626[/C][C]-0.348249539625742[/C][/ROW]
[ROW][C]2[/C][C]102.37[/C][C]102.079757225778[/C][C]0.290242774221512[/C][/ROW]
[ROW][C]3[/C][C]102.38[/C][C]102.087641914096[/C][C]0.292358085904087[/C][/ROW]
[ROW][C]4[/C][C]102.86[/C][C]103.284824728583[/C][C]-0.424824728582917[/C][/ROW]
[ROW][C]5[/C][C]102.87[/C][C]103.237516598678[/C][C]-0.36751659867845[/C][/ROW]
[ROW][C]6[/C][C]102.92[/C][C]103.237516598678[/C][C]-0.317516598678453[/C][/ROW]
[ROW][C]7[/C][C]102.95[/C][C]103.233574254520[/C][C]-0.283574254519745[/C][/ROW]
[ROW][C]8[/C][C]103.02[/C][C]103.267084179869[/C][C]-0.247084179868747[/C][/ROW]
[ROW][C]9[/C][C]104.08[/C][C]105.120900506038[/C][C]-1.04090050603842[/C][/ROW]
[ROW][C]10[/C][C]104.16[/C][C]105.081925016820[/C][C]-0.921925016819762[/C][/ROW]
[ROW][C]11[/C][C]104.24[/C][C]105.056299779788[/C][C]-0.81629977978818[/C][/ROW]
[ROW][C]12[/C][C]104.33[/C][C]105.028562378038[/C][C]-0.698562378037574[/C][/ROW]
[ROW][C]13[/C][C]104.73[/C][C]105.032082996201[/C][C]-0.30208299620124[/C][/ROW]
[ROW][C]14[/C][C]104.86[/C][C]105.024198307884[/C][C]-0.164198307883835[/C][/ROW]
[ROW][C]15[/C][C]105.03[/C][C]104.959456588994[/C][C]0.0705434110060713[/C][/ROW]
[ROW][C]16[/C][C]105.62[/C][C]106.107151287652[/C][C]-0.487151287652227[/C][/ROW]
[ROW][C]17[/C][C]105.63[/C][C]106.0834972227[/C][C]-0.453497222700007[/C][/ROW]
[ROW][C]18[/C][C]105.63[/C][C]106.0834972227[/C][C]-0.453497222700007[/C][/ROW]
[ROW][C]19[/C][C]105.94[/C][C]106.226342491600[/C][C]-0.286342491599596[/C][/ROW]
[ROW][C]20[/C][C]106.61[/C][C]106.214515459123[/C][C]0.395484540876522[/C][/ROW]
[ROW][C]21[/C][C]107.69[/C][C]108.348853518297[/C][C]-0.658853518297459[/C][/ROW]
[ROW][C]22[/C][C]107.78[/C][C]108.390516721726[/C][C]-0.610516721726502[/C][/ROW]
[ROW][C]23[/C][C]107.93[/C][C]108.552152832233[/C][C]-0.622152832233414[/C][/ROW]
[ROW][C]24[/C][C]108.48[/C][C]108.610481317034[/C][C]-0.130481317034084[/C][/ROW]
[ROW][C]25[/C][C]108.14[/C][C]107.075491797674[/C][C]1.06450820232595[/C][/ROW]
[ROW][C]26[/C][C]108.48[/C][C]107.073520625595[/C][C]1.40647937440531[/C][/ROW]
[ROW][C]27[/C][C]108.48[/C][C]107.030154839849[/C][C]1.44984516015107[/C][/ROW]
[ROW][C]28[/C][C]108.89[/C][C]108.429038016947[/C][C]0.460961983052936[/C][/ROW]
[ROW][C]29[/C][C]108.93[/C][C]108.417134244539[/C][C]0.512865755461242[/C][/ROW]
[ROW][C]30[/C][C]109.21[/C][C]108.375739630872[/C][C]0.834260369127633[/C][/ROW]
[ROW][C]31[/C][C]109.47[/C][C]108.306748608095[/C][C]1.16325139190498[/C][/ROW]
[ROW][C]32[/C][C]109.8[/C][C]108.282172755665[/C][C]1.5178272443354[/C][/ROW]
[ROW][C]33[/C][C]111.73[/C][C]111.524100217867[/C][C]0.205899782132949[/C][/ROW]
[ROW][C]34[/C][C]111.85[/C][C]111.555395355823[/C][C]0.294604644176539[/C][/ROW]
[ROW][C]35[/C][C]112.12[/C][C]111.668481193702[/C][C]0.451518806297707[/C][/ROW]
[ROW][C]36[/C][C]112.15[/C][C]111.695744760343[/C][C]0.454255239656895[/C][/ROW]
[ROW][C]37[/C][C]112.17[/C][C]111.670119523312[/C][C]0.499880476688474[/C][/ROW]
[ROW][C]38[/C][C]112.67[/C][C]111.722457751942[/C][C]0.94754224805805[/C][/ROW]
[ROW][C]39[/C][C]112.8[/C][C]111.692890170752[/C][C]1.10710982924834[/C][/ROW]
[ROW][C]40[/C][C]113.44[/C][C]114.46060007867[/C][C]-1.02060007866994[/C][/ROW]
[ROW][C]41[/C][C]113.53[/C][C]114.452715390353[/C][C]-0.922715390352529[/C][/ROW]
[ROW][C]42[/C][C]114.53[/C][C]114.506282366190[/C][C]0.0237176338101247[/C][/ROW]
[ROW][C]43[/C][C]114.51[/C][C]114.429406655095[/C][C]0.0805933449048791[/C][/ROW]
[ROW][C]44[/C][C]115.05[/C][C]114.872342098582[/C][C]0.177657901417867[/C][/ROW]
[ROW][C]45[/C][C]116.67[/C][C]116.994867095671[/C][C]-0.324867095671171[/C][/ROW]
[ROW][C]46[/C][C]117.07[/C][C]117.118340099714[/C][C]-0.0483400997136127[/C][/ROW]
[ROW][C]47[/C][C]116.92[/C][C]117.122282443872[/C][C]-0.202282443872311[/C][/ROW]
[ROW][C]48[/C][C]117[/C][C]117.146947523460[/C][C]-0.146947523459616[/C][/ROW]
[ROW][C]49[/C][C]117.02[/C][C]117.161232050350[/C][C]-0.141232050349579[/C][/ROW]
[ROW][C]50[/C][C]117.35[/C][C]117.193781818254[/C][C]0.156218181745704[/C][/ROW]
[ROW][C]51[/C][C]117.36[/C][C]117.448549338825[/C][C]-0.0885493388252743[/C][/ROW]
[ROW][C]52[/C][C]117.82[/C][C]118.476581523753[/C][C]-0.656581523753373[/C][/ROW]
[ROW][C]53[/C][C]117.88[/C][C]118.494398812400[/C][C]-0.614398812399727[/C][/ROW]
[ROW][C]54[/C][C]118.24[/C][C]118.553546462005[/C][C]-0.31354646200501[/C][/ROW]
[ROW][C]55[/C][C]118.5[/C][C]118.557898388566[/C][C]-0.0578983885660175[/C][/ROW]
[ROW][C]56[/C][C]118.8[/C][C]118.577610109360[/C][C]0.222389890640454[/C][/ROW]
[ROW][C]57[/C][C]119.76[/C][C]119.940089338700[/C][C]-0.180089338699758[/C][/ROW]
[ROW][C]58[/C][C]120.09[/C][C]119.920377617906[/C][C]0.169622382093766[/C][/ROW]
[ROW][C]59[/C][C]120.16[/C][C]120.056362608640[/C][C]0.103637391359820[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=112202&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=112202&T=4

As an alternative you can also use a QR Code:

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	101.76	102.108249539626	-0.348249539625742
2	102.37	102.079757225778	0.290242774221512
3	102.38	102.087641914096	0.292358085904087
4	102.86	103.284824728583	-0.424824728582917
5	102.87	103.237516598678	-0.36751659867845
6	102.92	103.237516598678	-0.317516598678453
7	102.95	103.233574254520	-0.283574254519745
8	103.02	103.267084179869	-0.247084179868747
9	104.08	105.120900506038	-1.04090050603842
10	104.16	105.081925016820	-0.921925016819762
11	104.24	105.056299779788	-0.81629977978818
12	104.33	105.028562378038	-0.698562378037574
13	104.73	105.032082996201	-0.30208299620124
14	104.86	105.024198307884	-0.164198307883835
15	105.03	104.959456588994	0.0705434110060713
16	105.62	106.107151287652	-0.487151287652227
17	105.63	106.0834972227	-0.453497222700007
18	105.63	106.0834972227	-0.453497222700007
19	105.94	106.226342491600	-0.286342491599596
20	106.61	106.214515459123	0.395484540876522
21	107.69	108.348853518297	-0.658853518297459
22	107.78	108.390516721726	-0.610516721726502
23	107.93	108.552152832233	-0.622152832233414
24	108.48	108.610481317034	-0.130481317034084
25	108.14	107.075491797674	1.06450820232595
26	108.48	107.073520625595	1.40647937440531
27	108.48	107.030154839849	1.44984516015107
28	108.89	108.429038016947	0.460961983052936
29	108.93	108.417134244539	0.512865755461242
30	109.21	108.375739630872	0.834260369127633
31	109.47	108.306748608095	1.16325139190498
32	109.8	108.282172755665	1.5178272443354
33	111.73	111.524100217867	0.205899782132949
34	111.85	111.555395355823	0.294604644176539
35	112.12	111.668481193702	0.451518806297707
36	112.15	111.695744760343	0.454255239656895
37	112.17	111.670119523312	0.499880476688474
38	112.67	111.722457751942	0.94754224805805
39	112.8	111.692890170752	1.10710982924834
40	113.44	114.46060007867	-1.02060007866994
41	113.53	114.452715390353	-0.922715390352529
42	114.53	114.506282366190	0.0237176338101247
43	114.51	114.429406655095	0.0805933449048791
44	115.05	114.872342098582	0.177657901417867
45	116.67	116.994867095671	-0.324867095671171
46	117.07	117.118340099714	-0.0483400997136127
47	116.92	117.122282443872	-0.202282443872311
48	117	117.146947523460	-0.146947523459616
49	117.02	117.161232050350	-0.141232050349579
50	117.35	117.193781818254	0.156218181745704
51	117.36	117.448549338825	-0.0885493388252743
52	117.82	118.476581523753	-0.656581523753373
53	117.88	118.494398812400	-0.614398812399727
54	118.24	118.553546462005	-0.31354646200501
55	118.5	118.557898388566	-0.0578983885660175
56	118.8	118.577610109360	0.222389890640454
57	119.76	119.940089338700	-0.180089338699758
58	120.09	119.920377617906	0.169622382093766
59	120.16	120.056362608640	0.103637391359820

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
8	0.00189939408454785	0.00379878816909569	0.998100605915452
9	0.000196042168738265	0.00039208433747653	0.999803957831262
10	0.000393444255389006	0.000786888510778012	0.99960655574461
11	9.58428316132757e-05	0.000191685663226551	0.999904157168387
12	0.000721588258076576	0.00144317651615315	0.999278411741923
13	0.00153121607452339	0.00306243214904677	0.998468783925477
14	0.00136197193023516	0.00272394386047033	0.998638028069765
15	0.000464737692344033	0.000929475384688065	0.999535262307656
16	0.000242643102891222	0.000485286205782443	0.999757356897109
17	0.000113401902750261	0.000226803805500523	0.99988659809725
18	5.73520797175512e-05	0.000114704159435102	0.999942647920282
19	3.54653485324525e-05	7.09306970649049e-05	0.999964534651468
20	0.000217585687204713	0.000435171374409425	0.999782414312795
21	0.000239250916308418	0.000478501832616836	0.999760749083692
22	0.000367210756198378	0.000734421512396756	0.999632789243802
23	0.0048240010779645	0.009648002155929	0.995175998922035
24	0.0343076286227614	0.0686152572455229	0.965692371377239
25	0.164654566861623	0.329309133723246	0.835345433138377
26	0.340383743278409	0.680767486556819	0.659616256721591
27	0.394803245782915	0.78960649156583	0.605196754217085
28	0.388690987412482	0.777381974824964	0.611309012587518
29	0.403028080852865	0.80605616170573	0.596971919147135
30	0.385986153091527	0.771972306183054	0.614013846908473
31	0.365734139561010	0.731468279122021	0.63426586043899
32	0.422034780249257	0.844069560498514	0.577965219750743
33	0.426147267041596	0.852294534083192	0.573852732958404
34	0.413366356082934	0.826732712165867	0.586633643917066
35	0.581856248258073	0.836287503483855	0.418143751741927
36	0.649572551277578	0.700854897444844	0.350427448722422
37	0.678857438690081	0.642285122619838	0.321142561309919
38	0.613271631042193	0.773456737915614	0.386728368957807
39	0.545780384578332	0.908439230843336	0.454219615421668
40	0.79483745140399	0.410325097192019	0.205162548596009
41	0.959523557092634	0.0809528858147328	0.0404764429073664
42	0.933247230058293	0.133505539883415	0.0667527699417073
43	0.896440220425651	0.207119559148697	0.103559779574349
44	0.995557907086354	0.00888418582729244	0.00444209291364622
45	0.999691506428725	0.000616987142550151	0.000308493571275076
46	0.999652573748507	0.000694852502985126	0.000347426251492563
47	0.999279704836416	0.00144059032716703	0.000720295163583516
48	0.997416001094168	0.00516799781166453	0.00258399890583227
49	0.990996750188208	0.0180064996235846	0.00900324981179231
50	0.972742466776135	0.0545150664477299	0.0272575332238649
51	0.91755282315295	0.164894353694099	0.0824471768470496

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
8 & 0.00189939408454785 & 0.00379878816909569 & 0.998100605915452 \tabularnewline
9 & 0.000196042168738265 & 0.00039208433747653 & 0.999803957831262 \tabularnewline
10 & 0.000393444255389006 & 0.000786888510778012 & 0.99960655574461 \tabularnewline
11 & 9.58428316132757e-05 & 0.000191685663226551 & 0.999904157168387 \tabularnewline
12 & 0.000721588258076576 & 0.00144317651615315 & 0.999278411741923 \tabularnewline
13 & 0.00153121607452339 & 0.00306243214904677 & 0.998468783925477 \tabularnewline
14 & 0.00136197193023516 & 0.00272394386047033 & 0.998638028069765 \tabularnewline
15 & 0.000464737692344033 & 0.000929475384688065 & 0.999535262307656 \tabularnewline
16 & 0.000242643102891222 & 0.000485286205782443 & 0.999757356897109 \tabularnewline
17 & 0.000113401902750261 & 0.000226803805500523 & 0.99988659809725 \tabularnewline
18 & 5.73520797175512e-05 & 0.000114704159435102 & 0.999942647920282 \tabularnewline
19 & 3.54653485324525e-05 & 7.09306970649049e-05 & 0.999964534651468 \tabularnewline
20 & 0.000217585687204713 & 0.000435171374409425 & 0.999782414312795 \tabularnewline
21 & 0.000239250916308418 & 0.000478501832616836 & 0.999760749083692 \tabularnewline
22 & 0.000367210756198378 & 0.000734421512396756 & 0.999632789243802 \tabularnewline
23 & 0.0048240010779645 & 0.009648002155929 & 0.995175998922035 \tabularnewline
24 & 0.0343076286227614 & 0.0686152572455229 & 0.965692371377239 \tabularnewline
25 & 0.164654566861623 & 0.329309133723246 & 0.835345433138377 \tabularnewline
26 & 0.340383743278409 & 0.680767486556819 & 0.659616256721591 \tabularnewline
27 & 0.394803245782915 & 0.78960649156583 & 0.605196754217085 \tabularnewline
28 & 0.388690987412482 & 0.777381974824964 & 0.611309012587518 \tabularnewline
29 & 0.403028080852865 & 0.80605616170573 & 0.596971919147135 \tabularnewline
30 & 0.385986153091527 & 0.771972306183054 & 0.614013846908473 \tabularnewline
31 & 0.365734139561010 & 0.731468279122021 & 0.63426586043899 \tabularnewline
32 & 0.422034780249257 & 0.844069560498514 & 0.577965219750743 \tabularnewline
33 & 0.426147267041596 & 0.852294534083192 & 0.573852732958404 \tabularnewline
34 & 0.413366356082934 & 0.826732712165867 & 0.586633643917066 \tabularnewline
35 & 0.581856248258073 & 0.836287503483855 & 0.418143751741927 \tabularnewline
36 & 0.649572551277578 & 0.700854897444844 & 0.350427448722422 \tabularnewline
37 & 0.678857438690081 & 0.642285122619838 & 0.321142561309919 \tabularnewline
38 & 0.613271631042193 & 0.773456737915614 & 0.386728368957807 \tabularnewline
39 & 0.545780384578332 & 0.908439230843336 & 0.454219615421668 \tabularnewline
40 & 0.79483745140399 & 0.410325097192019 & 0.205162548596009 \tabularnewline
41 & 0.959523557092634 & 0.0809528858147328 & 0.0404764429073664 \tabularnewline
42 & 0.933247230058293 & 0.133505539883415 & 0.0667527699417073 \tabularnewline
43 & 0.896440220425651 & 0.207119559148697 & 0.103559779574349 \tabularnewline
44 & 0.995557907086354 & 0.00888418582729244 & 0.00444209291364622 \tabularnewline
45 & 0.999691506428725 & 0.000616987142550151 & 0.000308493571275076 \tabularnewline
46 & 0.999652573748507 & 0.000694852502985126 & 0.000347426251492563 \tabularnewline
47 & 0.999279704836416 & 0.00144059032716703 & 0.000720295163583516 \tabularnewline
48 & 0.997416001094168 & 0.00516799781166453 & 0.00258399890583227 \tabularnewline
49 & 0.990996750188208 & 0.0180064996235846 & 0.00900324981179231 \tabularnewline
50 & 0.972742466776135 & 0.0545150664477299 & 0.0272575332238649 \tabularnewline
51 & 0.91755282315295 & 0.164894353694099 & 0.0824471768470496 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=112202&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]8[/C][C]0.00189939408454785[/C][C]0.00379878816909569[/C][C]0.998100605915452[/C][/ROW]
[ROW][C]9[/C][C]0.000196042168738265[/C][C]0.00039208433747653[/C][C]0.999803957831262[/C][/ROW]
[ROW][C]10[/C][C]0.000393444255389006[/C][C]0.000786888510778012[/C][C]0.99960655574461[/C][/ROW]
[ROW][C]11[/C][C]9.58428316132757e-05[/C][C]0.000191685663226551[/C][C]0.999904157168387[/C][/ROW]
[ROW][C]12[/C][C]0.000721588258076576[/C][C]0.00144317651615315[/C][C]0.999278411741923[/C][/ROW]
[ROW][C]13[/C][C]0.00153121607452339[/C][C]0.00306243214904677[/C][C]0.998468783925477[/C][/ROW]
[ROW][C]14[/C][C]0.00136197193023516[/C][C]0.00272394386047033[/C][C]0.998638028069765[/C][/ROW]
[ROW][C]15[/C][C]0.000464737692344033[/C][C]0.000929475384688065[/C][C]0.999535262307656[/C][/ROW]
[ROW][C]16[/C][C]0.000242643102891222[/C][C]0.000485286205782443[/C][C]0.999757356897109[/C][/ROW]
[ROW][C]17[/C][C]0.000113401902750261[/C][C]0.000226803805500523[/C][C]0.99988659809725[/C][/ROW]
[ROW][C]18[/C][C]5.73520797175512e-05[/C][C]0.000114704159435102[/C][C]0.999942647920282[/C][/ROW]
[ROW][C]19[/C][C]3.54653485324525e-05[/C][C]7.09306970649049e-05[/C][C]0.999964534651468[/C][/ROW]
[ROW][C]20[/C][C]0.000217585687204713[/C][C]0.000435171374409425[/C][C]0.999782414312795[/C][/ROW]
[ROW][C]21[/C][C]0.000239250916308418[/C][C]0.000478501832616836[/C][C]0.999760749083692[/C][/ROW]
[ROW][C]22[/C][C]0.000367210756198378[/C][C]0.000734421512396756[/C][C]0.999632789243802[/C][/ROW]
[ROW][C]23[/C][C]0.0048240010779645[/C][C]0.009648002155929[/C][C]0.995175998922035[/C][/ROW]
[ROW][C]24[/C][C]0.0343076286227614[/C][C]0.0686152572455229[/C][C]0.965692371377239[/C][/ROW]
[ROW][C]25[/C][C]0.164654566861623[/C][C]0.329309133723246[/C][C]0.835345433138377[/C][/ROW]
[ROW][C]26[/C][C]0.340383743278409[/C][C]0.680767486556819[/C][C]0.659616256721591[/C][/ROW]
[ROW][C]27[/C][C]0.394803245782915[/C][C]0.78960649156583[/C][C]0.605196754217085[/C][/ROW]
[ROW][C]28[/C][C]0.388690987412482[/C][C]0.777381974824964[/C][C]0.611309012587518[/C][/ROW]
[ROW][C]29[/C][C]0.403028080852865[/C][C]0.80605616170573[/C][C]0.596971919147135[/C][/ROW]
[ROW][C]30[/C][C]0.385986153091527[/C][C]0.771972306183054[/C][C]0.614013846908473[/C][/ROW]
[ROW][C]31[/C][C]0.365734139561010[/C][C]0.731468279122021[/C][C]0.63426586043899[/C][/ROW]
[ROW][C]32[/C][C]0.422034780249257[/C][C]0.844069560498514[/C][C]0.577965219750743[/C][/ROW]
[ROW][C]33[/C][C]0.426147267041596[/C][C]0.852294534083192[/C][C]0.573852732958404[/C][/ROW]
[ROW][C]34[/C][C]0.413366356082934[/C][C]0.826732712165867[/C][C]0.586633643917066[/C][/ROW]
[ROW][C]35[/C][C]0.581856248258073[/C][C]0.836287503483855[/C][C]0.418143751741927[/C][/ROW]
[ROW][C]36[/C][C]0.649572551277578[/C][C]0.700854897444844[/C][C]0.350427448722422[/C][/ROW]
[ROW][C]37[/C][C]0.678857438690081[/C][C]0.642285122619838[/C][C]0.321142561309919[/C][/ROW]
[ROW][C]38[/C][C]0.613271631042193[/C][C]0.773456737915614[/C][C]0.386728368957807[/C][/ROW]
[ROW][C]39[/C][C]0.545780384578332[/C][C]0.908439230843336[/C][C]0.454219615421668[/C][/ROW]
[ROW][C]40[/C][C]0.79483745140399[/C][C]0.410325097192019[/C][C]0.205162548596009[/C][/ROW]
[ROW][C]41[/C][C]0.959523557092634[/C][C]0.0809528858147328[/C][C]0.0404764429073664[/C][/ROW]
[ROW][C]42[/C][C]0.933247230058293[/C][C]0.133505539883415[/C][C]0.0667527699417073[/C][/ROW]
[ROW][C]43[/C][C]0.896440220425651[/C][C]0.207119559148697[/C][C]0.103559779574349[/C][/ROW]
[ROW][C]44[/C][C]0.995557907086354[/C][C]0.00888418582729244[/C][C]0.00444209291364622[/C][/ROW]
[ROW][C]45[/C][C]0.999691506428725[/C][C]0.000616987142550151[/C][C]0.000308493571275076[/C][/ROW]
[ROW][C]46[/C][C]0.999652573748507[/C][C]0.000694852502985126[/C][C]0.000347426251492563[/C][/ROW]
[ROW][C]47[/C][C]0.999279704836416[/C][C]0.00144059032716703[/C][C]0.000720295163583516[/C][/ROW]
[ROW][C]48[/C][C]0.997416001094168[/C][C]0.00516799781166453[/C][C]0.00258399890583227[/C][/ROW]
[ROW][C]49[/C][C]0.990996750188208[/C][C]0.0180064996235846[/C][C]0.00900324981179231[/C][/ROW]
[ROW][C]50[/C][C]0.972742466776135[/C][C]0.0545150664477299[/C][C]0.0272575332238649[/C][/ROW]
[ROW][C]51[/C][C]0.91755282315295[/C][C]0.164894353694099[/C][C]0.0824471768470496[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=112202&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=112202&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
8	0.00189939408454785	0.00379878816909569	0.998100605915452
9	0.000196042168738265	0.00039208433747653	0.999803957831262
10	0.000393444255389006	0.000786888510778012	0.99960655574461
11	9.58428316132757e-05	0.000191685663226551	0.999904157168387
12	0.000721588258076576	0.00144317651615315	0.999278411741923
13	0.00153121607452339	0.00306243214904677	0.998468783925477
14	0.00136197193023516	0.00272394386047033	0.998638028069765
15	0.000464737692344033	0.000929475384688065	0.999535262307656
16	0.000242643102891222	0.000485286205782443	0.999757356897109
17	0.000113401902750261	0.000226803805500523	0.99988659809725
18	5.73520797175512e-05	0.000114704159435102	0.999942647920282
19	3.54653485324525e-05	7.09306970649049e-05	0.999964534651468
20	0.000217585687204713	0.000435171374409425	0.999782414312795
21	0.000239250916308418	0.000478501832616836	0.999760749083692
22	0.000367210756198378	0.000734421512396756	0.999632789243802
23	0.0048240010779645	0.009648002155929	0.995175998922035
24	0.0343076286227614	0.0686152572455229	0.965692371377239
25	0.164654566861623	0.329309133723246	0.835345433138377
26	0.340383743278409	0.680767486556819	0.659616256721591
27	0.394803245782915	0.78960649156583	0.605196754217085
28	0.388690987412482	0.777381974824964	0.611309012587518
29	0.403028080852865	0.80605616170573	0.596971919147135
30	0.385986153091527	0.771972306183054	0.614013846908473
31	0.365734139561010	0.731468279122021	0.63426586043899
32	0.422034780249257	0.844069560498514	0.577965219750743
33	0.426147267041596	0.852294534083192	0.573852732958404
34	0.413366356082934	0.826732712165867	0.586633643917066
35	0.581856248258073	0.836287503483855	0.418143751741927
36	0.649572551277578	0.700854897444844	0.350427448722422
37	0.678857438690081	0.642285122619838	0.321142561309919
38	0.613271631042193	0.773456737915614	0.386728368957807
39	0.545780384578332	0.908439230843336	0.454219615421668
40	0.79483745140399	0.410325097192019	0.205162548596009
41	0.959523557092634	0.0809528858147328	0.0404764429073664
42	0.933247230058293	0.133505539883415	0.0667527699417073
43	0.896440220425651	0.207119559148697	0.103559779574349
44	0.995557907086354	0.00888418582729244	0.00444209291364622
45	0.999691506428725	0.000616987142550151	0.000308493571275076
46	0.999652573748507	0.000694852502985126	0.000347426251492563
47	0.999279704836416	0.00144059032716703	0.000720295163583516
48	0.997416001094168	0.00516799781166453	0.00258399890583227
49	0.990996750188208	0.0180064996235846	0.00900324981179231
50	0.972742466776135	0.0545150664477299	0.0272575332238649
51	0.91755282315295	0.164894353694099	0.0824471768470496

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	21	0.477272727272727	NOK
5% type I error level	22	0.5	NOK
10% type I error level	25	0.568181818181818	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 & 21 & 0.477272727272727 & NOK \tabularnewline
5% type I error level & 22 & 0.5 & NOK \tabularnewline
10% type I error level & 25 & 0.568181818181818 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=112202&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]21[/C][C]0.477272727272727[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]22[/C][C]0.5[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]25[/C][C]0.568181818181818[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=112202&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=112202&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	21	0.477272727272727	NOK
5% type I error level	22	0.5	NOK
10% type I error level	25	0.568181818181818	NOK

Figure 1

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Figure 2

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Figure 3

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Figure 4

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Figure 5

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Figure 6

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Figure 7

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Figure 8

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Figure 9

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Figure 10

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Parameters (Session):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;

Parameters (R input):

par1 = 5 ; 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