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rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Fri, 10 Dec 2010 10:44:38 +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/10/t1291977824h1y3g1wxainwxq0.htm/, Retrieved Mon, 29 Apr 2024 12:54:27 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=107520, Retrieved Mon, 29 Apr 2024 12:54:27 +0000

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Estimated Impact

146

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

-     [Multiple Regression] [Competence to learn] [2010-11-17 07:43:53] [b98453cac15ba1066b407e146608df68]
-   PD  [Multiple Regression] [vrijetijdsbesteding] [2010-11-19 11:37:04] [74deae64b71f9d77c839af86f7c687b5]
-   P       [Multiple Regression] [] [2010-12-10 10:44:38] [c05c5ae4ce2db58f67fd725429d7f25c] [Current]

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Dataseries X:

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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

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	7 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 7 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=107520&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]7 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=107520&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=107520&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	7 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

Multiple Linear Regression - Estimated Regression Equation
bios[t] = -174.117057776151 -0.222841174760951schouwburg[t] -0.0355988545204506eedagsacttractie[t] + 1.94413794987770huurDVD[t] + 1.04536917079398vrijetijdsbesteding[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
bios[t] =  -174.117057776151 -0.222841174760951schouwburg[t] -0.0355988545204506eedagsacttractie[t] +  1.94413794987770huurDVD[t] +  1.04536917079398vrijetijdsbesteding[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=107520&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]bios[t] =  -174.117057776151 -0.222841174760951schouwburg[t] -0.0355988545204506eedagsacttractie[t] +  1.94413794987770huurDVD[t] +  1.04536917079398vrijetijdsbesteding[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=107520&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=107520&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
bios[t] = -174.117057776151 -0.222841174760951schouwburg[t] -0.0355988545204506eedagsacttractie[t] + 1.94413794987770huurDVD[t] + 1.04536917079398vrijetijdsbesteding[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)	-174.117057776151	42.558301	-4.0913	0.000147	7.3e-05
schouwburg	-0.222841174760951	0.171312	-1.3008	0.198956	0.099478
eedagsacttractie	-0.0355988545204506	0.23151	-0.1538	0.878377	0.439188
huurDVD	1.94413794987770	0.514586	3.7781	0.000402	0.000201
vrijetijdsbesteding	1.04536917079398	0.383945	2.7227	0.008746	0.004373

\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) & -174.117057776151 & 42.558301 & -4.0913 & 0.000147 & 7.3e-05 \tabularnewline
schouwburg & -0.222841174760951 & 0.171312 & -1.3008 & 0.198956 & 0.099478 \tabularnewline
eedagsacttractie & -0.0355988545204506 & 0.23151 & -0.1538 & 0.878377 & 0.439188 \tabularnewline
huurDVD & 1.94413794987770 & 0.514586 & 3.7781 & 0.000402 & 0.000201 \tabularnewline
vrijetijdsbesteding & 1.04536917079398 & 0.383945 & 2.7227 & 0.008746 & 0.004373 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=107520&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]-174.117057776151[/C][C]42.558301[/C][C]-4.0913[/C][C]0.000147[/C][C]7.3e-05[/C][/ROW]
[ROW][C]schouwburg[/C][C]-0.222841174760951[/C][C]0.171312[/C][C]-1.3008[/C][C]0.198956[/C][C]0.099478[/C][/ROW]
[ROW][C]eedagsacttractie[/C][C]-0.0355988545204506[/C][C]0.23151[/C][C]-0.1538[/C][C]0.878377[/C][C]0.439188[/C][/ROW]
[ROW][C]huurDVD[/C][C]1.94413794987770[/C][C]0.514586[/C][C]3.7781[/C][C]0.000402[/C][C]0.000201[/C][/ROW]
[ROW][C]vrijetijdsbesteding[/C][C]1.04536917079398[/C][C]0.383945[/C][C]2.7227[/C][C]0.008746[/C][C]0.004373[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=107520&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=107520&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)	-174.117057776151	42.558301	-4.0913	0.000147	7.3e-05
schouwburg	-0.222841174760951	0.171312	-1.3008	0.198956	0.099478
eedagsacttractie	-0.0355988545204506	0.23151	-0.1538	0.878377	0.439188
huurDVD	1.94413794987770	0.514586	3.7781	0.000402	0.000201
vrijetijdsbesteding	1.04536917079398	0.383945	2.7227	0.008746	0.004373

Multiple Linear Regression - Regression Statistics
Multiple R	0.958447028955653
R-squared	0.918620707313919
Adjusted R-squared	0.91247887390365
F-TEST (value)	149.567831940512
F-TEST (DF numerator)	4
F-TEST (DF denominator)	53
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.93511710571985
Sum Squared Residuals	198.467945281026

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.958447028955653 \tabularnewline
R-squared & 0.918620707313919 \tabularnewline
Adjusted R-squared & 0.91247887390365 \tabularnewline
F-TEST (value) & 149.567831940512 \tabularnewline
F-TEST (DF numerator) & 4 \tabularnewline
F-TEST (DF denominator) & 53 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 1.93511710571985 \tabularnewline
Sum Squared Residuals & 198.467945281026 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=107520&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.958447028955653[/C][/ROW]
[ROW][C]R-squared[/C][C]0.918620707313919[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.91247887390365[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]149.567831940512[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]4[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]53[/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]1.93511710571985[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]198.467945281026[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=107520&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=107520&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	0.958447028955653
R-squared	0.918620707313919
Adjusted R-squared	0.91247887390365
F-TEST (value)	149.567831940512
F-TEST (DF numerator)	4
F-TEST (DF denominator)	53
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.93511710571985
Sum Squared Residuals	198.467945281026

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	101.82	99.1289908915415	2.69100910845848
2	101.68	99.8833143627193	1.79668563728074
3	101.68	99.8160025364321	1.86399746356790
4	102.45	100.209341222613	2.2406587773867
5	102.45	100.686388022292	1.76361197770811
6	102.45	100.738656480832	1.71134351916842
7	102.45	100.808900314953	1.64109968504703
8	102.45	100.551572705429	1.89842729457067
9	102.45	100.468078067730	1.98192193227043
10	102.52	101.018300709364	1.50169929063624
11	102.52	101.354668176511	1.16533182348863
12	102.85	102.109758304841	0.740241695158742
13	102.85	102.525677561411	0.324322438588753
14	102.85	102.739341071610	0.110658928390447
15	103.25	104.025212462075	-0.775212462074827
16	103.25	104.54942325109	-1.29942325109011
17	103.25	104.793173496783	-1.54317349678334
18	103.25	104.793173496783	-1.54317349678334
19	104.45	105.117237939729	-0.667237939729479
20	104.45	105.934283561154	-1.48428356115412
21	104.45	105.639556052060	-1.18955605206038
22	104.8	105.733639277432	-0.933639277431848
23	104.8	104.296251534151	0.503748465848773
24	105.29	104.871204578088	0.418795421912094
25	105.29	105.487366581976	-0.197366581975693
26	105.29	105.862233479544	-0.572233479544439
27	105.29	106.289943828518	-0.99994382851753
28	106.04	106.497236279260	-0.457236279260268
29	105.94	106.539051046092	-0.599051046092042
30	105.94	107.240023383389	-1.30002338338865
31	105.94	108.192267650252	-2.2522676502523
32	106.28	109.178805000074	-2.89880500007395
33	106.48	108.831938448889	-2.35193844888935
34	107.19	109.482299995852	-2.29229999585162
35	108.14	109.764549671966	-1.624549671966
36	108.22	109.620938331601	-1.40093833160082
37	108.22	109.894583648501	-1.67458364850079
38	108.61	110.358944095401	-1.74894409540145
39	108.61	110.786462780086	-2.17646278008633
40	108.61	111.232294231551	-2.62229423155125
41	108.61	111.404142974918	-2.79414297491784
42	109.06	112.449512145712	-3.38951214571182
43	109.06	113.186818562748	-4.12681856274822
44	112.93	113.926290330466	-0.996290330465953
45	115.84	116.624235092372	-0.784235092371702
46	118.57	118.152384370809	0.417615629191276
47	118.57	117.956696236192	0.613303763807938
48	118.86	118.137532667349	0.722467332650521
49	118.98	118.158440050765	0.82155994923465
50	119.27	118.522853256626	0.74714674337387
51	119.39	116.161458649483	3.22854135051672
52	119.49	116.811053104864	2.67894689513566
53	119.59	116.815451116616	2.77454888338433
54	120.12	117.230666777099	2.88933322290095
55	120.14	117.483021382007	2.65697861799329
56	120.14	117.602218338257	2.53778166174286
57	120.14	117.743377395894	2.39662260410551
58	120.14	118.282763017244	1.85723698275573

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 101.82 & 99.1289908915415 & 2.69100910845848 \tabularnewline
2 & 101.68 & 99.8833143627193 & 1.79668563728074 \tabularnewline
3 & 101.68 & 99.8160025364321 & 1.86399746356790 \tabularnewline
4 & 102.45 & 100.209341222613 & 2.2406587773867 \tabularnewline
5 & 102.45 & 100.686388022292 & 1.76361197770811 \tabularnewline
6 & 102.45 & 100.738656480832 & 1.71134351916842 \tabularnewline
7 & 102.45 & 100.808900314953 & 1.64109968504703 \tabularnewline
8 & 102.45 & 100.551572705429 & 1.89842729457067 \tabularnewline
9 & 102.45 & 100.468078067730 & 1.98192193227043 \tabularnewline
10 & 102.52 & 101.018300709364 & 1.50169929063624 \tabularnewline
11 & 102.52 & 101.354668176511 & 1.16533182348863 \tabularnewline
12 & 102.85 & 102.109758304841 & 0.740241695158742 \tabularnewline
13 & 102.85 & 102.525677561411 & 0.324322438588753 \tabularnewline
14 & 102.85 & 102.739341071610 & 0.110658928390447 \tabularnewline
15 & 103.25 & 104.025212462075 & -0.775212462074827 \tabularnewline
16 & 103.25 & 104.54942325109 & -1.29942325109011 \tabularnewline
17 & 103.25 & 104.793173496783 & -1.54317349678334 \tabularnewline
18 & 103.25 & 104.793173496783 & -1.54317349678334 \tabularnewline
19 & 104.45 & 105.117237939729 & -0.667237939729479 \tabularnewline
20 & 104.45 & 105.934283561154 & -1.48428356115412 \tabularnewline
21 & 104.45 & 105.639556052060 & -1.18955605206038 \tabularnewline
22 & 104.8 & 105.733639277432 & -0.933639277431848 \tabularnewline
23 & 104.8 & 104.296251534151 & 0.503748465848773 \tabularnewline
24 & 105.29 & 104.871204578088 & 0.418795421912094 \tabularnewline
25 & 105.29 & 105.487366581976 & -0.197366581975693 \tabularnewline
26 & 105.29 & 105.862233479544 & -0.572233479544439 \tabularnewline
27 & 105.29 & 106.289943828518 & -0.99994382851753 \tabularnewline
28 & 106.04 & 106.497236279260 & -0.457236279260268 \tabularnewline
29 & 105.94 & 106.539051046092 & -0.599051046092042 \tabularnewline
30 & 105.94 & 107.240023383389 & -1.30002338338865 \tabularnewline
31 & 105.94 & 108.192267650252 & -2.2522676502523 \tabularnewline
32 & 106.28 & 109.178805000074 & -2.89880500007395 \tabularnewline
33 & 106.48 & 108.831938448889 & -2.35193844888935 \tabularnewline
34 & 107.19 & 109.482299995852 & -2.29229999585162 \tabularnewline
35 & 108.14 & 109.764549671966 & -1.624549671966 \tabularnewline
36 & 108.22 & 109.620938331601 & -1.40093833160082 \tabularnewline
37 & 108.22 & 109.894583648501 & -1.67458364850079 \tabularnewline
38 & 108.61 & 110.358944095401 & -1.74894409540145 \tabularnewline
39 & 108.61 & 110.786462780086 & -2.17646278008633 \tabularnewline
40 & 108.61 & 111.232294231551 & -2.62229423155125 \tabularnewline
41 & 108.61 & 111.404142974918 & -2.79414297491784 \tabularnewline
42 & 109.06 & 112.449512145712 & -3.38951214571182 \tabularnewline
43 & 109.06 & 113.186818562748 & -4.12681856274822 \tabularnewline
44 & 112.93 & 113.926290330466 & -0.996290330465953 \tabularnewline
45 & 115.84 & 116.624235092372 & -0.784235092371702 \tabularnewline
46 & 118.57 & 118.152384370809 & 0.417615629191276 \tabularnewline
47 & 118.57 & 117.956696236192 & 0.613303763807938 \tabularnewline
48 & 118.86 & 118.137532667349 & 0.722467332650521 \tabularnewline
49 & 118.98 & 118.158440050765 & 0.82155994923465 \tabularnewline
50 & 119.27 & 118.522853256626 & 0.74714674337387 \tabularnewline
51 & 119.39 & 116.161458649483 & 3.22854135051672 \tabularnewline
52 & 119.49 & 116.811053104864 & 2.67894689513566 \tabularnewline
53 & 119.59 & 116.815451116616 & 2.77454888338433 \tabularnewline
54 & 120.12 & 117.230666777099 & 2.88933322290095 \tabularnewline
55 & 120.14 & 117.483021382007 & 2.65697861799329 \tabularnewline
56 & 120.14 & 117.602218338257 & 2.53778166174286 \tabularnewline
57 & 120.14 & 117.743377395894 & 2.39662260410551 \tabularnewline
58 & 120.14 & 118.282763017244 & 1.85723698275573 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=107520&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]101.82[/C][C]99.1289908915415[/C][C]2.69100910845848[/C][/ROW]
[ROW][C]2[/C][C]101.68[/C][C]99.8833143627193[/C][C]1.79668563728074[/C][/ROW]
[ROW][C]3[/C][C]101.68[/C][C]99.8160025364321[/C][C]1.86399746356790[/C][/ROW]
[ROW][C]4[/C][C]102.45[/C][C]100.209341222613[/C][C]2.2406587773867[/C][/ROW]
[ROW][C]5[/C][C]102.45[/C][C]100.686388022292[/C][C]1.76361197770811[/C][/ROW]
[ROW][C]6[/C][C]102.45[/C][C]100.738656480832[/C][C]1.71134351916842[/C][/ROW]
[ROW][C]7[/C][C]102.45[/C][C]100.808900314953[/C][C]1.64109968504703[/C][/ROW]
[ROW][C]8[/C][C]102.45[/C][C]100.551572705429[/C][C]1.89842729457067[/C][/ROW]
[ROW][C]9[/C][C]102.45[/C][C]100.468078067730[/C][C]1.98192193227043[/C][/ROW]
[ROW][C]10[/C][C]102.52[/C][C]101.018300709364[/C][C]1.50169929063624[/C][/ROW]
[ROW][C]11[/C][C]102.52[/C][C]101.354668176511[/C][C]1.16533182348863[/C][/ROW]
[ROW][C]12[/C][C]102.85[/C][C]102.109758304841[/C][C]0.740241695158742[/C][/ROW]
[ROW][C]13[/C][C]102.85[/C][C]102.525677561411[/C][C]0.324322438588753[/C][/ROW]
[ROW][C]14[/C][C]102.85[/C][C]102.739341071610[/C][C]0.110658928390447[/C][/ROW]
[ROW][C]15[/C][C]103.25[/C][C]104.025212462075[/C][C]-0.775212462074827[/C][/ROW]
[ROW][C]16[/C][C]103.25[/C][C]104.54942325109[/C][C]-1.29942325109011[/C][/ROW]
[ROW][C]17[/C][C]103.25[/C][C]104.793173496783[/C][C]-1.54317349678334[/C][/ROW]
[ROW][C]18[/C][C]103.25[/C][C]104.793173496783[/C][C]-1.54317349678334[/C][/ROW]
[ROW][C]19[/C][C]104.45[/C][C]105.117237939729[/C][C]-0.667237939729479[/C][/ROW]
[ROW][C]20[/C][C]104.45[/C][C]105.934283561154[/C][C]-1.48428356115412[/C][/ROW]
[ROW][C]21[/C][C]104.45[/C][C]105.639556052060[/C][C]-1.18955605206038[/C][/ROW]
[ROW][C]22[/C][C]104.8[/C][C]105.733639277432[/C][C]-0.933639277431848[/C][/ROW]
[ROW][C]23[/C][C]104.8[/C][C]104.296251534151[/C][C]0.503748465848773[/C][/ROW]
[ROW][C]24[/C][C]105.29[/C][C]104.871204578088[/C][C]0.418795421912094[/C][/ROW]
[ROW][C]25[/C][C]105.29[/C][C]105.487366581976[/C][C]-0.197366581975693[/C][/ROW]
[ROW][C]26[/C][C]105.29[/C][C]105.862233479544[/C][C]-0.572233479544439[/C][/ROW]
[ROW][C]27[/C][C]105.29[/C][C]106.289943828518[/C][C]-0.99994382851753[/C][/ROW]
[ROW][C]28[/C][C]106.04[/C][C]106.497236279260[/C][C]-0.457236279260268[/C][/ROW]
[ROW][C]29[/C][C]105.94[/C][C]106.539051046092[/C][C]-0.599051046092042[/C][/ROW]
[ROW][C]30[/C][C]105.94[/C][C]107.240023383389[/C][C]-1.30002338338865[/C][/ROW]
[ROW][C]31[/C][C]105.94[/C][C]108.192267650252[/C][C]-2.2522676502523[/C][/ROW]
[ROW][C]32[/C][C]106.28[/C][C]109.178805000074[/C][C]-2.89880500007395[/C][/ROW]
[ROW][C]33[/C][C]106.48[/C][C]108.831938448889[/C][C]-2.35193844888935[/C][/ROW]
[ROW][C]34[/C][C]107.19[/C][C]109.482299995852[/C][C]-2.29229999585162[/C][/ROW]
[ROW][C]35[/C][C]108.14[/C][C]109.764549671966[/C][C]-1.624549671966[/C][/ROW]
[ROW][C]36[/C][C]108.22[/C][C]109.620938331601[/C][C]-1.40093833160082[/C][/ROW]
[ROW][C]37[/C][C]108.22[/C][C]109.894583648501[/C][C]-1.67458364850079[/C][/ROW]
[ROW][C]38[/C][C]108.61[/C][C]110.358944095401[/C][C]-1.74894409540145[/C][/ROW]
[ROW][C]39[/C][C]108.61[/C][C]110.786462780086[/C][C]-2.17646278008633[/C][/ROW]
[ROW][C]40[/C][C]108.61[/C][C]111.232294231551[/C][C]-2.62229423155125[/C][/ROW]
[ROW][C]41[/C][C]108.61[/C][C]111.404142974918[/C][C]-2.79414297491784[/C][/ROW]
[ROW][C]42[/C][C]109.06[/C][C]112.449512145712[/C][C]-3.38951214571182[/C][/ROW]
[ROW][C]43[/C][C]109.06[/C][C]113.186818562748[/C][C]-4.12681856274822[/C][/ROW]
[ROW][C]44[/C][C]112.93[/C][C]113.926290330466[/C][C]-0.996290330465953[/C][/ROW]
[ROW][C]45[/C][C]115.84[/C][C]116.624235092372[/C][C]-0.784235092371702[/C][/ROW]
[ROW][C]46[/C][C]118.57[/C][C]118.152384370809[/C][C]0.417615629191276[/C][/ROW]
[ROW][C]47[/C][C]118.57[/C][C]117.956696236192[/C][C]0.613303763807938[/C][/ROW]
[ROW][C]48[/C][C]118.86[/C][C]118.137532667349[/C][C]0.722467332650521[/C][/ROW]
[ROW][C]49[/C][C]118.98[/C][C]118.158440050765[/C][C]0.82155994923465[/C][/ROW]
[ROW][C]50[/C][C]119.27[/C][C]118.522853256626[/C][C]0.74714674337387[/C][/ROW]
[ROW][C]51[/C][C]119.39[/C][C]116.161458649483[/C][C]3.22854135051672[/C][/ROW]
[ROW][C]52[/C][C]119.49[/C][C]116.811053104864[/C][C]2.67894689513566[/C][/ROW]
[ROW][C]53[/C][C]119.59[/C][C]116.815451116616[/C][C]2.77454888338433[/C][/ROW]
[ROW][C]54[/C][C]120.12[/C][C]117.230666777099[/C][C]2.88933322290095[/C][/ROW]
[ROW][C]55[/C][C]120.14[/C][C]117.483021382007[/C][C]2.65697861799329[/C][/ROW]
[ROW][C]56[/C][C]120.14[/C][C]117.602218338257[/C][C]2.53778166174286[/C][/ROW]
[ROW][C]57[/C][C]120.14[/C][C]117.743377395894[/C][C]2.39662260410551[/C][/ROW]
[ROW][C]58[/C][C]120.14[/C][C]118.282763017244[/C][C]1.85723698275573[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=107520&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=107520&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	101.82	99.1289908915415	2.69100910845848
2	101.68	99.8833143627193	1.79668563728074
3	101.68	99.8160025364321	1.86399746356790
4	102.45	100.209341222613	2.2406587773867
5	102.45	100.686388022292	1.76361197770811
6	102.45	100.738656480832	1.71134351916842
7	102.45	100.808900314953	1.64109968504703
8	102.45	100.551572705429	1.89842729457067
9	102.45	100.468078067730	1.98192193227043
10	102.52	101.018300709364	1.50169929063624
11	102.52	101.354668176511	1.16533182348863
12	102.85	102.109758304841	0.740241695158742
13	102.85	102.525677561411	0.324322438588753
14	102.85	102.739341071610	0.110658928390447
15	103.25	104.025212462075	-0.775212462074827
16	103.25	104.54942325109	-1.29942325109011
17	103.25	104.793173496783	-1.54317349678334
18	103.25	104.793173496783	-1.54317349678334
19	104.45	105.117237939729	-0.667237939729479
20	104.45	105.934283561154	-1.48428356115412
21	104.45	105.639556052060	-1.18955605206038
22	104.8	105.733639277432	-0.933639277431848
23	104.8	104.296251534151	0.503748465848773
24	105.29	104.871204578088	0.418795421912094
25	105.29	105.487366581976	-0.197366581975693
26	105.29	105.862233479544	-0.572233479544439
27	105.29	106.289943828518	-0.99994382851753
28	106.04	106.497236279260	-0.457236279260268
29	105.94	106.539051046092	-0.599051046092042
30	105.94	107.240023383389	-1.30002338338865
31	105.94	108.192267650252	-2.2522676502523
32	106.28	109.178805000074	-2.89880500007395
33	106.48	108.831938448889	-2.35193844888935
34	107.19	109.482299995852	-2.29229999585162
35	108.14	109.764549671966	-1.624549671966
36	108.22	109.620938331601	-1.40093833160082
37	108.22	109.894583648501	-1.67458364850079
38	108.61	110.358944095401	-1.74894409540145
39	108.61	110.786462780086	-2.17646278008633
40	108.61	111.232294231551	-2.62229423155125
41	108.61	111.404142974918	-2.79414297491784
42	109.06	112.449512145712	-3.38951214571182
43	109.06	113.186818562748	-4.12681856274822
44	112.93	113.926290330466	-0.996290330465953
45	115.84	116.624235092372	-0.784235092371702
46	118.57	118.152384370809	0.417615629191276
47	118.57	117.956696236192	0.613303763807938
48	118.86	118.137532667349	0.722467332650521
49	118.98	118.158440050765	0.82155994923465
50	119.27	118.522853256626	0.74714674337387
51	119.39	116.161458649483	3.22854135051672
52	119.49	116.811053104864	2.67894689513566
53	119.59	116.815451116616	2.77454888338433
54	120.12	117.230666777099	2.88933322290095
55	120.14	117.483021382007	2.65697861799329
56	120.14	117.602218338257	2.53778166174286
57	120.14	117.743377395894	2.39662260410551
58	120.14	118.282763017244	1.85723698275573

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
8	1.13598124113785e-06	2.27196248227571e-06	0.999998864018759
9	9.7025212824279e-09	1.94050425648558e-08	0.999999990297479
10	2.52824073213857e-09	5.05648146427715e-09	0.99999999747176
11	5.54012850145514e-11	1.10802570029103e-10	0.9999999999446
12	1.59918093481165e-09	3.19836186962331e-09	0.99999999840082
13	2.97258277185938e-10	5.94516554371876e-10	0.999999999702742
14	1.93145792500399e-11	3.86291585000797e-11	0.999999999980685
15	1.38598836815930e-12	2.77197673631861e-12	0.999999999998614
16	2.49644826937587e-10	4.99289653875175e-10	0.999999999750355
17	1.21167577926635e-10	2.42335155853271e-10	0.999999999878832
18	2.39715237498871e-11	4.79430474997741e-11	0.999999999976029
19	6.10354653733489e-09	1.22070930746698e-08	0.999999993896453
20	1.55240657835622e-09	3.10481315671244e-09	0.999999998447593
21	3.87622631679294e-10	7.75245263358588e-10	0.999999999612377
22	2.05100380557893e-10	4.10200761115786e-10	0.9999999997949
23	1.68989075731191e-10	3.37978151462382e-10	0.99999999983101
24	2.46514572704650e-10	4.93029145409301e-10	0.999999999753485
25	1.30323316801563e-10	2.60646633603125e-10	0.999999999869677
26	5.8593958269158e-11	1.17187916538316e-10	0.999999999941406
27	1.53441913446854e-11	3.06883826893707e-11	0.999999999984656
28	1.25077354724220e-11	2.50154709448441e-11	0.999999999987492
29	1.90926202867996e-11	3.81852405735993e-11	0.999999999980907
30	2.16696546353056e-11	4.33393092706112e-11	0.99999999997833
31	1.41703728469706e-11	2.83407456939411e-11	0.99999999998583
32	5.51593918059057e-12	1.10318783611811e-11	0.999999999994484
33	2.45292467131246e-12	4.90584934262492e-12	0.999999999997547
34	1.26589138451746e-12	2.53178276903491e-12	0.999999999998734
35	9.015429546489e-11	1.80308590929780e-10	0.999999999909846
36	8.1674834475244e-10	1.63349668950488e-09	0.999999999183252
37	2.70869271842375e-09	5.4173854368475e-09	0.999999997291307
38	4.07929231129996e-09	8.15858462259992e-09	0.999999995920708
39	1.19246499472185e-08	2.38492998944371e-08	0.99999998807535
40	5.98593285585271e-09	1.19718657117054e-08	0.999999994014067
41	4.09608423020220e-09	8.19216846040439e-09	0.999999995903916
42	1.80530425354315e-09	3.61060850708631e-09	0.999999998194696
43	4.64181317624015e-07	9.28362635248031e-07	0.999999535818682
44	0.225556161495110	0.451112322990219	0.77444383850489
45	0.999933456590562	0.000133086818876887	6.65434094384437e-05
46	0.999984652596758	3.06948064850012e-05	1.53474032425006e-05
47	0.999986263947679	2.74721046426459e-05	1.37360523213230e-05
48	0.999929472802998	0.000141054394003494	7.05271970017472e-05
49	0.999478207472525	0.00104358505494942	0.00052179252747471
50	0.995907631262439	0.00818473747512257	0.00409236873756129

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
8 & 1.13598124113785e-06 & 2.27196248227571e-06 & 0.999998864018759 \tabularnewline
9 & 9.7025212824279e-09 & 1.94050425648558e-08 & 0.999999990297479 \tabularnewline
10 & 2.52824073213857e-09 & 5.05648146427715e-09 & 0.99999999747176 \tabularnewline
11 & 5.54012850145514e-11 & 1.10802570029103e-10 & 0.9999999999446 \tabularnewline
12 & 1.59918093481165e-09 & 3.19836186962331e-09 & 0.99999999840082 \tabularnewline
13 & 2.97258277185938e-10 & 5.94516554371876e-10 & 0.999999999702742 \tabularnewline
14 & 1.93145792500399e-11 & 3.86291585000797e-11 & 0.999999999980685 \tabularnewline
15 & 1.38598836815930e-12 & 2.77197673631861e-12 & 0.999999999998614 \tabularnewline
16 & 2.49644826937587e-10 & 4.99289653875175e-10 & 0.999999999750355 \tabularnewline
17 & 1.21167577926635e-10 & 2.42335155853271e-10 & 0.999999999878832 \tabularnewline
18 & 2.39715237498871e-11 & 4.79430474997741e-11 & 0.999999999976029 \tabularnewline
19 & 6.10354653733489e-09 & 1.22070930746698e-08 & 0.999999993896453 \tabularnewline
20 & 1.55240657835622e-09 & 3.10481315671244e-09 & 0.999999998447593 \tabularnewline
21 & 3.87622631679294e-10 & 7.75245263358588e-10 & 0.999999999612377 \tabularnewline
22 & 2.05100380557893e-10 & 4.10200761115786e-10 & 0.9999999997949 \tabularnewline
23 & 1.68989075731191e-10 & 3.37978151462382e-10 & 0.99999999983101 \tabularnewline
24 & 2.46514572704650e-10 & 4.93029145409301e-10 & 0.999999999753485 \tabularnewline
25 & 1.30323316801563e-10 & 2.60646633603125e-10 & 0.999999999869677 \tabularnewline
26 & 5.8593958269158e-11 & 1.17187916538316e-10 & 0.999999999941406 \tabularnewline
27 & 1.53441913446854e-11 & 3.06883826893707e-11 & 0.999999999984656 \tabularnewline
28 & 1.25077354724220e-11 & 2.50154709448441e-11 & 0.999999999987492 \tabularnewline
29 & 1.90926202867996e-11 & 3.81852405735993e-11 & 0.999999999980907 \tabularnewline
30 & 2.16696546353056e-11 & 4.33393092706112e-11 & 0.99999999997833 \tabularnewline
31 & 1.41703728469706e-11 & 2.83407456939411e-11 & 0.99999999998583 \tabularnewline
32 & 5.51593918059057e-12 & 1.10318783611811e-11 & 0.999999999994484 \tabularnewline
33 & 2.45292467131246e-12 & 4.90584934262492e-12 & 0.999999999997547 \tabularnewline
34 & 1.26589138451746e-12 & 2.53178276903491e-12 & 0.999999999998734 \tabularnewline
35 & 9.015429546489e-11 & 1.80308590929780e-10 & 0.999999999909846 \tabularnewline
36 & 8.1674834475244e-10 & 1.63349668950488e-09 & 0.999999999183252 \tabularnewline
37 & 2.70869271842375e-09 & 5.4173854368475e-09 & 0.999999997291307 \tabularnewline
38 & 4.07929231129996e-09 & 8.15858462259992e-09 & 0.999999995920708 \tabularnewline
39 & 1.19246499472185e-08 & 2.38492998944371e-08 & 0.99999998807535 \tabularnewline
40 & 5.98593285585271e-09 & 1.19718657117054e-08 & 0.999999994014067 \tabularnewline
41 & 4.09608423020220e-09 & 8.19216846040439e-09 & 0.999999995903916 \tabularnewline
42 & 1.80530425354315e-09 & 3.61060850708631e-09 & 0.999999998194696 \tabularnewline
43 & 4.64181317624015e-07 & 9.28362635248031e-07 & 0.999999535818682 \tabularnewline
44 & 0.225556161495110 & 0.451112322990219 & 0.77444383850489 \tabularnewline
45 & 0.999933456590562 & 0.000133086818876887 & 6.65434094384437e-05 \tabularnewline
46 & 0.999984652596758 & 3.06948064850012e-05 & 1.53474032425006e-05 \tabularnewline
47 & 0.999986263947679 & 2.74721046426459e-05 & 1.37360523213230e-05 \tabularnewline
48 & 0.999929472802998 & 0.000141054394003494 & 7.05271970017472e-05 \tabularnewline
49 & 0.999478207472525 & 0.00104358505494942 & 0.00052179252747471 \tabularnewline
50 & 0.995907631262439 & 0.00818473747512257 & 0.00409236873756129 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=107520&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]1.13598124113785e-06[/C][C]2.27196248227571e-06[/C][C]0.999998864018759[/C][/ROW]
[ROW][C]9[/C][C]9.7025212824279e-09[/C][C]1.94050425648558e-08[/C][C]0.999999990297479[/C][/ROW]
[ROW][C]10[/C][C]2.52824073213857e-09[/C][C]5.05648146427715e-09[/C][C]0.99999999747176[/C][/ROW]
[ROW][C]11[/C][C]5.54012850145514e-11[/C][C]1.10802570029103e-10[/C][C]0.9999999999446[/C][/ROW]
[ROW][C]12[/C][C]1.59918093481165e-09[/C][C]3.19836186962331e-09[/C][C]0.99999999840082[/C][/ROW]
[ROW][C]13[/C][C]2.97258277185938e-10[/C][C]5.94516554371876e-10[/C][C]0.999999999702742[/C][/ROW]
[ROW][C]14[/C][C]1.93145792500399e-11[/C][C]3.86291585000797e-11[/C][C]0.999999999980685[/C][/ROW]
[ROW][C]15[/C][C]1.38598836815930e-12[/C][C]2.77197673631861e-12[/C][C]0.999999999998614[/C][/ROW]
[ROW][C]16[/C][C]2.49644826937587e-10[/C][C]4.99289653875175e-10[/C][C]0.999999999750355[/C][/ROW]
[ROW][C]17[/C][C]1.21167577926635e-10[/C][C]2.42335155853271e-10[/C][C]0.999999999878832[/C][/ROW]
[ROW][C]18[/C][C]2.39715237498871e-11[/C][C]4.79430474997741e-11[/C][C]0.999999999976029[/C][/ROW]
[ROW][C]19[/C][C]6.10354653733489e-09[/C][C]1.22070930746698e-08[/C][C]0.999999993896453[/C][/ROW]
[ROW][C]20[/C][C]1.55240657835622e-09[/C][C]3.10481315671244e-09[/C][C]0.999999998447593[/C][/ROW]
[ROW][C]21[/C][C]3.87622631679294e-10[/C][C]7.75245263358588e-10[/C][C]0.999999999612377[/C][/ROW]
[ROW][C]22[/C][C]2.05100380557893e-10[/C][C]4.10200761115786e-10[/C][C]0.9999999997949[/C][/ROW]
[ROW][C]23[/C][C]1.68989075731191e-10[/C][C]3.37978151462382e-10[/C][C]0.99999999983101[/C][/ROW]
[ROW][C]24[/C][C]2.46514572704650e-10[/C][C]4.93029145409301e-10[/C][C]0.999999999753485[/C][/ROW]
[ROW][C]25[/C][C]1.30323316801563e-10[/C][C]2.60646633603125e-10[/C][C]0.999999999869677[/C][/ROW]
[ROW][C]26[/C][C]5.8593958269158e-11[/C][C]1.17187916538316e-10[/C][C]0.999999999941406[/C][/ROW]
[ROW][C]27[/C][C]1.53441913446854e-11[/C][C]3.06883826893707e-11[/C][C]0.999999999984656[/C][/ROW]
[ROW][C]28[/C][C]1.25077354724220e-11[/C][C]2.50154709448441e-11[/C][C]0.999999999987492[/C][/ROW]
[ROW][C]29[/C][C]1.90926202867996e-11[/C][C]3.81852405735993e-11[/C][C]0.999999999980907[/C][/ROW]
[ROW][C]30[/C][C]2.16696546353056e-11[/C][C]4.33393092706112e-11[/C][C]0.99999999997833[/C][/ROW]
[ROW][C]31[/C][C]1.41703728469706e-11[/C][C]2.83407456939411e-11[/C][C]0.99999999998583[/C][/ROW]
[ROW][C]32[/C][C]5.51593918059057e-12[/C][C]1.10318783611811e-11[/C][C]0.999999999994484[/C][/ROW]
[ROW][C]33[/C][C]2.45292467131246e-12[/C][C]4.90584934262492e-12[/C][C]0.999999999997547[/C][/ROW]
[ROW][C]34[/C][C]1.26589138451746e-12[/C][C]2.53178276903491e-12[/C][C]0.999999999998734[/C][/ROW]
[ROW][C]35[/C][C]9.015429546489e-11[/C][C]1.80308590929780e-10[/C][C]0.999999999909846[/C][/ROW]
[ROW][C]36[/C][C]8.1674834475244e-10[/C][C]1.63349668950488e-09[/C][C]0.999999999183252[/C][/ROW]
[ROW][C]37[/C][C]2.70869271842375e-09[/C][C]5.4173854368475e-09[/C][C]0.999999997291307[/C][/ROW]
[ROW][C]38[/C][C]4.07929231129996e-09[/C][C]8.15858462259992e-09[/C][C]0.999999995920708[/C][/ROW]
[ROW][C]39[/C][C]1.19246499472185e-08[/C][C]2.38492998944371e-08[/C][C]0.99999998807535[/C][/ROW]
[ROW][C]40[/C][C]5.98593285585271e-09[/C][C]1.19718657117054e-08[/C][C]0.999999994014067[/C][/ROW]
[ROW][C]41[/C][C]4.09608423020220e-09[/C][C]8.19216846040439e-09[/C][C]0.999999995903916[/C][/ROW]
[ROW][C]42[/C][C]1.80530425354315e-09[/C][C]3.61060850708631e-09[/C][C]0.999999998194696[/C][/ROW]
[ROW][C]43[/C][C]4.64181317624015e-07[/C][C]9.28362635248031e-07[/C][C]0.999999535818682[/C][/ROW]
[ROW][C]44[/C][C]0.225556161495110[/C][C]0.451112322990219[/C][C]0.77444383850489[/C][/ROW]
[ROW][C]45[/C][C]0.999933456590562[/C][C]0.000133086818876887[/C][C]6.65434094384437e-05[/C][/ROW]
[ROW][C]46[/C][C]0.999984652596758[/C][C]3.06948064850012e-05[/C][C]1.53474032425006e-05[/C][/ROW]
[ROW][C]47[/C][C]0.999986263947679[/C][C]2.74721046426459e-05[/C][C]1.37360523213230e-05[/C][/ROW]
[ROW][C]48[/C][C]0.999929472802998[/C][C]0.000141054394003494[/C][C]7.05271970017472e-05[/C][/ROW]
[ROW][C]49[/C][C]0.999478207472525[/C][C]0.00104358505494942[/C][C]0.00052179252747471[/C][/ROW]
[ROW][C]50[/C][C]0.995907631262439[/C][C]0.00818473747512257[/C][C]0.00409236873756129[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=107520&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=107520&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	1.13598124113785e-06	2.27196248227571e-06	0.999998864018759
9	9.7025212824279e-09	1.94050425648558e-08	0.999999990297479
10	2.52824073213857e-09	5.05648146427715e-09	0.99999999747176
11	5.54012850145514e-11	1.10802570029103e-10	0.9999999999446
12	1.59918093481165e-09	3.19836186962331e-09	0.99999999840082
13	2.97258277185938e-10	5.94516554371876e-10	0.999999999702742
14	1.93145792500399e-11	3.86291585000797e-11	0.999999999980685
15	1.38598836815930e-12	2.77197673631861e-12	0.999999999998614
16	2.49644826937587e-10	4.99289653875175e-10	0.999999999750355
17	1.21167577926635e-10	2.42335155853271e-10	0.999999999878832
18	2.39715237498871e-11	4.79430474997741e-11	0.999999999976029
19	6.10354653733489e-09	1.22070930746698e-08	0.999999993896453
20	1.55240657835622e-09	3.10481315671244e-09	0.999999998447593
21	3.87622631679294e-10	7.75245263358588e-10	0.999999999612377
22	2.05100380557893e-10	4.10200761115786e-10	0.9999999997949
23	1.68989075731191e-10	3.37978151462382e-10	0.99999999983101
24	2.46514572704650e-10	4.93029145409301e-10	0.999999999753485
25	1.30323316801563e-10	2.60646633603125e-10	0.999999999869677
26	5.8593958269158e-11	1.17187916538316e-10	0.999999999941406
27	1.53441913446854e-11	3.06883826893707e-11	0.999999999984656
28	1.25077354724220e-11	2.50154709448441e-11	0.999999999987492
29	1.90926202867996e-11	3.81852405735993e-11	0.999999999980907
30	2.16696546353056e-11	4.33393092706112e-11	0.99999999997833
31	1.41703728469706e-11	2.83407456939411e-11	0.99999999998583
32	5.51593918059057e-12	1.10318783611811e-11	0.999999999994484
33	2.45292467131246e-12	4.90584934262492e-12	0.999999999997547
34	1.26589138451746e-12	2.53178276903491e-12	0.999999999998734
35	9.015429546489e-11	1.80308590929780e-10	0.999999999909846
36	8.1674834475244e-10	1.63349668950488e-09	0.999999999183252
37	2.70869271842375e-09	5.4173854368475e-09	0.999999997291307
38	4.07929231129996e-09	8.15858462259992e-09	0.999999995920708
39	1.19246499472185e-08	2.38492998944371e-08	0.99999998807535
40	5.98593285585271e-09	1.19718657117054e-08	0.999999994014067
41	4.09608423020220e-09	8.19216846040439e-09	0.999999995903916
42	1.80530425354315e-09	3.61060850708631e-09	0.999999998194696
43	4.64181317624015e-07	9.28362635248031e-07	0.999999535818682
44	0.225556161495110	0.451112322990219	0.77444383850489
45	0.999933456590562	0.000133086818876887	6.65434094384437e-05
46	0.999984652596758	3.06948064850012e-05	1.53474032425006e-05
47	0.999986263947679	2.74721046426459e-05	1.37360523213230e-05
48	0.999929472802998	0.000141054394003494	7.05271970017472e-05
49	0.999478207472525	0.00104358505494942	0.00052179252747471
50	0.995907631262439	0.00818473747512257	0.00409236873756129

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	42	0.976744186046512	NOK
5% type I error level	42	0.976744186046512	NOK
10% type I error level	42	0.976744186046512	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 & 42 & 0.976744186046512 & NOK \tabularnewline
5% type I error level & 42 & 0.976744186046512 & NOK \tabularnewline
10% type I error level & 42 & 0.976744186046512 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=107520&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]42[/C][C]0.976744186046512[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]42[/C][C]0.976744186046512[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]42[/C][C]0.976744186046512[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=107520&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=107520&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	42	0.976744186046512	NOK
5% type I error level	42	0.976744186046512	NOK
10% type I error level	42	0.976744186046512	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 = 2 ; par2 = quantiles ; par3 = 2 ; par4 = no ;

Parameters (R input):

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

R code (references can be found in the software module):

library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,mysum$coefficients[i,1])
a<-table.element(a, round(mysum$coefficients[i,2],6))
a<-table.element(a, round(mysum$coefficients[i,3],4))
a<-table.element(a, round(mysum$coefficients[i,4],6))
a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, sqrt(mysum$r.squared))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, mysum$r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, mysum$adj.r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, mysum$fstatistic[1])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[3])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a, mysum$sigma)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, sum(myerror*myerror))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,x[i])
a<-table.element(a,x[i]-mysum$resid[i])
a<-table.element(a,mysum$resid[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
if (n > n25) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-values',header=TRUE)
a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'breakpoint index',header=TRUE)
a<-table.element(a,'greater',header=TRUE)
a<-table.element(a,'2-sided',header=TRUE)
a<-table.element(a,'less',header=TRUE)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
a<-table.element(a,mypoint,header=TRUE)
a<-table.element(a,gqarr[mypoint-kp3+1,1])
a<-table.element(a,gqarr[mypoint-kp3+1,2])
a<-table.element(a,gqarr[mypoint-kp3+1,3])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,numsignificant1)
a<-table.element(a,numsignificant1/numgqtests)
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,numsignificant5)
a<-table.element(a,numsignificant5/numgqtests)
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,numsignificant10)
a<-table.element(a,numsignificant10/numgqtests)
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code