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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Fri, 10 Dec 2010 14:15:08 +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/t1291990424ny41ywjp34sk4b3.htm/, Retrieved Mon, 29 Apr 2024 09:57:07 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=107705, Retrieved Mon, 29 Apr 2024 09:57:07 +0000

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Estimated Impact

233

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 09:31:08] [8a9a6f7c332640af31ddca253a8ded58]
-   P     [Multiple Regression] [Multiple Regressi...] [2010-12-10 14:04:56] [8a9a6f7c332640af31ddca253a8ded58]
-   P         [Multiple Regression] [Multilpe Regressi...] [2010-12-10 14:15:08] [df17410ebb98883e83037e1662207ccb] [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

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	9 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 & 9 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=107705&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]9 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=107705&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=107705&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	9 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

Multiple Linear Regression - Estimated Regression Equation
Schouwburgabonnement[t] = -111.154156194121 -0.138834616969009Bioscoop[t] -0.666006206665054Eendagsattracties[t] + 0.965145274240743DVDhuren[t] + 1.94875805403434Vrijetijdsbesteding[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Schouwburgabonnement[t] =  -111.154156194121 -0.138834616969009Bioscoop[t] -0.666006206665054Eendagsattracties[t] +  0.965145274240743DVDhuren[t] +  1.94875805403434Vrijetijdsbesteding[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=107705&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Schouwburgabonnement[t] =  -111.154156194121 -0.138834616969009Bioscoop[t] -0.666006206665054Eendagsattracties[t] +  0.965145274240743DVDhuren[t] +  1.94875805403434Vrijetijdsbesteding[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=107705&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=107705&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
Schouwburgabonnement[t] = -111.154156194121 -0.138834616969009Bioscoop[t] -0.666006206665054Eendagsattracties[t] + 0.965145274240743DVDhuren[t] + 1.94875805403434Vrijetijdsbesteding[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)	-111.154156194121	35.379075	-3.1418	0.002747	0.001374
Bioscoop	-0.138834616969009	0.106731	-1.3008	0.198956	0.099478
Eendagsattracties	-0.666006206665054	0.158233	-4.209	1e-04	5e-05
DVDhuren	0.965145274240743	0.437984	2.2036	0.031919	0.015959
Vrijetijdsbesteding	1.94875805403434	0.181752	10.7221	0	0

\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) & -111.154156194121 & 35.379075 & -3.1418 & 0.002747 & 0.001374 \tabularnewline
Bioscoop & -0.138834616969009 & 0.106731 & -1.3008 & 0.198956 & 0.099478 \tabularnewline
Eendagsattracties & -0.666006206665054 & 0.158233 & -4.209 & 1e-04 & 5e-05 \tabularnewline
DVDhuren & 0.965145274240743 & 0.437984 & 2.2036 & 0.031919 & 0.015959 \tabularnewline
Vrijetijdsbesteding & 1.94875805403434 & 0.181752 & 10.7221 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=107705&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]-111.154156194121[/C][C]35.379075[/C][C]-3.1418[/C][C]0.002747[/C][C]0.001374[/C][/ROW]
[ROW][C]Bioscoop[/C][C]-0.138834616969009[/C][C]0.106731[/C][C]-1.3008[/C][C]0.198956[/C][C]0.099478[/C][/ROW]
[ROW][C]Eendagsattracties[/C][C]-0.666006206665054[/C][C]0.158233[/C][C]-4.209[/C][C]1e-04[/C][C]5e-05[/C][/ROW]
[ROW][C]DVDhuren[/C][C]0.965145274240743[/C][C]0.437984[/C][C]2.2036[/C][C]0.031919[/C][C]0.015959[/C][/ROW]
[ROW][C]Vrijetijdsbesteding[/C][C]1.94875805403434[/C][C]0.181752[/C][C]10.7221[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=107705&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=107705&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)	-111.154156194121	35.379075	-3.1418	0.002747	0.001374
Bioscoop	-0.138834616969009	0.106731	-1.3008	0.198956	0.099478
Eendagsattracties	-0.666006206665054	0.158233	-4.209	1e-04	5e-05
DVDhuren	0.965145274240743	0.437984	2.2036	0.031919	0.015959
Vrijetijdsbesteding	1.94875805403434	0.181752	10.7221	0	0

Multiple Linear Regression - Regression Statistics
Multiple R	0.982168707956716
R-squared	0.964655370889366
Adjusted R-squared	0.961987851711204
F-TEST (value)	361.630153884919
F-TEST (DF numerator)	4
F-TEST (DF denominator)	53
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.52741979807648
Sum Squared Residuals	123.649595696468

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.982168707956716 \tabularnewline
R-squared & 0.964655370889366 \tabularnewline
Adjusted R-squared & 0.961987851711204 \tabularnewline
F-TEST (value) & 361.630153884919 \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 & 1.52741979807648 \tabularnewline
Sum Squared Residuals & 123.649595696468 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=107705&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.982168707956716[/C][/ROW]
[ROW][C]R-squared[/C][C]0.964655370889366[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.961987851711204[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]361.630153884919[/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]1.52741979807648[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]123.649595696468[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=107705&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=107705&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	0.982168707956716
R-squared	0.964655370889366
Adjusted R-squared	0.961987851711204
F-TEST (value)	361.630153884919
F-TEST (DF numerator)	4
F-TEST (DF denominator)	53
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.52741979807648
Sum Squared Residuals	123.649595696468

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	107.34	107.026917187518	0.313082812482223
2	107.34	108.293005163310	-0.95300516330991
3	107.34	108.273886932881	-0.9338869328806
4	107.34	107.427721174346	-0.0877211743459105
5	107.34	107.678843620704	-0.338843620704044
6	107.34	107.776281523406	-0.436281523405752
7	107.34	107.854047170512	-0.514047170511609
8	107.34	107.826385537673	-0.486385537673072
9	112.6	109.882417622207	2.71758237779292
10	112.6	110.260234709160	2.33976529084022
11	112.6	110.541604239134	2.05839576086619
12	112.6	110.999326433639	1.60067356636100
13	112.61	111.778829655253	0.83117034474726
14	112.61	112.070774013247	0.539225986753183
15	112.61	112.896661841962	-0.286661841962286
16	112.61	112.314812956513	0.295187043486589
17	112.61	112.450117969963	0.159882030037384
18	112.61	112.450117969963	0.159882030037384
19	112.61	112.887631426350	-0.277631426350456
20	112.61	114.251208039008	-1.64120803900792
21	118.65	116.317260926395	2.33273907360462
22	118.65	116.444057035319	2.20594296468068
23	118.65	115.944951618547	2.70504838145293
24	118.65	116.948739585951	1.70126041404886
25	114.29	116.286161847579	-1.99616184757945
26	114.29	116.958391038694	-2.66839103869355
27	114.29	117.170722999027	-2.88072299902651
28	114.29	115.849620874404	-1.55962087440412
29	114.29	115.941454658262	-1.65145465826241
30	114.29	116.689787420983	-2.39978742098255
31	114.29	117.534265361016	-3.24426536101576
32	114.29	118.448649689577	-4.15864968957707
33	123.33	122.008259661106	1.32174033889377
34	123.33	122.404127273587	0.92587272641265
35	123.33	122.798399062056	0.531600937943915
36	123.33	122.758891959638	0.571108040362074
37	123.33	122.92333600637	0.406663993630103
38	123.33	123.814615174542	-0.484615174541932
39	123.33	124.212725512703	-0.882725512702505
40	123.33	121.284071751495	2.04592824850540
41	123.33	121.498065787327	1.83193421267266
42	123.33	123.384348263726	-0.0543482637256264
43	123.33	123.721779759599	-0.391779759598824
44	123.33	124.323682215789	-0.993682215788958
45	129.03	128.205881498806	0.824118501193542
46	128.76	129.127544664185	-0.367544664184801
47	128.76	128.815928050595	-0.0559280505948416
48	128.76	128.979823919709	-0.219823919708622
49	128.76	129.002138926753	-0.24213892675302
50	128.76	129.614618498406	-0.854618498405737
51	128.76	128.439968690336	0.320031309663901
52	128.76	127.876208171417	0.883791828583244
53	128.76	127.950295834735	0.809704165265292
54	128.76	128.597569292678	0.162430707321695
55	128.76	129.091818241645	-0.331818241645451
56	128.76	129.579931130432	-0.81993113043168
57	132.63	131.450738862305	1.17926113769534
58	132.63	132.19034354756	0.43965645243994

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 107.34 & 107.026917187518 & 0.313082812482223 \tabularnewline
2 & 107.34 & 108.293005163310 & -0.95300516330991 \tabularnewline
3 & 107.34 & 108.273886932881 & -0.9338869328806 \tabularnewline
4 & 107.34 & 107.427721174346 & -0.0877211743459105 \tabularnewline
5 & 107.34 & 107.678843620704 & -0.338843620704044 \tabularnewline
6 & 107.34 & 107.776281523406 & -0.436281523405752 \tabularnewline
7 & 107.34 & 107.854047170512 & -0.514047170511609 \tabularnewline
8 & 107.34 & 107.826385537673 & -0.486385537673072 \tabularnewline
9 & 112.6 & 109.882417622207 & 2.71758237779292 \tabularnewline
10 & 112.6 & 110.260234709160 & 2.33976529084022 \tabularnewline
11 & 112.6 & 110.541604239134 & 2.05839576086619 \tabularnewline
12 & 112.6 & 110.999326433639 & 1.60067356636100 \tabularnewline
13 & 112.61 & 111.778829655253 & 0.83117034474726 \tabularnewline
14 & 112.61 & 112.070774013247 & 0.539225986753183 \tabularnewline
15 & 112.61 & 112.896661841962 & -0.286661841962286 \tabularnewline
16 & 112.61 & 112.314812956513 & 0.295187043486589 \tabularnewline
17 & 112.61 & 112.450117969963 & 0.159882030037384 \tabularnewline
18 & 112.61 & 112.450117969963 & 0.159882030037384 \tabularnewline
19 & 112.61 & 112.887631426350 & -0.277631426350456 \tabularnewline
20 & 112.61 & 114.251208039008 & -1.64120803900792 \tabularnewline
21 & 118.65 & 116.317260926395 & 2.33273907360462 \tabularnewline
22 & 118.65 & 116.444057035319 & 2.20594296468068 \tabularnewline
23 & 118.65 & 115.944951618547 & 2.70504838145293 \tabularnewline
24 & 118.65 & 116.948739585951 & 1.70126041404886 \tabularnewline
25 & 114.29 & 116.286161847579 & -1.99616184757945 \tabularnewline
26 & 114.29 & 116.958391038694 & -2.66839103869355 \tabularnewline
27 & 114.29 & 117.170722999027 & -2.88072299902651 \tabularnewline
28 & 114.29 & 115.849620874404 & -1.55962087440412 \tabularnewline
29 & 114.29 & 115.941454658262 & -1.65145465826241 \tabularnewline
30 & 114.29 & 116.689787420983 & -2.39978742098255 \tabularnewline
31 & 114.29 & 117.534265361016 & -3.24426536101576 \tabularnewline
32 & 114.29 & 118.448649689577 & -4.15864968957707 \tabularnewline
33 & 123.33 & 122.008259661106 & 1.32174033889377 \tabularnewline
34 & 123.33 & 122.404127273587 & 0.92587272641265 \tabularnewline
35 & 123.33 & 122.798399062056 & 0.531600937943915 \tabularnewline
36 & 123.33 & 122.758891959638 & 0.571108040362074 \tabularnewline
37 & 123.33 & 122.92333600637 & 0.406663993630103 \tabularnewline
38 & 123.33 & 123.814615174542 & -0.484615174541932 \tabularnewline
39 & 123.33 & 124.212725512703 & -0.882725512702505 \tabularnewline
40 & 123.33 & 121.284071751495 & 2.04592824850540 \tabularnewline
41 & 123.33 & 121.498065787327 & 1.83193421267266 \tabularnewline
42 & 123.33 & 123.384348263726 & -0.0543482637256264 \tabularnewline
43 & 123.33 & 123.721779759599 & -0.391779759598824 \tabularnewline
44 & 123.33 & 124.323682215789 & -0.993682215788958 \tabularnewline
45 & 129.03 & 128.205881498806 & 0.824118501193542 \tabularnewline
46 & 128.76 & 129.127544664185 & -0.367544664184801 \tabularnewline
47 & 128.76 & 128.815928050595 & -0.0559280505948416 \tabularnewline
48 & 128.76 & 128.979823919709 & -0.219823919708622 \tabularnewline
49 & 128.76 & 129.002138926753 & -0.24213892675302 \tabularnewline
50 & 128.76 & 129.614618498406 & -0.854618498405737 \tabularnewline
51 & 128.76 & 128.439968690336 & 0.320031309663901 \tabularnewline
52 & 128.76 & 127.876208171417 & 0.883791828583244 \tabularnewline
53 & 128.76 & 127.950295834735 & 0.809704165265292 \tabularnewline
54 & 128.76 & 128.597569292678 & 0.162430707321695 \tabularnewline
55 & 128.76 & 129.091818241645 & -0.331818241645451 \tabularnewline
56 & 128.76 & 129.579931130432 & -0.81993113043168 \tabularnewline
57 & 132.63 & 131.450738862305 & 1.17926113769534 \tabularnewline
58 & 132.63 & 132.19034354756 & 0.43965645243994 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=107705&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]107.34[/C][C]107.026917187518[/C][C]0.313082812482223[/C][/ROW]
[ROW][C]2[/C][C]107.34[/C][C]108.293005163310[/C][C]-0.95300516330991[/C][/ROW]
[ROW][C]3[/C][C]107.34[/C][C]108.273886932881[/C][C]-0.9338869328806[/C][/ROW]
[ROW][C]4[/C][C]107.34[/C][C]107.427721174346[/C][C]-0.0877211743459105[/C][/ROW]
[ROW][C]5[/C][C]107.34[/C][C]107.678843620704[/C][C]-0.338843620704044[/C][/ROW]
[ROW][C]6[/C][C]107.34[/C][C]107.776281523406[/C][C]-0.436281523405752[/C][/ROW]
[ROW][C]7[/C][C]107.34[/C][C]107.854047170512[/C][C]-0.514047170511609[/C][/ROW]
[ROW][C]8[/C][C]107.34[/C][C]107.826385537673[/C][C]-0.486385537673072[/C][/ROW]
[ROW][C]9[/C][C]112.6[/C][C]109.882417622207[/C][C]2.71758237779292[/C][/ROW]
[ROW][C]10[/C][C]112.6[/C][C]110.260234709160[/C][C]2.33976529084022[/C][/ROW]
[ROW][C]11[/C][C]112.6[/C][C]110.541604239134[/C][C]2.05839576086619[/C][/ROW]
[ROW][C]12[/C][C]112.6[/C][C]110.999326433639[/C][C]1.60067356636100[/C][/ROW]
[ROW][C]13[/C][C]112.61[/C][C]111.778829655253[/C][C]0.83117034474726[/C][/ROW]
[ROW][C]14[/C][C]112.61[/C][C]112.070774013247[/C][C]0.539225986753183[/C][/ROW]
[ROW][C]15[/C][C]112.61[/C][C]112.896661841962[/C][C]-0.286661841962286[/C][/ROW]
[ROW][C]16[/C][C]112.61[/C][C]112.314812956513[/C][C]0.295187043486589[/C][/ROW]
[ROW][C]17[/C][C]112.61[/C][C]112.450117969963[/C][C]0.159882030037384[/C][/ROW]
[ROW][C]18[/C][C]112.61[/C][C]112.450117969963[/C][C]0.159882030037384[/C][/ROW]
[ROW][C]19[/C][C]112.61[/C][C]112.887631426350[/C][C]-0.277631426350456[/C][/ROW]
[ROW][C]20[/C][C]112.61[/C][C]114.251208039008[/C][C]-1.64120803900792[/C][/ROW]
[ROW][C]21[/C][C]118.65[/C][C]116.317260926395[/C][C]2.33273907360462[/C][/ROW]
[ROW][C]22[/C][C]118.65[/C][C]116.444057035319[/C][C]2.20594296468068[/C][/ROW]
[ROW][C]23[/C][C]118.65[/C][C]115.944951618547[/C][C]2.70504838145293[/C][/ROW]
[ROW][C]24[/C][C]118.65[/C][C]116.948739585951[/C][C]1.70126041404886[/C][/ROW]
[ROW][C]25[/C][C]114.29[/C][C]116.286161847579[/C][C]-1.99616184757945[/C][/ROW]
[ROW][C]26[/C][C]114.29[/C][C]116.958391038694[/C][C]-2.66839103869355[/C][/ROW]
[ROW][C]27[/C][C]114.29[/C][C]117.170722999027[/C][C]-2.88072299902651[/C][/ROW]
[ROW][C]28[/C][C]114.29[/C][C]115.849620874404[/C][C]-1.55962087440412[/C][/ROW]
[ROW][C]29[/C][C]114.29[/C][C]115.941454658262[/C][C]-1.65145465826241[/C][/ROW]
[ROW][C]30[/C][C]114.29[/C][C]116.689787420983[/C][C]-2.39978742098255[/C][/ROW]
[ROW][C]31[/C][C]114.29[/C][C]117.534265361016[/C][C]-3.24426536101576[/C][/ROW]
[ROW][C]32[/C][C]114.29[/C][C]118.448649689577[/C][C]-4.15864968957707[/C][/ROW]
[ROW][C]33[/C][C]123.33[/C][C]122.008259661106[/C][C]1.32174033889377[/C][/ROW]
[ROW][C]34[/C][C]123.33[/C][C]122.404127273587[/C][C]0.92587272641265[/C][/ROW]
[ROW][C]35[/C][C]123.33[/C][C]122.798399062056[/C][C]0.531600937943915[/C][/ROW]
[ROW][C]36[/C][C]123.33[/C][C]122.758891959638[/C][C]0.571108040362074[/C][/ROW]
[ROW][C]37[/C][C]123.33[/C][C]122.92333600637[/C][C]0.406663993630103[/C][/ROW]
[ROW][C]38[/C][C]123.33[/C][C]123.814615174542[/C][C]-0.484615174541932[/C][/ROW]
[ROW][C]39[/C][C]123.33[/C][C]124.212725512703[/C][C]-0.882725512702505[/C][/ROW]
[ROW][C]40[/C][C]123.33[/C][C]121.284071751495[/C][C]2.04592824850540[/C][/ROW]
[ROW][C]41[/C][C]123.33[/C][C]121.498065787327[/C][C]1.83193421267266[/C][/ROW]
[ROW][C]42[/C][C]123.33[/C][C]123.384348263726[/C][C]-0.0543482637256264[/C][/ROW]
[ROW][C]43[/C][C]123.33[/C][C]123.721779759599[/C][C]-0.391779759598824[/C][/ROW]
[ROW][C]44[/C][C]123.33[/C][C]124.323682215789[/C][C]-0.993682215788958[/C][/ROW]
[ROW][C]45[/C][C]129.03[/C][C]128.205881498806[/C][C]0.824118501193542[/C][/ROW]
[ROW][C]46[/C][C]128.76[/C][C]129.127544664185[/C][C]-0.367544664184801[/C][/ROW]
[ROW][C]47[/C][C]128.76[/C][C]128.815928050595[/C][C]-0.0559280505948416[/C][/ROW]
[ROW][C]48[/C][C]128.76[/C][C]128.979823919709[/C][C]-0.219823919708622[/C][/ROW]
[ROW][C]49[/C][C]128.76[/C][C]129.002138926753[/C][C]-0.24213892675302[/C][/ROW]
[ROW][C]50[/C][C]128.76[/C][C]129.614618498406[/C][C]-0.854618498405737[/C][/ROW]
[ROW][C]51[/C][C]128.76[/C][C]128.439968690336[/C][C]0.320031309663901[/C][/ROW]
[ROW][C]52[/C][C]128.76[/C][C]127.876208171417[/C][C]0.883791828583244[/C][/ROW]
[ROW][C]53[/C][C]128.76[/C][C]127.950295834735[/C][C]0.809704165265292[/C][/ROW]
[ROW][C]54[/C][C]128.76[/C][C]128.597569292678[/C][C]0.162430707321695[/C][/ROW]
[ROW][C]55[/C][C]128.76[/C][C]129.091818241645[/C][C]-0.331818241645451[/C][/ROW]
[ROW][C]56[/C][C]128.76[/C][C]129.579931130432[/C][C]-0.81993113043168[/C][/ROW]
[ROW][C]57[/C][C]132.63[/C][C]131.450738862305[/C][C]1.17926113769534[/C][/ROW]
[ROW][C]58[/C][C]132.63[/C][C]132.19034354756[/C][C]0.43965645243994[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=107705&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=107705&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	107.34	107.026917187518	0.313082812482223
2	107.34	108.293005163310	-0.95300516330991
3	107.34	108.273886932881	-0.9338869328806
4	107.34	107.427721174346	-0.0877211743459105
5	107.34	107.678843620704	-0.338843620704044
6	107.34	107.776281523406	-0.436281523405752
7	107.34	107.854047170512	-0.514047170511609
8	107.34	107.826385537673	-0.486385537673072
9	112.6	109.882417622207	2.71758237779292
10	112.6	110.260234709160	2.33976529084022
11	112.6	110.541604239134	2.05839576086619
12	112.6	110.999326433639	1.60067356636100
13	112.61	111.778829655253	0.83117034474726
14	112.61	112.070774013247	0.539225986753183
15	112.61	112.896661841962	-0.286661841962286
16	112.61	112.314812956513	0.295187043486589
17	112.61	112.450117969963	0.159882030037384
18	112.61	112.450117969963	0.159882030037384
19	112.61	112.887631426350	-0.277631426350456
20	112.61	114.251208039008	-1.64120803900792
21	118.65	116.317260926395	2.33273907360462
22	118.65	116.444057035319	2.20594296468068
23	118.65	115.944951618547	2.70504838145293
24	118.65	116.948739585951	1.70126041404886
25	114.29	116.286161847579	-1.99616184757945
26	114.29	116.958391038694	-2.66839103869355
27	114.29	117.170722999027	-2.88072299902651
28	114.29	115.849620874404	-1.55962087440412
29	114.29	115.941454658262	-1.65145465826241
30	114.29	116.689787420983	-2.39978742098255
31	114.29	117.534265361016	-3.24426536101576
32	114.29	118.448649689577	-4.15864968957707
33	123.33	122.008259661106	1.32174033889377
34	123.33	122.404127273587	0.92587272641265
35	123.33	122.798399062056	0.531600937943915
36	123.33	122.758891959638	0.571108040362074
37	123.33	122.92333600637	0.406663993630103
38	123.33	123.814615174542	-0.484615174541932
39	123.33	124.212725512703	-0.882725512702505
40	123.33	121.284071751495	2.04592824850540
41	123.33	121.498065787327	1.83193421267266
42	123.33	123.384348263726	-0.0543482637256264
43	123.33	123.721779759599	-0.391779759598824
44	123.33	124.323682215789	-0.993682215788958
45	129.03	128.205881498806	0.824118501193542
46	128.76	129.127544664185	-0.367544664184801
47	128.76	128.815928050595	-0.0559280505948416
48	128.76	128.979823919709	-0.219823919708622
49	128.76	129.002138926753	-0.24213892675302
50	128.76	129.614618498406	-0.854618498405737
51	128.76	128.439968690336	0.320031309663901
52	128.76	127.876208171417	0.883791828583244
53	128.76	127.950295834735	0.809704165265292
54	128.76	128.597569292678	0.162430707321695
55	128.76	129.091818241645	-0.331818241645451
56	128.76	129.579931130432	-0.81993113043168
57	132.63	131.450738862305	1.17926113769534
58	132.63	132.19034354756	0.43965645243994

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
8	0	0	1
9	0.00221863467358035	0.00443726934716069	0.99778136532642
10	0.00387637260973758	0.00775274521947517	0.996123627390262
11	0.00125744809800876	0.00251489619601752	0.99874255190199
12	0.0144968695595750	0.0289937391191500	0.985503130440425
13	0.0329638367444320	0.0659276734888641	0.967036163255568
14	0.0347528448175066	0.0695056896350131	0.965247155182493
15	0.0187916551382971	0.0375833102765943	0.981208344861703
16	0.0103691700412704	0.0207383400825407	0.98963082995873
17	0.00522657437216752	0.0104531487443350	0.994773425627832
18	0.00252004950709234	0.00504009901418468	0.997479950492908
19	0.00152700931555798	0.00305401863111596	0.998472990684442
20	0.00782709921445009	0.0156541984289002	0.99217290078555
21	0.00790673867621891	0.0158134773524378	0.992093261323781
22	0.0117063748473429	0.0234127496946857	0.988293625152657
23	0.0592748571553909	0.118549714310782	0.94072514284461
24	0.23633032914009	0.47266065828018	0.76366967085991
25	0.676766887628902	0.646466224742196	0.323233112371098
26	0.879834478753871	0.240331042492258	0.120165521246129
27	0.92830439941046	0.143391201179080	0.0716956005895402
28	0.903892394884816	0.192215210230368	0.096107605115184
29	0.877887457200315	0.24422508559937	0.122112542799685
30	0.856072399546703	0.287855200906595	0.143927600453297
31	0.901723495277021	0.196553009445957	0.0982765047229785
32	0.998274466503103	0.00345106699379431	0.00172553349689716
33	0.997863283398662	0.00427343320267568	0.00213671660133784
34	0.997935157546327	0.00412968490734691	0.00206484245367346
35	0.998280666339612	0.00343866732077679	0.00171933366038840
36	0.997974123152594	0.00405175369481125	0.00202587684740563
37	0.997359625053978	0.00528074989204477	0.00264037494602239
38	0.99469858968265	0.0106028206347017	0.00530141031735086
39	0.989486427123236	0.0210271457535285	0.0105135728767643
40	0.996701281156467	0.00659743768706599	0.00329871884353299
41	0.999438916370973	0.00112216725805409	0.000561083629027047
42	0.998554567598187	0.00289086480362656	0.00144543240181328
43	0.99631669576534	0.0073666084693204	0.0036833042346602
44	0.999141552519935	0.00171689496013013	0.000858447480065063
45	0.999364530149377	0.00127093970124689	0.000635469850623445
46	0.998773355189448	0.00245328962110346	0.00122664481055173
47	0.997214089621967	0.00557182075606572	0.00278591037803286
48	0.99060159927498	0.0187968014500397	0.00939840072501983
49	0.96893438313358	0.06213123373284	0.03106561686642
50	0.91363097680653	0.17273804638694	0.08636902319347

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
8 & 0 & 0 & 1 \tabularnewline
9 & 0.00221863467358035 & 0.00443726934716069 & 0.99778136532642 \tabularnewline
10 & 0.00387637260973758 & 0.00775274521947517 & 0.996123627390262 \tabularnewline
11 & 0.00125744809800876 & 0.00251489619601752 & 0.99874255190199 \tabularnewline
12 & 0.0144968695595750 & 0.0289937391191500 & 0.985503130440425 \tabularnewline
13 & 0.0329638367444320 & 0.0659276734888641 & 0.967036163255568 \tabularnewline
14 & 0.0347528448175066 & 0.0695056896350131 & 0.965247155182493 \tabularnewline
15 & 0.0187916551382971 & 0.0375833102765943 & 0.981208344861703 \tabularnewline
16 & 0.0103691700412704 & 0.0207383400825407 & 0.98963082995873 \tabularnewline
17 & 0.00522657437216752 & 0.0104531487443350 & 0.994773425627832 \tabularnewline
18 & 0.00252004950709234 & 0.00504009901418468 & 0.997479950492908 \tabularnewline
19 & 0.00152700931555798 & 0.00305401863111596 & 0.998472990684442 \tabularnewline
20 & 0.00782709921445009 & 0.0156541984289002 & 0.99217290078555 \tabularnewline
21 & 0.00790673867621891 & 0.0158134773524378 & 0.992093261323781 \tabularnewline
22 & 0.0117063748473429 & 0.0234127496946857 & 0.988293625152657 \tabularnewline
23 & 0.0592748571553909 & 0.118549714310782 & 0.94072514284461 \tabularnewline
24 & 0.23633032914009 & 0.47266065828018 & 0.76366967085991 \tabularnewline
25 & 0.676766887628902 & 0.646466224742196 & 0.323233112371098 \tabularnewline
26 & 0.879834478753871 & 0.240331042492258 & 0.120165521246129 \tabularnewline
27 & 0.92830439941046 & 0.143391201179080 & 0.0716956005895402 \tabularnewline
28 & 0.903892394884816 & 0.192215210230368 & 0.096107605115184 \tabularnewline
29 & 0.877887457200315 & 0.24422508559937 & 0.122112542799685 \tabularnewline
30 & 0.856072399546703 & 0.287855200906595 & 0.143927600453297 \tabularnewline
31 & 0.901723495277021 & 0.196553009445957 & 0.0982765047229785 \tabularnewline
32 & 0.998274466503103 & 0.00345106699379431 & 0.00172553349689716 \tabularnewline
33 & 0.997863283398662 & 0.00427343320267568 & 0.00213671660133784 \tabularnewline
34 & 0.997935157546327 & 0.00412968490734691 & 0.00206484245367346 \tabularnewline
35 & 0.998280666339612 & 0.00343866732077679 & 0.00171933366038840 \tabularnewline
36 & 0.997974123152594 & 0.00405175369481125 & 0.00202587684740563 \tabularnewline
37 & 0.997359625053978 & 0.00528074989204477 & 0.00264037494602239 \tabularnewline
38 & 0.99469858968265 & 0.0106028206347017 & 0.00530141031735086 \tabularnewline
39 & 0.989486427123236 & 0.0210271457535285 & 0.0105135728767643 \tabularnewline
40 & 0.996701281156467 & 0.00659743768706599 & 0.00329871884353299 \tabularnewline
41 & 0.999438916370973 & 0.00112216725805409 & 0.000561083629027047 \tabularnewline
42 & 0.998554567598187 & 0.00289086480362656 & 0.00144543240181328 \tabularnewline
43 & 0.99631669576534 & 0.0073666084693204 & 0.0036833042346602 \tabularnewline
44 & 0.999141552519935 & 0.00171689496013013 & 0.000858447480065063 \tabularnewline
45 & 0.999364530149377 & 0.00127093970124689 & 0.000635469850623445 \tabularnewline
46 & 0.998773355189448 & 0.00245328962110346 & 0.00122664481055173 \tabularnewline
47 & 0.997214089621967 & 0.00557182075606572 & 0.00278591037803286 \tabularnewline
48 & 0.99060159927498 & 0.0187968014500397 & 0.00939840072501983 \tabularnewline
49 & 0.96893438313358 & 0.06213123373284 & 0.03106561686642 \tabularnewline
50 & 0.91363097680653 & 0.17273804638694 & 0.08636902319347 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=107705&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[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]9[/C][C]0.00221863467358035[/C][C]0.00443726934716069[/C][C]0.99778136532642[/C][/ROW]
[ROW][C]10[/C][C]0.00387637260973758[/C][C]0.00775274521947517[/C][C]0.996123627390262[/C][/ROW]
[ROW][C]11[/C][C]0.00125744809800876[/C][C]0.00251489619601752[/C][C]0.99874255190199[/C][/ROW]
[ROW][C]12[/C][C]0.0144968695595750[/C][C]0.0289937391191500[/C][C]0.985503130440425[/C][/ROW]
[ROW][C]13[/C][C]0.0329638367444320[/C][C]0.0659276734888641[/C][C]0.967036163255568[/C][/ROW]
[ROW][C]14[/C][C]0.0347528448175066[/C][C]0.0695056896350131[/C][C]0.965247155182493[/C][/ROW]
[ROW][C]15[/C][C]0.0187916551382971[/C][C]0.0375833102765943[/C][C]0.981208344861703[/C][/ROW]
[ROW][C]16[/C][C]0.0103691700412704[/C][C]0.0207383400825407[/C][C]0.98963082995873[/C][/ROW]
[ROW][C]17[/C][C]0.00522657437216752[/C][C]0.0104531487443350[/C][C]0.994773425627832[/C][/ROW]
[ROW][C]18[/C][C]0.00252004950709234[/C][C]0.00504009901418468[/C][C]0.997479950492908[/C][/ROW]
[ROW][C]19[/C][C]0.00152700931555798[/C][C]0.00305401863111596[/C][C]0.998472990684442[/C][/ROW]
[ROW][C]20[/C][C]0.00782709921445009[/C][C]0.0156541984289002[/C][C]0.99217290078555[/C][/ROW]
[ROW][C]21[/C][C]0.00790673867621891[/C][C]0.0158134773524378[/C][C]0.992093261323781[/C][/ROW]
[ROW][C]22[/C][C]0.0117063748473429[/C][C]0.0234127496946857[/C][C]0.988293625152657[/C][/ROW]
[ROW][C]23[/C][C]0.0592748571553909[/C][C]0.118549714310782[/C][C]0.94072514284461[/C][/ROW]
[ROW][C]24[/C][C]0.23633032914009[/C][C]0.47266065828018[/C][C]0.76366967085991[/C][/ROW]
[ROW][C]25[/C][C]0.676766887628902[/C][C]0.646466224742196[/C][C]0.323233112371098[/C][/ROW]
[ROW][C]26[/C][C]0.879834478753871[/C][C]0.240331042492258[/C][C]0.120165521246129[/C][/ROW]
[ROW][C]27[/C][C]0.92830439941046[/C][C]0.143391201179080[/C][C]0.0716956005895402[/C][/ROW]
[ROW][C]28[/C][C]0.903892394884816[/C][C]0.192215210230368[/C][C]0.096107605115184[/C][/ROW]
[ROW][C]29[/C][C]0.877887457200315[/C][C]0.24422508559937[/C][C]0.122112542799685[/C][/ROW]
[ROW][C]30[/C][C]0.856072399546703[/C][C]0.287855200906595[/C][C]0.143927600453297[/C][/ROW]
[ROW][C]31[/C][C]0.901723495277021[/C][C]0.196553009445957[/C][C]0.0982765047229785[/C][/ROW]
[ROW][C]32[/C][C]0.998274466503103[/C][C]0.00345106699379431[/C][C]0.00172553349689716[/C][/ROW]
[ROW][C]33[/C][C]0.997863283398662[/C][C]0.00427343320267568[/C][C]0.00213671660133784[/C][/ROW]
[ROW][C]34[/C][C]0.997935157546327[/C][C]0.00412968490734691[/C][C]0.00206484245367346[/C][/ROW]
[ROW][C]35[/C][C]0.998280666339612[/C][C]0.00343866732077679[/C][C]0.00171933366038840[/C][/ROW]
[ROW][C]36[/C][C]0.997974123152594[/C][C]0.00405175369481125[/C][C]0.00202587684740563[/C][/ROW]
[ROW][C]37[/C][C]0.997359625053978[/C][C]0.00528074989204477[/C][C]0.00264037494602239[/C][/ROW]
[ROW][C]38[/C][C]0.99469858968265[/C][C]0.0106028206347017[/C][C]0.00530141031735086[/C][/ROW]
[ROW][C]39[/C][C]0.989486427123236[/C][C]0.0210271457535285[/C][C]0.0105135728767643[/C][/ROW]
[ROW][C]40[/C][C]0.996701281156467[/C][C]0.00659743768706599[/C][C]0.00329871884353299[/C][/ROW]
[ROW][C]41[/C][C]0.999438916370973[/C][C]0.00112216725805409[/C][C]0.000561083629027047[/C][/ROW]
[ROW][C]42[/C][C]0.998554567598187[/C][C]0.00289086480362656[/C][C]0.00144543240181328[/C][/ROW]
[ROW][C]43[/C][C]0.99631669576534[/C][C]0.0073666084693204[/C][C]0.0036833042346602[/C][/ROW]
[ROW][C]44[/C][C]0.999141552519935[/C][C]0.00171689496013013[/C][C]0.000858447480065063[/C][/ROW]
[ROW][C]45[/C][C]0.999364530149377[/C][C]0.00127093970124689[/C][C]0.000635469850623445[/C][/ROW]
[ROW][C]46[/C][C]0.998773355189448[/C][C]0.00245328962110346[/C][C]0.00122664481055173[/C][/ROW]
[ROW][C]47[/C][C]0.997214089621967[/C][C]0.00557182075606572[/C][C]0.00278591037803286[/C][/ROW]
[ROW][C]48[/C][C]0.99060159927498[/C][C]0.0187968014500397[/C][C]0.00939840072501983[/C][/ROW]
[ROW][C]49[/C][C]0.96893438313358[/C][C]0.06213123373284[/C][C]0.03106561686642[/C][/ROW]
[ROW][C]50[/C][C]0.91363097680653[/C][C]0.17273804638694[/C][C]0.08636902319347[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=107705&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=107705&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	0	1
9	0.00221863467358035	0.00443726934716069	0.99778136532642
10	0.00387637260973758	0.00775274521947517	0.996123627390262
11	0.00125744809800876	0.00251489619601752	0.99874255190199
12	0.0144968695595750	0.0289937391191500	0.985503130440425
13	0.0329638367444320	0.0659276734888641	0.967036163255568
14	0.0347528448175066	0.0695056896350131	0.965247155182493
15	0.0187916551382971	0.0375833102765943	0.981208344861703
16	0.0103691700412704	0.0207383400825407	0.98963082995873
17	0.00522657437216752	0.0104531487443350	0.994773425627832
18	0.00252004950709234	0.00504009901418468	0.997479950492908
19	0.00152700931555798	0.00305401863111596	0.998472990684442
20	0.00782709921445009	0.0156541984289002	0.99217290078555
21	0.00790673867621891	0.0158134773524378	0.992093261323781
22	0.0117063748473429	0.0234127496946857	0.988293625152657
23	0.0592748571553909	0.118549714310782	0.94072514284461
24	0.23633032914009	0.47266065828018	0.76366967085991
25	0.676766887628902	0.646466224742196	0.323233112371098
26	0.879834478753871	0.240331042492258	0.120165521246129
27	0.92830439941046	0.143391201179080	0.0716956005895402
28	0.903892394884816	0.192215210230368	0.096107605115184
29	0.877887457200315	0.24422508559937	0.122112542799685
30	0.856072399546703	0.287855200906595	0.143927600453297
31	0.901723495277021	0.196553009445957	0.0982765047229785
32	0.998274466503103	0.00345106699379431	0.00172553349689716
33	0.997863283398662	0.00427343320267568	0.00213671660133784
34	0.997935157546327	0.00412968490734691	0.00206484245367346
35	0.998280666339612	0.00343866732077679	0.00171933366038840
36	0.997974123152594	0.00405175369481125	0.00202587684740563
37	0.997359625053978	0.00528074989204477	0.00264037494602239
38	0.99469858968265	0.0106028206347017	0.00530141031735086
39	0.989486427123236	0.0210271457535285	0.0105135728767643
40	0.996701281156467	0.00659743768706599	0.00329871884353299
41	0.999438916370973	0.00112216725805409	0.000561083629027047
42	0.998554567598187	0.00289086480362656	0.00144543240181328
43	0.99631669576534	0.0073666084693204	0.0036833042346602
44	0.999141552519935	0.00171689496013013	0.000858447480065063
45	0.999364530149377	0.00127093970124689	0.000635469850623445
46	0.998773355189448	0.00245328962110346	0.00122664481055173
47	0.997214089621967	0.00557182075606572	0.00278591037803286
48	0.99060159927498	0.0187968014500397	0.00939840072501983
49	0.96893438313358	0.06213123373284	0.03106561686642
50	0.91363097680653	0.17273804638694	0.08636902319347

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	20	0.465116279069767	NOK
5% type I error level	30	0.697674418604651	NOK
10% type I error level	33	0.767441860465116	NOK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 20 & 0.465116279069767 & NOK \tabularnewline
5% type I error level & 30 & 0.697674418604651 & NOK \tabularnewline
10% type I error level & 33 & 0.767441860465116 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=107705&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]20[/C][C]0.465116279069767[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]30[/C][C]0.697674418604651[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]33[/C][C]0.767441860465116[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=107705&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=107705&T=6

As an alternative you can also use a QR Code:

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	20	0.465116279069767	NOK
5% type I error level	30	0.697674418604651	NOK
10% type I error level	33	0.767441860465116	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 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 2 ; 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