Repository of Reproducible Computations

Free Statistics

of Irreproducible Research!

Author's title

Author

*The author of this computation has been verified*

R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Tue, 24 Nov 2009 11:24:19 -0700

Cite this page as follows

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2009/Nov/24/t12590871317jkygbwgjhk4zew.htm/, Retrieved Mon, 30 Jun 2025 15:18:02 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=59206, Retrieved Mon, 30 Jun 2025 15:18:02 +0000

QR Codes:

Paste this QR Code to cite your computation.

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

304

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

-     [Multiple Regression] [Q1 The Seatbeltlaw] [2007-11-14 19:27:43] [8cd6641b921d30ebe00b648d1481bba0]
- RMPD  [Multiple Regression] [Seatbelt] [2009-11-12 13:54:52] [b98453cac15ba1066b407e146608df68]
-   PD    [Multiple Regression] [] [2009-11-20 13:55:26] [5482608004c1d7bbf873930172393a2d]
-   PD        [Multiple Regression] [workshop 7/module1] [2009-11-24 18:24:19] [f94f05f163a3ee3ab544c4fef41db0eb] [Current]

Feedback Forum

Post a new message

Dataseries X:

Download CSV

Histogram

Boxplots

114.08	136.49
112.95	142.62
135.31	141.71
134.31	149.51
133.03	147.39
140.11	131.96
124.69	136.38
131.68	127.34
150.95	133.85
137.26	125.14
130.51	141.25
143.15	149.32
118.01	120.92
122.56	134.85
147.97	131.93
135.74	134.22
151.62	143.07
154.82	145.37
145.59	134.32
147.12	126.31
175.86	162.21
140.66	124.09
152.69	153.91
154.38	154.34
132.45	138.70
136.44	150.98
153.24	146.39
154.11	178.30
155.93	168.23
142.53	162.52
148.73	158.86
147.73	152.17
166.79	171.01
144.30	171.49
156.07	189.62
161.70	177.46
152.10	179.98
140.45	156.96
155.56	167.89
174.53	194.78
167.16	192.78
159.48	165.06
173.22	196.60
176.13	151.64
180.31	187.02
185.84	210.99
169.43	219.08
195.25	235.68
174.99	241.44
156.42	187.46
182.08	229.57
182.00	208.44
153.28	215.09
136.72	217.00
130.19	171.08
132.04	178.41
143.89	196.34
133.38	172.11
127.98	154.93
150.45	182.26
133.55	181.74

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	4 seconds
R Server	'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 & 4 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=59206&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]4 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=59206&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=59206&T=0

As an alternative you can also use a QR Code:

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

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	4 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

Multiple Linear Regression - Estimated Regression Equation
InvoerEU[t] = + 82.0823843016541 + 0.405853219646718InvoerAM[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
InvoerEU[t] =  +  82.0823843016541 +  0.405853219646718InvoerAM[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=59206&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]InvoerEU[t] =  +  82.0823843016541 +  0.405853219646718InvoerAM[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=59206&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=59206&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
InvoerEU[t] = + 82.0823843016541 + 0.405853219646718InvoerAM[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)	82.0823843016541	10.050437	8.167	0	0
InvoerAM	0.405853219646718	0.059794	6.7875	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) & 82.0823843016541 & 10.050437 & 8.167 & 0 & 0 \tabularnewline
InvoerAM & 0.405853219646718 & 0.059794 & 6.7875 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=59206&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]82.0823843016541[/C][C]10.050437[/C][C]8.167[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]InvoerAM[/C][C]0.405853219646718[/C][C]0.059794[/C][C]6.7875[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=59206&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=59206&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)	82.0823843016541	10.050437	8.167	0	0
InvoerAM	0.405853219646718	0.059794	6.7875	0	0

Multiple Linear Regression - Regression Statistics
Multiple R	0.662171795257425
R-squared	0.438471486434441
Adjusted R-squared	0.428954054001127
F-TEST (value)	46.0703545317146
F-TEST (DF numerator)	1
F-TEST (DF denominator)	59
p-value	6.18692874709836e-09
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	14.0076440268092
Sum Squared Residuals	11576.6313797264

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.662171795257425 \tabularnewline
R-squared & 0.438471486434441 \tabularnewline
Adjusted R-squared & 0.428954054001127 \tabularnewline
F-TEST (value) & 46.0703545317146 \tabularnewline
F-TEST (DF numerator) & 1 \tabularnewline
F-TEST (DF denominator) & 59 \tabularnewline
p-value & 6.18692874709836e-09 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 14.0076440268092 \tabularnewline
Sum Squared Residuals & 11576.6313797264 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=59206&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.662171795257425[/C][/ROW]
[ROW][C]R-squared[/C][C]0.438471486434441[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.428954054001127[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]46.0703545317146[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]1[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]59[/C][/ROW]
[ROW][C]p-value[/C][C]6.18692874709836e-09[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]14.0076440268092[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]11576.6313797264[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=59206&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=59206&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	0.662171795257425
R-squared	0.438471486434441
Adjusted R-squared	0.428954054001127
F-TEST (value)	46.0703545317146
F-TEST (DF numerator)	1
F-TEST (DF denominator)	59
p-value	6.18692874709836e-09
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	14.0076440268092
Sum Squared Residuals	11576.6313797264

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	114.08	137.477290251235	-23.3972902512352
2	112.95	139.965170487669	-27.0151704876691
3	135.31	139.595844057791	-4.2858440577906
4	134.31	142.761499171035	-8.451499171035
5	133.03	141.901090345384	-8.87109034538395
6	140.11	135.638775166235	4.47122483376492
7	124.69	137.432646397074	-12.7426463970736
8	131.68	133.763733291467	-2.08373329146725
9	150.95	136.405837751367	14.5441622486326
10	137.26	132.870856208244	4.38914379175551
11	130.51	139.409151576753	-8.89915157675312
12	143.15	142.684387059302	0.465612940697883
13	118.01	131.158155621335	-13.1481556213353
14	122.56	136.811690971014	-14.2516909710141
15	147.97	135.626599569646	12.3434004303543
16	135.74	136.556003442637	-0.816003442636669
17	151.62	140.14780443651	11.4721955634899
18	154.82	141.081266841698	13.7387331583024
19	145.59	136.596588764601	8.99341123539866
20	147.12	133.345704475231	13.7742955247689
21	175.86	147.915835060548	27.9441649394517
22	140.66	132.444710327615	8.21528967238457
23	152.69	144.547253337481	8.14274666251944
24	154.38	144.721770221929	9.65822977807134
25	132.45	138.374225866654	-5.92422586665398
26	136.44	143.358103403916	-6.91810340391568
27	153.24	141.495237125737	11.7447628742628
28	154.11	154.446013364664	-0.336013364664014
29	155.93	150.359071442822	5.57092855717844
30	142.53	148.041649558639	-5.51164955863881
31	148.73	146.556226774732	2.17377322526817
32	147.73	143.841068735295	3.88893126470472
33	166.79	151.487343393439	15.3026566065606
34	144.3	151.68215293887	-7.38215293886986
35	156.07	159.040271811065	-2.97027181106488
36	161.7	154.105096660161	7.5949033398392
37	152.1	155.127846773671	-3.02784677367051
38	140.45	145.785105657403	-5.33510565740307
39	155.56	150.221081348142	5.33891865185832
40	174.53	161.134474424442	13.3955255755581
41	167.16	160.322767985149	6.83723201485149
42	159.48	149.072516736541	10.4074832634585
43	173.22	161.873127284199	11.3468727158010
44	176.13	143.625966528883	32.5040334711175
45	180.31	157.985053439983	22.3249465600166
46	185.84	167.713355114915	18.1266448850848
47	169.43	170.996707661857	-1.56670766185720
48	195.25	177.733871107993	17.5161288920073
49	174.99	180.071585653158	-5.08158565315781
50	156.42	158.163628856628	-1.74362885662798
51	182.08	175.254107935951	6.82589206404874
52	182	166.678429404816	15.3215705951839
53	153.28	169.377353315467	-16.0973533154668
54	136.72	170.152532964992	-33.432532964992
55	130.19	151.515753118815	-21.3257531188147
56	132.04	154.490657218825	-22.4506572188252
57	143.89	161.767605447091	-17.8776054470908
58	133.38	151.933781935051	-18.5537819350508
59	127.98	144.961223621520	-16.9812236215202
60	150.45	156.053192114465	-5.60319211446504
61	133.55	155.842148440249	-22.2921484402487

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 114.08 & 137.477290251235 & -23.3972902512352 \tabularnewline
2 & 112.95 & 139.965170487669 & -27.0151704876691 \tabularnewline
3 & 135.31 & 139.595844057791 & -4.2858440577906 \tabularnewline
4 & 134.31 & 142.761499171035 & -8.451499171035 \tabularnewline
5 & 133.03 & 141.901090345384 & -8.87109034538395 \tabularnewline
6 & 140.11 & 135.638775166235 & 4.47122483376492 \tabularnewline
7 & 124.69 & 137.432646397074 & -12.7426463970736 \tabularnewline
8 & 131.68 & 133.763733291467 & -2.08373329146725 \tabularnewline
9 & 150.95 & 136.405837751367 & 14.5441622486326 \tabularnewline
10 & 137.26 & 132.870856208244 & 4.38914379175551 \tabularnewline
11 & 130.51 & 139.409151576753 & -8.89915157675312 \tabularnewline
12 & 143.15 & 142.684387059302 & 0.465612940697883 \tabularnewline
13 & 118.01 & 131.158155621335 & -13.1481556213353 \tabularnewline
14 & 122.56 & 136.811690971014 & -14.2516909710141 \tabularnewline
15 & 147.97 & 135.626599569646 & 12.3434004303543 \tabularnewline
16 & 135.74 & 136.556003442637 & -0.816003442636669 \tabularnewline
17 & 151.62 & 140.14780443651 & 11.4721955634899 \tabularnewline
18 & 154.82 & 141.081266841698 & 13.7387331583024 \tabularnewline
19 & 145.59 & 136.596588764601 & 8.99341123539866 \tabularnewline
20 & 147.12 & 133.345704475231 & 13.7742955247689 \tabularnewline
21 & 175.86 & 147.915835060548 & 27.9441649394517 \tabularnewline
22 & 140.66 & 132.444710327615 & 8.21528967238457 \tabularnewline
23 & 152.69 & 144.547253337481 & 8.14274666251944 \tabularnewline
24 & 154.38 & 144.721770221929 & 9.65822977807134 \tabularnewline
25 & 132.45 & 138.374225866654 & -5.92422586665398 \tabularnewline
26 & 136.44 & 143.358103403916 & -6.91810340391568 \tabularnewline
27 & 153.24 & 141.495237125737 & 11.7447628742628 \tabularnewline
28 & 154.11 & 154.446013364664 & -0.336013364664014 \tabularnewline
29 & 155.93 & 150.359071442822 & 5.57092855717844 \tabularnewline
30 & 142.53 & 148.041649558639 & -5.51164955863881 \tabularnewline
31 & 148.73 & 146.556226774732 & 2.17377322526817 \tabularnewline
32 & 147.73 & 143.841068735295 & 3.88893126470472 \tabularnewline
33 & 166.79 & 151.487343393439 & 15.3026566065606 \tabularnewline
34 & 144.3 & 151.68215293887 & -7.38215293886986 \tabularnewline
35 & 156.07 & 159.040271811065 & -2.97027181106488 \tabularnewline
36 & 161.7 & 154.105096660161 & 7.5949033398392 \tabularnewline
37 & 152.1 & 155.127846773671 & -3.02784677367051 \tabularnewline
38 & 140.45 & 145.785105657403 & -5.33510565740307 \tabularnewline
39 & 155.56 & 150.221081348142 & 5.33891865185832 \tabularnewline
40 & 174.53 & 161.134474424442 & 13.3955255755581 \tabularnewline
41 & 167.16 & 160.322767985149 & 6.83723201485149 \tabularnewline
42 & 159.48 & 149.072516736541 & 10.4074832634585 \tabularnewline
43 & 173.22 & 161.873127284199 & 11.3468727158010 \tabularnewline
44 & 176.13 & 143.625966528883 & 32.5040334711175 \tabularnewline
45 & 180.31 & 157.985053439983 & 22.3249465600166 \tabularnewline
46 & 185.84 & 167.713355114915 & 18.1266448850848 \tabularnewline
47 & 169.43 & 170.996707661857 & -1.56670766185720 \tabularnewline
48 & 195.25 & 177.733871107993 & 17.5161288920073 \tabularnewline
49 & 174.99 & 180.071585653158 & -5.08158565315781 \tabularnewline
50 & 156.42 & 158.163628856628 & -1.74362885662798 \tabularnewline
51 & 182.08 & 175.254107935951 & 6.82589206404874 \tabularnewline
52 & 182 & 166.678429404816 & 15.3215705951839 \tabularnewline
53 & 153.28 & 169.377353315467 & -16.0973533154668 \tabularnewline
54 & 136.72 & 170.152532964992 & -33.432532964992 \tabularnewline
55 & 130.19 & 151.515753118815 & -21.3257531188147 \tabularnewline
56 & 132.04 & 154.490657218825 & -22.4506572188252 \tabularnewline
57 & 143.89 & 161.767605447091 & -17.8776054470908 \tabularnewline
58 & 133.38 & 151.933781935051 & -18.5537819350508 \tabularnewline
59 & 127.98 & 144.961223621520 & -16.9812236215202 \tabularnewline
60 & 150.45 & 156.053192114465 & -5.60319211446504 \tabularnewline
61 & 133.55 & 155.842148440249 & -22.2921484402487 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=59206&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]114.08[/C][C]137.477290251235[/C][C]-23.3972902512352[/C][/ROW]
[ROW][C]2[/C][C]112.95[/C][C]139.965170487669[/C][C]-27.0151704876691[/C][/ROW]
[ROW][C]3[/C][C]135.31[/C][C]139.595844057791[/C][C]-4.2858440577906[/C][/ROW]
[ROW][C]4[/C][C]134.31[/C][C]142.761499171035[/C][C]-8.451499171035[/C][/ROW]
[ROW][C]5[/C][C]133.03[/C][C]141.901090345384[/C][C]-8.87109034538395[/C][/ROW]
[ROW][C]6[/C][C]140.11[/C][C]135.638775166235[/C][C]4.47122483376492[/C][/ROW]
[ROW][C]7[/C][C]124.69[/C][C]137.432646397074[/C][C]-12.7426463970736[/C][/ROW]
[ROW][C]8[/C][C]131.68[/C][C]133.763733291467[/C][C]-2.08373329146725[/C][/ROW]
[ROW][C]9[/C][C]150.95[/C][C]136.405837751367[/C][C]14.5441622486326[/C][/ROW]
[ROW][C]10[/C][C]137.26[/C][C]132.870856208244[/C][C]4.38914379175551[/C][/ROW]
[ROW][C]11[/C][C]130.51[/C][C]139.409151576753[/C][C]-8.89915157675312[/C][/ROW]
[ROW][C]12[/C][C]143.15[/C][C]142.684387059302[/C][C]0.465612940697883[/C][/ROW]
[ROW][C]13[/C][C]118.01[/C][C]131.158155621335[/C][C]-13.1481556213353[/C][/ROW]
[ROW][C]14[/C][C]122.56[/C][C]136.811690971014[/C][C]-14.2516909710141[/C][/ROW]
[ROW][C]15[/C][C]147.97[/C][C]135.626599569646[/C][C]12.3434004303543[/C][/ROW]
[ROW][C]16[/C][C]135.74[/C][C]136.556003442637[/C][C]-0.816003442636669[/C][/ROW]
[ROW][C]17[/C][C]151.62[/C][C]140.14780443651[/C][C]11.4721955634899[/C][/ROW]
[ROW][C]18[/C][C]154.82[/C][C]141.081266841698[/C][C]13.7387331583024[/C][/ROW]
[ROW][C]19[/C][C]145.59[/C][C]136.596588764601[/C][C]8.99341123539866[/C][/ROW]
[ROW][C]20[/C][C]147.12[/C][C]133.345704475231[/C][C]13.7742955247689[/C][/ROW]
[ROW][C]21[/C][C]175.86[/C][C]147.915835060548[/C][C]27.9441649394517[/C][/ROW]
[ROW][C]22[/C][C]140.66[/C][C]132.444710327615[/C][C]8.21528967238457[/C][/ROW]
[ROW][C]23[/C][C]152.69[/C][C]144.547253337481[/C][C]8.14274666251944[/C][/ROW]
[ROW][C]24[/C][C]154.38[/C][C]144.721770221929[/C][C]9.65822977807134[/C][/ROW]
[ROW][C]25[/C][C]132.45[/C][C]138.374225866654[/C][C]-5.92422586665398[/C][/ROW]
[ROW][C]26[/C][C]136.44[/C][C]143.358103403916[/C][C]-6.91810340391568[/C][/ROW]
[ROW][C]27[/C][C]153.24[/C][C]141.495237125737[/C][C]11.7447628742628[/C][/ROW]
[ROW][C]28[/C][C]154.11[/C][C]154.446013364664[/C][C]-0.336013364664014[/C][/ROW]
[ROW][C]29[/C][C]155.93[/C][C]150.359071442822[/C][C]5.57092855717844[/C][/ROW]
[ROW][C]30[/C][C]142.53[/C][C]148.041649558639[/C][C]-5.51164955863881[/C][/ROW]
[ROW][C]31[/C][C]148.73[/C][C]146.556226774732[/C][C]2.17377322526817[/C][/ROW]
[ROW][C]32[/C][C]147.73[/C][C]143.841068735295[/C][C]3.88893126470472[/C][/ROW]
[ROW][C]33[/C][C]166.79[/C][C]151.487343393439[/C][C]15.3026566065606[/C][/ROW]
[ROW][C]34[/C][C]144.3[/C][C]151.68215293887[/C][C]-7.38215293886986[/C][/ROW]
[ROW][C]35[/C][C]156.07[/C][C]159.040271811065[/C][C]-2.97027181106488[/C][/ROW]
[ROW][C]36[/C][C]161.7[/C][C]154.105096660161[/C][C]7.5949033398392[/C][/ROW]
[ROW][C]37[/C][C]152.1[/C][C]155.127846773671[/C][C]-3.02784677367051[/C][/ROW]
[ROW][C]38[/C][C]140.45[/C][C]145.785105657403[/C][C]-5.33510565740307[/C][/ROW]
[ROW][C]39[/C][C]155.56[/C][C]150.221081348142[/C][C]5.33891865185832[/C][/ROW]
[ROW][C]40[/C][C]174.53[/C][C]161.134474424442[/C][C]13.3955255755581[/C][/ROW]
[ROW][C]41[/C][C]167.16[/C][C]160.322767985149[/C][C]6.83723201485149[/C][/ROW]
[ROW][C]42[/C][C]159.48[/C][C]149.072516736541[/C][C]10.4074832634585[/C][/ROW]
[ROW][C]43[/C][C]173.22[/C][C]161.873127284199[/C][C]11.3468727158010[/C][/ROW]
[ROW][C]44[/C][C]176.13[/C][C]143.625966528883[/C][C]32.5040334711175[/C][/ROW]
[ROW][C]45[/C][C]180.31[/C][C]157.985053439983[/C][C]22.3249465600166[/C][/ROW]
[ROW][C]46[/C][C]185.84[/C][C]167.713355114915[/C][C]18.1266448850848[/C][/ROW]
[ROW][C]47[/C][C]169.43[/C][C]170.996707661857[/C][C]-1.56670766185720[/C][/ROW]
[ROW][C]48[/C][C]195.25[/C][C]177.733871107993[/C][C]17.5161288920073[/C][/ROW]
[ROW][C]49[/C][C]174.99[/C][C]180.071585653158[/C][C]-5.08158565315781[/C][/ROW]
[ROW][C]50[/C][C]156.42[/C][C]158.163628856628[/C][C]-1.74362885662798[/C][/ROW]
[ROW][C]51[/C][C]182.08[/C][C]175.254107935951[/C][C]6.82589206404874[/C][/ROW]
[ROW][C]52[/C][C]182[/C][C]166.678429404816[/C][C]15.3215705951839[/C][/ROW]
[ROW][C]53[/C][C]153.28[/C][C]169.377353315467[/C][C]-16.0973533154668[/C][/ROW]
[ROW][C]54[/C][C]136.72[/C][C]170.152532964992[/C][C]-33.432532964992[/C][/ROW]
[ROW][C]55[/C][C]130.19[/C][C]151.515753118815[/C][C]-21.3257531188147[/C][/ROW]
[ROW][C]56[/C][C]132.04[/C][C]154.490657218825[/C][C]-22.4506572188252[/C][/ROW]
[ROW][C]57[/C][C]143.89[/C][C]161.767605447091[/C][C]-17.8776054470908[/C][/ROW]
[ROW][C]58[/C][C]133.38[/C][C]151.933781935051[/C][C]-18.5537819350508[/C][/ROW]
[ROW][C]59[/C][C]127.98[/C][C]144.961223621520[/C][C]-16.9812236215202[/C][/ROW]
[ROW][C]60[/C][C]150.45[/C][C]156.053192114465[/C][C]-5.60319211446504[/C][/ROW]
[ROW][C]61[/C][C]133.55[/C][C]155.842148440249[/C][C]-22.2921484402487[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=59206&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=59206&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	114.08	137.477290251235	-23.3972902512352
2	112.95	139.965170487669	-27.0151704876691
3	135.31	139.595844057791	-4.2858440577906
4	134.31	142.761499171035	-8.451499171035
5	133.03	141.901090345384	-8.87109034538395
6	140.11	135.638775166235	4.47122483376492
7	124.69	137.432646397074	-12.7426463970736
8	131.68	133.763733291467	-2.08373329146725
9	150.95	136.405837751367	14.5441622486326
10	137.26	132.870856208244	4.38914379175551
11	130.51	139.409151576753	-8.89915157675312
12	143.15	142.684387059302	0.465612940697883
13	118.01	131.158155621335	-13.1481556213353
14	122.56	136.811690971014	-14.2516909710141
15	147.97	135.626599569646	12.3434004303543
16	135.74	136.556003442637	-0.816003442636669
17	151.62	140.14780443651	11.4721955634899
18	154.82	141.081266841698	13.7387331583024
19	145.59	136.596588764601	8.99341123539866
20	147.12	133.345704475231	13.7742955247689
21	175.86	147.915835060548	27.9441649394517
22	140.66	132.444710327615	8.21528967238457
23	152.69	144.547253337481	8.14274666251944
24	154.38	144.721770221929	9.65822977807134
25	132.45	138.374225866654	-5.92422586665398
26	136.44	143.358103403916	-6.91810340391568
27	153.24	141.495237125737	11.7447628742628
28	154.11	154.446013364664	-0.336013364664014
29	155.93	150.359071442822	5.57092855717844
30	142.53	148.041649558639	-5.51164955863881
31	148.73	146.556226774732	2.17377322526817
32	147.73	143.841068735295	3.88893126470472
33	166.79	151.487343393439	15.3026566065606
34	144.3	151.68215293887	-7.38215293886986
35	156.07	159.040271811065	-2.97027181106488
36	161.7	154.105096660161	7.5949033398392
37	152.1	155.127846773671	-3.02784677367051
38	140.45	145.785105657403	-5.33510565740307
39	155.56	150.221081348142	5.33891865185832
40	174.53	161.134474424442	13.3955255755581
41	167.16	160.322767985149	6.83723201485149
42	159.48	149.072516736541	10.4074832634585
43	173.22	161.873127284199	11.3468727158010
44	176.13	143.625966528883	32.5040334711175
45	180.31	157.985053439983	22.3249465600166
46	185.84	167.713355114915	18.1266448850848
47	169.43	170.996707661857	-1.56670766185720
48	195.25	177.733871107993	17.5161288920073
49	174.99	180.071585653158	-5.08158565315781
50	156.42	158.163628856628	-1.74362885662798
51	182.08	175.254107935951	6.82589206404874
52	182	166.678429404816	15.3215705951839
53	153.28	169.377353315467	-16.0973533154668
54	136.72	170.152532964992	-33.432532964992
55	130.19	151.515753118815	-21.3257531188147
56	132.04	154.490657218825	-22.4506572188252
57	143.89	161.767605447091	-17.8776054470908
58	133.38	151.933781935051	-18.5537819350508
59	127.98	144.961223621520	-16.9812236215202
60	150.45	156.053192114465	-5.60319211446504
61	133.55	155.842148440249	-22.2921484402487

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
5	0.326189404113174	0.652378808226349	0.673810595886826
6	0.53395555111065	0.9320888977787	0.46604444888935
7	0.401683345183835	0.80336669036767	0.598316654816165
8	0.287483874154723	0.574967748309445	0.712516125845277
9	0.444946976899485	0.889893953798969	0.555053023100515
10	0.334427756784060	0.668855513568119	0.66557224321594
11	0.245667223357378	0.491334446714756	0.754332776642622
12	0.245455456284912	0.490910912569823	0.754544543715088
13	0.255882170926344	0.511764341852688	0.744117829073656
14	0.228607012201552	0.457214024403105	0.771392987798448
15	0.270652801046952	0.541305602093904	0.729347198953048
16	0.207173549307149	0.414347098614298	0.792826450692851
17	0.247299305905333	0.494598611810667	0.752700694094667
18	0.293979928809691	0.587959857619383	0.706020071190309
19	0.264975352601441	0.529950705202882	0.735024647398559
20	0.260498326825826	0.520996653651652	0.739501673174174
21	0.489071672935862	0.978143345871723	0.510928327064138
22	0.446792937682433	0.893585875364867	0.553207062317567
23	0.384135820350593	0.768271640701187	0.615864179649407
24	0.33085228468771	0.66170456937542	0.66914771531229
25	0.275546628188883	0.551093256377766	0.724453371811117
26	0.237520477304389	0.475040954608778	0.762479522695611
27	0.212467741885758	0.424935483771517	0.787532258114242
28	0.168750033336141	0.337500066672281	0.83124996666386
29	0.127837377876334	0.255674755752669	0.872162622123666
30	0.101138416100487	0.202276832200974	0.898861583899513
31	0.0716534128556518	0.143306825711304	0.928346587144348
32	0.0502357330508296	0.100471466101659	0.94976426694917
33	0.0485425242379713	0.0970850484759425	0.951457475762029
34	0.0396526468854478	0.0793052937708957	0.960347353114552
35	0.0280188135182369	0.0560376270364737	0.971981186481763
36	0.0197707850220263	0.0395415700440527	0.980229214977974
37	0.0129625866340042	0.0259251732680084	0.987037413365996
38	0.00838577949259833	0.0167715589851967	0.991614220507402
39	0.00528297407998074	0.0105659481599615	0.99471702592002
40	0.00441798654885528	0.00883597309771056	0.995582013451145
41	0.00273056966232526	0.00546113932465051	0.997269430337675
42	0.00219312796157651	0.00438625592315302	0.997806872038423
43	0.00163618991097458	0.00327237982194916	0.998363810089025
44	0.0550037723720441	0.110007544744088	0.944996227627956
45	0.181538892118653	0.363077784237305	0.818461107881347
46	0.273131858208746	0.546263716417493	0.726868141791254
47	0.226360009961055	0.45272001992211	0.773639990038945
48	0.282069578535098	0.564139157070195	0.717930421464902
49	0.240464625684858	0.480929251369715	0.759535374315143
50	0.224293787068309	0.448587574136619	0.77570621293169
51	0.236474793661368	0.472949587322736	0.763525206338632
52	0.8951951853279	0.209609629344198	0.104804814672099
53	0.901283299328306	0.197433401343387	0.0987167006716937
54	0.943303300619555	0.113393398760890	0.0566966993804448
55	0.896730496419142	0.206539007161715	0.103269503580857
56	0.835865026720133	0.328269946559734	0.164134973279867

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
5 & 0.326189404113174 & 0.652378808226349 & 0.673810595886826 \tabularnewline
6 & 0.53395555111065 & 0.9320888977787 & 0.46604444888935 \tabularnewline
7 & 0.401683345183835 & 0.80336669036767 & 0.598316654816165 \tabularnewline
8 & 0.287483874154723 & 0.574967748309445 & 0.712516125845277 \tabularnewline
9 & 0.444946976899485 & 0.889893953798969 & 0.555053023100515 \tabularnewline
10 & 0.334427756784060 & 0.668855513568119 & 0.66557224321594 \tabularnewline
11 & 0.245667223357378 & 0.491334446714756 & 0.754332776642622 \tabularnewline
12 & 0.245455456284912 & 0.490910912569823 & 0.754544543715088 \tabularnewline
13 & 0.255882170926344 & 0.511764341852688 & 0.744117829073656 \tabularnewline
14 & 0.228607012201552 & 0.457214024403105 & 0.771392987798448 \tabularnewline
15 & 0.270652801046952 & 0.541305602093904 & 0.729347198953048 \tabularnewline
16 & 0.207173549307149 & 0.414347098614298 & 0.792826450692851 \tabularnewline
17 & 0.247299305905333 & 0.494598611810667 & 0.752700694094667 \tabularnewline
18 & 0.293979928809691 & 0.587959857619383 & 0.706020071190309 \tabularnewline
19 & 0.264975352601441 & 0.529950705202882 & 0.735024647398559 \tabularnewline
20 & 0.260498326825826 & 0.520996653651652 & 0.739501673174174 \tabularnewline
21 & 0.489071672935862 & 0.978143345871723 & 0.510928327064138 \tabularnewline
22 & 0.446792937682433 & 0.893585875364867 & 0.553207062317567 \tabularnewline
23 & 0.384135820350593 & 0.768271640701187 & 0.615864179649407 \tabularnewline
24 & 0.33085228468771 & 0.66170456937542 & 0.66914771531229 \tabularnewline
25 & 0.275546628188883 & 0.551093256377766 & 0.724453371811117 \tabularnewline
26 & 0.237520477304389 & 0.475040954608778 & 0.762479522695611 \tabularnewline
27 & 0.212467741885758 & 0.424935483771517 & 0.787532258114242 \tabularnewline
28 & 0.168750033336141 & 0.337500066672281 & 0.83124996666386 \tabularnewline
29 & 0.127837377876334 & 0.255674755752669 & 0.872162622123666 \tabularnewline
30 & 0.101138416100487 & 0.202276832200974 & 0.898861583899513 \tabularnewline
31 & 0.0716534128556518 & 0.143306825711304 & 0.928346587144348 \tabularnewline
32 & 0.0502357330508296 & 0.100471466101659 & 0.94976426694917 \tabularnewline
33 & 0.0485425242379713 & 0.0970850484759425 & 0.951457475762029 \tabularnewline
34 & 0.0396526468854478 & 0.0793052937708957 & 0.960347353114552 \tabularnewline
35 & 0.0280188135182369 & 0.0560376270364737 & 0.971981186481763 \tabularnewline
36 & 0.0197707850220263 & 0.0395415700440527 & 0.980229214977974 \tabularnewline
37 & 0.0129625866340042 & 0.0259251732680084 & 0.987037413365996 \tabularnewline
38 & 0.00838577949259833 & 0.0167715589851967 & 0.991614220507402 \tabularnewline
39 & 0.00528297407998074 & 0.0105659481599615 & 0.99471702592002 \tabularnewline
40 & 0.00441798654885528 & 0.00883597309771056 & 0.995582013451145 \tabularnewline
41 & 0.00273056966232526 & 0.00546113932465051 & 0.997269430337675 \tabularnewline
42 & 0.00219312796157651 & 0.00438625592315302 & 0.997806872038423 \tabularnewline
43 & 0.00163618991097458 & 0.00327237982194916 & 0.998363810089025 \tabularnewline
44 & 0.0550037723720441 & 0.110007544744088 & 0.944996227627956 \tabularnewline
45 & 0.181538892118653 & 0.363077784237305 & 0.818461107881347 \tabularnewline
46 & 0.273131858208746 & 0.546263716417493 & 0.726868141791254 \tabularnewline
47 & 0.226360009961055 & 0.45272001992211 & 0.773639990038945 \tabularnewline
48 & 0.282069578535098 & 0.564139157070195 & 0.717930421464902 \tabularnewline
49 & 0.240464625684858 & 0.480929251369715 & 0.759535374315143 \tabularnewline
50 & 0.224293787068309 & 0.448587574136619 & 0.77570621293169 \tabularnewline
51 & 0.236474793661368 & 0.472949587322736 & 0.763525206338632 \tabularnewline
52 & 0.8951951853279 & 0.209609629344198 & 0.104804814672099 \tabularnewline
53 & 0.901283299328306 & 0.197433401343387 & 0.0987167006716937 \tabularnewline
54 & 0.943303300619555 & 0.113393398760890 & 0.0566966993804448 \tabularnewline
55 & 0.896730496419142 & 0.206539007161715 & 0.103269503580857 \tabularnewline
56 & 0.835865026720133 & 0.328269946559734 & 0.164134973279867 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=59206&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]5[/C][C]0.326189404113174[/C][C]0.652378808226349[/C][C]0.673810595886826[/C][/ROW]
[ROW][C]6[/C][C]0.53395555111065[/C][C]0.9320888977787[/C][C]0.46604444888935[/C][/ROW]
[ROW][C]7[/C][C]0.401683345183835[/C][C]0.80336669036767[/C][C]0.598316654816165[/C][/ROW]
[ROW][C]8[/C][C]0.287483874154723[/C][C]0.574967748309445[/C][C]0.712516125845277[/C][/ROW]
[ROW][C]9[/C][C]0.444946976899485[/C][C]0.889893953798969[/C][C]0.555053023100515[/C][/ROW]
[ROW][C]10[/C][C]0.334427756784060[/C][C]0.668855513568119[/C][C]0.66557224321594[/C][/ROW]
[ROW][C]11[/C][C]0.245667223357378[/C][C]0.491334446714756[/C][C]0.754332776642622[/C][/ROW]
[ROW][C]12[/C][C]0.245455456284912[/C][C]0.490910912569823[/C][C]0.754544543715088[/C][/ROW]
[ROW][C]13[/C][C]0.255882170926344[/C][C]0.511764341852688[/C][C]0.744117829073656[/C][/ROW]
[ROW][C]14[/C][C]0.228607012201552[/C][C]0.457214024403105[/C][C]0.771392987798448[/C][/ROW]
[ROW][C]15[/C][C]0.270652801046952[/C][C]0.541305602093904[/C][C]0.729347198953048[/C][/ROW]
[ROW][C]16[/C][C]0.207173549307149[/C][C]0.414347098614298[/C][C]0.792826450692851[/C][/ROW]
[ROW][C]17[/C][C]0.247299305905333[/C][C]0.494598611810667[/C][C]0.752700694094667[/C][/ROW]
[ROW][C]18[/C][C]0.293979928809691[/C][C]0.587959857619383[/C][C]0.706020071190309[/C][/ROW]
[ROW][C]19[/C][C]0.264975352601441[/C][C]0.529950705202882[/C][C]0.735024647398559[/C][/ROW]
[ROW][C]20[/C][C]0.260498326825826[/C][C]0.520996653651652[/C][C]0.739501673174174[/C][/ROW]
[ROW][C]21[/C][C]0.489071672935862[/C][C]0.978143345871723[/C][C]0.510928327064138[/C][/ROW]
[ROW][C]22[/C][C]0.446792937682433[/C][C]0.893585875364867[/C][C]0.553207062317567[/C][/ROW]
[ROW][C]23[/C][C]0.384135820350593[/C][C]0.768271640701187[/C][C]0.615864179649407[/C][/ROW]
[ROW][C]24[/C][C]0.33085228468771[/C][C]0.66170456937542[/C][C]0.66914771531229[/C][/ROW]
[ROW][C]25[/C][C]0.275546628188883[/C][C]0.551093256377766[/C][C]0.724453371811117[/C][/ROW]
[ROW][C]26[/C][C]0.237520477304389[/C][C]0.475040954608778[/C][C]0.762479522695611[/C][/ROW]
[ROW][C]27[/C][C]0.212467741885758[/C][C]0.424935483771517[/C][C]0.787532258114242[/C][/ROW]
[ROW][C]28[/C][C]0.168750033336141[/C][C]0.337500066672281[/C][C]0.83124996666386[/C][/ROW]
[ROW][C]29[/C][C]0.127837377876334[/C][C]0.255674755752669[/C][C]0.872162622123666[/C][/ROW]
[ROW][C]30[/C][C]0.101138416100487[/C][C]0.202276832200974[/C][C]0.898861583899513[/C][/ROW]
[ROW][C]31[/C][C]0.0716534128556518[/C][C]0.143306825711304[/C][C]0.928346587144348[/C][/ROW]
[ROW][C]32[/C][C]0.0502357330508296[/C][C]0.100471466101659[/C][C]0.94976426694917[/C][/ROW]
[ROW][C]33[/C][C]0.0485425242379713[/C][C]0.0970850484759425[/C][C]0.951457475762029[/C][/ROW]
[ROW][C]34[/C][C]0.0396526468854478[/C][C]0.0793052937708957[/C][C]0.960347353114552[/C][/ROW]
[ROW][C]35[/C][C]0.0280188135182369[/C][C]0.0560376270364737[/C][C]0.971981186481763[/C][/ROW]
[ROW][C]36[/C][C]0.0197707850220263[/C][C]0.0395415700440527[/C][C]0.980229214977974[/C][/ROW]
[ROW][C]37[/C][C]0.0129625866340042[/C][C]0.0259251732680084[/C][C]0.987037413365996[/C][/ROW]
[ROW][C]38[/C][C]0.00838577949259833[/C][C]0.0167715589851967[/C][C]0.991614220507402[/C][/ROW]
[ROW][C]39[/C][C]0.00528297407998074[/C][C]0.0105659481599615[/C][C]0.99471702592002[/C][/ROW]
[ROW][C]40[/C][C]0.00441798654885528[/C][C]0.00883597309771056[/C][C]0.995582013451145[/C][/ROW]
[ROW][C]41[/C][C]0.00273056966232526[/C][C]0.00546113932465051[/C][C]0.997269430337675[/C][/ROW]
[ROW][C]42[/C][C]0.00219312796157651[/C][C]0.00438625592315302[/C][C]0.997806872038423[/C][/ROW]
[ROW][C]43[/C][C]0.00163618991097458[/C][C]0.00327237982194916[/C][C]0.998363810089025[/C][/ROW]
[ROW][C]44[/C][C]0.0550037723720441[/C][C]0.110007544744088[/C][C]0.944996227627956[/C][/ROW]
[ROW][C]45[/C][C]0.181538892118653[/C][C]0.363077784237305[/C][C]0.818461107881347[/C][/ROW]
[ROW][C]46[/C][C]0.273131858208746[/C][C]0.546263716417493[/C][C]0.726868141791254[/C][/ROW]
[ROW][C]47[/C][C]0.226360009961055[/C][C]0.45272001992211[/C][C]0.773639990038945[/C][/ROW]
[ROW][C]48[/C][C]0.282069578535098[/C][C]0.564139157070195[/C][C]0.717930421464902[/C][/ROW]
[ROW][C]49[/C][C]0.240464625684858[/C][C]0.480929251369715[/C][C]0.759535374315143[/C][/ROW]
[ROW][C]50[/C][C]0.224293787068309[/C][C]0.448587574136619[/C][C]0.77570621293169[/C][/ROW]
[ROW][C]51[/C][C]0.236474793661368[/C][C]0.472949587322736[/C][C]0.763525206338632[/C][/ROW]
[ROW][C]52[/C][C]0.8951951853279[/C][C]0.209609629344198[/C][C]0.104804814672099[/C][/ROW]
[ROW][C]53[/C][C]0.901283299328306[/C][C]0.197433401343387[/C][C]0.0987167006716937[/C][/ROW]
[ROW][C]54[/C][C]0.943303300619555[/C][C]0.113393398760890[/C][C]0.0566966993804448[/C][/ROW]
[ROW][C]55[/C][C]0.896730496419142[/C][C]0.206539007161715[/C][C]0.103269503580857[/C][/ROW]
[ROW][C]56[/C][C]0.835865026720133[/C][C]0.328269946559734[/C][C]0.164134973279867[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=59206&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=59206&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
5	0.326189404113174	0.652378808226349	0.673810595886826
6	0.53395555111065	0.9320888977787	0.46604444888935
7	0.401683345183835	0.80336669036767	0.598316654816165
8	0.287483874154723	0.574967748309445	0.712516125845277
9	0.444946976899485	0.889893953798969	0.555053023100515
10	0.334427756784060	0.668855513568119	0.66557224321594
11	0.245667223357378	0.491334446714756	0.754332776642622
12	0.245455456284912	0.490910912569823	0.754544543715088
13	0.255882170926344	0.511764341852688	0.744117829073656
14	0.228607012201552	0.457214024403105	0.771392987798448
15	0.270652801046952	0.541305602093904	0.729347198953048
16	0.207173549307149	0.414347098614298	0.792826450692851
17	0.247299305905333	0.494598611810667	0.752700694094667
18	0.293979928809691	0.587959857619383	0.706020071190309
19	0.264975352601441	0.529950705202882	0.735024647398559
20	0.260498326825826	0.520996653651652	0.739501673174174
21	0.489071672935862	0.978143345871723	0.510928327064138
22	0.446792937682433	0.893585875364867	0.553207062317567
23	0.384135820350593	0.768271640701187	0.615864179649407
24	0.33085228468771	0.66170456937542	0.66914771531229
25	0.275546628188883	0.551093256377766	0.724453371811117
26	0.237520477304389	0.475040954608778	0.762479522695611
27	0.212467741885758	0.424935483771517	0.787532258114242
28	0.168750033336141	0.337500066672281	0.83124996666386
29	0.127837377876334	0.255674755752669	0.872162622123666
30	0.101138416100487	0.202276832200974	0.898861583899513
31	0.0716534128556518	0.143306825711304	0.928346587144348
32	0.0502357330508296	0.100471466101659	0.94976426694917
33	0.0485425242379713	0.0970850484759425	0.951457475762029
34	0.0396526468854478	0.0793052937708957	0.960347353114552
35	0.0280188135182369	0.0560376270364737	0.971981186481763
36	0.0197707850220263	0.0395415700440527	0.980229214977974
37	0.0129625866340042	0.0259251732680084	0.987037413365996
38	0.00838577949259833	0.0167715589851967	0.991614220507402
39	0.00528297407998074	0.0105659481599615	0.99471702592002
40	0.00441798654885528	0.00883597309771056	0.995582013451145
41	0.00273056966232526	0.00546113932465051	0.997269430337675
42	0.00219312796157651	0.00438625592315302	0.997806872038423
43	0.00163618991097458	0.00327237982194916	0.998363810089025
44	0.0550037723720441	0.110007544744088	0.944996227627956
45	0.181538892118653	0.363077784237305	0.818461107881347
46	0.273131858208746	0.546263716417493	0.726868141791254
47	0.226360009961055	0.45272001992211	0.773639990038945
48	0.282069578535098	0.564139157070195	0.717930421464902
49	0.240464625684858	0.480929251369715	0.759535374315143
50	0.224293787068309	0.448587574136619	0.77570621293169
51	0.236474793661368	0.472949587322736	0.763525206338632
52	0.8951951853279	0.209609629344198	0.104804814672099
53	0.901283299328306	0.197433401343387	0.0987167006716937
54	0.943303300619555	0.113393398760890	0.0566966993804448
55	0.896730496419142	0.206539007161715	0.103269503580857
56	0.835865026720133	0.328269946559734	0.164134973279867

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	4	0.0769230769230769	NOK
5% type I error level	8	0.153846153846154	NOK
10% type I error level	11	0.211538461538462	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 & 4 & 0.0769230769230769 & NOK \tabularnewline
5% type I error level & 8 & 0.153846153846154 & NOK \tabularnewline
10% type I error level & 11 & 0.211538461538462 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=59206&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]4[/C][C]0.0769230769230769[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]8[/C][C]0.153846153846154[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]11[/C][C]0.211538461538462[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=59206&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=59206&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	4	0.0769230769230769	NOK
5% type I error level	8	0.153846153846154	NOK
10% type I error level	11	0.211538461538462	NOK

Figure 1

PNG link

Postscript link

PDF link

Figure 2

PNG link

Postscript link

PDF link

Figure 3

PNG link

Postscript link

PDF link

Figure 4

PNG link

Postscript link

PDF link

Figure 5

PNG link

Postscript link

PDF link

Figure 6

PNG link

Postscript link

PDF link

Figure 7

PNG link

Postscript link

PDF link

Figure 8

PNG link

Postscript link

PDF link

Figure 9

PNG link

Postscript link

PDF link

Figure 10

PNG link

Postscript link

PDF link

Parameters (Session):

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