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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Wed, 18 Nov 2009 11:01:24 -0700

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2009/Nov/18/t1258567370cndndeoknz4y84a.htm/, Retrieved Wed, 01 May 2024 14:24:18 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=57565, Retrieved Wed, 01 May 2024 14:24:18 +0000

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No (this computation is public)

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Estimated Impact

169

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]
- RM D  [Multiple Regression] [Seatbelt] [2009-11-12 14:10:54] [b98453cac15ba1066b407e146608df68]
-   PD      [Multiple Regression] [] [2009-11-18 18:01:24] [7dd0431c761b876151627bfbf92230c8] [Current]

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Dataseries X:

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1.6	8.3	1.8	1.6
1.5	7.5	1.6	1.8
1.5	7.2	1.5	1.6
1.3	7.4	1.5	1.5
1.4	8.8	1.3	1.5
1.4	9.3	1.4	1.3
1.3	9.3	1.4	1.4
1.3	8.7	1.3	1.4
1.2	8.2	1.3	1.3
1.1	8.3	1.2	1.3
1.4	8.5	1.1	1.2
1.2	8.6	1.4	1.1
1.5	8.5	1.2	1.4
1.1	8.2	1.5	1.2
1.3	8.1	1.1	1.5
1.5	7.9	1.3	1.1
1.1	8.6	1.5	1.3
1.4	8.7	1.1	1.5
1.3	8.7	1.4	1.1
1.5	8.5	1.3	1.4
1.6	8.4	1.5	1.3
1.7	8.5	1.6	1.5
1.1	8.7	1.7	1.6
1.6	8.7	1.1	1.7
1.3	8.6	1.6	1.1
1.7	8.5	1.3	1.6
1.6	8.3	1.7	1.3
1.7	8	1.6	1.7
1.9	8.2	1.7	1.6
1.8	8.1	1.9	1.7
1.9	8.1	1.8	1.9
1.6	8	1.9	1.8
1.5	7.9	1.6	1.9
1.6	7.9	1.5	1.6
1.6	8	1.6	1.5
1.7	8	1.6	1.6
2	7.9	1.7	1.6
2	8	2	1.7
1.9	7.7	2	2
1.7	7.2	1.9	2
1.8	7.5	1.7	1.9
1.9	7.3	1.8	1.7
1.7	7	1.9	1.8
2	7	1.7	1.9
2.1	7	2	1.7
2.4	7.2	2.1	2
2.5	7.3	2.4	2.1
2.5	7.1	2.5	2.4
2.6	6.8	2.5	2.5
2.2	6.4	2.6	2.5
2.5	6.1	2.2	2.6
2.8	6.5	2.5	2.2
2.8	7.7	2.8	2.5
2.9	7.9	2.8	2.8
3	7.5	2.9	2.8
3.1	6.9	3	2.9
2.9	6.6	3.1	3
2.7	6.9	2.9	3.1

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	6 seconds
R Server	'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 & 6 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57565&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]6 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57565&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57565&T=0

As an alternative you can also use a QR Code:

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

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	6 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

Multiple Linear Regression - Estimated Regression Equation
Y[t] = + 0.125897900427605 + 0.00944079300588357X[t] + 0.39313092210009Y1[t] + 0.35998595852647Y2[t] + 0.0090037694087649t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Y[t] =  +  0.125897900427605 +  0.00944079300588357X[t] +  0.39313092210009Y1[t] +  0.35998595852647Y2[t] +  0.0090037694087649t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57565&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Y[t] =  +  0.125897900427605 +  0.00944079300588357X[t] +  0.39313092210009Y1[t] +  0.35998595852647Y2[t] +  0.0090037694087649t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57565&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57565&T=1

As an alternative you can also use a QR Code:

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

Multiple Linear Regression - Estimated Regression Equation
Y[t] = + 0.125897900427605 + 0.00944079300588357X[t] + 0.39313092210009Y1[t] + 0.35998595852647Y2[t] + 0.0090037694087649t + 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)	0.125897900427605	0.506992	0.2483	0.804844	0.402422
X	0.00944079300588357	0.052382	0.1802	0.857658	0.428829
Y1	0.39313092210009	0.123489	3.1835	0.002436	0.001218
Y2	0.35998595852647	0.125166	2.8761	0.005788	0.002894
t	0.0090037694087649	0.00288	3.1264	0.002871	0.001436

\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) & 0.125897900427605 & 0.506992 & 0.2483 & 0.804844 & 0.402422 \tabularnewline
X & 0.00944079300588357 & 0.052382 & 0.1802 & 0.857658 & 0.428829 \tabularnewline
Y1 & 0.39313092210009 & 0.123489 & 3.1835 & 0.002436 & 0.001218 \tabularnewline
Y2 & 0.35998595852647 & 0.125166 & 2.8761 & 0.005788 & 0.002894 \tabularnewline
t & 0.0090037694087649 & 0.00288 & 3.1264 & 0.002871 & 0.001436 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57565&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]0.125897900427605[/C][C]0.506992[/C][C]0.2483[/C][C]0.804844[/C][C]0.402422[/C][/ROW]
[ROW][C]X[/C][C]0.00944079300588357[/C][C]0.052382[/C][C]0.1802[/C][C]0.857658[/C][C]0.428829[/C][/ROW]
[ROW][C]Y1[/C][C]0.39313092210009[/C][C]0.123489[/C][C]3.1835[/C][C]0.002436[/C][C]0.001218[/C][/ROW]
[ROW][C]Y2[/C][C]0.35998595852647[/C][C]0.125166[/C][C]2.8761[/C][C]0.005788[/C][C]0.002894[/C][/ROW]
[ROW][C]t[/C][C]0.0090037694087649[/C][C]0.00288[/C][C]3.1264[/C][C]0.002871[/C][C]0.001436[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57565&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57565&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)	0.125897900427605	0.506992	0.2483	0.804844	0.402422
X	0.00944079300588357	0.052382	0.1802	0.857658	0.428829
Y1	0.39313092210009	0.123489	3.1835	0.002436	0.001218
Y2	0.35998595852647	0.125166	2.8761	0.005788	0.002894
t	0.0090037694087649	0.00288	3.1264	0.002871	0.001436

Multiple Linear Regression - Regression Statistics
Multiple R	0.942776116416407
R-squared	0.888826805685203
Adjusted R-squared	0.880436375925596
F-TEST (value)	105.933406410735
F-TEST (DF numerator)	4
F-TEST (DF denominator)	53
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.188487886954718
Sum Squared Residuals	1.88296722701868

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.942776116416407 \tabularnewline
R-squared & 0.888826805685203 \tabularnewline
Adjusted R-squared & 0.880436375925596 \tabularnewline
F-TEST (value) & 105.933406410735 \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 & 0.188487886954718 \tabularnewline
Sum Squared Residuals & 1.88296722701868 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57565&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.942776116416407[/C][/ROW]
[ROW][C]R-squared[/C][C]0.888826805685203[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.880436375925596[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]105.933406410735[/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]0.188487886954718[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]1.88296722701868[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57565&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57565&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	0.942776116416407
R-squared	0.888826805685203
Adjusted R-squared	0.880436375925596
F-TEST (value)	105.933406410735
F-TEST (DF numerator)	4
F-TEST (DF denominator)	53
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.188487886954718
Sum Squared Residuals	1.88296722701868

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	1.6	1.49687344520772	0.103126554792285
2	1.5	1.49169558749705	0.00830441250294869
3	1.5	1.38655683508875	0.113443164911251
4	1.3	1.36145016724604	-0.0614501672460424
5	1.4	1.30504486244303	0.0949551375569734
6	1.4	1.28608492885945	0.113915071140552
7	1.3	1.33108729412086	-0.0310872941208596
8	1.3	1.29511349551609	0.00488650448391445
9	1.2	1.26339827256926	-0.0633982725692618
10	1.1	1.23403302906861	-0.134033029068606
11	1.4	1.16961326901589	0.230386730984108
12	1.2	1.26150179850262	-0.0615017985026249
13	1.5	1.29893109174872	0.201068908251276
14	1.1	1.35104470818046	-0.251044708180457
15	1.3	1.30984781700654	-0.00984781700653888
16	1.5	1.25159522882356	0.248404771176443
17	1.1	1.41783092946175	-0.317830929461752
18	1.4	1.34252360103636	0.0574763989636363
19	1.3	1.32547226366457	-0.0254722636645674
20	1.5	1.40127056982009	0.0987294301799123
21	1.6	1.45195784849564	0.148042151504365
22	1.7	1.57321598112029	0.126784018879708
23	1.1	1.65941959719289	-0.559419597192889
24	1.6	1.46854340919425	0.131456590805753
25	1.3	1.45717698523659	-0.157176985236587
26	1.7	1.52729037797797	0.172709622022029
27	1.6	1.58366257006765	0.0163374299323458
28	1.7	1.69451539277523	0.00548460722476676
29	1.9	1.70872181714254	0.191278182857463
30	1.8	1.83140628752338	-0.0314062875233781
31	1.9	1.87309415642743	0.0269058435725718
32	1.6	1.88446834289297	-0.284468342892967
33	1.5	1.81058735222376	-0.310587352223763
34	1.6	1.67228224186458	-0.0722822418645782
35	1.6	1.68554458693129	-0.0855445869312934
36	1.7	1.73054695219271	-0.0305469521927055
37	2	1.77791973451089	0.222080265489109
38	2	1.94180545570292	0.0581945442970819
39	1.9	2.05597277476786	-0.155972774767859
40	1.7	2.02094305546367	-0.320943055463673
41	1.8	1.91815428250154	-0.118154282501538
42	1.9	1.89258579381384	0.00741420618615849
43	1.7	1.97406901338350	-0.274069013383497
44	2	1.94044519422489	0.0595548057751088
45	2.1	1.99539104855839	0.104608951441611
46	2.4	2.15359185633628	0.246408143663719
47	2.5	2.31747757752831	0.182522422471692
48	2.5	2.47190206810385	0.028097931896154
49	2.6	2.51407219546349	0.0859278045365071
50	2.2	2.55861273987991	-0.358612739879913
51	2.5	2.44353049839952	0.0564695016004754
52	2.8	2.43025547823008	0.369744521769918
53	2.8	2.67652326343387	0.123476736566125
54	2.9	2.79541097900176	0.104589020998243
55	3	2.83995152341818	0.160048476581822
56	3.1	2.91860250508607	0.181397494913932
57	2.9	3.00008572465572	-0.100085724655725
58	2.7	2.96929414339888	-0.269294143398883

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 1.6 & 1.49687344520772 & 0.103126554792285 \tabularnewline
2 & 1.5 & 1.49169558749705 & 0.00830441250294869 \tabularnewline
3 & 1.5 & 1.38655683508875 & 0.113443164911251 \tabularnewline
4 & 1.3 & 1.36145016724604 & -0.0614501672460424 \tabularnewline
5 & 1.4 & 1.30504486244303 & 0.0949551375569734 \tabularnewline
6 & 1.4 & 1.28608492885945 & 0.113915071140552 \tabularnewline
7 & 1.3 & 1.33108729412086 & -0.0310872941208596 \tabularnewline
8 & 1.3 & 1.29511349551609 & 0.00488650448391445 \tabularnewline
9 & 1.2 & 1.26339827256926 & -0.0633982725692618 \tabularnewline
10 & 1.1 & 1.23403302906861 & -0.134033029068606 \tabularnewline
11 & 1.4 & 1.16961326901589 & 0.230386730984108 \tabularnewline
12 & 1.2 & 1.26150179850262 & -0.0615017985026249 \tabularnewline
13 & 1.5 & 1.29893109174872 & 0.201068908251276 \tabularnewline
14 & 1.1 & 1.35104470818046 & -0.251044708180457 \tabularnewline
15 & 1.3 & 1.30984781700654 & -0.00984781700653888 \tabularnewline
16 & 1.5 & 1.25159522882356 & 0.248404771176443 \tabularnewline
17 & 1.1 & 1.41783092946175 & -0.317830929461752 \tabularnewline
18 & 1.4 & 1.34252360103636 & 0.0574763989636363 \tabularnewline
19 & 1.3 & 1.32547226366457 & -0.0254722636645674 \tabularnewline
20 & 1.5 & 1.40127056982009 & 0.0987294301799123 \tabularnewline
21 & 1.6 & 1.45195784849564 & 0.148042151504365 \tabularnewline
22 & 1.7 & 1.57321598112029 & 0.126784018879708 \tabularnewline
23 & 1.1 & 1.65941959719289 & -0.559419597192889 \tabularnewline
24 & 1.6 & 1.46854340919425 & 0.131456590805753 \tabularnewline
25 & 1.3 & 1.45717698523659 & -0.157176985236587 \tabularnewline
26 & 1.7 & 1.52729037797797 & 0.172709622022029 \tabularnewline
27 & 1.6 & 1.58366257006765 & 0.0163374299323458 \tabularnewline
28 & 1.7 & 1.69451539277523 & 0.00548460722476676 \tabularnewline
29 & 1.9 & 1.70872181714254 & 0.191278182857463 \tabularnewline
30 & 1.8 & 1.83140628752338 & -0.0314062875233781 \tabularnewline
31 & 1.9 & 1.87309415642743 & 0.0269058435725718 \tabularnewline
32 & 1.6 & 1.88446834289297 & -0.284468342892967 \tabularnewline
33 & 1.5 & 1.81058735222376 & -0.310587352223763 \tabularnewline
34 & 1.6 & 1.67228224186458 & -0.0722822418645782 \tabularnewline
35 & 1.6 & 1.68554458693129 & -0.0855445869312934 \tabularnewline
36 & 1.7 & 1.73054695219271 & -0.0305469521927055 \tabularnewline
37 & 2 & 1.77791973451089 & 0.222080265489109 \tabularnewline
38 & 2 & 1.94180545570292 & 0.0581945442970819 \tabularnewline
39 & 1.9 & 2.05597277476786 & -0.155972774767859 \tabularnewline
40 & 1.7 & 2.02094305546367 & -0.320943055463673 \tabularnewline
41 & 1.8 & 1.91815428250154 & -0.118154282501538 \tabularnewline
42 & 1.9 & 1.89258579381384 & 0.00741420618615849 \tabularnewline
43 & 1.7 & 1.97406901338350 & -0.274069013383497 \tabularnewline
44 & 2 & 1.94044519422489 & 0.0595548057751088 \tabularnewline
45 & 2.1 & 1.99539104855839 & 0.104608951441611 \tabularnewline
46 & 2.4 & 2.15359185633628 & 0.246408143663719 \tabularnewline
47 & 2.5 & 2.31747757752831 & 0.182522422471692 \tabularnewline
48 & 2.5 & 2.47190206810385 & 0.028097931896154 \tabularnewline
49 & 2.6 & 2.51407219546349 & 0.0859278045365071 \tabularnewline
50 & 2.2 & 2.55861273987991 & -0.358612739879913 \tabularnewline
51 & 2.5 & 2.44353049839952 & 0.0564695016004754 \tabularnewline
52 & 2.8 & 2.43025547823008 & 0.369744521769918 \tabularnewline
53 & 2.8 & 2.67652326343387 & 0.123476736566125 \tabularnewline
54 & 2.9 & 2.79541097900176 & 0.104589020998243 \tabularnewline
55 & 3 & 2.83995152341818 & 0.160048476581822 \tabularnewline
56 & 3.1 & 2.91860250508607 & 0.181397494913932 \tabularnewline
57 & 2.9 & 3.00008572465572 & -0.100085724655725 \tabularnewline
58 & 2.7 & 2.96929414339888 & -0.269294143398883 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57565&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]1.6[/C][C]1.49687344520772[/C][C]0.103126554792285[/C][/ROW]
[ROW][C]2[/C][C]1.5[/C][C]1.49169558749705[/C][C]0.00830441250294869[/C][/ROW]
[ROW][C]3[/C][C]1.5[/C][C]1.38655683508875[/C][C]0.113443164911251[/C][/ROW]
[ROW][C]4[/C][C]1.3[/C][C]1.36145016724604[/C][C]-0.0614501672460424[/C][/ROW]
[ROW][C]5[/C][C]1.4[/C][C]1.30504486244303[/C][C]0.0949551375569734[/C][/ROW]
[ROW][C]6[/C][C]1.4[/C][C]1.28608492885945[/C][C]0.113915071140552[/C][/ROW]
[ROW][C]7[/C][C]1.3[/C][C]1.33108729412086[/C][C]-0.0310872941208596[/C][/ROW]
[ROW][C]8[/C][C]1.3[/C][C]1.29511349551609[/C][C]0.00488650448391445[/C][/ROW]
[ROW][C]9[/C][C]1.2[/C][C]1.26339827256926[/C][C]-0.0633982725692618[/C][/ROW]
[ROW][C]10[/C][C]1.1[/C][C]1.23403302906861[/C][C]-0.134033029068606[/C][/ROW]
[ROW][C]11[/C][C]1.4[/C][C]1.16961326901589[/C][C]0.230386730984108[/C][/ROW]
[ROW][C]12[/C][C]1.2[/C][C]1.26150179850262[/C][C]-0.0615017985026249[/C][/ROW]
[ROW][C]13[/C][C]1.5[/C][C]1.29893109174872[/C][C]0.201068908251276[/C][/ROW]
[ROW][C]14[/C][C]1.1[/C][C]1.35104470818046[/C][C]-0.251044708180457[/C][/ROW]
[ROW][C]15[/C][C]1.3[/C][C]1.30984781700654[/C][C]-0.00984781700653888[/C][/ROW]
[ROW][C]16[/C][C]1.5[/C][C]1.25159522882356[/C][C]0.248404771176443[/C][/ROW]
[ROW][C]17[/C][C]1.1[/C][C]1.41783092946175[/C][C]-0.317830929461752[/C][/ROW]
[ROW][C]18[/C][C]1.4[/C][C]1.34252360103636[/C][C]0.0574763989636363[/C][/ROW]
[ROW][C]19[/C][C]1.3[/C][C]1.32547226366457[/C][C]-0.0254722636645674[/C][/ROW]
[ROW][C]20[/C][C]1.5[/C][C]1.40127056982009[/C][C]0.0987294301799123[/C][/ROW]
[ROW][C]21[/C][C]1.6[/C][C]1.45195784849564[/C][C]0.148042151504365[/C][/ROW]
[ROW][C]22[/C][C]1.7[/C][C]1.57321598112029[/C][C]0.126784018879708[/C][/ROW]
[ROW][C]23[/C][C]1.1[/C][C]1.65941959719289[/C][C]-0.559419597192889[/C][/ROW]
[ROW][C]24[/C][C]1.6[/C][C]1.46854340919425[/C][C]0.131456590805753[/C][/ROW]
[ROW][C]25[/C][C]1.3[/C][C]1.45717698523659[/C][C]-0.157176985236587[/C][/ROW]
[ROW][C]26[/C][C]1.7[/C][C]1.52729037797797[/C][C]0.172709622022029[/C][/ROW]
[ROW][C]27[/C][C]1.6[/C][C]1.58366257006765[/C][C]0.0163374299323458[/C][/ROW]
[ROW][C]28[/C][C]1.7[/C][C]1.69451539277523[/C][C]0.00548460722476676[/C][/ROW]
[ROW][C]29[/C][C]1.9[/C][C]1.70872181714254[/C][C]0.191278182857463[/C][/ROW]
[ROW][C]30[/C][C]1.8[/C][C]1.83140628752338[/C][C]-0.0314062875233781[/C][/ROW]
[ROW][C]31[/C][C]1.9[/C][C]1.87309415642743[/C][C]0.0269058435725718[/C][/ROW]
[ROW][C]32[/C][C]1.6[/C][C]1.88446834289297[/C][C]-0.284468342892967[/C][/ROW]
[ROW][C]33[/C][C]1.5[/C][C]1.81058735222376[/C][C]-0.310587352223763[/C][/ROW]
[ROW][C]34[/C][C]1.6[/C][C]1.67228224186458[/C][C]-0.0722822418645782[/C][/ROW]
[ROW][C]35[/C][C]1.6[/C][C]1.68554458693129[/C][C]-0.0855445869312934[/C][/ROW]
[ROW][C]36[/C][C]1.7[/C][C]1.73054695219271[/C][C]-0.0305469521927055[/C][/ROW]
[ROW][C]37[/C][C]2[/C][C]1.77791973451089[/C][C]0.222080265489109[/C][/ROW]
[ROW][C]38[/C][C]2[/C][C]1.94180545570292[/C][C]0.0581945442970819[/C][/ROW]
[ROW][C]39[/C][C]1.9[/C][C]2.05597277476786[/C][C]-0.155972774767859[/C][/ROW]
[ROW][C]40[/C][C]1.7[/C][C]2.02094305546367[/C][C]-0.320943055463673[/C][/ROW]
[ROW][C]41[/C][C]1.8[/C][C]1.91815428250154[/C][C]-0.118154282501538[/C][/ROW]
[ROW][C]42[/C][C]1.9[/C][C]1.89258579381384[/C][C]0.00741420618615849[/C][/ROW]
[ROW][C]43[/C][C]1.7[/C][C]1.97406901338350[/C][C]-0.274069013383497[/C][/ROW]
[ROW][C]44[/C][C]2[/C][C]1.94044519422489[/C][C]0.0595548057751088[/C][/ROW]
[ROW][C]45[/C][C]2.1[/C][C]1.99539104855839[/C][C]0.104608951441611[/C][/ROW]
[ROW][C]46[/C][C]2.4[/C][C]2.15359185633628[/C][C]0.246408143663719[/C][/ROW]
[ROW][C]47[/C][C]2.5[/C][C]2.31747757752831[/C][C]0.182522422471692[/C][/ROW]
[ROW][C]48[/C][C]2.5[/C][C]2.47190206810385[/C][C]0.028097931896154[/C][/ROW]
[ROW][C]49[/C][C]2.6[/C][C]2.51407219546349[/C][C]0.0859278045365071[/C][/ROW]
[ROW][C]50[/C][C]2.2[/C][C]2.55861273987991[/C][C]-0.358612739879913[/C][/ROW]
[ROW][C]51[/C][C]2.5[/C][C]2.44353049839952[/C][C]0.0564695016004754[/C][/ROW]
[ROW][C]52[/C][C]2.8[/C][C]2.43025547823008[/C][C]0.369744521769918[/C][/ROW]
[ROW][C]53[/C][C]2.8[/C][C]2.67652326343387[/C][C]0.123476736566125[/C][/ROW]
[ROW][C]54[/C][C]2.9[/C][C]2.79541097900176[/C][C]0.104589020998243[/C][/ROW]
[ROW][C]55[/C][C]3[/C][C]2.83995152341818[/C][C]0.160048476581822[/C][/ROW]
[ROW][C]56[/C][C]3.1[/C][C]2.91860250508607[/C][C]0.181397494913932[/C][/ROW]
[ROW][C]57[/C][C]2.9[/C][C]3.00008572465572[/C][C]-0.100085724655725[/C][/ROW]
[ROW][C]58[/C][C]2.7[/C][C]2.96929414339888[/C][C]-0.269294143398883[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57565&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57565&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	1.6	1.49687344520772	0.103126554792285
2	1.5	1.49169558749705	0.00830441250294869
3	1.5	1.38655683508875	0.113443164911251
4	1.3	1.36145016724604	-0.0614501672460424
5	1.4	1.30504486244303	0.0949551375569734
6	1.4	1.28608492885945	0.113915071140552
7	1.3	1.33108729412086	-0.0310872941208596
8	1.3	1.29511349551609	0.00488650448391445
9	1.2	1.26339827256926	-0.0633982725692618
10	1.1	1.23403302906861	-0.134033029068606
11	1.4	1.16961326901589	0.230386730984108
12	1.2	1.26150179850262	-0.0615017985026249
13	1.5	1.29893109174872	0.201068908251276
14	1.1	1.35104470818046	-0.251044708180457
15	1.3	1.30984781700654	-0.00984781700653888
16	1.5	1.25159522882356	0.248404771176443
17	1.1	1.41783092946175	-0.317830929461752
18	1.4	1.34252360103636	0.0574763989636363
19	1.3	1.32547226366457	-0.0254722636645674
20	1.5	1.40127056982009	0.0987294301799123
21	1.6	1.45195784849564	0.148042151504365
22	1.7	1.57321598112029	0.126784018879708
23	1.1	1.65941959719289	-0.559419597192889
24	1.6	1.46854340919425	0.131456590805753
25	1.3	1.45717698523659	-0.157176985236587
26	1.7	1.52729037797797	0.172709622022029
27	1.6	1.58366257006765	0.0163374299323458
28	1.7	1.69451539277523	0.00548460722476676
29	1.9	1.70872181714254	0.191278182857463
30	1.8	1.83140628752338	-0.0314062875233781
31	1.9	1.87309415642743	0.0269058435725718
32	1.6	1.88446834289297	-0.284468342892967
33	1.5	1.81058735222376	-0.310587352223763
34	1.6	1.67228224186458	-0.0722822418645782
35	1.6	1.68554458693129	-0.0855445869312934
36	1.7	1.73054695219271	-0.0305469521927055
37	2	1.77791973451089	0.222080265489109
38	2	1.94180545570292	0.0581945442970819
39	1.9	2.05597277476786	-0.155972774767859
40	1.7	2.02094305546367	-0.320943055463673
41	1.8	1.91815428250154	-0.118154282501538
42	1.9	1.89258579381384	0.00741420618615849
43	1.7	1.97406901338350	-0.274069013383497
44	2	1.94044519422489	0.0595548057751088
45	2.1	1.99539104855839	0.104608951441611
46	2.4	2.15359185633628	0.246408143663719
47	2.5	2.31747757752831	0.182522422471692
48	2.5	2.47190206810385	0.028097931896154
49	2.6	2.51407219546349	0.0859278045365071
50	2.2	2.55861273987991	-0.358612739879913
51	2.5	2.44353049839952	0.0564695016004754
52	2.8	2.43025547823008	0.369744521769918
53	2.8	2.67652326343387	0.123476736566125
54	2.9	2.79541097900176	0.104589020998243
55	3	2.83995152341818	0.160048476581822
56	3.1	2.91860250508607	0.181397494913932
57	2.9	3.00008572465572	-0.100085724655725
58	2.7	2.96929414339888	-0.269294143398883

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
8	0.0627955954478451	0.125591190895690	0.937204404552155
9	0.0192078981272032	0.0384157962544064	0.980792101872797
10	0.00608235804448552	0.0121647160889710	0.993917641955514
11	0.0858155998254255	0.171631199650851	0.914184400174574
12	0.0454719297815119	0.0909438595630238	0.954528070218488
13	0.0932562808927185	0.186512561785437	0.906743719107282
14	0.0778926285407004	0.155785257081401	0.9221073714593
15	0.0458759925100906	0.0917519850201812	0.95412400748991
16	0.181575604052821	0.363151208105643	0.818424395947179
17	0.175923852753216	0.351847705506432	0.824076147246784
18	0.138740739351622	0.277481478703245	0.861259260648378
19	0.105288661714443	0.210577323428887	0.894711338285556
20	0.106601909719025	0.213203819438050	0.893398090280975
21	0.145802692918965	0.291605385837929	0.854197307081035
22	0.154422880218274	0.308845760436547	0.845577119781726
23	0.521539274738019	0.956921450523962	0.478460725261981
24	0.506116836535814	0.987766326928372	0.493883163464186
25	0.473733346586997	0.947466693173995	0.526266653413003
26	0.553275664027776	0.893448671944448	0.446724335972224
27	0.514065968829683	0.971868062340634	0.485934031170317
28	0.477315537830808	0.954631075661617	0.522684462169192
29	0.608318460762233	0.783363078475534	0.391681539237767
30	0.553347267273295	0.89330546545341	0.446652732726705
31	0.659809334834905	0.68038133033019	0.340190665165095
32	0.635837445155578	0.728325109688843	0.364162554844422
33	0.652592122045238	0.694815755909523	0.347407877954762
34	0.582355544541346	0.835288910917309	0.417644455458654
35	0.504918789840074	0.990162420319852	0.495081210159926
36	0.420676020510547	0.841352041021095	0.579323979489453
37	0.526553618565518	0.946892762868964	0.473446381434482
38	0.493642966815098	0.987285933630196	0.506357033184902
39	0.431446044314813	0.862892088629626	0.568553955685187
40	0.391939226203284	0.783878452406568	0.608060773796716
41	0.307878399925071	0.615756799850142	0.692121600074929
42	0.239855130925645	0.479710261851289	0.760144869074355
43	0.425076284655705	0.85015256931141	0.574923715344295
44	0.358011704803248	0.716023409606496	0.641988295196752
45	0.450867411134635	0.90173482226927	0.549132588865365
46	0.414719684644353	0.829439369288707	0.585280315355647
47	0.346651849714245	0.693303699428491	0.653348150285755
48	0.240585722005056	0.481171444010112	0.759414277994944
49	0.225725089585705	0.45145017917141	0.774274910414295
50	0.606282762219178	0.787434475561643	0.393717237780822

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
8 & 0.0627955954478451 & 0.125591190895690 & 0.937204404552155 \tabularnewline
9 & 0.0192078981272032 & 0.0384157962544064 & 0.980792101872797 \tabularnewline
10 & 0.00608235804448552 & 0.0121647160889710 & 0.993917641955514 \tabularnewline
11 & 0.0858155998254255 & 0.171631199650851 & 0.914184400174574 \tabularnewline
12 & 0.0454719297815119 & 0.0909438595630238 & 0.954528070218488 \tabularnewline
13 & 0.0932562808927185 & 0.186512561785437 & 0.906743719107282 \tabularnewline
14 & 0.0778926285407004 & 0.155785257081401 & 0.9221073714593 \tabularnewline
15 & 0.0458759925100906 & 0.0917519850201812 & 0.95412400748991 \tabularnewline
16 & 0.181575604052821 & 0.363151208105643 & 0.818424395947179 \tabularnewline
17 & 0.175923852753216 & 0.351847705506432 & 0.824076147246784 \tabularnewline
18 & 0.138740739351622 & 0.277481478703245 & 0.861259260648378 \tabularnewline
19 & 0.105288661714443 & 0.210577323428887 & 0.894711338285556 \tabularnewline
20 & 0.106601909719025 & 0.213203819438050 & 0.893398090280975 \tabularnewline
21 & 0.145802692918965 & 0.291605385837929 & 0.854197307081035 \tabularnewline
22 & 0.154422880218274 & 0.308845760436547 & 0.845577119781726 \tabularnewline
23 & 0.521539274738019 & 0.956921450523962 & 0.478460725261981 \tabularnewline
24 & 0.506116836535814 & 0.987766326928372 & 0.493883163464186 \tabularnewline
25 & 0.473733346586997 & 0.947466693173995 & 0.526266653413003 \tabularnewline
26 & 0.553275664027776 & 0.893448671944448 & 0.446724335972224 \tabularnewline
27 & 0.514065968829683 & 0.971868062340634 & 0.485934031170317 \tabularnewline
28 & 0.477315537830808 & 0.954631075661617 & 0.522684462169192 \tabularnewline
29 & 0.608318460762233 & 0.783363078475534 & 0.391681539237767 \tabularnewline
30 & 0.553347267273295 & 0.89330546545341 & 0.446652732726705 \tabularnewline
31 & 0.659809334834905 & 0.68038133033019 & 0.340190665165095 \tabularnewline
32 & 0.635837445155578 & 0.728325109688843 & 0.364162554844422 \tabularnewline
33 & 0.652592122045238 & 0.694815755909523 & 0.347407877954762 \tabularnewline
34 & 0.582355544541346 & 0.835288910917309 & 0.417644455458654 \tabularnewline
35 & 0.504918789840074 & 0.990162420319852 & 0.495081210159926 \tabularnewline
36 & 0.420676020510547 & 0.841352041021095 & 0.579323979489453 \tabularnewline
37 & 0.526553618565518 & 0.946892762868964 & 0.473446381434482 \tabularnewline
38 & 0.493642966815098 & 0.987285933630196 & 0.506357033184902 \tabularnewline
39 & 0.431446044314813 & 0.862892088629626 & 0.568553955685187 \tabularnewline
40 & 0.391939226203284 & 0.783878452406568 & 0.608060773796716 \tabularnewline
41 & 0.307878399925071 & 0.615756799850142 & 0.692121600074929 \tabularnewline
42 & 0.239855130925645 & 0.479710261851289 & 0.760144869074355 \tabularnewline
43 & 0.425076284655705 & 0.85015256931141 & 0.574923715344295 \tabularnewline
44 & 0.358011704803248 & 0.716023409606496 & 0.641988295196752 \tabularnewline
45 & 0.450867411134635 & 0.90173482226927 & 0.549132588865365 \tabularnewline
46 & 0.414719684644353 & 0.829439369288707 & 0.585280315355647 \tabularnewline
47 & 0.346651849714245 & 0.693303699428491 & 0.653348150285755 \tabularnewline
48 & 0.240585722005056 & 0.481171444010112 & 0.759414277994944 \tabularnewline
49 & 0.225725089585705 & 0.45145017917141 & 0.774274910414295 \tabularnewline
50 & 0.606282762219178 & 0.787434475561643 & 0.393717237780822 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57565&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]0.0627955954478451[/C][C]0.125591190895690[/C][C]0.937204404552155[/C][/ROW]
[ROW][C]9[/C][C]0.0192078981272032[/C][C]0.0384157962544064[/C][C]0.980792101872797[/C][/ROW]
[ROW][C]10[/C][C]0.00608235804448552[/C][C]0.0121647160889710[/C][C]0.993917641955514[/C][/ROW]
[ROW][C]11[/C][C]0.0858155998254255[/C][C]0.171631199650851[/C][C]0.914184400174574[/C][/ROW]
[ROW][C]12[/C][C]0.0454719297815119[/C][C]0.0909438595630238[/C][C]0.954528070218488[/C][/ROW]
[ROW][C]13[/C][C]0.0932562808927185[/C][C]0.186512561785437[/C][C]0.906743719107282[/C][/ROW]
[ROW][C]14[/C][C]0.0778926285407004[/C][C]0.155785257081401[/C][C]0.9221073714593[/C][/ROW]
[ROW][C]15[/C][C]0.0458759925100906[/C][C]0.0917519850201812[/C][C]0.95412400748991[/C][/ROW]
[ROW][C]16[/C][C]0.181575604052821[/C][C]0.363151208105643[/C][C]0.818424395947179[/C][/ROW]
[ROW][C]17[/C][C]0.175923852753216[/C][C]0.351847705506432[/C][C]0.824076147246784[/C][/ROW]
[ROW][C]18[/C][C]0.138740739351622[/C][C]0.277481478703245[/C][C]0.861259260648378[/C][/ROW]
[ROW][C]19[/C][C]0.105288661714443[/C][C]0.210577323428887[/C][C]0.894711338285556[/C][/ROW]
[ROW][C]20[/C][C]0.106601909719025[/C][C]0.213203819438050[/C][C]0.893398090280975[/C][/ROW]
[ROW][C]21[/C][C]0.145802692918965[/C][C]0.291605385837929[/C][C]0.854197307081035[/C][/ROW]
[ROW][C]22[/C][C]0.154422880218274[/C][C]0.308845760436547[/C][C]0.845577119781726[/C][/ROW]
[ROW][C]23[/C][C]0.521539274738019[/C][C]0.956921450523962[/C][C]0.478460725261981[/C][/ROW]
[ROW][C]24[/C][C]0.506116836535814[/C][C]0.987766326928372[/C][C]0.493883163464186[/C][/ROW]
[ROW][C]25[/C][C]0.473733346586997[/C][C]0.947466693173995[/C][C]0.526266653413003[/C][/ROW]
[ROW][C]26[/C][C]0.553275664027776[/C][C]0.893448671944448[/C][C]0.446724335972224[/C][/ROW]
[ROW][C]27[/C][C]0.514065968829683[/C][C]0.971868062340634[/C][C]0.485934031170317[/C][/ROW]
[ROW][C]28[/C][C]0.477315537830808[/C][C]0.954631075661617[/C][C]0.522684462169192[/C][/ROW]
[ROW][C]29[/C][C]0.608318460762233[/C][C]0.783363078475534[/C][C]0.391681539237767[/C][/ROW]
[ROW][C]30[/C][C]0.553347267273295[/C][C]0.89330546545341[/C][C]0.446652732726705[/C][/ROW]
[ROW][C]31[/C][C]0.659809334834905[/C][C]0.68038133033019[/C][C]0.340190665165095[/C][/ROW]
[ROW][C]32[/C][C]0.635837445155578[/C][C]0.728325109688843[/C][C]0.364162554844422[/C][/ROW]
[ROW][C]33[/C][C]0.652592122045238[/C][C]0.694815755909523[/C][C]0.347407877954762[/C][/ROW]
[ROW][C]34[/C][C]0.582355544541346[/C][C]0.835288910917309[/C][C]0.417644455458654[/C][/ROW]
[ROW][C]35[/C][C]0.504918789840074[/C][C]0.990162420319852[/C][C]0.495081210159926[/C][/ROW]
[ROW][C]36[/C][C]0.420676020510547[/C][C]0.841352041021095[/C][C]0.579323979489453[/C][/ROW]
[ROW][C]37[/C][C]0.526553618565518[/C][C]0.946892762868964[/C][C]0.473446381434482[/C][/ROW]
[ROW][C]38[/C][C]0.493642966815098[/C][C]0.987285933630196[/C][C]0.506357033184902[/C][/ROW]
[ROW][C]39[/C][C]0.431446044314813[/C][C]0.862892088629626[/C][C]0.568553955685187[/C][/ROW]
[ROW][C]40[/C][C]0.391939226203284[/C][C]0.783878452406568[/C][C]0.608060773796716[/C][/ROW]
[ROW][C]41[/C][C]0.307878399925071[/C][C]0.615756799850142[/C][C]0.692121600074929[/C][/ROW]
[ROW][C]42[/C][C]0.239855130925645[/C][C]0.479710261851289[/C][C]0.760144869074355[/C][/ROW]
[ROW][C]43[/C][C]0.425076284655705[/C][C]0.85015256931141[/C][C]0.574923715344295[/C][/ROW]
[ROW][C]44[/C][C]0.358011704803248[/C][C]0.716023409606496[/C][C]0.641988295196752[/C][/ROW]
[ROW][C]45[/C][C]0.450867411134635[/C][C]0.90173482226927[/C][C]0.549132588865365[/C][/ROW]
[ROW][C]46[/C][C]0.414719684644353[/C][C]0.829439369288707[/C][C]0.585280315355647[/C][/ROW]
[ROW][C]47[/C][C]0.346651849714245[/C][C]0.693303699428491[/C][C]0.653348150285755[/C][/ROW]
[ROW][C]48[/C][C]0.240585722005056[/C][C]0.481171444010112[/C][C]0.759414277994944[/C][/ROW]
[ROW][C]49[/C][C]0.225725089585705[/C][C]0.45145017917141[/C][C]0.774274910414295[/C][/ROW]
[ROW][C]50[/C][C]0.606282762219178[/C][C]0.787434475561643[/C][C]0.393717237780822[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57565&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57565&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	0.0627955954478451	0.125591190895690	0.937204404552155
9	0.0192078981272032	0.0384157962544064	0.980792101872797
10	0.00608235804448552	0.0121647160889710	0.993917641955514
11	0.0858155998254255	0.171631199650851	0.914184400174574
12	0.0454719297815119	0.0909438595630238	0.954528070218488
13	0.0932562808927185	0.186512561785437	0.906743719107282
14	0.0778926285407004	0.155785257081401	0.9221073714593
15	0.0458759925100906	0.0917519850201812	0.95412400748991
16	0.181575604052821	0.363151208105643	0.818424395947179
17	0.175923852753216	0.351847705506432	0.824076147246784
18	0.138740739351622	0.277481478703245	0.861259260648378
19	0.105288661714443	0.210577323428887	0.894711338285556
20	0.106601909719025	0.213203819438050	0.893398090280975
21	0.145802692918965	0.291605385837929	0.854197307081035
22	0.154422880218274	0.308845760436547	0.845577119781726
23	0.521539274738019	0.956921450523962	0.478460725261981
24	0.506116836535814	0.987766326928372	0.493883163464186
25	0.473733346586997	0.947466693173995	0.526266653413003
26	0.553275664027776	0.893448671944448	0.446724335972224
27	0.514065968829683	0.971868062340634	0.485934031170317
28	0.477315537830808	0.954631075661617	0.522684462169192
29	0.608318460762233	0.783363078475534	0.391681539237767
30	0.553347267273295	0.89330546545341	0.446652732726705
31	0.659809334834905	0.68038133033019	0.340190665165095
32	0.635837445155578	0.728325109688843	0.364162554844422
33	0.652592122045238	0.694815755909523	0.347407877954762
34	0.582355544541346	0.835288910917309	0.417644455458654
35	0.504918789840074	0.990162420319852	0.495081210159926
36	0.420676020510547	0.841352041021095	0.579323979489453
37	0.526553618565518	0.946892762868964	0.473446381434482
38	0.493642966815098	0.987285933630196	0.506357033184902
39	0.431446044314813	0.862892088629626	0.568553955685187
40	0.391939226203284	0.783878452406568	0.608060773796716
41	0.307878399925071	0.615756799850142	0.692121600074929
42	0.239855130925645	0.479710261851289	0.760144869074355
43	0.425076284655705	0.85015256931141	0.574923715344295
44	0.358011704803248	0.716023409606496	0.641988295196752
45	0.450867411134635	0.90173482226927	0.549132588865365
46	0.414719684644353	0.829439369288707	0.585280315355647
47	0.346651849714245	0.693303699428491	0.653348150285755
48	0.240585722005056	0.481171444010112	0.759414277994944
49	0.225725089585705	0.45145017917141	0.774274910414295
50	0.606282762219178	0.787434475561643	0.393717237780822

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	0	0	OK
5% type I error level	2	0.0465116279069767	OK
10% type I error level	4	0.0930232558139535	OK

\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 & 0 & 0 & OK \tabularnewline
5% type I error level & 2 & 0.0465116279069767 & OK \tabularnewline
10% type I error level & 4 & 0.0930232558139535 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57565&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]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]2[/C][C]0.0465116279069767[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]4[/C][C]0.0930232558139535[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57565&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57565&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	0	0	OK
5% type I error level	2	0.0465116279069767	OK
10% type I error level	4	0.0930232558139535	OK

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 = Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = 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