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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

Fri, 20 Nov 2009 05:03:21 -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/20/t1258718686c7j6wv1p1zjtdsx.htm/, Retrieved Tue, 23 Apr 2024 22:59:22 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=58060, Retrieved Tue, 23 Apr 2024 22:59:22 +0000

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Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords

WS7,MR1

Estimated Impact

161

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

-       [Multiple Regression] [] [2009-11-20 12:03:21] [30f5b608e5a1bbbae86b1702c0071566] [Current]
-   PD    [Multiple Regression] [ws7] [2009-12-26 18:13:42] [74be16979710d4c4e7c6647856088456] 

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Dataseries X:

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Histogram

Boxplots

1,3	2
1,2	2,1
1,1	2,1
1,4	2,5
1,2	2,2
1,5	2,3
1,1	2,3
1,3	2,2
1,5	2,2
1,1	1,6
1,4	1,8
1,3	1,7
1,5	1,9
1,6	1,8
1,7	1,9
1,1	1,5
1,6	1
1,3	0,8
1,7	1,1
1,6	1,5
1,7	1,7
1,9	2,3
1,8	2,4
1,9	3
1,6	3
1,5	3,2
1,6	3,2
1,6	3,2
1,7	3,5
2	4
2	4,3
1,9	4,1
1,7	4
1,8	4,1
1,9	4,2
1,7	4,5
2	5,6
2,1	6,5
2,4	7,6
2,5	8,5
2,5	8,7
2,6	8,3
2,2	8,3
2,5	8,5
2,8	8,7
2,8	8,7
2,9	8,5
3	7,9
3,1	7
2,9	5,8
2,7	4,5
2,2	3,7
2,5	3,1
2,3	2,7
2,6	2,3
2,3	1,8
2,2	1,5
1,8	1,2
1,8	1

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	3 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 & 3 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58060&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]3 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=58060&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58060&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	3 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

Multiple Linear Regression - Estimated Regression Equation
inflatie[t] = + 1.28825292718720 + 0.164679951696550inflatie_levensmiddelen[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
inflatie[t] =  +  1.28825292718720 +  0.164679951696550inflatie_levensmiddelen[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58060&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]inflatie[t] =  +  1.28825292718720 +  0.164679951696550inflatie_levensmiddelen[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58060&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58060&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
inflatie[t] = + 1.28825292718720 + 0.164679951696550inflatie_levensmiddelen[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)	1.28825292718720	0.086087	14.9646	0	0
inflatie_levensmiddelen	0.164679951696550	0.019171	8.5899	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) & 1.28825292718720 & 0.086087 & 14.9646 & 0 & 0 \tabularnewline
inflatie_levensmiddelen & 0.164679951696550 & 0.019171 & 8.5899 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58060&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]1.28825292718720[/C][C]0.086087[/C][C]14.9646[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]inflatie_levensmiddelen[/C][C]0.164679951696550[/C][C]0.019171[/C][C]8.5899[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58060&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58060&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)	1.28825292718720	0.086087	14.9646	0	0
inflatie_levensmiddelen	0.164679951696550	0.019171	8.5899	0	0

Multiple Linear Regression - Regression Statistics
Multiple R	0.751116916172864
R-squared	0.564176621761034
Adjusted R-squared	0.556530597581403
F-TEST (value)	73.7869261862917
F-TEST (DF numerator)	1
F-TEST (DF denominator)	57
p-value	7.26485538393717e-12
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.362405795242342
Sum Squared Residuals	7.48626374423835

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.751116916172864 \tabularnewline
R-squared & 0.564176621761034 \tabularnewline
Adjusted R-squared & 0.556530597581403 \tabularnewline
F-TEST (value) & 73.7869261862917 \tabularnewline
F-TEST (DF numerator) & 1 \tabularnewline
F-TEST (DF denominator) & 57 \tabularnewline
p-value & 7.26485538393717e-12 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.362405795242342 \tabularnewline
Sum Squared Residuals & 7.48626374423835 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58060&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.751116916172864[/C][/ROW]
[ROW][C]R-squared[/C][C]0.564176621761034[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.556530597581403[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]73.7869261862917[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]1[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]57[/C][/ROW]
[ROW][C]p-value[/C][C]7.26485538393717e-12[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.362405795242342[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]7.48626374423835[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58060&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58060&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	0.751116916172864
R-squared	0.564176621761034
Adjusted R-squared	0.556530597581403
F-TEST (value)	73.7869261862917
F-TEST (DF numerator)	1
F-TEST (DF denominator)	57
p-value	7.26485538393717e-12
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.362405795242342
Sum Squared Residuals	7.48626374423835

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	1.3	1.61761283058029	-0.317612830580293
2	1.2	1.63408082574995	-0.434080825749952
3	1.1	1.63408082574995	-0.534080825749951
4	1.4	1.69995280642857	-0.299952806428571
5	1.2	1.65054882091961	-0.450548820919606
6	1.5	1.66701681608926	-0.167016816089261
7	1.1	1.66701681608926	-0.567016816089261
8	1.3	1.65054882091961	-0.350548820919606
9	1.5	1.65054882091961	-0.150548820919606
10	1.1	1.55174084990168	-0.451740849901676
11	1.4	1.58467684024099	-0.184676840240986
12	1.3	1.56820884507133	-0.268208845071331
13	1.5	1.60114483541064	-0.101144835410641
14	1.6	1.58467684024099	0.0153231597590140
15	1.7	1.60114483541064	0.0988551645893589
16	1.1	1.53527285473202	-0.435272854732021
17	1.6	1.45293287888375	0.147067121116254
18	1.3	1.41999688854444	-0.119996888544437
19	1.7	1.4694008740534	0.230599125946599
20	1.6	1.53527285473202	0.0647271452679789
21	1.7	1.56820884507133	0.131791154928669
22	1.9	1.66701681608926	0.232983183910739
23	1.8	1.68348481125892	0.116515188741084
24	1.9	1.78229278227685	0.117707217723154
25	1.6	1.78229278227685	-0.182292782276846
26	1.5	1.81522877261616	-0.315228772616156
27	1.6	1.81522877261616	-0.215228772616155
28	1.6	1.81522877261616	-0.215228772616155
29	1.7	1.86463275812512	-0.164632758125120
30	2	1.94697273397340	0.0530272660266048
31	2	1.99637671948236	0.00362328051763999
32	1.9	1.96344072914305	-0.0634407291430502
33	1.7	1.94697273397340	-0.246972733973395
34	1.8	1.96344072914305	-0.16344072914305
35	1.9	1.97990872431271	-0.0799087243127052
36	1.7	2.02931270982167	-0.32931270982167
37	2	2.21046065668787	-0.210460656687874
38	2.1	2.35867261321477	-0.258672613214769
39	2.4	2.53982056008097	-0.139820560080974
40	2.5	2.68803251660787	-0.188032516607868
41	2.5	2.72096850694718	-0.220968506947178
42	2.6	2.65509652626856	-0.0550965262685584
43	2.2	2.65509652626856	-0.455096526268558
44	2.5	2.68803251660787	-0.188032516607868
45	2.8	2.72096850694718	0.0790314930528216
46	2.8	2.72096850694718	0.0790314930528216
47	2.9	2.68803251660787	0.211967483392132
48	3	2.58922454558994	0.410775454410061
49	3.1	2.44101258906304	0.658987410936956
50	2.9	2.24339664702718	0.656603352972816
51	2.7	2.02931270982167	0.67068729017833
52	2.2	1.89756874846443	0.30243125153557
53	2.5	1.7987607774465	0.701239222553499
54	2.3	1.73288879676788	0.567111203232119
55	2.6	1.66701681608926	0.932983183910739
56	2.3	1.58467684024099	0.715323159759014
57	2.2	1.53527285473202	0.664727145267979
58	1.8	1.48586886922306	0.314131130776944
59	1.8	1.45293287888375	0.347067121116254

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 1.3 & 1.61761283058029 & -0.317612830580293 \tabularnewline
2 & 1.2 & 1.63408082574995 & -0.434080825749952 \tabularnewline
3 & 1.1 & 1.63408082574995 & -0.534080825749951 \tabularnewline
4 & 1.4 & 1.69995280642857 & -0.299952806428571 \tabularnewline
5 & 1.2 & 1.65054882091961 & -0.450548820919606 \tabularnewline
6 & 1.5 & 1.66701681608926 & -0.167016816089261 \tabularnewline
7 & 1.1 & 1.66701681608926 & -0.567016816089261 \tabularnewline
8 & 1.3 & 1.65054882091961 & -0.350548820919606 \tabularnewline
9 & 1.5 & 1.65054882091961 & -0.150548820919606 \tabularnewline
10 & 1.1 & 1.55174084990168 & -0.451740849901676 \tabularnewline
11 & 1.4 & 1.58467684024099 & -0.184676840240986 \tabularnewline
12 & 1.3 & 1.56820884507133 & -0.268208845071331 \tabularnewline
13 & 1.5 & 1.60114483541064 & -0.101144835410641 \tabularnewline
14 & 1.6 & 1.58467684024099 & 0.0153231597590140 \tabularnewline
15 & 1.7 & 1.60114483541064 & 0.0988551645893589 \tabularnewline
16 & 1.1 & 1.53527285473202 & -0.435272854732021 \tabularnewline
17 & 1.6 & 1.45293287888375 & 0.147067121116254 \tabularnewline
18 & 1.3 & 1.41999688854444 & -0.119996888544437 \tabularnewline
19 & 1.7 & 1.4694008740534 & 0.230599125946599 \tabularnewline
20 & 1.6 & 1.53527285473202 & 0.0647271452679789 \tabularnewline
21 & 1.7 & 1.56820884507133 & 0.131791154928669 \tabularnewline
22 & 1.9 & 1.66701681608926 & 0.232983183910739 \tabularnewline
23 & 1.8 & 1.68348481125892 & 0.116515188741084 \tabularnewline
24 & 1.9 & 1.78229278227685 & 0.117707217723154 \tabularnewline
25 & 1.6 & 1.78229278227685 & -0.182292782276846 \tabularnewline
26 & 1.5 & 1.81522877261616 & -0.315228772616156 \tabularnewline
27 & 1.6 & 1.81522877261616 & -0.215228772616155 \tabularnewline
28 & 1.6 & 1.81522877261616 & -0.215228772616155 \tabularnewline
29 & 1.7 & 1.86463275812512 & -0.164632758125120 \tabularnewline
30 & 2 & 1.94697273397340 & 0.0530272660266048 \tabularnewline
31 & 2 & 1.99637671948236 & 0.00362328051763999 \tabularnewline
32 & 1.9 & 1.96344072914305 & -0.0634407291430502 \tabularnewline
33 & 1.7 & 1.94697273397340 & -0.246972733973395 \tabularnewline
34 & 1.8 & 1.96344072914305 & -0.16344072914305 \tabularnewline
35 & 1.9 & 1.97990872431271 & -0.0799087243127052 \tabularnewline
36 & 1.7 & 2.02931270982167 & -0.32931270982167 \tabularnewline
37 & 2 & 2.21046065668787 & -0.210460656687874 \tabularnewline
38 & 2.1 & 2.35867261321477 & -0.258672613214769 \tabularnewline
39 & 2.4 & 2.53982056008097 & -0.139820560080974 \tabularnewline
40 & 2.5 & 2.68803251660787 & -0.188032516607868 \tabularnewline
41 & 2.5 & 2.72096850694718 & -0.220968506947178 \tabularnewline
42 & 2.6 & 2.65509652626856 & -0.0550965262685584 \tabularnewline
43 & 2.2 & 2.65509652626856 & -0.455096526268558 \tabularnewline
44 & 2.5 & 2.68803251660787 & -0.188032516607868 \tabularnewline
45 & 2.8 & 2.72096850694718 & 0.0790314930528216 \tabularnewline
46 & 2.8 & 2.72096850694718 & 0.0790314930528216 \tabularnewline
47 & 2.9 & 2.68803251660787 & 0.211967483392132 \tabularnewline
48 & 3 & 2.58922454558994 & 0.410775454410061 \tabularnewline
49 & 3.1 & 2.44101258906304 & 0.658987410936956 \tabularnewline
50 & 2.9 & 2.24339664702718 & 0.656603352972816 \tabularnewline
51 & 2.7 & 2.02931270982167 & 0.67068729017833 \tabularnewline
52 & 2.2 & 1.89756874846443 & 0.30243125153557 \tabularnewline
53 & 2.5 & 1.7987607774465 & 0.701239222553499 \tabularnewline
54 & 2.3 & 1.73288879676788 & 0.567111203232119 \tabularnewline
55 & 2.6 & 1.66701681608926 & 0.932983183910739 \tabularnewline
56 & 2.3 & 1.58467684024099 & 0.715323159759014 \tabularnewline
57 & 2.2 & 1.53527285473202 & 0.664727145267979 \tabularnewline
58 & 1.8 & 1.48586886922306 & 0.314131130776944 \tabularnewline
59 & 1.8 & 1.45293287888375 & 0.347067121116254 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58060&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]1.3[/C][C]1.61761283058029[/C][C]-0.317612830580293[/C][/ROW]
[ROW][C]2[/C][C]1.2[/C][C]1.63408082574995[/C][C]-0.434080825749952[/C][/ROW]
[ROW][C]3[/C][C]1.1[/C][C]1.63408082574995[/C][C]-0.534080825749951[/C][/ROW]
[ROW][C]4[/C][C]1.4[/C][C]1.69995280642857[/C][C]-0.299952806428571[/C][/ROW]
[ROW][C]5[/C][C]1.2[/C][C]1.65054882091961[/C][C]-0.450548820919606[/C][/ROW]
[ROW][C]6[/C][C]1.5[/C][C]1.66701681608926[/C][C]-0.167016816089261[/C][/ROW]
[ROW][C]7[/C][C]1.1[/C][C]1.66701681608926[/C][C]-0.567016816089261[/C][/ROW]
[ROW][C]8[/C][C]1.3[/C][C]1.65054882091961[/C][C]-0.350548820919606[/C][/ROW]
[ROW][C]9[/C][C]1.5[/C][C]1.65054882091961[/C][C]-0.150548820919606[/C][/ROW]
[ROW][C]10[/C][C]1.1[/C][C]1.55174084990168[/C][C]-0.451740849901676[/C][/ROW]
[ROW][C]11[/C][C]1.4[/C][C]1.58467684024099[/C][C]-0.184676840240986[/C][/ROW]
[ROW][C]12[/C][C]1.3[/C][C]1.56820884507133[/C][C]-0.268208845071331[/C][/ROW]
[ROW][C]13[/C][C]1.5[/C][C]1.60114483541064[/C][C]-0.101144835410641[/C][/ROW]
[ROW][C]14[/C][C]1.6[/C][C]1.58467684024099[/C][C]0.0153231597590140[/C][/ROW]
[ROW][C]15[/C][C]1.7[/C][C]1.60114483541064[/C][C]0.0988551645893589[/C][/ROW]
[ROW][C]16[/C][C]1.1[/C][C]1.53527285473202[/C][C]-0.435272854732021[/C][/ROW]
[ROW][C]17[/C][C]1.6[/C][C]1.45293287888375[/C][C]0.147067121116254[/C][/ROW]
[ROW][C]18[/C][C]1.3[/C][C]1.41999688854444[/C][C]-0.119996888544437[/C][/ROW]
[ROW][C]19[/C][C]1.7[/C][C]1.4694008740534[/C][C]0.230599125946599[/C][/ROW]
[ROW][C]20[/C][C]1.6[/C][C]1.53527285473202[/C][C]0.0647271452679789[/C][/ROW]
[ROW][C]21[/C][C]1.7[/C][C]1.56820884507133[/C][C]0.131791154928669[/C][/ROW]
[ROW][C]22[/C][C]1.9[/C][C]1.66701681608926[/C][C]0.232983183910739[/C][/ROW]
[ROW][C]23[/C][C]1.8[/C][C]1.68348481125892[/C][C]0.116515188741084[/C][/ROW]
[ROW][C]24[/C][C]1.9[/C][C]1.78229278227685[/C][C]0.117707217723154[/C][/ROW]
[ROW][C]25[/C][C]1.6[/C][C]1.78229278227685[/C][C]-0.182292782276846[/C][/ROW]
[ROW][C]26[/C][C]1.5[/C][C]1.81522877261616[/C][C]-0.315228772616156[/C][/ROW]
[ROW][C]27[/C][C]1.6[/C][C]1.81522877261616[/C][C]-0.215228772616155[/C][/ROW]
[ROW][C]28[/C][C]1.6[/C][C]1.81522877261616[/C][C]-0.215228772616155[/C][/ROW]
[ROW][C]29[/C][C]1.7[/C][C]1.86463275812512[/C][C]-0.164632758125120[/C][/ROW]
[ROW][C]30[/C][C]2[/C][C]1.94697273397340[/C][C]0.0530272660266048[/C][/ROW]
[ROW][C]31[/C][C]2[/C][C]1.99637671948236[/C][C]0.00362328051763999[/C][/ROW]
[ROW][C]32[/C][C]1.9[/C][C]1.96344072914305[/C][C]-0.0634407291430502[/C][/ROW]
[ROW][C]33[/C][C]1.7[/C][C]1.94697273397340[/C][C]-0.246972733973395[/C][/ROW]
[ROW][C]34[/C][C]1.8[/C][C]1.96344072914305[/C][C]-0.16344072914305[/C][/ROW]
[ROW][C]35[/C][C]1.9[/C][C]1.97990872431271[/C][C]-0.0799087243127052[/C][/ROW]
[ROW][C]36[/C][C]1.7[/C][C]2.02931270982167[/C][C]-0.32931270982167[/C][/ROW]
[ROW][C]37[/C][C]2[/C][C]2.21046065668787[/C][C]-0.210460656687874[/C][/ROW]
[ROW][C]38[/C][C]2.1[/C][C]2.35867261321477[/C][C]-0.258672613214769[/C][/ROW]
[ROW][C]39[/C][C]2.4[/C][C]2.53982056008097[/C][C]-0.139820560080974[/C][/ROW]
[ROW][C]40[/C][C]2.5[/C][C]2.68803251660787[/C][C]-0.188032516607868[/C][/ROW]
[ROW][C]41[/C][C]2.5[/C][C]2.72096850694718[/C][C]-0.220968506947178[/C][/ROW]
[ROW][C]42[/C][C]2.6[/C][C]2.65509652626856[/C][C]-0.0550965262685584[/C][/ROW]
[ROW][C]43[/C][C]2.2[/C][C]2.65509652626856[/C][C]-0.455096526268558[/C][/ROW]
[ROW][C]44[/C][C]2.5[/C][C]2.68803251660787[/C][C]-0.188032516607868[/C][/ROW]
[ROW][C]45[/C][C]2.8[/C][C]2.72096850694718[/C][C]0.0790314930528216[/C][/ROW]
[ROW][C]46[/C][C]2.8[/C][C]2.72096850694718[/C][C]0.0790314930528216[/C][/ROW]
[ROW][C]47[/C][C]2.9[/C][C]2.68803251660787[/C][C]0.211967483392132[/C][/ROW]
[ROW][C]48[/C][C]3[/C][C]2.58922454558994[/C][C]0.410775454410061[/C][/ROW]
[ROW][C]49[/C][C]3.1[/C][C]2.44101258906304[/C][C]0.658987410936956[/C][/ROW]
[ROW][C]50[/C][C]2.9[/C][C]2.24339664702718[/C][C]0.656603352972816[/C][/ROW]
[ROW][C]51[/C][C]2.7[/C][C]2.02931270982167[/C][C]0.67068729017833[/C][/ROW]
[ROW][C]52[/C][C]2.2[/C][C]1.89756874846443[/C][C]0.30243125153557[/C][/ROW]
[ROW][C]53[/C][C]2.5[/C][C]1.7987607774465[/C][C]0.701239222553499[/C][/ROW]
[ROW][C]54[/C][C]2.3[/C][C]1.73288879676788[/C][C]0.567111203232119[/C][/ROW]
[ROW][C]55[/C][C]2.6[/C][C]1.66701681608926[/C][C]0.932983183910739[/C][/ROW]
[ROW][C]56[/C][C]2.3[/C][C]1.58467684024099[/C][C]0.715323159759014[/C][/ROW]
[ROW][C]57[/C][C]2.2[/C][C]1.53527285473202[/C][C]0.664727145267979[/C][/ROW]
[ROW][C]58[/C][C]1.8[/C][C]1.48586886922306[/C][C]0.314131130776944[/C][/ROW]
[ROW][C]59[/C][C]1.8[/C][C]1.45293287888375[/C][C]0.347067121116254[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58060&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58060&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	1.3	1.61761283058029	-0.317612830580293
2	1.2	1.63408082574995	-0.434080825749952
3	1.1	1.63408082574995	-0.534080825749951
4	1.4	1.69995280642857	-0.299952806428571
5	1.2	1.65054882091961	-0.450548820919606
6	1.5	1.66701681608926	-0.167016816089261
7	1.1	1.66701681608926	-0.567016816089261
8	1.3	1.65054882091961	-0.350548820919606
9	1.5	1.65054882091961	-0.150548820919606
10	1.1	1.55174084990168	-0.451740849901676
11	1.4	1.58467684024099	-0.184676840240986
12	1.3	1.56820884507133	-0.268208845071331
13	1.5	1.60114483541064	-0.101144835410641
14	1.6	1.58467684024099	0.0153231597590140
15	1.7	1.60114483541064	0.0988551645893589
16	1.1	1.53527285473202	-0.435272854732021
17	1.6	1.45293287888375	0.147067121116254
18	1.3	1.41999688854444	-0.119996888544437
19	1.7	1.4694008740534	0.230599125946599
20	1.6	1.53527285473202	0.0647271452679789
21	1.7	1.56820884507133	0.131791154928669
22	1.9	1.66701681608926	0.232983183910739
23	1.8	1.68348481125892	0.116515188741084
24	1.9	1.78229278227685	0.117707217723154
25	1.6	1.78229278227685	-0.182292782276846
26	1.5	1.81522877261616	-0.315228772616156
27	1.6	1.81522877261616	-0.215228772616155
28	1.6	1.81522877261616	-0.215228772616155
29	1.7	1.86463275812512	-0.164632758125120
30	2	1.94697273397340	0.0530272660266048
31	2	1.99637671948236	0.00362328051763999
32	1.9	1.96344072914305	-0.0634407291430502
33	1.7	1.94697273397340	-0.246972733973395
34	1.8	1.96344072914305	-0.16344072914305
35	1.9	1.97990872431271	-0.0799087243127052
36	1.7	2.02931270982167	-0.32931270982167
37	2	2.21046065668787	-0.210460656687874
38	2.1	2.35867261321477	-0.258672613214769
39	2.4	2.53982056008097	-0.139820560080974
40	2.5	2.68803251660787	-0.188032516607868
41	2.5	2.72096850694718	-0.220968506947178
42	2.6	2.65509652626856	-0.0550965262685584
43	2.2	2.65509652626856	-0.455096526268558
44	2.5	2.68803251660787	-0.188032516607868
45	2.8	2.72096850694718	0.0790314930528216
46	2.8	2.72096850694718	0.0790314930528216
47	2.9	2.68803251660787	0.211967483392132
48	3	2.58922454558994	0.410775454410061
49	3.1	2.44101258906304	0.658987410936956
50	2.9	2.24339664702718	0.656603352972816
51	2.7	2.02931270982167	0.67068729017833
52	2.2	1.89756874846443	0.30243125153557
53	2.5	1.7987607774465	0.701239222553499
54	2.3	1.73288879676788	0.567111203232119
55	2.6	1.66701681608926	0.932983183910739
56	2.3	1.58467684024099	0.715323159759014
57	2.2	1.53527285473202	0.664727145267979
58	1.8	1.48586886922306	0.314131130776944
59	1.8	1.45293287888375	0.347067121116254

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
5	0.0326281988823356	0.0652563977646713	0.967371801117664
6	0.0312791131206282	0.0625582262412564	0.968720886879372
7	0.0334535820699092	0.0669071641398184	0.96654641793009
8	0.0139172228652256	0.0278344457304512	0.986082777134774
9	0.0153673028819489	0.0307346057638978	0.984632697118051
10	0.00731906686724517	0.0146381337344903	0.992680933132755
11	0.0071688129781305	0.014337625956261	0.99283118702187
12	0.00380905741026834	0.00761811482053668	0.996190942589732
13	0.00417021279225777	0.00834042558451554	0.995829787207742
14	0.00686800367209979	0.0137360073441996	0.9931319963279
15	0.0135327503080857	0.0270655006161715	0.986467249691914
16	0.0167105508664688	0.0334211017329376	0.983289449133531
17	0.0146887295205489	0.0293774590410978	0.985311270479451
18	0.0102876728739955	0.0205753457479909	0.989712327126005
19	0.0110314378362361	0.0220628756724722	0.988968562163764
20	0.00838905171496416	0.0167781034299283	0.991610948285036
21	0.00906764759072576	0.0181352951814515	0.990932352409274
22	0.0274202458799108	0.0548404917598216	0.97257975412009
23	0.0341856306235717	0.0683712612471434	0.965814369376428
24	0.0425868890206574	0.0851737780413147	0.957413110979343
25	0.0334514938249024	0.0669029876498048	0.966548506175098
26	0.0316498855965211	0.0632997711930422	0.968350114403479
27	0.0276757431856122	0.0553514863712244	0.972324256814388
28	0.0256738777502495	0.0513477555004991	0.97432612224975
29	0.0235285421198603	0.0470570842397207	0.97647145788014
30	0.0225559979463835	0.0451119958927670	0.977444002053617
31	0.0181348440763620	0.0362696881527241	0.981865155923638
32	0.0144767957283773	0.0289535914567547	0.985523204271623
33	0.0176566954004093	0.0353133908008187	0.98234330459959
34	0.0189524824530508	0.0379049649061016	0.98104751754695
35	0.0186212025386530	0.0372424050773059	0.981378797461347
36	0.0433751849182819	0.0867503698365638	0.956624815081718
37	0.0546489362400105	0.109297872480021	0.94535106375999
38	0.0706432840472426	0.141286568094485	0.929356715952757
39	0.0606111118357189	0.121222223671438	0.939388888164281
40	0.0501021740004277	0.100204348000855	0.949897825999572
41	0.0455963963399208	0.0911927926798416	0.95440360366008
42	0.0360858680603622	0.0721717361207244	0.963914131939638
43	0.166880666234147	0.333761332468293	0.833119333765853
44	0.286818556982526	0.573637113965053	0.713181443017474
45	0.315964686942831	0.631929373885663	0.684035313057169
46	0.404670408399941	0.809340816799882	0.595329591600059
47	0.517770955861056	0.964458088277888	0.482229044138944
48	0.603176053704825	0.79364789259035	0.396823946295175
49	0.641815574773598	0.716368850452803	0.358184425226402
50	0.637247177067797	0.725505645864406	0.362752822932203
51	0.609667216464404	0.780665567071192	0.390332783535596
52	0.813931751903546	0.372136496192907	0.186068248096454
53	0.778995620284944	0.442008759430112	0.221004379715056
54	0.97354526310127	0.0529094737974597	0.0264547368987298

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
5 & 0.0326281988823356 & 0.0652563977646713 & 0.967371801117664 \tabularnewline
6 & 0.0312791131206282 & 0.0625582262412564 & 0.968720886879372 \tabularnewline
7 & 0.0334535820699092 & 0.0669071641398184 & 0.96654641793009 \tabularnewline
8 & 0.0139172228652256 & 0.0278344457304512 & 0.986082777134774 \tabularnewline
9 & 0.0153673028819489 & 0.0307346057638978 & 0.984632697118051 \tabularnewline
10 & 0.00731906686724517 & 0.0146381337344903 & 0.992680933132755 \tabularnewline
11 & 0.0071688129781305 & 0.014337625956261 & 0.99283118702187 \tabularnewline
12 & 0.00380905741026834 & 0.00761811482053668 & 0.996190942589732 \tabularnewline
13 & 0.00417021279225777 & 0.00834042558451554 & 0.995829787207742 \tabularnewline
14 & 0.00686800367209979 & 0.0137360073441996 & 0.9931319963279 \tabularnewline
15 & 0.0135327503080857 & 0.0270655006161715 & 0.986467249691914 \tabularnewline
16 & 0.0167105508664688 & 0.0334211017329376 & 0.983289449133531 \tabularnewline
17 & 0.0146887295205489 & 0.0293774590410978 & 0.985311270479451 \tabularnewline
18 & 0.0102876728739955 & 0.0205753457479909 & 0.989712327126005 \tabularnewline
19 & 0.0110314378362361 & 0.0220628756724722 & 0.988968562163764 \tabularnewline
20 & 0.00838905171496416 & 0.0167781034299283 & 0.991610948285036 \tabularnewline
21 & 0.00906764759072576 & 0.0181352951814515 & 0.990932352409274 \tabularnewline
22 & 0.0274202458799108 & 0.0548404917598216 & 0.97257975412009 \tabularnewline
23 & 0.0341856306235717 & 0.0683712612471434 & 0.965814369376428 \tabularnewline
24 & 0.0425868890206574 & 0.0851737780413147 & 0.957413110979343 \tabularnewline
25 & 0.0334514938249024 & 0.0669029876498048 & 0.966548506175098 \tabularnewline
26 & 0.0316498855965211 & 0.0632997711930422 & 0.968350114403479 \tabularnewline
27 & 0.0276757431856122 & 0.0553514863712244 & 0.972324256814388 \tabularnewline
28 & 0.0256738777502495 & 0.0513477555004991 & 0.97432612224975 \tabularnewline
29 & 0.0235285421198603 & 0.0470570842397207 & 0.97647145788014 \tabularnewline
30 & 0.0225559979463835 & 0.0451119958927670 & 0.977444002053617 \tabularnewline
31 & 0.0181348440763620 & 0.0362696881527241 & 0.981865155923638 \tabularnewline
32 & 0.0144767957283773 & 0.0289535914567547 & 0.985523204271623 \tabularnewline
33 & 0.0176566954004093 & 0.0353133908008187 & 0.98234330459959 \tabularnewline
34 & 0.0189524824530508 & 0.0379049649061016 & 0.98104751754695 \tabularnewline
35 & 0.0186212025386530 & 0.0372424050773059 & 0.981378797461347 \tabularnewline
36 & 0.0433751849182819 & 0.0867503698365638 & 0.956624815081718 \tabularnewline
37 & 0.0546489362400105 & 0.109297872480021 & 0.94535106375999 \tabularnewline
38 & 0.0706432840472426 & 0.141286568094485 & 0.929356715952757 \tabularnewline
39 & 0.0606111118357189 & 0.121222223671438 & 0.939388888164281 \tabularnewline
40 & 0.0501021740004277 & 0.100204348000855 & 0.949897825999572 \tabularnewline
41 & 0.0455963963399208 & 0.0911927926798416 & 0.95440360366008 \tabularnewline
42 & 0.0360858680603622 & 0.0721717361207244 & 0.963914131939638 \tabularnewline
43 & 0.166880666234147 & 0.333761332468293 & 0.833119333765853 \tabularnewline
44 & 0.286818556982526 & 0.573637113965053 & 0.713181443017474 \tabularnewline
45 & 0.315964686942831 & 0.631929373885663 & 0.684035313057169 \tabularnewline
46 & 0.404670408399941 & 0.809340816799882 & 0.595329591600059 \tabularnewline
47 & 0.517770955861056 & 0.964458088277888 & 0.482229044138944 \tabularnewline
48 & 0.603176053704825 & 0.79364789259035 & 0.396823946295175 \tabularnewline
49 & 0.641815574773598 & 0.716368850452803 & 0.358184425226402 \tabularnewline
50 & 0.637247177067797 & 0.725505645864406 & 0.362752822932203 \tabularnewline
51 & 0.609667216464404 & 0.780665567071192 & 0.390332783535596 \tabularnewline
52 & 0.813931751903546 & 0.372136496192907 & 0.186068248096454 \tabularnewline
53 & 0.778995620284944 & 0.442008759430112 & 0.221004379715056 \tabularnewline
54 & 0.97354526310127 & 0.0529094737974597 & 0.0264547368987298 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58060&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]0.0326281988823356[/C][C]0.0652563977646713[/C][C]0.967371801117664[/C][/ROW]
[ROW][C]6[/C][C]0.0312791131206282[/C][C]0.0625582262412564[/C][C]0.968720886879372[/C][/ROW]
[ROW][C]7[/C][C]0.0334535820699092[/C][C]0.0669071641398184[/C][C]0.96654641793009[/C][/ROW]
[ROW][C]8[/C][C]0.0139172228652256[/C][C]0.0278344457304512[/C][C]0.986082777134774[/C][/ROW]
[ROW][C]9[/C][C]0.0153673028819489[/C][C]0.0307346057638978[/C][C]0.984632697118051[/C][/ROW]
[ROW][C]10[/C][C]0.00731906686724517[/C][C]0.0146381337344903[/C][C]0.992680933132755[/C][/ROW]
[ROW][C]11[/C][C]0.0071688129781305[/C][C]0.014337625956261[/C][C]0.99283118702187[/C][/ROW]
[ROW][C]12[/C][C]0.00380905741026834[/C][C]0.00761811482053668[/C][C]0.996190942589732[/C][/ROW]
[ROW][C]13[/C][C]0.00417021279225777[/C][C]0.00834042558451554[/C][C]0.995829787207742[/C][/ROW]
[ROW][C]14[/C][C]0.00686800367209979[/C][C]0.0137360073441996[/C][C]0.9931319963279[/C][/ROW]
[ROW][C]15[/C][C]0.0135327503080857[/C][C]0.0270655006161715[/C][C]0.986467249691914[/C][/ROW]
[ROW][C]16[/C][C]0.0167105508664688[/C][C]0.0334211017329376[/C][C]0.983289449133531[/C][/ROW]
[ROW][C]17[/C][C]0.0146887295205489[/C][C]0.0293774590410978[/C][C]0.985311270479451[/C][/ROW]
[ROW][C]18[/C][C]0.0102876728739955[/C][C]0.0205753457479909[/C][C]0.989712327126005[/C][/ROW]
[ROW][C]19[/C][C]0.0110314378362361[/C][C]0.0220628756724722[/C][C]0.988968562163764[/C][/ROW]
[ROW][C]20[/C][C]0.00838905171496416[/C][C]0.0167781034299283[/C][C]0.991610948285036[/C][/ROW]
[ROW][C]21[/C][C]0.00906764759072576[/C][C]0.0181352951814515[/C][C]0.990932352409274[/C][/ROW]
[ROW][C]22[/C][C]0.0274202458799108[/C][C]0.0548404917598216[/C][C]0.97257975412009[/C][/ROW]
[ROW][C]23[/C][C]0.0341856306235717[/C][C]0.0683712612471434[/C][C]0.965814369376428[/C][/ROW]
[ROW][C]24[/C][C]0.0425868890206574[/C][C]0.0851737780413147[/C][C]0.957413110979343[/C][/ROW]
[ROW][C]25[/C][C]0.0334514938249024[/C][C]0.0669029876498048[/C][C]0.966548506175098[/C][/ROW]
[ROW][C]26[/C][C]0.0316498855965211[/C][C]0.0632997711930422[/C][C]0.968350114403479[/C][/ROW]
[ROW][C]27[/C][C]0.0276757431856122[/C][C]0.0553514863712244[/C][C]0.972324256814388[/C][/ROW]
[ROW][C]28[/C][C]0.0256738777502495[/C][C]0.0513477555004991[/C][C]0.97432612224975[/C][/ROW]
[ROW][C]29[/C][C]0.0235285421198603[/C][C]0.0470570842397207[/C][C]0.97647145788014[/C][/ROW]
[ROW][C]30[/C][C]0.0225559979463835[/C][C]0.0451119958927670[/C][C]0.977444002053617[/C][/ROW]
[ROW][C]31[/C][C]0.0181348440763620[/C][C]0.0362696881527241[/C][C]0.981865155923638[/C][/ROW]
[ROW][C]32[/C][C]0.0144767957283773[/C][C]0.0289535914567547[/C][C]0.985523204271623[/C][/ROW]
[ROW][C]33[/C][C]0.0176566954004093[/C][C]0.0353133908008187[/C][C]0.98234330459959[/C][/ROW]
[ROW][C]34[/C][C]0.0189524824530508[/C][C]0.0379049649061016[/C][C]0.98104751754695[/C][/ROW]
[ROW][C]35[/C][C]0.0186212025386530[/C][C]0.0372424050773059[/C][C]0.981378797461347[/C][/ROW]
[ROW][C]36[/C][C]0.0433751849182819[/C][C]0.0867503698365638[/C][C]0.956624815081718[/C][/ROW]
[ROW][C]37[/C][C]0.0546489362400105[/C][C]0.109297872480021[/C][C]0.94535106375999[/C][/ROW]
[ROW][C]38[/C][C]0.0706432840472426[/C][C]0.141286568094485[/C][C]0.929356715952757[/C][/ROW]
[ROW][C]39[/C][C]0.0606111118357189[/C][C]0.121222223671438[/C][C]0.939388888164281[/C][/ROW]
[ROW][C]40[/C][C]0.0501021740004277[/C][C]0.100204348000855[/C][C]0.949897825999572[/C][/ROW]
[ROW][C]41[/C][C]0.0455963963399208[/C][C]0.0911927926798416[/C][C]0.95440360366008[/C][/ROW]
[ROW][C]42[/C][C]0.0360858680603622[/C][C]0.0721717361207244[/C][C]0.963914131939638[/C][/ROW]
[ROW][C]43[/C][C]0.166880666234147[/C][C]0.333761332468293[/C][C]0.833119333765853[/C][/ROW]
[ROW][C]44[/C][C]0.286818556982526[/C][C]0.573637113965053[/C][C]0.713181443017474[/C][/ROW]
[ROW][C]45[/C][C]0.315964686942831[/C][C]0.631929373885663[/C][C]0.684035313057169[/C][/ROW]
[ROW][C]46[/C][C]0.404670408399941[/C][C]0.809340816799882[/C][C]0.595329591600059[/C][/ROW]
[ROW][C]47[/C][C]0.517770955861056[/C][C]0.964458088277888[/C][C]0.482229044138944[/C][/ROW]
[ROW][C]48[/C][C]0.603176053704825[/C][C]0.79364789259035[/C][C]0.396823946295175[/C][/ROW]
[ROW][C]49[/C][C]0.641815574773598[/C][C]0.716368850452803[/C][C]0.358184425226402[/C][/ROW]
[ROW][C]50[/C][C]0.637247177067797[/C][C]0.725505645864406[/C][C]0.362752822932203[/C][/ROW]
[ROW][C]51[/C][C]0.609667216464404[/C][C]0.780665567071192[/C][C]0.390332783535596[/C][/ROW]
[ROW][C]52[/C][C]0.813931751903546[/C][C]0.372136496192907[/C][C]0.186068248096454[/C][/ROW]
[ROW][C]53[/C][C]0.778995620284944[/C][C]0.442008759430112[/C][C]0.221004379715056[/C][/ROW]
[ROW][C]54[/C][C]0.97354526310127[/C][C]0.0529094737974597[/C][C]0.0264547368987298[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58060&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58060&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	0.0326281988823356	0.0652563977646713	0.967371801117664
6	0.0312791131206282	0.0625582262412564	0.968720886879372
7	0.0334535820699092	0.0669071641398184	0.96654641793009
8	0.0139172228652256	0.0278344457304512	0.986082777134774
9	0.0153673028819489	0.0307346057638978	0.984632697118051
10	0.00731906686724517	0.0146381337344903	0.992680933132755
11	0.0071688129781305	0.014337625956261	0.99283118702187
12	0.00380905741026834	0.00761811482053668	0.996190942589732
13	0.00417021279225777	0.00834042558451554	0.995829787207742
14	0.00686800367209979	0.0137360073441996	0.9931319963279
15	0.0135327503080857	0.0270655006161715	0.986467249691914
16	0.0167105508664688	0.0334211017329376	0.983289449133531
17	0.0146887295205489	0.0293774590410978	0.985311270479451
18	0.0102876728739955	0.0205753457479909	0.989712327126005
19	0.0110314378362361	0.0220628756724722	0.988968562163764
20	0.00838905171496416	0.0167781034299283	0.991610948285036
21	0.00906764759072576	0.0181352951814515	0.990932352409274
22	0.0274202458799108	0.0548404917598216	0.97257975412009
23	0.0341856306235717	0.0683712612471434	0.965814369376428
24	0.0425868890206574	0.0851737780413147	0.957413110979343
25	0.0334514938249024	0.0669029876498048	0.966548506175098
26	0.0316498855965211	0.0632997711930422	0.968350114403479
27	0.0276757431856122	0.0553514863712244	0.972324256814388
28	0.0256738777502495	0.0513477555004991	0.97432612224975
29	0.0235285421198603	0.0470570842397207	0.97647145788014
30	0.0225559979463835	0.0451119958927670	0.977444002053617
31	0.0181348440763620	0.0362696881527241	0.981865155923638
32	0.0144767957283773	0.0289535914567547	0.985523204271623
33	0.0176566954004093	0.0353133908008187	0.98234330459959
34	0.0189524824530508	0.0379049649061016	0.98104751754695
35	0.0186212025386530	0.0372424050773059	0.981378797461347
36	0.0433751849182819	0.0867503698365638	0.956624815081718
37	0.0546489362400105	0.109297872480021	0.94535106375999
38	0.0706432840472426	0.141286568094485	0.929356715952757
39	0.0606111118357189	0.121222223671438	0.939388888164281
40	0.0501021740004277	0.100204348000855	0.949897825999572
41	0.0455963963399208	0.0911927926798416	0.95440360366008
42	0.0360858680603622	0.0721717361207244	0.963914131939638
43	0.166880666234147	0.333761332468293	0.833119333765853
44	0.286818556982526	0.573637113965053	0.713181443017474
45	0.315964686942831	0.631929373885663	0.684035313057169
46	0.404670408399941	0.809340816799882	0.595329591600059
47	0.517770955861056	0.964458088277888	0.482229044138944
48	0.603176053704825	0.79364789259035	0.396823946295175
49	0.641815574773598	0.716368850452803	0.358184425226402
50	0.637247177067797	0.725505645864406	0.362752822932203
51	0.609667216464404	0.780665567071192	0.390332783535596
52	0.813931751903546	0.372136496192907	0.186068248096454
53	0.778995620284944	0.442008759430112	0.221004379715056
54	0.97354526310127	0.0529094737974597	0.0264547368987298

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	2	0.04	NOK
5% type I error level	21	0.42	NOK
10% type I error level	35	0.7	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 & 2 & 0.04 & NOK \tabularnewline
5% type I error level & 21 & 0.42 & NOK \tabularnewline
10% type I error level & 35 & 0.7 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58060&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]2[/C][C]0.04[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]21[/C][C]0.42[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]35[/C][C]0.7[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58060&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58060&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	2	0.04	NOK
5% type I error level	21	0.42	NOK
10% type I error level	35	0.7	NOK

Figure 1

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Figure 2

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Figure 3

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Figure 4

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Figure 5

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Figure 6

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Figure 7

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Figure 8

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Figure 9

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Figure 10

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Parameters (Session):

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

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