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Author's title

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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Fri, 20 Nov 2009 05:04:37 -0700

Cite this page as follows

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2009/Nov/20/t1258718743b9u56ernrjevgxx.htm/, Retrieved Fri, 26 Apr 2024 21:18:16 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=58063, Retrieved Fri, 26 Apr 2024 21:18:16 +0000

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Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords

ETSHWW7(2)

Estimated Impact

177

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

-     [Multiple Regression] [invoer-textiel] [2008-12-19 19:19:44] [5e74953d94072114d25d7276793b561e]
-   PD  [Multiple Regression] [invoer-textiel] [2008-12-19 19:31:41] [5e74953d94072114d25d7276793b561e]
-   PD    [Multiple Regression] [werkloosheid/invoer] [2008-12-19 20:08:51] [5e74953d94072114d25d7276793b561e]
-  M D        [Multiple Regression] [Workshop 7: Multi...] [2009-11-20 12:04:37] [af31b947d6acaef3c71f428c4bb503e9] [Current]

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Dataseries X:

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Histogram

Boxplots

1.43	0.51
1.43	0.51
1.43	0.51
1.43	0.51
1.43	0.52
1.43	0.52
1.44	0.52
1.48	0.53
1.48	0.53
1.48	0.52
1.48	0.52
1.48	0.52
1.48	0.52
1.48	0.52
1.48	0.52
1.48	0.52
1.48	0.52
1.48	0.52
1.48	0.52
1.48	0.53
1.48	0.53
1.48	0.53
1.48	0.54
1.48	0.54
1.48	0.54
1.48	0.54
1.48	0.54
1.48	0.54
1.48	0.54
1.48	0.54
1.48	0.54
1.48	0.54
1.48	0.53
1.48	0.53
1.48	0.53
1.48	0.53
1.48	0.53
1.57	0.54
1.58	0.55
1.58	0.55
1.58	0.55
1.58	0.55
1.59	0.55
1.6	0.55
1.6	0.55
1.61	0.55
1.61	0.56
1.61	0.56
1.62	0.56
1.63	0.56
1.63	0.56
1.64	0.55
1.64	0.56
1.64	0.55
1.64	0.55
1.64	0.56
1.65	0.55
1.65	0.55
1.65	0.55
1.65	0.55

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=58063&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=58063&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58063&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
Broodprijs[t] = -0.799665390788996 + 4.33374490677863Bakmeelprijs[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Broodprijs[t] =  -0.799665390788996 +  4.33374490677863Bakmeelprijs[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58063&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Broodprijs[t] =  -0.799665390788996 +  4.33374490677863Bakmeelprijs[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58063&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58063&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
Broodprijs[t] = -0.799665390788996 + 4.33374490677863Bakmeelprijs[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)	-0.799665390788996	0.171007	-4.6762	1.8e-05	9e-06
Bakmeelprijs	4.33374490677863	0.318424	13.61	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) & -0.799665390788996 & 0.171007 & -4.6762 & 1.8e-05 & 9e-06 \tabularnewline
Bakmeelprijs & 4.33374490677863 & 0.318424 & 13.61 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58063&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]-0.799665390788996[/C][C]0.171007[/C][C]-4.6762[/C][C]1.8e-05[/C][C]9e-06[/C][/ROW]
[ROW][C]Bakmeelprijs[/C][C]4.33374490677863[/C][C]0.318424[/C][C]13.61[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58063&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58063&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)	-0.799665390788996	0.171007	-4.6762	1.8e-05	9e-06
Bakmeelprijs	4.33374490677863	0.318424	13.61	0	0

Multiple Linear Regression - Regression Statistics
Multiple R	0.872665100613162
R-squared	0.76154437782818
Adjusted R-squared	0.757433073997631
F-TEST (value)	185.231841093718
F-TEST (DF numerator)	1
F-TEST (DF denominator)	58
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.0369952185155945
Sum Squared Residuals	0.079381479194962

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.872665100613162 \tabularnewline
R-squared & 0.76154437782818 \tabularnewline
Adjusted R-squared & 0.757433073997631 \tabularnewline
F-TEST (value) & 185.231841093718 \tabularnewline
F-TEST (DF numerator) & 1 \tabularnewline
F-TEST (DF denominator) & 58 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.0369952185155945 \tabularnewline
Sum Squared Residuals & 0.079381479194962 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58063&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.872665100613162[/C][/ROW]
[ROW][C]R-squared[/C][C]0.76154437782818[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.757433073997631[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]185.231841093718[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]1[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]58[/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]0.0369952185155945[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]0.079381479194962[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58063&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58063&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	0.872665100613162
R-squared	0.76154437782818
Adjusted R-squared	0.757433073997631
F-TEST (value)	185.231841093718
F-TEST (DF numerator)	1
F-TEST (DF denominator)	58
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.0369952185155945
Sum Squared Residuals	0.079381479194962

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	1.43	1.41054451166811	0.0194554883318857
2	1.43	1.41054451166811	0.0194554883318930
3	1.43	1.41054451166811	0.0194554883318934
4	1.43	1.41054451166811	0.0194554883318934
5	1.43	1.45388196073589	-0.0238819607358929
6	1.43	1.45388196073589	-0.0238819607358929
7	1.44	1.45388196073589	-0.0138819607358929
8	1.48	1.49721940980368	-0.0172194098036792
9	1.48	1.49721940980368	-0.0172194098036792
10	1.48	1.45388196073589	0.0261180392641071
11	1.48	1.45388196073589	0.0261180392641071
12	1.48	1.45388196073589	0.0261180392641071
13	1.48	1.45388196073589	0.0261180392641071
14	1.48	1.45388196073589	0.0261180392641071
15	1.48	1.45388196073589	0.0261180392641071
16	1.48	1.45388196073589	0.0261180392641071
17	1.48	1.45388196073589	0.0261180392641071
18	1.48	1.45388196073589	0.0261180392641071
19	1.48	1.45388196073589	0.0261180392641071
20	1.48	1.49721940980368	-0.0172194098036792
21	1.48	1.49721940980368	-0.0172194098036792
22	1.48	1.49721940980368	-0.0172194098036792
23	1.48	1.54055685887147	-0.0605568588714655
24	1.48	1.54055685887147	-0.0605568588714655
25	1.48	1.54055685887147	-0.0605568588714655
26	1.48	1.54055685887147	-0.0605568588714655
27	1.48	1.54055685887147	-0.0605568588714655
28	1.48	1.54055685887147	-0.0605568588714655
29	1.48	1.54055685887147	-0.0605568588714655
30	1.48	1.54055685887147	-0.0605568588714655
31	1.48	1.54055685887147	-0.0605568588714655
32	1.48	1.54055685887147	-0.0605568588714655
33	1.48	1.49721940980368	-0.0172194098036792
34	1.48	1.49721940980368	-0.0172194098036792
35	1.48	1.49721940980368	-0.0172194098036792
36	1.48	1.49721940980368	-0.0172194098036792
37	1.48	1.49721940980368	-0.0172194098036792
38	1.57	1.54055685887147	0.0294431411285346
39	1.58	1.58389430793925	-0.00389430793925175
40	1.58	1.58389430793925	-0.00389430793925175
41	1.58	1.58389430793925	-0.00389430793925175
42	1.58	1.58389430793925	-0.00389430793925175
43	1.59	1.58389430793925	0.00610569206074826
44	1.6	1.58389430793925	0.0161056920607483
45	1.6	1.58389430793925	0.0161056920607483
46	1.61	1.58389430793925	0.0261056920607483
47	1.61	1.62723175700704	-0.0172317570070380
48	1.61	1.62723175700704	-0.0172317570070380
49	1.62	1.62723175700704	-0.00723175700703803
50	1.63	1.62723175700704	0.00276824299296176
51	1.63	1.62723175700704	0.00276824299296176
52	1.64	1.58389430793925	0.0561056920607481
53	1.64	1.62723175700704	0.0127682429929618
54	1.64	1.58389430793925	0.0561056920607481
55	1.64	1.58389430793925	0.0561056920607481
56	1.64	1.62723175700704	0.0127682429929618
57	1.65	1.58389430793925	0.0661056920607481
58	1.65	1.58389430793925	0.0661056920607481
59	1.65	1.58389430793925	0.0661056920607481
60	1.65	1.58389430793925	0.0661056920607481

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 1.43 & 1.41054451166811 & 0.0194554883318857 \tabularnewline
2 & 1.43 & 1.41054451166811 & 0.0194554883318930 \tabularnewline
3 & 1.43 & 1.41054451166811 & 0.0194554883318934 \tabularnewline
4 & 1.43 & 1.41054451166811 & 0.0194554883318934 \tabularnewline
5 & 1.43 & 1.45388196073589 & -0.0238819607358929 \tabularnewline
6 & 1.43 & 1.45388196073589 & -0.0238819607358929 \tabularnewline
7 & 1.44 & 1.45388196073589 & -0.0138819607358929 \tabularnewline
8 & 1.48 & 1.49721940980368 & -0.0172194098036792 \tabularnewline
9 & 1.48 & 1.49721940980368 & -0.0172194098036792 \tabularnewline
10 & 1.48 & 1.45388196073589 & 0.0261180392641071 \tabularnewline
11 & 1.48 & 1.45388196073589 & 0.0261180392641071 \tabularnewline
12 & 1.48 & 1.45388196073589 & 0.0261180392641071 \tabularnewline
13 & 1.48 & 1.45388196073589 & 0.0261180392641071 \tabularnewline
14 & 1.48 & 1.45388196073589 & 0.0261180392641071 \tabularnewline
15 & 1.48 & 1.45388196073589 & 0.0261180392641071 \tabularnewline
16 & 1.48 & 1.45388196073589 & 0.0261180392641071 \tabularnewline
17 & 1.48 & 1.45388196073589 & 0.0261180392641071 \tabularnewline
18 & 1.48 & 1.45388196073589 & 0.0261180392641071 \tabularnewline
19 & 1.48 & 1.45388196073589 & 0.0261180392641071 \tabularnewline
20 & 1.48 & 1.49721940980368 & -0.0172194098036792 \tabularnewline
21 & 1.48 & 1.49721940980368 & -0.0172194098036792 \tabularnewline
22 & 1.48 & 1.49721940980368 & -0.0172194098036792 \tabularnewline
23 & 1.48 & 1.54055685887147 & -0.0605568588714655 \tabularnewline
24 & 1.48 & 1.54055685887147 & -0.0605568588714655 \tabularnewline
25 & 1.48 & 1.54055685887147 & -0.0605568588714655 \tabularnewline
26 & 1.48 & 1.54055685887147 & -0.0605568588714655 \tabularnewline
27 & 1.48 & 1.54055685887147 & -0.0605568588714655 \tabularnewline
28 & 1.48 & 1.54055685887147 & -0.0605568588714655 \tabularnewline
29 & 1.48 & 1.54055685887147 & -0.0605568588714655 \tabularnewline
30 & 1.48 & 1.54055685887147 & -0.0605568588714655 \tabularnewline
31 & 1.48 & 1.54055685887147 & -0.0605568588714655 \tabularnewline
32 & 1.48 & 1.54055685887147 & -0.0605568588714655 \tabularnewline
33 & 1.48 & 1.49721940980368 & -0.0172194098036792 \tabularnewline
34 & 1.48 & 1.49721940980368 & -0.0172194098036792 \tabularnewline
35 & 1.48 & 1.49721940980368 & -0.0172194098036792 \tabularnewline
36 & 1.48 & 1.49721940980368 & -0.0172194098036792 \tabularnewline
37 & 1.48 & 1.49721940980368 & -0.0172194098036792 \tabularnewline
38 & 1.57 & 1.54055685887147 & 0.0294431411285346 \tabularnewline
39 & 1.58 & 1.58389430793925 & -0.00389430793925175 \tabularnewline
40 & 1.58 & 1.58389430793925 & -0.00389430793925175 \tabularnewline
41 & 1.58 & 1.58389430793925 & -0.00389430793925175 \tabularnewline
42 & 1.58 & 1.58389430793925 & -0.00389430793925175 \tabularnewline
43 & 1.59 & 1.58389430793925 & 0.00610569206074826 \tabularnewline
44 & 1.6 & 1.58389430793925 & 0.0161056920607483 \tabularnewline
45 & 1.6 & 1.58389430793925 & 0.0161056920607483 \tabularnewline
46 & 1.61 & 1.58389430793925 & 0.0261056920607483 \tabularnewline
47 & 1.61 & 1.62723175700704 & -0.0172317570070380 \tabularnewline
48 & 1.61 & 1.62723175700704 & -0.0172317570070380 \tabularnewline
49 & 1.62 & 1.62723175700704 & -0.00723175700703803 \tabularnewline
50 & 1.63 & 1.62723175700704 & 0.00276824299296176 \tabularnewline
51 & 1.63 & 1.62723175700704 & 0.00276824299296176 \tabularnewline
52 & 1.64 & 1.58389430793925 & 0.0561056920607481 \tabularnewline
53 & 1.64 & 1.62723175700704 & 0.0127682429929618 \tabularnewline
54 & 1.64 & 1.58389430793925 & 0.0561056920607481 \tabularnewline
55 & 1.64 & 1.58389430793925 & 0.0561056920607481 \tabularnewline
56 & 1.64 & 1.62723175700704 & 0.0127682429929618 \tabularnewline
57 & 1.65 & 1.58389430793925 & 0.0661056920607481 \tabularnewline
58 & 1.65 & 1.58389430793925 & 0.0661056920607481 \tabularnewline
59 & 1.65 & 1.58389430793925 & 0.0661056920607481 \tabularnewline
60 & 1.65 & 1.58389430793925 & 0.0661056920607481 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58063&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]1.43[/C][C]1.41054451166811[/C][C]0.0194554883318857[/C][/ROW]
[ROW][C]2[/C][C]1.43[/C][C]1.41054451166811[/C][C]0.0194554883318930[/C][/ROW]
[ROW][C]3[/C][C]1.43[/C][C]1.41054451166811[/C][C]0.0194554883318934[/C][/ROW]
[ROW][C]4[/C][C]1.43[/C][C]1.41054451166811[/C][C]0.0194554883318934[/C][/ROW]
[ROW][C]5[/C][C]1.43[/C][C]1.45388196073589[/C][C]-0.0238819607358929[/C][/ROW]
[ROW][C]6[/C][C]1.43[/C][C]1.45388196073589[/C][C]-0.0238819607358929[/C][/ROW]
[ROW][C]7[/C][C]1.44[/C][C]1.45388196073589[/C][C]-0.0138819607358929[/C][/ROW]
[ROW][C]8[/C][C]1.48[/C][C]1.49721940980368[/C][C]-0.0172194098036792[/C][/ROW]
[ROW][C]9[/C][C]1.48[/C][C]1.49721940980368[/C][C]-0.0172194098036792[/C][/ROW]
[ROW][C]10[/C][C]1.48[/C][C]1.45388196073589[/C][C]0.0261180392641071[/C][/ROW]
[ROW][C]11[/C][C]1.48[/C][C]1.45388196073589[/C][C]0.0261180392641071[/C][/ROW]
[ROW][C]12[/C][C]1.48[/C][C]1.45388196073589[/C][C]0.0261180392641071[/C][/ROW]
[ROW][C]13[/C][C]1.48[/C][C]1.45388196073589[/C][C]0.0261180392641071[/C][/ROW]
[ROW][C]14[/C][C]1.48[/C][C]1.45388196073589[/C][C]0.0261180392641071[/C][/ROW]
[ROW][C]15[/C][C]1.48[/C][C]1.45388196073589[/C][C]0.0261180392641071[/C][/ROW]
[ROW][C]16[/C][C]1.48[/C][C]1.45388196073589[/C][C]0.0261180392641071[/C][/ROW]
[ROW][C]17[/C][C]1.48[/C][C]1.45388196073589[/C][C]0.0261180392641071[/C][/ROW]
[ROW][C]18[/C][C]1.48[/C][C]1.45388196073589[/C][C]0.0261180392641071[/C][/ROW]
[ROW][C]19[/C][C]1.48[/C][C]1.45388196073589[/C][C]0.0261180392641071[/C][/ROW]
[ROW][C]20[/C][C]1.48[/C][C]1.49721940980368[/C][C]-0.0172194098036792[/C][/ROW]
[ROW][C]21[/C][C]1.48[/C][C]1.49721940980368[/C][C]-0.0172194098036792[/C][/ROW]
[ROW][C]22[/C][C]1.48[/C][C]1.49721940980368[/C][C]-0.0172194098036792[/C][/ROW]
[ROW][C]23[/C][C]1.48[/C][C]1.54055685887147[/C][C]-0.0605568588714655[/C][/ROW]
[ROW][C]24[/C][C]1.48[/C][C]1.54055685887147[/C][C]-0.0605568588714655[/C][/ROW]
[ROW][C]25[/C][C]1.48[/C][C]1.54055685887147[/C][C]-0.0605568588714655[/C][/ROW]
[ROW][C]26[/C][C]1.48[/C][C]1.54055685887147[/C][C]-0.0605568588714655[/C][/ROW]
[ROW][C]27[/C][C]1.48[/C][C]1.54055685887147[/C][C]-0.0605568588714655[/C][/ROW]
[ROW][C]28[/C][C]1.48[/C][C]1.54055685887147[/C][C]-0.0605568588714655[/C][/ROW]
[ROW][C]29[/C][C]1.48[/C][C]1.54055685887147[/C][C]-0.0605568588714655[/C][/ROW]
[ROW][C]30[/C][C]1.48[/C][C]1.54055685887147[/C][C]-0.0605568588714655[/C][/ROW]
[ROW][C]31[/C][C]1.48[/C][C]1.54055685887147[/C][C]-0.0605568588714655[/C][/ROW]
[ROW][C]32[/C][C]1.48[/C][C]1.54055685887147[/C][C]-0.0605568588714655[/C][/ROW]
[ROW][C]33[/C][C]1.48[/C][C]1.49721940980368[/C][C]-0.0172194098036792[/C][/ROW]
[ROW][C]34[/C][C]1.48[/C][C]1.49721940980368[/C][C]-0.0172194098036792[/C][/ROW]
[ROW][C]35[/C][C]1.48[/C][C]1.49721940980368[/C][C]-0.0172194098036792[/C][/ROW]
[ROW][C]36[/C][C]1.48[/C][C]1.49721940980368[/C][C]-0.0172194098036792[/C][/ROW]
[ROW][C]37[/C][C]1.48[/C][C]1.49721940980368[/C][C]-0.0172194098036792[/C][/ROW]
[ROW][C]38[/C][C]1.57[/C][C]1.54055685887147[/C][C]0.0294431411285346[/C][/ROW]
[ROW][C]39[/C][C]1.58[/C][C]1.58389430793925[/C][C]-0.00389430793925175[/C][/ROW]
[ROW][C]40[/C][C]1.58[/C][C]1.58389430793925[/C][C]-0.00389430793925175[/C][/ROW]
[ROW][C]41[/C][C]1.58[/C][C]1.58389430793925[/C][C]-0.00389430793925175[/C][/ROW]
[ROW][C]42[/C][C]1.58[/C][C]1.58389430793925[/C][C]-0.00389430793925175[/C][/ROW]
[ROW][C]43[/C][C]1.59[/C][C]1.58389430793925[/C][C]0.00610569206074826[/C][/ROW]
[ROW][C]44[/C][C]1.6[/C][C]1.58389430793925[/C][C]0.0161056920607483[/C][/ROW]
[ROW][C]45[/C][C]1.6[/C][C]1.58389430793925[/C][C]0.0161056920607483[/C][/ROW]
[ROW][C]46[/C][C]1.61[/C][C]1.58389430793925[/C][C]0.0261056920607483[/C][/ROW]
[ROW][C]47[/C][C]1.61[/C][C]1.62723175700704[/C][C]-0.0172317570070380[/C][/ROW]
[ROW][C]48[/C][C]1.61[/C][C]1.62723175700704[/C][C]-0.0172317570070380[/C][/ROW]
[ROW][C]49[/C][C]1.62[/C][C]1.62723175700704[/C][C]-0.00723175700703803[/C][/ROW]
[ROW][C]50[/C][C]1.63[/C][C]1.62723175700704[/C][C]0.00276824299296176[/C][/ROW]
[ROW][C]51[/C][C]1.63[/C][C]1.62723175700704[/C][C]0.00276824299296176[/C][/ROW]
[ROW][C]52[/C][C]1.64[/C][C]1.58389430793925[/C][C]0.0561056920607481[/C][/ROW]
[ROW][C]53[/C][C]1.64[/C][C]1.62723175700704[/C][C]0.0127682429929618[/C][/ROW]
[ROW][C]54[/C][C]1.64[/C][C]1.58389430793925[/C][C]0.0561056920607481[/C][/ROW]
[ROW][C]55[/C][C]1.64[/C][C]1.58389430793925[/C][C]0.0561056920607481[/C][/ROW]
[ROW][C]56[/C][C]1.64[/C][C]1.62723175700704[/C][C]0.0127682429929618[/C][/ROW]
[ROW][C]57[/C][C]1.65[/C][C]1.58389430793925[/C][C]0.0661056920607481[/C][/ROW]
[ROW][C]58[/C][C]1.65[/C][C]1.58389430793925[/C][C]0.0661056920607481[/C][/ROW]
[ROW][C]59[/C][C]1.65[/C][C]1.58389430793925[/C][C]0.0661056920607481[/C][/ROW]
[ROW][C]60[/C][C]1.65[/C][C]1.58389430793925[/C][C]0.0661056920607481[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58063&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58063&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	1.43	1.41054451166811	0.0194554883318857
2	1.43	1.41054451166811	0.0194554883318930
3	1.43	1.41054451166811	0.0194554883318934
4	1.43	1.41054451166811	0.0194554883318934
5	1.43	1.45388196073589	-0.0238819607358929
6	1.43	1.45388196073589	-0.0238819607358929
7	1.44	1.45388196073589	-0.0138819607358929
8	1.48	1.49721940980368	-0.0172194098036792
9	1.48	1.49721940980368	-0.0172194098036792
10	1.48	1.45388196073589	0.0261180392641071
11	1.48	1.45388196073589	0.0261180392641071
12	1.48	1.45388196073589	0.0261180392641071
13	1.48	1.45388196073589	0.0261180392641071
14	1.48	1.45388196073589	0.0261180392641071
15	1.48	1.45388196073589	0.0261180392641071
16	1.48	1.45388196073589	0.0261180392641071
17	1.48	1.45388196073589	0.0261180392641071
18	1.48	1.45388196073589	0.0261180392641071
19	1.48	1.45388196073589	0.0261180392641071
20	1.48	1.49721940980368	-0.0172194098036792
21	1.48	1.49721940980368	-0.0172194098036792
22	1.48	1.49721940980368	-0.0172194098036792
23	1.48	1.54055685887147	-0.0605568588714655
24	1.48	1.54055685887147	-0.0605568588714655
25	1.48	1.54055685887147	-0.0605568588714655
26	1.48	1.54055685887147	-0.0605568588714655
27	1.48	1.54055685887147	-0.0605568588714655
28	1.48	1.54055685887147	-0.0605568588714655
29	1.48	1.54055685887147	-0.0605568588714655
30	1.48	1.54055685887147	-0.0605568588714655
31	1.48	1.54055685887147	-0.0605568588714655
32	1.48	1.54055685887147	-0.0605568588714655
33	1.48	1.49721940980368	-0.0172194098036792
34	1.48	1.49721940980368	-0.0172194098036792
35	1.48	1.49721940980368	-0.0172194098036792
36	1.48	1.49721940980368	-0.0172194098036792
37	1.48	1.49721940980368	-0.0172194098036792
38	1.57	1.54055685887147	0.0294431411285346
39	1.58	1.58389430793925	-0.00389430793925175
40	1.58	1.58389430793925	-0.00389430793925175
41	1.58	1.58389430793925	-0.00389430793925175
42	1.58	1.58389430793925	-0.00389430793925175
43	1.59	1.58389430793925	0.00610569206074826
44	1.6	1.58389430793925	0.0161056920607483
45	1.6	1.58389430793925	0.0161056920607483
46	1.61	1.58389430793925	0.0261056920607483
47	1.61	1.62723175700704	-0.0172317570070380
48	1.61	1.62723175700704	-0.0172317570070380
49	1.62	1.62723175700704	-0.00723175700703803
50	1.63	1.62723175700704	0.00276824299296176
51	1.63	1.62723175700704	0.00276824299296176
52	1.64	1.58389430793925	0.0561056920607481
53	1.64	1.62723175700704	0.0127682429929618
54	1.64	1.58389430793925	0.0561056920607481
55	1.64	1.58389430793925	0.0561056920607481
56	1.64	1.62723175700704	0.0127682429929618
57	1.65	1.58389430793925	0.0661056920607481
58	1.65	1.58389430793925	0.0661056920607481
59	1.65	1.58389430793925	0.0661056920607481
60	1.65	1.58389430793925	0.0661056920607481

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
5	3.87809722182447e-43	7.75619444364894e-43	1
6	2.9509596315842e-57	5.9019192631684e-57	1
7	2.34628318179399e-05	4.69256636358798e-05	0.999976537168182
8	0.00266678037503573	0.00533356075007146	0.997333219624964
9	0.00132467439023061	0.00264934878046122	0.99867532560977
10	0.00435640286759149	0.00871280573518298	0.995643597132408
11	0.00581996243685427	0.0116399248737085	0.994180037563146
12	0.00575872637859902	0.0115174527571980	0.994241273621401
13	0.00493483165616793	0.00986966331233585	0.995065168343832
14	0.00391269541033363	0.00782539082066727	0.996087304589666
15	0.00298451177964293	0.00596902355928586	0.997015488220357
16	0.00226121308412699	0.00452242616825399	0.997738786915873
17	0.00176380365653319	0.00352760731306637	0.998236196343467
18	0.00149152660771334	0.00298305321542667	0.998508473392287
19	0.00149590927221329	0.00299181854442658	0.998504090727787
20	0.000829121719615177	0.00165824343923035	0.999170878280385
21	0.000430609055176752	0.000861218110353505	0.999569390944823
22	0.000214341723004837	0.000428683446009675	0.999785658276995
23	0.000268366294626426	0.000536732589252853	0.999731633705374
24	0.000239495787454188	0.000478991574908376	0.999760504212546
25	0.000194810394255097	0.000389620788510195	0.999805189605745
26	0.000157444449036668	0.000314888898073337	0.999842555550963
27	0.000132769845537928	0.000265539691075855	0.999867230154462
28	0.000121662719489014	0.000243325438978027	0.999878337280511
29	0.000126544327395977	0.000253088654791955	0.999873455672604
30	0.000157771970800566	0.000315543941601131	0.9998422280292
31	0.000254014080982601	0.000508028161965202	0.999745985919017
32	0.000588265807509683	0.00117653161501937	0.99941173419249
33	0.000359292761615733	0.000718585523231467	0.999640707238384
34	0.000234855976478907	0.000469711952957813	0.999765144023521
35	0.000181558850937985	0.000363117701875970	0.999818441149062
36	0.000223499809986715	0.000446999619973429	0.999776500190013
37	0.00185270601742747	0.00370541203485494	0.998147293982573
38	0.0810689362989787	0.162137872597957	0.918931063701021
39	0.265136424321379	0.530272848642759	0.73486357567862
40	0.470530099916091	0.941060199832182	0.529469900083909
41	0.671191464576196	0.657617070847607	0.328808535423804
42	0.855259499350093	0.289481001299814	0.144740500649907
43	0.949181829965576	0.101636340068848	0.0508181700344241
44	0.983395145465249	0.0332097090695022	0.0166048545347511
45	0.998069052569961	0.00386189486007740	0.00193094743003870
46	0.99990701040552	0.000185979188961044	9.29895944805221e-05
47	0.999933652561918	0.000132694876164707	6.63474380823537e-05
48	0.99998853282455	2.29343508989433e-05	1.14671754494716e-05
49	0.999994593162615	1.08136747693225e-05	5.40683738466127e-06
50	0.999983746285248	3.25074295048613e-05	1.62537147524307e-05
51	0.999972244670005	5.5510659990631e-05	2.77553299953155e-05
52	0.999942042837733	0.000115914324532931	5.79571622664656e-05
53	0.99962129976882	0.000757400462360212	0.000378700231180106
54	0.999261053091167	0.00147789381766683	0.000738946908833416
55	1	0	0

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
5 & 3.87809722182447e-43 & 7.75619444364894e-43 & 1 \tabularnewline
6 & 2.9509596315842e-57 & 5.9019192631684e-57 & 1 \tabularnewline
7 & 2.34628318179399e-05 & 4.69256636358798e-05 & 0.999976537168182 \tabularnewline
8 & 0.00266678037503573 & 0.00533356075007146 & 0.997333219624964 \tabularnewline
9 & 0.00132467439023061 & 0.00264934878046122 & 0.99867532560977 \tabularnewline
10 & 0.00435640286759149 & 0.00871280573518298 & 0.995643597132408 \tabularnewline
11 & 0.00581996243685427 & 0.0116399248737085 & 0.994180037563146 \tabularnewline
12 & 0.00575872637859902 & 0.0115174527571980 & 0.994241273621401 \tabularnewline
13 & 0.00493483165616793 & 0.00986966331233585 & 0.995065168343832 \tabularnewline
14 & 0.00391269541033363 & 0.00782539082066727 & 0.996087304589666 \tabularnewline
15 & 0.00298451177964293 & 0.00596902355928586 & 0.997015488220357 \tabularnewline
16 & 0.00226121308412699 & 0.00452242616825399 & 0.997738786915873 \tabularnewline
17 & 0.00176380365653319 & 0.00352760731306637 & 0.998236196343467 \tabularnewline
18 & 0.00149152660771334 & 0.00298305321542667 & 0.998508473392287 \tabularnewline
19 & 0.00149590927221329 & 0.00299181854442658 & 0.998504090727787 \tabularnewline
20 & 0.000829121719615177 & 0.00165824343923035 & 0.999170878280385 \tabularnewline
21 & 0.000430609055176752 & 0.000861218110353505 & 0.999569390944823 \tabularnewline
22 & 0.000214341723004837 & 0.000428683446009675 & 0.999785658276995 \tabularnewline
23 & 0.000268366294626426 & 0.000536732589252853 & 0.999731633705374 \tabularnewline
24 & 0.000239495787454188 & 0.000478991574908376 & 0.999760504212546 \tabularnewline
25 & 0.000194810394255097 & 0.000389620788510195 & 0.999805189605745 \tabularnewline
26 & 0.000157444449036668 & 0.000314888898073337 & 0.999842555550963 \tabularnewline
27 & 0.000132769845537928 & 0.000265539691075855 & 0.999867230154462 \tabularnewline
28 & 0.000121662719489014 & 0.000243325438978027 & 0.999878337280511 \tabularnewline
29 & 0.000126544327395977 & 0.000253088654791955 & 0.999873455672604 \tabularnewline
30 & 0.000157771970800566 & 0.000315543941601131 & 0.9998422280292 \tabularnewline
31 & 0.000254014080982601 & 0.000508028161965202 & 0.999745985919017 \tabularnewline
32 & 0.000588265807509683 & 0.00117653161501937 & 0.99941173419249 \tabularnewline
33 & 0.000359292761615733 & 0.000718585523231467 & 0.999640707238384 \tabularnewline
34 & 0.000234855976478907 & 0.000469711952957813 & 0.999765144023521 \tabularnewline
35 & 0.000181558850937985 & 0.000363117701875970 & 0.999818441149062 \tabularnewline
36 & 0.000223499809986715 & 0.000446999619973429 & 0.999776500190013 \tabularnewline
37 & 0.00185270601742747 & 0.00370541203485494 & 0.998147293982573 \tabularnewline
38 & 0.0810689362989787 & 0.162137872597957 & 0.918931063701021 \tabularnewline
39 & 0.265136424321379 & 0.530272848642759 & 0.73486357567862 \tabularnewline
40 & 0.470530099916091 & 0.941060199832182 & 0.529469900083909 \tabularnewline
41 & 0.671191464576196 & 0.657617070847607 & 0.328808535423804 \tabularnewline
42 & 0.855259499350093 & 0.289481001299814 & 0.144740500649907 \tabularnewline
43 & 0.949181829965576 & 0.101636340068848 & 0.0508181700344241 \tabularnewline
44 & 0.983395145465249 & 0.0332097090695022 & 0.0166048545347511 \tabularnewline
45 & 0.998069052569961 & 0.00386189486007740 & 0.00193094743003870 \tabularnewline
46 & 0.99990701040552 & 0.000185979188961044 & 9.29895944805221e-05 \tabularnewline
47 & 0.999933652561918 & 0.000132694876164707 & 6.63474380823537e-05 \tabularnewline
48 & 0.99998853282455 & 2.29343508989433e-05 & 1.14671754494716e-05 \tabularnewline
49 & 0.999994593162615 & 1.08136747693225e-05 & 5.40683738466127e-06 \tabularnewline
50 & 0.999983746285248 & 3.25074295048613e-05 & 1.62537147524307e-05 \tabularnewline
51 & 0.999972244670005 & 5.5510659990631e-05 & 2.77553299953155e-05 \tabularnewline
52 & 0.999942042837733 & 0.000115914324532931 & 5.79571622664656e-05 \tabularnewline
53 & 0.99962129976882 & 0.000757400462360212 & 0.000378700231180106 \tabularnewline
54 & 0.999261053091167 & 0.00147789381766683 & 0.000738946908833416 \tabularnewline
55 & 1 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58063&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]3.87809722182447e-43[/C][C]7.75619444364894e-43[/C][C]1[/C][/ROW]
[ROW][C]6[/C][C]2.9509596315842e-57[/C][C]5.9019192631684e-57[/C][C]1[/C][/ROW]
[ROW][C]7[/C][C]2.34628318179399e-05[/C][C]4.69256636358798e-05[/C][C]0.999976537168182[/C][/ROW]
[ROW][C]8[/C][C]0.00266678037503573[/C][C]0.00533356075007146[/C][C]0.997333219624964[/C][/ROW]
[ROW][C]9[/C][C]0.00132467439023061[/C][C]0.00264934878046122[/C][C]0.99867532560977[/C][/ROW]
[ROW][C]10[/C][C]0.00435640286759149[/C][C]0.00871280573518298[/C][C]0.995643597132408[/C][/ROW]
[ROW][C]11[/C][C]0.00581996243685427[/C][C]0.0116399248737085[/C][C]0.994180037563146[/C][/ROW]
[ROW][C]12[/C][C]0.00575872637859902[/C][C]0.0115174527571980[/C][C]0.994241273621401[/C][/ROW]
[ROW][C]13[/C][C]0.00493483165616793[/C][C]0.00986966331233585[/C][C]0.995065168343832[/C][/ROW]
[ROW][C]14[/C][C]0.00391269541033363[/C][C]0.00782539082066727[/C][C]0.996087304589666[/C][/ROW]
[ROW][C]15[/C][C]0.00298451177964293[/C][C]0.00596902355928586[/C][C]0.997015488220357[/C][/ROW]
[ROW][C]16[/C][C]0.00226121308412699[/C][C]0.00452242616825399[/C][C]0.997738786915873[/C][/ROW]
[ROW][C]17[/C][C]0.00176380365653319[/C][C]0.00352760731306637[/C][C]0.998236196343467[/C][/ROW]
[ROW][C]18[/C][C]0.00149152660771334[/C][C]0.00298305321542667[/C][C]0.998508473392287[/C][/ROW]
[ROW][C]19[/C][C]0.00149590927221329[/C][C]0.00299181854442658[/C][C]0.998504090727787[/C][/ROW]
[ROW][C]20[/C][C]0.000829121719615177[/C][C]0.00165824343923035[/C][C]0.999170878280385[/C][/ROW]
[ROW][C]21[/C][C]0.000430609055176752[/C][C]0.000861218110353505[/C][C]0.999569390944823[/C][/ROW]
[ROW][C]22[/C][C]0.000214341723004837[/C][C]0.000428683446009675[/C][C]0.999785658276995[/C][/ROW]
[ROW][C]23[/C][C]0.000268366294626426[/C][C]0.000536732589252853[/C][C]0.999731633705374[/C][/ROW]
[ROW][C]24[/C][C]0.000239495787454188[/C][C]0.000478991574908376[/C][C]0.999760504212546[/C][/ROW]
[ROW][C]25[/C][C]0.000194810394255097[/C][C]0.000389620788510195[/C][C]0.999805189605745[/C][/ROW]
[ROW][C]26[/C][C]0.000157444449036668[/C][C]0.000314888898073337[/C][C]0.999842555550963[/C][/ROW]
[ROW][C]27[/C][C]0.000132769845537928[/C][C]0.000265539691075855[/C][C]0.999867230154462[/C][/ROW]
[ROW][C]28[/C][C]0.000121662719489014[/C][C]0.000243325438978027[/C][C]0.999878337280511[/C][/ROW]
[ROW][C]29[/C][C]0.000126544327395977[/C][C]0.000253088654791955[/C][C]0.999873455672604[/C][/ROW]
[ROW][C]30[/C][C]0.000157771970800566[/C][C]0.000315543941601131[/C][C]0.9998422280292[/C][/ROW]
[ROW][C]31[/C][C]0.000254014080982601[/C][C]0.000508028161965202[/C][C]0.999745985919017[/C][/ROW]
[ROW][C]32[/C][C]0.000588265807509683[/C][C]0.00117653161501937[/C][C]0.99941173419249[/C][/ROW]
[ROW][C]33[/C][C]0.000359292761615733[/C][C]0.000718585523231467[/C][C]0.999640707238384[/C][/ROW]
[ROW][C]34[/C][C]0.000234855976478907[/C][C]0.000469711952957813[/C][C]0.999765144023521[/C][/ROW]
[ROW][C]35[/C][C]0.000181558850937985[/C][C]0.000363117701875970[/C][C]0.999818441149062[/C][/ROW]
[ROW][C]36[/C][C]0.000223499809986715[/C][C]0.000446999619973429[/C][C]0.999776500190013[/C][/ROW]
[ROW][C]37[/C][C]0.00185270601742747[/C][C]0.00370541203485494[/C][C]0.998147293982573[/C][/ROW]
[ROW][C]38[/C][C]0.0810689362989787[/C][C]0.162137872597957[/C][C]0.918931063701021[/C][/ROW]
[ROW][C]39[/C][C]0.265136424321379[/C][C]0.530272848642759[/C][C]0.73486357567862[/C][/ROW]
[ROW][C]40[/C][C]0.470530099916091[/C][C]0.941060199832182[/C][C]0.529469900083909[/C][/ROW]
[ROW][C]41[/C][C]0.671191464576196[/C][C]0.657617070847607[/C][C]0.328808535423804[/C][/ROW]
[ROW][C]42[/C][C]0.855259499350093[/C][C]0.289481001299814[/C][C]0.144740500649907[/C][/ROW]
[ROW][C]43[/C][C]0.949181829965576[/C][C]0.101636340068848[/C][C]0.0508181700344241[/C][/ROW]
[ROW][C]44[/C][C]0.983395145465249[/C][C]0.0332097090695022[/C][C]0.0166048545347511[/C][/ROW]
[ROW][C]45[/C][C]0.998069052569961[/C][C]0.00386189486007740[/C][C]0.00193094743003870[/C][/ROW]
[ROW][C]46[/C][C]0.99990701040552[/C][C]0.000185979188961044[/C][C]9.29895944805221e-05[/C][/ROW]
[ROW][C]47[/C][C]0.999933652561918[/C][C]0.000132694876164707[/C][C]6.63474380823537e-05[/C][/ROW]
[ROW][C]48[/C][C]0.99998853282455[/C][C]2.29343508989433e-05[/C][C]1.14671754494716e-05[/C][/ROW]
[ROW][C]49[/C][C]0.999994593162615[/C][C]1.08136747693225e-05[/C][C]5.40683738466127e-06[/C][/ROW]
[ROW][C]50[/C][C]0.999983746285248[/C][C]3.25074295048613e-05[/C][C]1.62537147524307e-05[/C][/ROW]
[ROW][C]51[/C][C]0.999972244670005[/C][C]5.5510659990631e-05[/C][C]2.77553299953155e-05[/C][/ROW]
[ROW][C]52[/C][C]0.999942042837733[/C][C]0.000115914324532931[/C][C]5.79571622664656e-05[/C][/ROW]
[ROW][C]53[/C][C]0.99962129976882[/C][C]0.000757400462360212[/C][C]0.000378700231180106[/C][/ROW]
[ROW][C]54[/C][C]0.999261053091167[/C][C]0.00147789381766683[/C][C]0.000738946908833416[/C][/ROW]
[ROW][C]55[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58063&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58063&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	3.87809722182447e-43	7.75619444364894e-43	1
6	2.9509596315842e-57	5.9019192631684e-57	1
7	2.34628318179399e-05	4.69256636358798e-05	0.999976537168182
8	0.00266678037503573	0.00533356075007146	0.997333219624964
9	0.00132467439023061	0.00264934878046122	0.99867532560977
10	0.00435640286759149	0.00871280573518298	0.995643597132408
11	0.00581996243685427	0.0116399248737085	0.994180037563146
12	0.00575872637859902	0.0115174527571980	0.994241273621401
13	0.00493483165616793	0.00986966331233585	0.995065168343832
14	0.00391269541033363	0.00782539082066727	0.996087304589666
15	0.00298451177964293	0.00596902355928586	0.997015488220357
16	0.00226121308412699	0.00452242616825399	0.997738786915873
17	0.00176380365653319	0.00352760731306637	0.998236196343467
18	0.00149152660771334	0.00298305321542667	0.998508473392287
19	0.00149590927221329	0.00299181854442658	0.998504090727787
20	0.000829121719615177	0.00165824343923035	0.999170878280385
21	0.000430609055176752	0.000861218110353505	0.999569390944823
22	0.000214341723004837	0.000428683446009675	0.999785658276995
23	0.000268366294626426	0.000536732589252853	0.999731633705374
24	0.000239495787454188	0.000478991574908376	0.999760504212546
25	0.000194810394255097	0.000389620788510195	0.999805189605745
26	0.000157444449036668	0.000314888898073337	0.999842555550963
27	0.000132769845537928	0.000265539691075855	0.999867230154462
28	0.000121662719489014	0.000243325438978027	0.999878337280511
29	0.000126544327395977	0.000253088654791955	0.999873455672604
30	0.000157771970800566	0.000315543941601131	0.9998422280292
31	0.000254014080982601	0.000508028161965202	0.999745985919017
32	0.000588265807509683	0.00117653161501937	0.99941173419249
33	0.000359292761615733	0.000718585523231467	0.999640707238384
34	0.000234855976478907	0.000469711952957813	0.999765144023521
35	0.000181558850937985	0.000363117701875970	0.999818441149062
36	0.000223499809986715	0.000446999619973429	0.999776500190013
37	0.00185270601742747	0.00370541203485494	0.998147293982573
38	0.0810689362989787	0.162137872597957	0.918931063701021
39	0.265136424321379	0.530272848642759	0.73486357567862
40	0.470530099916091	0.941060199832182	0.529469900083909
41	0.671191464576196	0.657617070847607	0.328808535423804
42	0.855259499350093	0.289481001299814	0.144740500649907
43	0.949181829965576	0.101636340068848	0.0508181700344241
44	0.983395145465249	0.0332097090695022	0.0166048545347511
45	0.998069052569961	0.00386189486007740	0.00193094743003870
46	0.99990701040552	0.000185979188961044	9.29895944805221e-05
47	0.999933652561918	0.000132694876164707	6.63474380823537e-05
48	0.99998853282455	2.29343508989433e-05	1.14671754494716e-05
49	0.999994593162615	1.08136747693225e-05	5.40683738466127e-06
50	0.999983746285248	3.25074295048613e-05	1.62537147524307e-05
51	0.999972244670005	5.5510659990631e-05	2.77553299953155e-05
52	0.999942042837733	0.000115914324532931	5.79571622664656e-05
53	0.99962129976882	0.000757400462360212	0.000378700231180106
54	0.999261053091167	0.00147789381766683	0.000738946908833416
55	1	0	0

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	42	0.823529411764706	NOK
5% type I error level	45	0.88235294117647	NOK
10% type I error level	45	0.88235294117647	NOK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 42 & 0.823529411764706 & NOK \tabularnewline
5% type I error level & 45 & 0.88235294117647 & NOK \tabularnewline
10% type I error level & 45 & 0.88235294117647 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58063&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]42[/C][C]0.823529411764706[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]45[/C][C]0.88235294117647[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]45[/C][C]0.88235294117647[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58063&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58063&T=6

As an alternative you can also use a QR Code:

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	42	0.823529411764706	NOK
5% type I error level	45	0.88235294117647	NOK
10% type I error level	45	0.88235294117647	NOK

Figure 1

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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