Repository of Reproducible Computations

Free Statistics

of Irreproducible Research!

Author's title

Author

*The author of this computation has been verified*

R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Mon, 21 Dec 2009 04:18:14 -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/Dec/21/t1261394443avja5ao25vaax7v.htm/, Retrieved Sat, 04 May 2024 13:59:12 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=70102, Retrieved Sat, 04 May 2024 13:59:12 +0000

QR Codes:

Paste this QR Code to cite your computation.

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

135

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

-     [Multiple Regression] [Q1 The Seatbeltlaw] [2007-11-14 19:27:43] [8cd6641b921d30ebe00b648d1481bba0]
- RMPD  [Multiple Regression] [Seatbelt] [2009-11-12 14:03:14] [b98453cac15ba1066b407e146608df68]
-    D    [Multiple Regression] [] [2009-11-18 12:06:02] [6ba840d2473f9a55d7b3e13093db69b8]
-    D      [Multiple Regression] [] [2009-12-15 14:57:54] [6ba840d2473f9a55d7b3e13093db69b8]
-   PD          [Multiple Regression] [] [2009-12-21 11:18:14] [830aa0f7fb5acd5849dbc0c6ad889830] [Current]

Feedback Forum

Post a new message

Dataseries X:

Download CSV

Histogram

Boxplots

2.6	30.5
2.4	28.6
2.5	30
2.7	28.2
3.2	27.6
2.8	24.9
2.8	23.8
3	24.3
3.1	23.6
3.1	24.2
3	28.1
2.4	30.1
2.7	31.1
3	32
2.7	32.4
2.7	34
2	35.1
2.4	37.1
2.6	37.3
2.4	38.1
2.3	39.5
2.4	38.3
2.5	37.3
2.6	38.7
2.6	37.5
2.6	38.7
2.7	37.9
2.8	36.6
2.6	35.5
2.6	37.6
2	38.6
2	40.3
2.1	39
1.9	36.8
2	36.5
2.5	34.1
2.9	34.2
3.3	31.9
3.5	33.7
3.8	33.5
4.6	33.8
4.4	29.9
5.3	32.3
5.8	30.5
5.9	28.5
5.6	29
5.8	23.8
5.5	17.9
4.6	9.9
4.2	3
4	4.2
3.5	0.4
2.3	0
2.2	2.4
1.4	4.2
0.6	8.2
0	9
0.5	13.6
0.1	14
0.1	17.6

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

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 4 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=70102&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]4 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=70102&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=70102&T=0

As an alternative you can also use a QR Code:

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

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

Multiple Linear Regression - Estimated Regression Equation
Y[t] = + 2.63835457876613 + 0.0084265340572524X[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Y[t] =  +  2.63835457876613 +  0.0084265340572524X[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=70102&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Y[t] =  +  2.63835457876613 +  0.0084265340572524X[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=70102&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=70102&T=1

As an alternative you can also use a QR Code:

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

Multiple Linear Regression - Estimated Regression Equation
Y[t] = + 2.63835457876613 + 0.0084265340572524X[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)	2.63835457876613	0.449474	5.8699	0	0
X	0.0084265340572524	0.015094	0.5583	0.578806	0.289403

\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) & 2.63835457876613 & 0.449474 & 5.8699 & 0 & 0 \tabularnewline
X & 0.0084265340572524 & 0.015094 & 0.5583 & 0.578806 & 0.289403 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=70102&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]2.63835457876613[/C][C]0.449474[/C][C]5.8699[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]X[/C][C]0.0084265340572524[/C][C]0.015094[/C][C]0.5583[/C][C]0.578806[/C][C]0.289403[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=70102&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=70102&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)	2.63835457876613	0.449474	5.8699	0	0
X	0.0084265340572524	0.015094	0.5583	0.578806	0.289403

Multiple Linear Regression - Regression Statistics
Multiple R	0.0731087363377837
R-squared	0.00534488732890758
Adjusted R-squared	-0.0118043387516285
F-TEST (value)	0.311669302381749
F-TEST (DF numerator)	1
F-TEST (DF denominator)	58
p-value	0.578806475583351
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.33847340344487
Sum Squared Residuals	103.907641000298

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.0731087363377837 \tabularnewline
R-squared & 0.00534488732890758 \tabularnewline
Adjusted R-squared & -0.0118043387516285 \tabularnewline
F-TEST (value) & 0.311669302381749 \tabularnewline
F-TEST (DF numerator) & 1 \tabularnewline
F-TEST (DF denominator) & 58 \tabularnewline
p-value & 0.578806475583351 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 1.33847340344487 \tabularnewline
Sum Squared Residuals & 103.907641000298 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=70102&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.0731087363377837[/C][/ROW]
[ROW][C]R-squared[/C][C]0.00534488732890758[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]-0.0118043387516285[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]0.311669302381749[/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]0.578806475583351[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]1.33847340344487[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]103.907641000298[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=70102&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=70102&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	0.0731087363377837
R-squared	0.00534488732890758
Adjusted R-squared	-0.0118043387516285
F-TEST (value)	0.311669302381749
F-TEST (DF numerator)	1
F-TEST (DF denominator)	58
p-value	0.578806475583351
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.33847340344487
Sum Squared Residuals	103.907641000298

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	2.6	2.89536386751233	-0.295363867512332
2	2.4	2.87935345280355	-0.479353452803550
3	2.5	2.89115060048370	-0.391150600483703
4	2.7	2.87598283918065	-0.175982839180649
5	3.2	2.8709269187463	0.329073081253702
6	2.8	2.84817527679172	-0.0481752767917163
7	2.8	2.83890608932874	-0.0389060893287387
8	3	2.84311935635736	0.156880643642635
9	3.1	2.83722078251729	0.262779217482712
10	3.1	2.84227670295164	0.257723297048361
11	3	2.87514018577492	0.124859814225076
12	2.4	2.89199325388943	-0.491993253889429
13	2.7	2.90041978794668	-0.200419787946681
14	3	2.90800366859821	0.0919963314017917
15	2.7	2.91137428222111	-0.211374282221109
16	2.7	2.92485673671271	-0.224856736712713
17	2	2.93412592417569	-0.93412592417569
18	2.4	2.95097899229020	-0.550978992290196
19	2.6	2.95266429910165	-0.352664299101646
20	2.4	2.95940552634745	-0.559405526347448
21	2.3	2.9712026740276	-0.671202674027601
22	2.4	2.9610908331589	-0.561090833158899
23	2.5	2.95266429910165	-0.452664299101646
24	2.6	2.9644614467818	-0.364461446781799
25	2.6	2.95434960591310	-0.354349605913096
26	2.6	2.9644614467818	-0.364461446781799
27	2.7	2.957720219536	-0.257720219535997
28	2.8	2.94676572526157	-0.146765725261570
29	2.6	2.93749653779859	-0.337496537798592
30	2.6	2.95519225931882	-0.355192259318822
31	2	2.96361879337607	-0.963618793376074
32	2	2.97794390127340	-0.977943901273403
33	2.1	2.96698940699898	-0.866989406998975
34	1.9	2.94845103207302	-1.04845103207302
35	2	2.94592307185584	-0.945923071855844
36	2.5	2.92569939011844	-0.425699390118438
37	2.9	2.92654204352416	-0.0265420435241637
38	3.3	2.90716101519248	0.392838984807517
39	3.5	2.92232877649554	0.577671223504463
40	3.8	2.92064346968409	0.879356530315913
41	4.6	2.92317142990126	1.67682857009874
42	4.4	2.89030794707798	1.50969205292202
43	5.3	2.91053162881538	2.38946837118462
44	5.8	2.89536386751233	2.90463613248767
45	5.9	2.87851079939782	3.02148920060218
46	5.6	2.88272406642645	2.71727593357355
47	5.8	2.83890608932874	2.96109391067126
48	5.5	2.78918953839095	2.71081046160905
49	4.6	2.72177726593293	1.87822273406707
50	4.2	2.66363418093789	1.53636581906211
51	4	2.67374602180659	1.32625397819341
52	3.5	2.64172519238903	0.858274807610968
53	2.3	2.63835457876613	-0.338354578766131
54	2.2	2.65857826050354	-0.458578260503537
55	1.4	2.67374602180659	-1.27374602180659
56	0.6	2.7074521580356	-2.1074521580356
57	0	2.71419338528140	-2.71419338528140
58	0.5	2.75295544194476	-2.25295544194476
59	0.1	2.75632605556767	-2.65632605556767
60	0.1	2.78666157817377	-2.68666157817377

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 2.6 & 2.89536386751233 & -0.295363867512332 \tabularnewline
2 & 2.4 & 2.87935345280355 & -0.479353452803550 \tabularnewline
3 & 2.5 & 2.89115060048370 & -0.391150600483703 \tabularnewline
4 & 2.7 & 2.87598283918065 & -0.175982839180649 \tabularnewline
5 & 3.2 & 2.8709269187463 & 0.329073081253702 \tabularnewline
6 & 2.8 & 2.84817527679172 & -0.0481752767917163 \tabularnewline
7 & 2.8 & 2.83890608932874 & -0.0389060893287387 \tabularnewline
8 & 3 & 2.84311935635736 & 0.156880643642635 \tabularnewline
9 & 3.1 & 2.83722078251729 & 0.262779217482712 \tabularnewline
10 & 3.1 & 2.84227670295164 & 0.257723297048361 \tabularnewline
11 & 3 & 2.87514018577492 & 0.124859814225076 \tabularnewline
12 & 2.4 & 2.89199325388943 & -0.491993253889429 \tabularnewline
13 & 2.7 & 2.90041978794668 & -0.200419787946681 \tabularnewline
14 & 3 & 2.90800366859821 & 0.0919963314017917 \tabularnewline
15 & 2.7 & 2.91137428222111 & -0.211374282221109 \tabularnewline
16 & 2.7 & 2.92485673671271 & -0.224856736712713 \tabularnewline
17 & 2 & 2.93412592417569 & -0.93412592417569 \tabularnewline
18 & 2.4 & 2.95097899229020 & -0.550978992290196 \tabularnewline
19 & 2.6 & 2.95266429910165 & -0.352664299101646 \tabularnewline
20 & 2.4 & 2.95940552634745 & -0.559405526347448 \tabularnewline
21 & 2.3 & 2.9712026740276 & -0.671202674027601 \tabularnewline
22 & 2.4 & 2.9610908331589 & -0.561090833158899 \tabularnewline
23 & 2.5 & 2.95266429910165 & -0.452664299101646 \tabularnewline
24 & 2.6 & 2.9644614467818 & -0.364461446781799 \tabularnewline
25 & 2.6 & 2.95434960591310 & -0.354349605913096 \tabularnewline
26 & 2.6 & 2.9644614467818 & -0.364461446781799 \tabularnewline
27 & 2.7 & 2.957720219536 & -0.257720219535997 \tabularnewline
28 & 2.8 & 2.94676572526157 & -0.146765725261570 \tabularnewline
29 & 2.6 & 2.93749653779859 & -0.337496537798592 \tabularnewline
30 & 2.6 & 2.95519225931882 & -0.355192259318822 \tabularnewline
31 & 2 & 2.96361879337607 & -0.963618793376074 \tabularnewline
32 & 2 & 2.97794390127340 & -0.977943901273403 \tabularnewline
33 & 2.1 & 2.96698940699898 & -0.866989406998975 \tabularnewline
34 & 1.9 & 2.94845103207302 & -1.04845103207302 \tabularnewline
35 & 2 & 2.94592307185584 & -0.945923071855844 \tabularnewline
36 & 2.5 & 2.92569939011844 & -0.425699390118438 \tabularnewline
37 & 2.9 & 2.92654204352416 & -0.0265420435241637 \tabularnewline
38 & 3.3 & 2.90716101519248 & 0.392838984807517 \tabularnewline
39 & 3.5 & 2.92232877649554 & 0.577671223504463 \tabularnewline
40 & 3.8 & 2.92064346968409 & 0.879356530315913 \tabularnewline
41 & 4.6 & 2.92317142990126 & 1.67682857009874 \tabularnewline
42 & 4.4 & 2.89030794707798 & 1.50969205292202 \tabularnewline
43 & 5.3 & 2.91053162881538 & 2.38946837118462 \tabularnewline
44 & 5.8 & 2.89536386751233 & 2.90463613248767 \tabularnewline
45 & 5.9 & 2.87851079939782 & 3.02148920060218 \tabularnewline
46 & 5.6 & 2.88272406642645 & 2.71727593357355 \tabularnewline
47 & 5.8 & 2.83890608932874 & 2.96109391067126 \tabularnewline
48 & 5.5 & 2.78918953839095 & 2.71081046160905 \tabularnewline
49 & 4.6 & 2.72177726593293 & 1.87822273406707 \tabularnewline
50 & 4.2 & 2.66363418093789 & 1.53636581906211 \tabularnewline
51 & 4 & 2.67374602180659 & 1.32625397819341 \tabularnewline
52 & 3.5 & 2.64172519238903 & 0.858274807610968 \tabularnewline
53 & 2.3 & 2.63835457876613 & -0.338354578766131 \tabularnewline
54 & 2.2 & 2.65857826050354 & -0.458578260503537 \tabularnewline
55 & 1.4 & 2.67374602180659 & -1.27374602180659 \tabularnewline
56 & 0.6 & 2.7074521580356 & -2.1074521580356 \tabularnewline
57 & 0 & 2.71419338528140 & -2.71419338528140 \tabularnewline
58 & 0.5 & 2.75295544194476 & -2.25295544194476 \tabularnewline
59 & 0.1 & 2.75632605556767 & -2.65632605556767 \tabularnewline
60 & 0.1 & 2.78666157817377 & -2.68666157817377 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=70102&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]2.6[/C][C]2.89536386751233[/C][C]-0.295363867512332[/C][/ROW]
[ROW][C]2[/C][C]2.4[/C][C]2.87935345280355[/C][C]-0.479353452803550[/C][/ROW]
[ROW][C]3[/C][C]2.5[/C][C]2.89115060048370[/C][C]-0.391150600483703[/C][/ROW]
[ROW][C]4[/C][C]2.7[/C][C]2.87598283918065[/C][C]-0.175982839180649[/C][/ROW]
[ROW][C]5[/C][C]3.2[/C][C]2.8709269187463[/C][C]0.329073081253702[/C][/ROW]
[ROW][C]6[/C][C]2.8[/C][C]2.84817527679172[/C][C]-0.0481752767917163[/C][/ROW]
[ROW][C]7[/C][C]2.8[/C][C]2.83890608932874[/C][C]-0.0389060893287387[/C][/ROW]
[ROW][C]8[/C][C]3[/C][C]2.84311935635736[/C][C]0.156880643642635[/C][/ROW]
[ROW][C]9[/C][C]3.1[/C][C]2.83722078251729[/C][C]0.262779217482712[/C][/ROW]
[ROW][C]10[/C][C]3.1[/C][C]2.84227670295164[/C][C]0.257723297048361[/C][/ROW]
[ROW][C]11[/C][C]3[/C][C]2.87514018577492[/C][C]0.124859814225076[/C][/ROW]
[ROW][C]12[/C][C]2.4[/C][C]2.89199325388943[/C][C]-0.491993253889429[/C][/ROW]
[ROW][C]13[/C][C]2.7[/C][C]2.90041978794668[/C][C]-0.200419787946681[/C][/ROW]
[ROW][C]14[/C][C]3[/C][C]2.90800366859821[/C][C]0.0919963314017917[/C][/ROW]
[ROW][C]15[/C][C]2.7[/C][C]2.91137428222111[/C][C]-0.211374282221109[/C][/ROW]
[ROW][C]16[/C][C]2.7[/C][C]2.92485673671271[/C][C]-0.224856736712713[/C][/ROW]
[ROW][C]17[/C][C]2[/C][C]2.93412592417569[/C][C]-0.93412592417569[/C][/ROW]
[ROW][C]18[/C][C]2.4[/C][C]2.95097899229020[/C][C]-0.550978992290196[/C][/ROW]
[ROW][C]19[/C][C]2.6[/C][C]2.95266429910165[/C][C]-0.352664299101646[/C][/ROW]
[ROW][C]20[/C][C]2.4[/C][C]2.95940552634745[/C][C]-0.559405526347448[/C][/ROW]
[ROW][C]21[/C][C]2.3[/C][C]2.9712026740276[/C][C]-0.671202674027601[/C][/ROW]
[ROW][C]22[/C][C]2.4[/C][C]2.9610908331589[/C][C]-0.561090833158899[/C][/ROW]
[ROW][C]23[/C][C]2.5[/C][C]2.95266429910165[/C][C]-0.452664299101646[/C][/ROW]
[ROW][C]24[/C][C]2.6[/C][C]2.9644614467818[/C][C]-0.364461446781799[/C][/ROW]
[ROW][C]25[/C][C]2.6[/C][C]2.95434960591310[/C][C]-0.354349605913096[/C][/ROW]
[ROW][C]26[/C][C]2.6[/C][C]2.9644614467818[/C][C]-0.364461446781799[/C][/ROW]
[ROW][C]27[/C][C]2.7[/C][C]2.957720219536[/C][C]-0.257720219535997[/C][/ROW]
[ROW][C]28[/C][C]2.8[/C][C]2.94676572526157[/C][C]-0.146765725261570[/C][/ROW]
[ROW][C]29[/C][C]2.6[/C][C]2.93749653779859[/C][C]-0.337496537798592[/C][/ROW]
[ROW][C]30[/C][C]2.6[/C][C]2.95519225931882[/C][C]-0.355192259318822[/C][/ROW]
[ROW][C]31[/C][C]2[/C][C]2.96361879337607[/C][C]-0.963618793376074[/C][/ROW]
[ROW][C]32[/C][C]2[/C][C]2.97794390127340[/C][C]-0.977943901273403[/C][/ROW]
[ROW][C]33[/C][C]2.1[/C][C]2.96698940699898[/C][C]-0.866989406998975[/C][/ROW]
[ROW][C]34[/C][C]1.9[/C][C]2.94845103207302[/C][C]-1.04845103207302[/C][/ROW]
[ROW][C]35[/C][C]2[/C][C]2.94592307185584[/C][C]-0.945923071855844[/C][/ROW]
[ROW][C]36[/C][C]2.5[/C][C]2.92569939011844[/C][C]-0.425699390118438[/C][/ROW]
[ROW][C]37[/C][C]2.9[/C][C]2.92654204352416[/C][C]-0.0265420435241637[/C][/ROW]
[ROW][C]38[/C][C]3.3[/C][C]2.90716101519248[/C][C]0.392838984807517[/C][/ROW]
[ROW][C]39[/C][C]3.5[/C][C]2.92232877649554[/C][C]0.577671223504463[/C][/ROW]
[ROW][C]40[/C][C]3.8[/C][C]2.92064346968409[/C][C]0.879356530315913[/C][/ROW]
[ROW][C]41[/C][C]4.6[/C][C]2.92317142990126[/C][C]1.67682857009874[/C][/ROW]
[ROW][C]42[/C][C]4.4[/C][C]2.89030794707798[/C][C]1.50969205292202[/C][/ROW]
[ROW][C]43[/C][C]5.3[/C][C]2.91053162881538[/C][C]2.38946837118462[/C][/ROW]
[ROW][C]44[/C][C]5.8[/C][C]2.89536386751233[/C][C]2.90463613248767[/C][/ROW]
[ROW][C]45[/C][C]5.9[/C][C]2.87851079939782[/C][C]3.02148920060218[/C][/ROW]
[ROW][C]46[/C][C]5.6[/C][C]2.88272406642645[/C][C]2.71727593357355[/C][/ROW]
[ROW][C]47[/C][C]5.8[/C][C]2.83890608932874[/C][C]2.96109391067126[/C][/ROW]
[ROW][C]48[/C][C]5.5[/C][C]2.78918953839095[/C][C]2.71081046160905[/C][/ROW]
[ROW][C]49[/C][C]4.6[/C][C]2.72177726593293[/C][C]1.87822273406707[/C][/ROW]
[ROW][C]50[/C][C]4.2[/C][C]2.66363418093789[/C][C]1.53636581906211[/C][/ROW]
[ROW][C]51[/C][C]4[/C][C]2.67374602180659[/C][C]1.32625397819341[/C][/ROW]
[ROW][C]52[/C][C]3.5[/C][C]2.64172519238903[/C][C]0.858274807610968[/C][/ROW]
[ROW][C]53[/C][C]2.3[/C][C]2.63835457876613[/C][C]-0.338354578766131[/C][/ROW]
[ROW][C]54[/C][C]2.2[/C][C]2.65857826050354[/C][C]-0.458578260503537[/C][/ROW]
[ROW][C]55[/C][C]1.4[/C][C]2.67374602180659[/C][C]-1.27374602180659[/C][/ROW]
[ROW][C]56[/C][C]0.6[/C][C]2.7074521580356[/C][C]-2.1074521580356[/C][/ROW]
[ROW][C]57[/C][C]0[/C][C]2.71419338528140[/C][C]-2.71419338528140[/C][/ROW]
[ROW][C]58[/C][C]0.5[/C][C]2.75295544194476[/C][C]-2.25295544194476[/C][/ROW]
[ROW][C]59[/C][C]0.1[/C][C]2.75632605556767[/C][C]-2.65632605556767[/C][/ROW]
[ROW][C]60[/C][C]0.1[/C][C]2.78666157817377[/C][C]-2.68666157817377[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=70102&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=70102&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	2.6	2.89536386751233	-0.295363867512332
2	2.4	2.87935345280355	-0.479353452803550
3	2.5	2.89115060048370	-0.391150600483703
4	2.7	2.87598283918065	-0.175982839180649
5	3.2	2.8709269187463	0.329073081253702
6	2.8	2.84817527679172	-0.0481752767917163
7	2.8	2.83890608932874	-0.0389060893287387
8	3	2.84311935635736	0.156880643642635
9	3.1	2.83722078251729	0.262779217482712
10	3.1	2.84227670295164	0.257723297048361
11	3	2.87514018577492	0.124859814225076
12	2.4	2.89199325388943	-0.491993253889429
13	2.7	2.90041978794668	-0.200419787946681
14	3	2.90800366859821	0.0919963314017917
15	2.7	2.91137428222111	-0.211374282221109
16	2.7	2.92485673671271	-0.224856736712713
17	2	2.93412592417569	-0.93412592417569
18	2.4	2.95097899229020	-0.550978992290196
19	2.6	2.95266429910165	-0.352664299101646
20	2.4	2.95940552634745	-0.559405526347448
21	2.3	2.9712026740276	-0.671202674027601
22	2.4	2.9610908331589	-0.561090833158899
23	2.5	2.95266429910165	-0.452664299101646
24	2.6	2.9644614467818	-0.364461446781799
25	2.6	2.95434960591310	-0.354349605913096
26	2.6	2.9644614467818	-0.364461446781799
27	2.7	2.957720219536	-0.257720219535997
28	2.8	2.94676572526157	-0.146765725261570
29	2.6	2.93749653779859	-0.337496537798592
30	2.6	2.95519225931882	-0.355192259318822
31	2	2.96361879337607	-0.963618793376074
32	2	2.97794390127340	-0.977943901273403
33	2.1	2.96698940699898	-0.866989406998975
34	1.9	2.94845103207302	-1.04845103207302
35	2	2.94592307185584	-0.945923071855844
36	2.5	2.92569939011844	-0.425699390118438
37	2.9	2.92654204352416	-0.0265420435241637
38	3.3	2.90716101519248	0.392838984807517
39	3.5	2.92232877649554	0.577671223504463
40	3.8	2.92064346968409	0.879356530315913
41	4.6	2.92317142990126	1.67682857009874
42	4.4	2.89030794707798	1.50969205292202
43	5.3	2.91053162881538	2.38946837118462
44	5.8	2.89536386751233	2.90463613248767
45	5.9	2.87851079939782	3.02148920060218
46	5.6	2.88272406642645	2.71727593357355
47	5.8	2.83890608932874	2.96109391067126
48	5.5	2.78918953839095	2.71081046160905
49	4.6	2.72177726593293	1.87822273406707
50	4.2	2.66363418093789	1.53636581906211
51	4	2.67374602180659	1.32625397819341
52	3.5	2.64172519238903	0.858274807610968
53	2.3	2.63835457876613	-0.338354578766131
54	2.2	2.65857826050354	-0.458578260503537
55	1.4	2.67374602180659	-1.27374602180659
56	0.6	2.7074521580356	-2.1074521580356
57	0	2.71419338528140	-2.71419338528140
58	0.5	2.75295544194476	-2.25295544194476
59	0.1	2.75632605556767	-2.65632605556767
60	0.1	2.78666157817377	-2.68666157817377

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
5	0.0114474240933169	0.0228948481866339	0.988552575906683
6	0.00298500936945455	0.0059700187389091	0.997014990630545
7	0.00056896212571626	0.00113792425143252	0.999431037874284
8	9.77474507862184e-05	0.000195494901572437	0.999902252549214
9	1.67431297662078e-05	3.34862595324156e-05	0.999983256870234
10	2.79960135118431e-06	5.59920270236863e-06	0.999997200398649
11	8.02080278311731e-07	1.60416055662346e-06	0.999999197919722
12	1.69215982171278e-07	3.38431964342555e-07	0.999999830784018
13	3.11196997634010e-08	6.22393995268019e-08	0.9999999688803
14	2.6862967816924e-08	5.3725935633848e-08	0.999999973137032
15	4.26364085413442e-09	8.52728170826885e-09	0.99999999573636
16	7.01530338111902e-10	1.40306067622380e-09	0.99999999929847
17	7.42465560293367e-10	1.48493112058673e-09	0.999999999257534
18	1.20607259947445e-10	2.41214519894889e-10	0.999999999879393
19	2.88886878206947e-11	5.77773756413893e-11	0.999999999971111
20	4.38062769055744e-12	8.7612553811149e-12	0.99999999999562
21	6.46425123327324e-13	1.29285024665465e-12	0.999999999999354
22	9.29840533979355e-14	1.85968106795871e-13	0.999999999999907
23	1.37029385672614e-14	2.74058771345227e-14	0.999999999999986
24	3.04697292313371e-15	6.09394584626741e-15	0.999999999999997
25	5.16053193033838e-16	1.03210638606768e-15	1
26	9.56843273447087e-17	1.91368654689417e-16	1
27	2.17251641861236e-17	4.34503283722471e-17	1
28	5.85499864891341e-18	1.17099972978268e-17	1
29	7.54519508467045e-19	1.50903901693409e-18	1
30	1.05711060918354e-19	2.11422121836708e-19	1
31	1.43638526280649e-19	2.87277052561299e-19	1
32	9.90280598568281e-20	1.98056119713656e-19	1
33	4.52827887082971e-20	9.05655774165942e-20	1
34	1.61674138336717e-19	3.23348276673433e-19	1
35	2.63381537289425e-19	5.26763074578849e-19	1
36	7.71703075450152e-20	1.54340615090030e-19	1
37	5.14164488050178e-20	1.02832897610036e-19	1
38	1.69878728152952e-19	3.39757456305903e-19	1
39	3.43260265762977e-18	6.86520531525955e-18	1
40	2.19123970455543e-16	4.38247940911086e-16	1
41	6.47178680749915e-13	1.29435736149983e-12	0.999999999999353
42	7.66672256424672e-12	1.53334451284934e-11	0.999999999992333
43	1.98434346182306e-09	3.96868692364611e-09	0.999999998015657
44	1.64470119507978e-07	3.28940239015956e-07	0.99999983552988
45	2.62573705213292e-06	5.25147410426583e-06	0.999997374262948
46	1.38287057936088e-05	2.76574115872176e-05	0.999986171294206
47	0.000265196804237043	0.000530393608474087	0.999734803195763
48	0.0269936081497211	0.0539872162994422	0.97300639185028
49	0.285018394387286	0.570036788774573	0.714981605612714
50	0.475644239424695	0.95128847884939	0.524355760575305
51	0.866457535425815	0.267084929148371	0.133542464574185
52	0.963472478832376	0.0730550423352479	0.0365275211676239
53	0.942647377292558	0.114705245414884	0.0573526227074419
54	0.96395812900575	0.0720837419885004	0.0360418709942502
55	0.974409513539867	0.0511809729202664	0.0255904864601332

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
5 & 0.0114474240933169 & 0.0228948481866339 & 0.988552575906683 \tabularnewline
6 & 0.00298500936945455 & 0.0059700187389091 & 0.997014990630545 \tabularnewline
7 & 0.00056896212571626 & 0.00113792425143252 & 0.999431037874284 \tabularnewline
8 & 9.77474507862184e-05 & 0.000195494901572437 & 0.999902252549214 \tabularnewline
9 & 1.67431297662078e-05 & 3.34862595324156e-05 & 0.999983256870234 \tabularnewline
10 & 2.79960135118431e-06 & 5.59920270236863e-06 & 0.999997200398649 \tabularnewline
11 & 8.02080278311731e-07 & 1.60416055662346e-06 & 0.999999197919722 \tabularnewline
12 & 1.69215982171278e-07 & 3.38431964342555e-07 & 0.999999830784018 \tabularnewline
13 & 3.11196997634010e-08 & 6.22393995268019e-08 & 0.9999999688803 \tabularnewline
14 & 2.6862967816924e-08 & 5.3725935633848e-08 & 0.999999973137032 \tabularnewline
15 & 4.26364085413442e-09 & 8.52728170826885e-09 & 0.99999999573636 \tabularnewline
16 & 7.01530338111902e-10 & 1.40306067622380e-09 & 0.99999999929847 \tabularnewline
17 & 7.42465560293367e-10 & 1.48493112058673e-09 & 0.999999999257534 \tabularnewline
18 & 1.20607259947445e-10 & 2.41214519894889e-10 & 0.999999999879393 \tabularnewline
19 & 2.88886878206947e-11 & 5.77773756413893e-11 & 0.999999999971111 \tabularnewline
20 & 4.38062769055744e-12 & 8.7612553811149e-12 & 0.99999999999562 \tabularnewline
21 & 6.46425123327324e-13 & 1.29285024665465e-12 & 0.999999999999354 \tabularnewline
22 & 9.29840533979355e-14 & 1.85968106795871e-13 & 0.999999999999907 \tabularnewline
23 & 1.37029385672614e-14 & 2.74058771345227e-14 & 0.999999999999986 \tabularnewline
24 & 3.04697292313371e-15 & 6.09394584626741e-15 & 0.999999999999997 \tabularnewline
25 & 5.16053193033838e-16 & 1.03210638606768e-15 & 1 \tabularnewline
26 & 9.56843273447087e-17 & 1.91368654689417e-16 & 1 \tabularnewline
27 & 2.17251641861236e-17 & 4.34503283722471e-17 & 1 \tabularnewline
28 & 5.85499864891341e-18 & 1.17099972978268e-17 & 1 \tabularnewline
29 & 7.54519508467045e-19 & 1.50903901693409e-18 & 1 \tabularnewline
30 & 1.05711060918354e-19 & 2.11422121836708e-19 & 1 \tabularnewline
31 & 1.43638526280649e-19 & 2.87277052561299e-19 & 1 \tabularnewline
32 & 9.90280598568281e-20 & 1.98056119713656e-19 & 1 \tabularnewline
33 & 4.52827887082971e-20 & 9.05655774165942e-20 & 1 \tabularnewline
34 & 1.61674138336717e-19 & 3.23348276673433e-19 & 1 \tabularnewline
35 & 2.63381537289425e-19 & 5.26763074578849e-19 & 1 \tabularnewline
36 & 7.71703075450152e-20 & 1.54340615090030e-19 & 1 \tabularnewline
37 & 5.14164488050178e-20 & 1.02832897610036e-19 & 1 \tabularnewline
38 & 1.69878728152952e-19 & 3.39757456305903e-19 & 1 \tabularnewline
39 & 3.43260265762977e-18 & 6.86520531525955e-18 & 1 \tabularnewline
40 & 2.19123970455543e-16 & 4.38247940911086e-16 & 1 \tabularnewline
41 & 6.47178680749915e-13 & 1.29435736149983e-12 & 0.999999999999353 \tabularnewline
42 & 7.66672256424672e-12 & 1.53334451284934e-11 & 0.999999999992333 \tabularnewline
43 & 1.98434346182306e-09 & 3.96868692364611e-09 & 0.999999998015657 \tabularnewline
44 & 1.64470119507978e-07 & 3.28940239015956e-07 & 0.99999983552988 \tabularnewline
45 & 2.62573705213292e-06 & 5.25147410426583e-06 & 0.999997374262948 \tabularnewline
46 & 1.38287057936088e-05 & 2.76574115872176e-05 & 0.999986171294206 \tabularnewline
47 & 0.000265196804237043 & 0.000530393608474087 & 0.999734803195763 \tabularnewline
48 & 0.0269936081497211 & 0.0539872162994422 & 0.97300639185028 \tabularnewline
49 & 0.285018394387286 & 0.570036788774573 & 0.714981605612714 \tabularnewline
50 & 0.475644239424695 & 0.95128847884939 & 0.524355760575305 \tabularnewline
51 & 0.866457535425815 & 0.267084929148371 & 0.133542464574185 \tabularnewline
52 & 0.963472478832376 & 0.0730550423352479 & 0.0365275211676239 \tabularnewline
53 & 0.942647377292558 & 0.114705245414884 & 0.0573526227074419 \tabularnewline
54 & 0.96395812900575 & 0.0720837419885004 & 0.0360418709942502 \tabularnewline
55 & 0.974409513539867 & 0.0511809729202664 & 0.0255904864601332 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=70102&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]0.0114474240933169[/C][C]0.0228948481866339[/C][C]0.988552575906683[/C][/ROW]
[ROW][C]6[/C][C]0.00298500936945455[/C][C]0.0059700187389091[/C][C]0.997014990630545[/C][/ROW]
[ROW][C]7[/C][C]0.00056896212571626[/C][C]0.00113792425143252[/C][C]0.999431037874284[/C][/ROW]
[ROW][C]8[/C][C]9.77474507862184e-05[/C][C]0.000195494901572437[/C][C]0.999902252549214[/C][/ROW]
[ROW][C]9[/C][C]1.67431297662078e-05[/C][C]3.34862595324156e-05[/C][C]0.999983256870234[/C][/ROW]
[ROW][C]10[/C][C]2.79960135118431e-06[/C][C]5.59920270236863e-06[/C][C]0.999997200398649[/C][/ROW]
[ROW][C]11[/C][C]8.02080278311731e-07[/C][C]1.60416055662346e-06[/C][C]0.999999197919722[/C][/ROW]
[ROW][C]12[/C][C]1.69215982171278e-07[/C][C]3.38431964342555e-07[/C][C]0.999999830784018[/C][/ROW]
[ROW][C]13[/C][C]3.11196997634010e-08[/C][C]6.22393995268019e-08[/C][C]0.9999999688803[/C][/ROW]
[ROW][C]14[/C][C]2.6862967816924e-08[/C][C]5.3725935633848e-08[/C][C]0.999999973137032[/C][/ROW]
[ROW][C]15[/C][C]4.26364085413442e-09[/C][C]8.52728170826885e-09[/C][C]0.99999999573636[/C][/ROW]
[ROW][C]16[/C][C]7.01530338111902e-10[/C][C]1.40306067622380e-09[/C][C]0.99999999929847[/C][/ROW]
[ROW][C]17[/C][C]7.42465560293367e-10[/C][C]1.48493112058673e-09[/C][C]0.999999999257534[/C][/ROW]
[ROW][C]18[/C][C]1.20607259947445e-10[/C][C]2.41214519894889e-10[/C][C]0.999999999879393[/C][/ROW]
[ROW][C]19[/C][C]2.88886878206947e-11[/C][C]5.77773756413893e-11[/C][C]0.999999999971111[/C][/ROW]
[ROW][C]20[/C][C]4.38062769055744e-12[/C][C]8.7612553811149e-12[/C][C]0.99999999999562[/C][/ROW]
[ROW][C]21[/C][C]6.46425123327324e-13[/C][C]1.29285024665465e-12[/C][C]0.999999999999354[/C][/ROW]
[ROW][C]22[/C][C]9.29840533979355e-14[/C][C]1.85968106795871e-13[/C][C]0.999999999999907[/C][/ROW]
[ROW][C]23[/C][C]1.37029385672614e-14[/C][C]2.74058771345227e-14[/C][C]0.999999999999986[/C][/ROW]
[ROW][C]24[/C][C]3.04697292313371e-15[/C][C]6.09394584626741e-15[/C][C]0.999999999999997[/C][/ROW]
[ROW][C]25[/C][C]5.16053193033838e-16[/C][C]1.03210638606768e-15[/C][C]1[/C][/ROW]
[ROW][C]26[/C][C]9.56843273447087e-17[/C][C]1.91368654689417e-16[/C][C]1[/C][/ROW]
[ROW][C]27[/C][C]2.17251641861236e-17[/C][C]4.34503283722471e-17[/C][C]1[/C][/ROW]
[ROW][C]28[/C][C]5.85499864891341e-18[/C][C]1.17099972978268e-17[/C][C]1[/C][/ROW]
[ROW][C]29[/C][C]7.54519508467045e-19[/C][C]1.50903901693409e-18[/C][C]1[/C][/ROW]
[ROW][C]30[/C][C]1.05711060918354e-19[/C][C]2.11422121836708e-19[/C][C]1[/C][/ROW]
[ROW][C]31[/C][C]1.43638526280649e-19[/C][C]2.87277052561299e-19[/C][C]1[/C][/ROW]
[ROW][C]32[/C][C]9.90280598568281e-20[/C][C]1.98056119713656e-19[/C][C]1[/C][/ROW]
[ROW][C]33[/C][C]4.52827887082971e-20[/C][C]9.05655774165942e-20[/C][C]1[/C][/ROW]
[ROW][C]34[/C][C]1.61674138336717e-19[/C][C]3.23348276673433e-19[/C][C]1[/C][/ROW]
[ROW][C]35[/C][C]2.63381537289425e-19[/C][C]5.26763074578849e-19[/C][C]1[/C][/ROW]
[ROW][C]36[/C][C]7.71703075450152e-20[/C][C]1.54340615090030e-19[/C][C]1[/C][/ROW]
[ROW][C]37[/C][C]5.14164488050178e-20[/C][C]1.02832897610036e-19[/C][C]1[/C][/ROW]
[ROW][C]38[/C][C]1.69878728152952e-19[/C][C]3.39757456305903e-19[/C][C]1[/C][/ROW]
[ROW][C]39[/C][C]3.43260265762977e-18[/C][C]6.86520531525955e-18[/C][C]1[/C][/ROW]
[ROW][C]40[/C][C]2.19123970455543e-16[/C][C]4.38247940911086e-16[/C][C]1[/C][/ROW]
[ROW][C]41[/C][C]6.47178680749915e-13[/C][C]1.29435736149983e-12[/C][C]0.999999999999353[/C][/ROW]
[ROW][C]42[/C][C]7.66672256424672e-12[/C][C]1.53334451284934e-11[/C][C]0.999999999992333[/C][/ROW]
[ROW][C]43[/C][C]1.98434346182306e-09[/C][C]3.96868692364611e-09[/C][C]0.999999998015657[/C][/ROW]
[ROW][C]44[/C][C]1.64470119507978e-07[/C][C]3.28940239015956e-07[/C][C]0.99999983552988[/C][/ROW]
[ROW][C]45[/C][C]2.62573705213292e-06[/C][C]5.25147410426583e-06[/C][C]0.999997374262948[/C][/ROW]
[ROW][C]46[/C][C]1.38287057936088e-05[/C][C]2.76574115872176e-05[/C][C]0.999986171294206[/C][/ROW]
[ROW][C]47[/C][C]0.000265196804237043[/C][C]0.000530393608474087[/C][C]0.999734803195763[/C][/ROW]
[ROW][C]48[/C][C]0.0269936081497211[/C][C]0.0539872162994422[/C][C]0.97300639185028[/C][/ROW]
[ROW][C]49[/C][C]0.285018394387286[/C][C]0.570036788774573[/C][C]0.714981605612714[/C][/ROW]
[ROW][C]50[/C][C]0.475644239424695[/C][C]0.95128847884939[/C][C]0.524355760575305[/C][/ROW]
[ROW][C]51[/C][C]0.866457535425815[/C][C]0.267084929148371[/C][C]0.133542464574185[/C][/ROW]
[ROW][C]52[/C][C]0.963472478832376[/C][C]0.0730550423352479[/C][C]0.0365275211676239[/C][/ROW]
[ROW][C]53[/C][C]0.942647377292558[/C][C]0.114705245414884[/C][C]0.0573526227074419[/C][/ROW]
[ROW][C]54[/C][C]0.96395812900575[/C][C]0.0720837419885004[/C][C]0.0360418709942502[/C][/ROW]
[ROW][C]55[/C][C]0.974409513539867[/C][C]0.0511809729202664[/C][C]0.0255904864601332[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=70102&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=70102&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	0.0114474240933169	0.0228948481866339	0.988552575906683
6	0.00298500936945455	0.0059700187389091	0.997014990630545
7	0.00056896212571626	0.00113792425143252	0.999431037874284
8	9.77474507862184e-05	0.000195494901572437	0.999902252549214
9	1.67431297662078e-05	3.34862595324156e-05	0.999983256870234
10	2.79960135118431e-06	5.59920270236863e-06	0.999997200398649
11	8.02080278311731e-07	1.60416055662346e-06	0.999999197919722
12	1.69215982171278e-07	3.38431964342555e-07	0.999999830784018
13	3.11196997634010e-08	6.22393995268019e-08	0.9999999688803
14	2.6862967816924e-08	5.3725935633848e-08	0.999999973137032
15	4.26364085413442e-09	8.52728170826885e-09	0.99999999573636
16	7.01530338111902e-10	1.40306067622380e-09	0.99999999929847
17	7.42465560293367e-10	1.48493112058673e-09	0.999999999257534
18	1.20607259947445e-10	2.41214519894889e-10	0.999999999879393
19	2.88886878206947e-11	5.77773756413893e-11	0.999999999971111
20	4.38062769055744e-12	8.7612553811149e-12	0.99999999999562
21	6.46425123327324e-13	1.29285024665465e-12	0.999999999999354
22	9.29840533979355e-14	1.85968106795871e-13	0.999999999999907
23	1.37029385672614e-14	2.74058771345227e-14	0.999999999999986
24	3.04697292313371e-15	6.09394584626741e-15	0.999999999999997
25	5.16053193033838e-16	1.03210638606768e-15	1
26	9.56843273447087e-17	1.91368654689417e-16	1
27	2.17251641861236e-17	4.34503283722471e-17	1
28	5.85499864891341e-18	1.17099972978268e-17	1
29	7.54519508467045e-19	1.50903901693409e-18	1
30	1.05711060918354e-19	2.11422121836708e-19	1
31	1.43638526280649e-19	2.87277052561299e-19	1
32	9.90280598568281e-20	1.98056119713656e-19	1
33	4.52827887082971e-20	9.05655774165942e-20	1
34	1.61674138336717e-19	3.23348276673433e-19	1
35	2.63381537289425e-19	5.26763074578849e-19	1
36	7.71703075450152e-20	1.54340615090030e-19	1
37	5.14164488050178e-20	1.02832897610036e-19	1
38	1.69878728152952e-19	3.39757456305903e-19	1
39	3.43260265762977e-18	6.86520531525955e-18	1
40	2.19123970455543e-16	4.38247940911086e-16	1
41	6.47178680749915e-13	1.29435736149983e-12	0.999999999999353
42	7.66672256424672e-12	1.53334451284934e-11	0.999999999992333
43	1.98434346182306e-09	3.96868692364611e-09	0.999999998015657
44	1.64470119507978e-07	3.28940239015956e-07	0.99999983552988
45	2.62573705213292e-06	5.25147410426583e-06	0.999997374262948
46	1.38287057936088e-05	2.76574115872176e-05	0.999986171294206
47	0.000265196804237043	0.000530393608474087	0.999734803195763
48	0.0269936081497211	0.0539872162994422	0.97300639185028
49	0.285018394387286	0.570036788774573	0.714981605612714
50	0.475644239424695	0.95128847884939	0.524355760575305
51	0.866457535425815	0.267084929148371	0.133542464574185
52	0.963472478832376	0.0730550423352479	0.0365275211676239
53	0.942647377292558	0.114705245414884	0.0573526227074419
54	0.96395812900575	0.0720837419885004	0.0360418709942502
55	0.974409513539867	0.0511809729202664	0.0255904864601332

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	43	0.843137254901961	NOK
10% type I error level	47	0.92156862745098	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 & 43 & 0.843137254901961 & NOK \tabularnewline
10% type I error level & 47 & 0.92156862745098 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=70102&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]43[/C][C]0.843137254901961[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]47[/C][C]0.92156862745098[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=70102&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=70102&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	43	0.843137254901961	NOK
10% type I error level	47	0.92156862745098	NOK

Figure 1

PNG link

Postscript link

PDF link

Figure 2

PNG link

Postscript link

PDF link

Figure 3

PNG link

Postscript link

PDF link

Figure 4

PNG link

Postscript link

PDF link

Figure 5

PNG link

Postscript link

PDF link

Figure 6

PNG link

Postscript link

PDF link

Figure 7

PNG link

Postscript link

PDF link

Figure 8

PNG link

Postscript link

PDF link

Figure 9

PNG link

Postscript link

PDF link

Figure 10

PNG link

Postscript link

PDF link

Parameters (Session):

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