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Author

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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Sat, 21 Nov 2009 06:40:01 -0700

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2009/Nov/21/t1258810949e3hdw2bje6ce5s8.htm/, Retrieved Mon, 03 Nov 2025 02:22:28 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=58539, Retrieved Mon, 03 Nov 2025 02:22:28 +0000

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Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords

cvm

Estimated Impact

479

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

-     [Multiple Regression] [Q1 The Seatbeltlaw] [2007-11-14 19:27:43] [8cd6641b921d30ebe00b648d1481bba0]
- RMPD  [Multiple Regression] [Seatbelt] [2009-11-12 13:54:52] [b98453cac15ba1066b407e146608df68]
-    D    [Multiple Regression] [model 1] [2009-11-17 14:36:29] [ed603017d2bee8fbd82b6d5ec04e12c3]
-    D      [Multiple Regression] [multiple regression] [2009-11-19 21:38:11] [ed603017d2bee8fbd82b6d5ec04e12c3]
-    D          [Multiple Regression] [W7: Multiple regr...] [2009-11-21 13:40:01] [a5ada8bd39e806b5b90f09589c89554a] [Current]

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Dataseries X:

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Boxplots

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

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=58539&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=58539&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58539&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
WMan>25[t] = + 6.38588796845063 -0.160470000726172Infl[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
WMan>25[t] =  +  6.38588796845063 -0.160470000726172Infl[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58539&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]WMan>25[t] =  +  6.38588796845063 -0.160470000726172Infl[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58539&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58539&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
WMan>25[t] = + 6.38588796845063 -0.160470000726172Infl[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)	6.38588796845063	0.12663	50.4296	0	0
Infl	-0.160470000726172	0.044702	-3.5897	0.000682	0.000341

\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) & 6.38588796845063 & 0.12663 & 50.4296 & 0 & 0 \tabularnewline
Infl & -0.160470000726172 & 0.044702 & -3.5897 & 0.000682 & 0.000341 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58539&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]6.38588796845063[/C][C]0.12663[/C][C]50.4296[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Infl[/C][C]-0.160470000726172[/C][C]0.044702[/C][C]-3.5897[/C][C]0.000682[/C][C]0.000341[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58539&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58539&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)	6.38588796845063	0.12663	50.4296	0	0
Infl	-0.160470000726172	0.044702	-3.5897	0.000682	0.000341

Multiple Linear Regression - Regression Statistics
Multiple R	0.426365369418309
R-squared	0.181787428239211
Adjusted R-squared	0.167680314932991
F-TEST (value)	12.8862244382097
F-TEST (DF numerator)	1
F-TEST (DF denominator)	58
p-value	0.000681690348453667
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.48835694565493
Sum Squared Residuals	13.8325653694259

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.426365369418309 \tabularnewline
R-squared & 0.181787428239211 \tabularnewline
Adjusted R-squared & 0.167680314932991 \tabularnewline
F-TEST (value) & 12.8862244382097 \tabularnewline
F-TEST (DF numerator) & 1 \tabularnewline
F-TEST (DF denominator) & 58 \tabularnewline
p-value & 0.000681690348453667 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.48835694565493 \tabularnewline
Sum Squared Residuals & 13.8325653694259 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58539&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.426365369418309[/C][/ROW]
[ROW][C]R-squared[/C][C]0.181787428239211[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.167680314932991[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]12.8862244382097[/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]0.000681690348453667[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.48835694565493[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]13.8325653694259[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58539&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58539&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	0.426365369418309
R-squared	0.181787428239211
Adjusted R-squared	0.167680314932991
F-TEST (value)	12.8862244382097
F-TEST (DF numerator)	1
F-TEST (DF denominator)	58
p-value	0.000681690348453667
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.48835694565493
Sum Squared Residuals	13.8325653694259

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	6.3	6.06494796699829	0.235052033001706
2	6.2	6.09704196714352	0.102958032856481
3	6.1	5.95261896648996	0.147381033510035
4	6.3	6.01680696678043	0.283193033219566
5	6.5	6.0809949670709	0.419005032929098
6	6.6	6.06494796699829	0.535052033001715
7	6.5	6.01680696678043	0.483193033219567
8	6.2	5.93657196641735	0.263428033582653
9	6.2	6.00075996670782	0.199240033292184
10	5.9	6.01680696678043	-0.116806966780433
11	6.1	5.95261896648996	0.147381033510035
12	6.1	5.95261896648996	0.147381033510035
13	6.1	5.92052496634473	0.179475033655269
14	6.1	5.90447796627211	0.195522033727886
15	6.1	6.03285396685305	0.0671460331469491
16	6.4	6.01680696678043	0.383193033219567
17	6.7	5.93657196641735	0.763428033582653
18	6.9	5.93657196641735	0.963428033582653
19	7	5.93657196641735	1.06342803358265
20	7	6.03285396685305	0.96714603314695
21	6.8	5.96866596656258	0.831334033437418
22	6.4	5.93657196641735	0.463428033582653
23	5.9	5.9847129666352	-0.0847129666351987
24	5.5	6.00075996670782	-0.500759966707816
25	5.5	6.01680696678043	-0.516806966780433
26	5.6	6.0809949670709	-0.480994967070903
27	5.8	6.11308896721614	-0.313088967216137
28	5.9	6.06494796699829	-0.164947966998285
29	6.1	6.04890096692567	0.0510990330743319
30	6.1	6.11308896721614	-0.0130889672161368
31	6	6.09704196714352	-0.0970419671435193
32	6	6.09704196714352	-0.0970419671435193
33	5.9	6.09704196714352	-0.197041967143519
34	5.5	6.1772769675066	-0.677276967506605
35	5.6	6.1772769675066	-0.577276967506606
36	5.4	6.1772769675066	-0.777276967506605
37	5.2	6.19332396757922	-0.993323967579222
38	5.2	6.16122996743399	-0.961229967433988
39	5.2	6.03285396685305	-0.83285396685305
40	5.5	5.92052496634473	-0.42052496634473
41	5.8	5.88843096619950	-0.0884309661994961
42	5.8	5.82424296590903	-0.0242429659090275
43	5.5	5.80819596583641	-0.30819596583641
44	5.3	5.67981996525547	-0.379819965255473
45	5.1	5.72796096547332	-0.627960965473325
46	5.2	5.56749096474715	-0.367490964747152
47	5.8	5.45516196423883	0.344838035761168
48	5.8	5.43911496416622	0.360885035833785
49	5.5	5.5193499645293	-0.0193499645293008
50	5	5.50330296445668	-0.503302964456684
51	4.9	5.615631964965	-0.715631964965004
52	5.3	5.87238396612688	-0.572383966126879
53	6.1	5.95261896648996	0.147381033510035
54	6.5	6.04890096692567	0.451099033074332
55	6.8	6.0809949670709	0.719005032929098
56	6.6	6.28960596801493	0.310394031985074
57	6.4	6.27355896794231	0.126441032057692
58	6.4	6.41798196859586	-0.0179819685958625
59	6.6	6.5463579691768	0.0536420308231993
60	6.7	6.65868696968512	0.0413130303148796

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 6.3 & 6.06494796699829 & 0.235052033001706 \tabularnewline
2 & 6.2 & 6.09704196714352 & 0.102958032856481 \tabularnewline
3 & 6.1 & 5.95261896648996 & 0.147381033510035 \tabularnewline
4 & 6.3 & 6.01680696678043 & 0.283193033219566 \tabularnewline
5 & 6.5 & 6.0809949670709 & 0.419005032929098 \tabularnewline
6 & 6.6 & 6.06494796699829 & 0.535052033001715 \tabularnewline
7 & 6.5 & 6.01680696678043 & 0.483193033219567 \tabularnewline
8 & 6.2 & 5.93657196641735 & 0.263428033582653 \tabularnewline
9 & 6.2 & 6.00075996670782 & 0.199240033292184 \tabularnewline
10 & 5.9 & 6.01680696678043 & -0.116806966780433 \tabularnewline
11 & 6.1 & 5.95261896648996 & 0.147381033510035 \tabularnewline
12 & 6.1 & 5.95261896648996 & 0.147381033510035 \tabularnewline
13 & 6.1 & 5.92052496634473 & 0.179475033655269 \tabularnewline
14 & 6.1 & 5.90447796627211 & 0.195522033727886 \tabularnewline
15 & 6.1 & 6.03285396685305 & 0.0671460331469491 \tabularnewline
16 & 6.4 & 6.01680696678043 & 0.383193033219567 \tabularnewline
17 & 6.7 & 5.93657196641735 & 0.763428033582653 \tabularnewline
18 & 6.9 & 5.93657196641735 & 0.963428033582653 \tabularnewline
19 & 7 & 5.93657196641735 & 1.06342803358265 \tabularnewline
20 & 7 & 6.03285396685305 & 0.96714603314695 \tabularnewline
21 & 6.8 & 5.96866596656258 & 0.831334033437418 \tabularnewline
22 & 6.4 & 5.93657196641735 & 0.463428033582653 \tabularnewline
23 & 5.9 & 5.9847129666352 & -0.0847129666351987 \tabularnewline
24 & 5.5 & 6.00075996670782 & -0.500759966707816 \tabularnewline
25 & 5.5 & 6.01680696678043 & -0.516806966780433 \tabularnewline
26 & 5.6 & 6.0809949670709 & -0.480994967070903 \tabularnewline
27 & 5.8 & 6.11308896721614 & -0.313088967216137 \tabularnewline
28 & 5.9 & 6.06494796699829 & -0.164947966998285 \tabularnewline
29 & 6.1 & 6.04890096692567 & 0.0510990330743319 \tabularnewline
30 & 6.1 & 6.11308896721614 & -0.0130889672161368 \tabularnewline
31 & 6 & 6.09704196714352 & -0.0970419671435193 \tabularnewline
32 & 6 & 6.09704196714352 & -0.0970419671435193 \tabularnewline
33 & 5.9 & 6.09704196714352 & -0.197041967143519 \tabularnewline
34 & 5.5 & 6.1772769675066 & -0.677276967506605 \tabularnewline
35 & 5.6 & 6.1772769675066 & -0.577276967506606 \tabularnewline
36 & 5.4 & 6.1772769675066 & -0.777276967506605 \tabularnewline
37 & 5.2 & 6.19332396757922 & -0.993323967579222 \tabularnewline
38 & 5.2 & 6.16122996743399 & -0.961229967433988 \tabularnewline
39 & 5.2 & 6.03285396685305 & -0.83285396685305 \tabularnewline
40 & 5.5 & 5.92052496634473 & -0.42052496634473 \tabularnewline
41 & 5.8 & 5.88843096619950 & -0.0884309661994961 \tabularnewline
42 & 5.8 & 5.82424296590903 & -0.0242429659090275 \tabularnewline
43 & 5.5 & 5.80819596583641 & -0.30819596583641 \tabularnewline
44 & 5.3 & 5.67981996525547 & -0.379819965255473 \tabularnewline
45 & 5.1 & 5.72796096547332 & -0.627960965473325 \tabularnewline
46 & 5.2 & 5.56749096474715 & -0.367490964747152 \tabularnewline
47 & 5.8 & 5.45516196423883 & 0.344838035761168 \tabularnewline
48 & 5.8 & 5.43911496416622 & 0.360885035833785 \tabularnewline
49 & 5.5 & 5.5193499645293 & -0.0193499645293008 \tabularnewline
50 & 5 & 5.50330296445668 & -0.503302964456684 \tabularnewline
51 & 4.9 & 5.615631964965 & -0.715631964965004 \tabularnewline
52 & 5.3 & 5.87238396612688 & -0.572383966126879 \tabularnewline
53 & 6.1 & 5.95261896648996 & 0.147381033510035 \tabularnewline
54 & 6.5 & 6.04890096692567 & 0.451099033074332 \tabularnewline
55 & 6.8 & 6.0809949670709 & 0.719005032929098 \tabularnewline
56 & 6.6 & 6.28960596801493 & 0.310394031985074 \tabularnewline
57 & 6.4 & 6.27355896794231 & 0.126441032057692 \tabularnewline
58 & 6.4 & 6.41798196859586 & -0.0179819685958625 \tabularnewline
59 & 6.6 & 6.5463579691768 & 0.0536420308231993 \tabularnewline
60 & 6.7 & 6.65868696968512 & 0.0413130303148796 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58539&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]6.3[/C][C]6.06494796699829[/C][C]0.235052033001706[/C][/ROW]
[ROW][C]2[/C][C]6.2[/C][C]6.09704196714352[/C][C]0.102958032856481[/C][/ROW]
[ROW][C]3[/C][C]6.1[/C][C]5.95261896648996[/C][C]0.147381033510035[/C][/ROW]
[ROW][C]4[/C][C]6.3[/C][C]6.01680696678043[/C][C]0.283193033219566[/C][/ROW]
[ROW][C]5[/C][C]6.5[/C][C]6.0809949670709[/C][C]0.419005032929098[/C][/ROW]
[ROW][C]6[/C][C]6.6[/C][C]6.06494796699829[/C][C]0.535052033001715[/C][/ROW]
[ROW][C]7[/C][C]6.5[/C][C]6.01680696678043[/C][C]0.483193033219567[/C][/ROW]
[ROW][C]8[/C][C]6.2[/C][C]5.93657196641735[/C][C]0.263428033582653[/C][/ROW]
[ROW][C]9[/C][C]6.2[/C][C]6.00075996670782[/C][C]0.199240033292184[/C][/ROW]
[ROW][C]10[/C][C]5.9[/C][C]6.01680696678043[/C][C]-0.116806966780433[/C][/ROW]
[ROW][C]11[/C][C]6.1[/C][C]5.95261896648996[/C][C]0.147381033510035[/C][/ROW]
[ROW][C]12[/C][C]6.1[/C][C]5.95261896648996[/C][C]0.147381033510035[/C][/ROW]
[ROW][C]13[/C][C]6.1[/C][C]5.92052496634473[/C][C]0.179475033655269[/C][/ROW]
[ROW][C]14[/C][C]6.1[/C][C]5.90447796627211[/C][C]0.195522033727886[/C][/ROW]
[ROW][C]15[/C][C]6.1[/C][C]6.03285396685305[/C][C]0.0671460331469491[/C][/ROW]
[ROW][C]16[/C][C]6.4[/C][C]6.01680696678043[/C][C]0.383193033219567[/C][/ROW]
[ROW][C]17[/C][C]6.7[/C][C]5.93657196641735[/C][C]0.763428033582653[/C][/ROW]
[ROW][C]18[/C][C]6.9[/C][C]5.93657196641735[/C][C]0.963428033582653[/C][/ROW]
[ROW][C]19[/C][C]7[/C][C]5.93657196641735[/C][C]1.06342803358265[/C][/ROW]
[ROW][C]20[/C][C]7[/C][C]6.03285396685305[/C][C]0.96714603314695[/C][/ROW]
[ROW][C]21[/C][C]6.8[/C][C]5.96866596656258[/C][C]0.831334033437418[/C][/ROW]
[ROW][C]22[/C][C]6.4[/C][C]5.93657196641735[/C][C]0.463428033582653[/C][/ROW]
[ROW][C]23[/C][C]5.9[/C][C]5.9847129666352[/C][C]-0.0847129666351987[/C][/ROW]
[ROW][C]24[/C][C]5.5[/C][C]6.00075996670782[/C][C]-0.500759966707816[/C][/ROW]
[ROW][C]25[/C][C]5.5[/C][C]6.01680696678043[/C][C]-0.516806966780433[/C][/ROW]
[ROW][C]26[/C][C]5.6[/C][C]6.0809949670709[/C][C]-0.480994967070903[/C][/ROW]
[ROW][C]27[/C][C]5.8[/C][C]6.11308896721614[/C][C]-0.313088967216137[/C][/ROW]
[ROW][C]28[/C][C]5.9[/C][C]6.06494796699829[/C][C]-0.164947966998285[/C][/ROW]
[ROW][C]29[/C][C]6.1[/C][C]6.04890096692567[/C][C]0.0510990330743319[/C][/ROW]
[ROW][C]30[/C][C]6.1[/C][C]6.11308896721614[/C][C]-0.0130889672161368[/C][/ROW]
[ROW][C]31[/C][C]6[/C][C]6.09704196714352[/C][C]-0.0970419671435193[/C][/ROW]
[ROW][C]32[/C][C]6[/C][C]6.09704196714352[/C][C]-0.0970419671435193[/C][/ROW]
[ROW][C]33[/C][C]5.9[/C][C]6.09704196714352[/C][C]-0.197041967143519[/C][/ROW]
[ROW][C]34[/C][C]5.5[/C][C]6.1772769675066[/C][C]-0.677276967506605[/C][/ROW]
[ROW][C]35[/C][C]5.6[/C][C]6.1772769675066[/C][C]-0.577276967506606[/C][/ROW]
[ROW][C]36[/C][C]5.4[/C][C]6.1772769675066[/C][C]-0.777276967506605[/C][/ROW]
[ROW][C]37[/C][C]5.2[/C][C]6.19332396757922[/C][C]-0.993323967579222[/C][/ROW]
[ROW][C]38[/C][C]5.2[/C][C]6.16122996743399[/C][C]-0.961229967433988[/C][/ROW]
[ROW][C]39[/C][C]5.2[/C][C]6.03285396685305[/C][C]-0.83285396685305[/C][/ROW]
[ROW][C]40[/C][C]5.5[/C][C]5.92052496634473[/C][C]-0.42052496634473[/C][/ROW]
[ROW][C]41[/C][C]5.8[/C][C]5.88843096619950[/C][C]-0.0884309661994961[/C][/ROW]
[ROW][C]42[/C][C]5.8[/C][C]5.82424296590903[/C][C]-0.0242429659090275[/C][/ROW]
[ROW][C]43[/C][C]5.5[/C][C]5.80819596583641[/C][C]-0.30819596583641[/C][/ROW]
[ROW][C]44[/C][C]5.3[/C][C]5.67981996525547[/C][C]-0.379819965255473[/C][/ROW]
[ROW][C]45[/C][C]5.1[/C][C]5.72796096547332[/C][C]-0.627960965473325[/C][/ROW]
[ROW][C]46[/C][C]5.2[/C][C]5.56749096474715[/C][C]-0.367490964747152[/C][/ROW]
[ROW][C]47[/C][C]5.8[/C][C]5.45516196423883[/C][C]0.344838035761168[/C][/ROW]
[ROW][C]48[/C][C]5.8[/C][C]5.43911496416622[/C][C]0.360885035833785[/C][/ROW]
[ROW][C]49[/C][C]5.5[/C][C]5.5193499645293[/C][C]-0.0193499645293008[/C][/ROW]
[ROW][C]50[/C][C]5[/C][C]5.50330296445668[/C][C]-0.503302964456684[/C][/ROW]
[ROW][C]51[/C][C]4.9[/C][C]5.615631964965[/C][C]-0.715631964965004[/C][/ROW]
[ROW][C]52[/C][C]5.3[/C][C]5.87238396612688[/C][C]-0.572383966126879[/C][/ROW]
[ROW][C]53[/C][C]6.1[/C][C]5.95261896648996[/C][C]0.147381033510035[/C][/ROW]
[ROW][C]54[/C][C]6.5[/C][C]6.04890096692567[/C][C]0.451099033074332[/C][/ROW]
[ROW][C]55[/C][C]6.8[/C][C]6.0809949670709[/C][C]0.719005032929098[/C][/ROW]
[ROW][C]56[/C][C]6.6[/C][C]6.28960596801493[/C][C]0.310394031985074[/C][/ROW]
[ROW][C]57[/C][C]6.4[/C][C]6.27355896794231[/C][C]0.126441032057692[/C][/ROW]
[ROW][C]58[/C][C]6.4[/C][C]6.41798196859586[/C][C]-0.0179819685958625[/C][/ROW]
[ROW][C]59[/C][C]6.6[/C][C]6.5463579691768[/C][C]0.0536420308231993[/C][/ROW]
[ROW][C]60[/C][C]6.7[/C][C]6.65868696968512[/C][C]0.0413130303148796[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58539&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58539&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	6.3	6.06494796699829	0.235052033001706
2	6.2	6.09704196714352	0.102958032856481
3	6.1	5.95261896648996	0.147381033510035
4	6.3	6.01680696678043	0.283193033219566
5	6.5	6.0809949670709	0.419005032929098
6	6.6	6.06494796699829	0.535052033001715
7	6.5	6.01680696678043	0.483193033219567
8	6.2	5.93657196641735	0.263428033582653
9	6.2	6.00075996670782	0.199240033292184
10	5.9	6.01680696678043	-0.116806966780433
11	6.1	5.95261896648996	0.147381033510035
12	6.1	5.95261896648996	0.147381033510035
13	6.1	5.92052496634473	0.179475033655269
14	6.1	5.90447796627211	0.195522033727886
15	6.1	6.03285396685305	0.0671460331469491
16	6.4	6.01680696678043	0.383193033219567
17	6.7	5.93657196641735	0.763428033582653
18	6.9	5.93657196641735	0.963428033582653
19	7	5.93657196641735	1.06342803358265
20	7	6.03285396685305	0.96714603314695
21	6.8	5.96866596656258	0.831334033437418
22	6.4	5.93657196641735	0.463428033582653
23	5.9	5.9847129666352	-0.0847129666351987
24	5.5	6.00075996670782	-0.500759966707816
25	5.5	6.01680696678043	-0.516806966780433
26	5.6	6.0809949670709	-0.480994967070903
27	5.8	6.11308896721614	-0.313088967216137
28	5.9	6.06494796699829	-0.164947966998285
29	6.1	6.04890096692567	0.0510990330743319
30	6.1	6.11308896721614	-0.0130889672161368
31	6	6.09704196714352	-0.0970419671435193
32	6	6.09704196714352	-0.0970419671435193
33	5.9	6.09704196714352	-0.197041967143519
34	5.5	6.1772769675066	-0.677276967506605
35	5.6	6.1772769675066	-0.577276967506606
36	5.4	6.1772769675066	-0.777276967506605
37	5.2	6.19332396757922	-0.993323967579222
38	5.2	6.16122996743399	-0.961229967433988
39	5.2	6.03285396685305	-0.83285396685305
40	5.5	5.92052496634473	-0.42052496634473
41	5.8	5.88843096619950	-0.0884309661994961
42	5.8	5.82424296590903	-0.0242429659090275
43	5.5	5.80819596583641	-0.30819596583641
44	5.3	5.67981996525547	-0.379819965255473
45	5.1	5.72796096547332	-0.627960965473325
46	5.2	5.56749096474715	-0.367490964747152
47	5.8	5.45516196423883	0.344838035761168
48	5.8	5.43911496416622	0.360885035833785
49	5.5	5.5193499645293	-0.0193499645293008
50	5	5.50330296445668	-0.503302964456684
51	4.9	5.615631964965	-0.715631964965004
52	5.3	5.87238396612688	-0.572383966126879
53	6.1	5.95261896648996	0.147381033510035
54	6.5	6.04890096692567	0.451099033074332
55	6.8	6.0809949670709	0.719005032929098
56	6.6	6.28960596801493	0.310394031985074
57	6.4	6.27355896794231	0.126441032057692
58	6.4	6.41798196859586	-0.0179819685958625
59	6.6	6.5463579691768	0.0536420308231993
60	6.7	6.65868696968512	0.0413130303148796

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
5	0.0274980816320827	0.0549961632641654	0.972501918367917
6	0.0265279601236305	0.0530559202472611	0.97347203987637
7	0.0152526478162500	0.0305052956325001	0.98474735218375
8	0.00478475311789227	0.00956950623578454	0.995215246882108
9	0.00161378932011810	0.00322757864023619	0.998386210679882
10	0.00414849135594805	0.0082969827118961	0.995851508644052
11	0.00150220832250578	0.00300441664501156	0.998497791677494
12	0.00051212153298762	0.00102424306597524	0.999487878467012
13	0.000165069933210901	0.000330139866421801	0.999834930066789
14	5.22067028762867e-05	0.000104413405752573	0.999947793297124
15	2.6221709576041e-05	5.2443419152082e-05	0.999973778290424
16	1.21096151177987e-05	2.42192302355973e-05	0.999987890384882
17	0.000204370546039924	0.000408741092079849	0.99979562945396
18	0.00329605166570514	0.00659210333141027	0.996703948334295
19	0.0256585484139815	0.051317096827963	0.974341451586018
20	0.0942678977668756	0.188535795533751	0.905732102233124
21	0.162926019432227	0.325852038864455	0.837073980567773
22	0.155546742565706	0.311093485131412	0.844453257434294
23	0.165352530691414	0.330705061382829	0.834647469308586
24	0.307506000135365	0.615012000270729	0.692493999864635
25	0.426320720573305	0.85264144114661	0.573679279426695
26	0.458209578796707	0.916419157593415	0.541790421203293
27	0.406262300800039	0.812524601600077	0.593737699199961
28	0.346410221939443	0.692820443878886	0.653589778060557
29	0.289374193247156	0.578748386494313	0.710625806752844
30	0.233584100678006	0.467168201356012	0.766415899321994
31	0.181716619756316	0.363433239512633	0.818283380243684
32	0.137745753148958	0.275491506297916	0.862254246851042
33	0.102489204013804	0.204978408027608	0.897510795986196
34	0.0993922756983946	0.198784551396789	0.900607724301605
35	0.0834254156259888	0.166850831251978	0.916574584374011
36	0.0949671354706091	0.189934270941218	0.90503286452939
37	0.171196124386296	0.342392248772592	0.828803875613704
38	0.321566705291908	0.643133410583815	0.678433294708092
39	0.581474842908859	0.837050314182281	0.418525157091141
40	0.672202294987515	0.65559541002497	0.327797705012485
41	0.648405104813611	0.703189790372779	0.351594895186389
42	0.63118988088586	0.73762023822828	0.36881011911414
43	0.656070136640316	0.687859726719367	0.343929863359684
44	0.700070305428296	0.599859389143407	0.299929694571704
45	0.787181528410556	0.425636943178888	0.212818471589444
46	0.771453676349413	0.457092647301173	0.228546323650587
47	0.754947221662361	0.490105556675279	0.245052778337639
48	0.792300567589804	0.415398864820391	0.207699432410196
49	0.746119320249597	0.507761359500806	0.253880679750403
50	0.684234581079164	0.631530837841672	0.315765418920836
51	0.781163866207255	0.43767226758549	0.218836133792745
52	0.982531294436327	0.0349374111273456	0.0174687055636728
53	0.990419384277556	0.0191612314448882	0.0095806157224441
54	0.972584606461741	0.0548307870765179	0.0274153935382590
55	0.98399671564356	0.0320065687128818	0.0160032843564409

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
5 & 0.0274980816320827 & 0.0549961632641654 & 0.972501918367917 \tabularnewline
6 & 0.0265279601236305 & 0.0530559202472611 & 0.97347203987637 \tabularnewline
7 & 0.0152526478162500 & 0.0305052956325001 & 0.98474735218375 \tabularnewline
8 & 0.00478475311789227 & 0.00956950623578454 & 0.995215246882108 \tabularnewline
9 & 0.00161378932011810 & 0.00322757864023619 & 0.998386210679882 \tabularnewline
10 & 0.00414849135594805 & 0.0082969827118961 & 0.995851508644052 \tabularnewline
11 & 0.00150220832250578 & 0.00300441664501156 & 0.998497791677494 \tabularnewline
12 & 0.00051212153298762 & 0.00102424306597524 & 0.999487878467012 \tabularnewline
13 & 0.000165069933210901 & 0.000330139866421801 & 0.999834930066789 \tabularnewline
14 & 5.22067028762867e-05 & 0.000104413405752573 & 0.999947793297124 \tabularnewline
15 & 2.6221709576041e-05 & 5.2443419152082e-05 & 0.999973778290424 \tabularnewline
16 & 1.21096151177987e-05 & 2.42192302355973e-05 & 0.999987890384882 \tabularnewline
17 & 0.000204370546039924 & 0.000408741092079849 & 0.99979562945396 \tabularnewline
18 & 0.00329605166570514 & 0.00659210333141027 & 0.996703948334295 \tabularnewline
19 & 0.0256585484139815 & 0.051317096827963 & 0.974341451586018 \tabularnewline
20 & 0.0942678977668756 & 0.188535795533751 & 0.905732102233124 \tabularnewline
21 & 0.162926019432227 & 0.325852038864455 & 0.837073980567773 \tabularnewline
22 & 0.155546742565706 & 0.311093485131412 & 0.844453257434294 \tabularnewline
23 & 0.165352530691414 & 0.330705061382829 & 0.834647469308586 \tabularnewline
24 & 0.307506000135365 & 0.615012000270729 & 0.692493999864635 \tabularnewline
25 & 0.426320720573305 & 0.85264144114661 & 0.573679279426695 \tabularnewline
26 & 0.458209578796707 & 0.916419157593415 & 0.541790421203293 \tabularnewline
27 & 0.406262300800039 & 0.812524601600077 & 0.593737699199961 \tabularnewline
28 & 0.346410221939443 & 0.692820443878886 & 0.653589778060557 \tabularnewline
29 & 0.289374193247156 & 0.578748386494313 & 0.710625806752844 \tabularnewline
30 & 0.233584100678006 & 0.467168201356012 & 0.766415899321994 \tabularnewline
31 & 0.181716619756316 & 0.363433239512633 & 0.818283380243684 \tabularnewline
32 & 0.137745753148958 & 0.275491506297916 & 0.862254246851042 \tabularnewline
33 & 0.102489204013804 & 0.204978408027608 & 0.897510795986196 \tabularnewline
34 & 0.0993922756983946 & 0.198784551396789 & 0.900607724301605 \tabularnewline
35 & 0.0834254156259888 & 0.166850831251978 & 0.916574584374011 \tabularnewline
36 & 0.0949671354706091 & 0.189934270941218 & 0.90503286452939 \tabularnewline
37 & 0.171196124386296 & 0.342392248772592 & 0.828803875613704 \tabularnewline
38 & 0.321566705291908 & 0.643133410583815 & 0.678433294708092 \tabularnewline
39 & 0.581474842908859 & 0.837050314182281 & 0.418525157091141 \tabularnewline
40 & 0.672202294987515 & 0.65559541002497 & 0.327797705012485 \tabularnewline
41 & 0.648405104813611 & 0.703189790372779 & 0.351594895186389 \tabularnewline
42 & 0.63118988088586 & 0.73762023822828 & 0.36881011911414 \tabularnewline
43 & 0.656070136640316 & 0.687859726719367 & 0.343929863359684 \tabularnewline
44 & 0.700070305428296 & 0.599859389143407 & 0.299929694571704 \tabularnewline
45 & 0.787181528410556 & 0.425636943178888 & 0.212818471589444 \tabularnewline
46 & 0.771453676349413 & 0.457092647301173 & 0.228546323650587 \tabularnewline
47 & 0.754947221662361 & 0.490105556675279 & 0.245052778337639 \tabularnewline
48 & 0.792300567589804 & 0.415398864820391 & 0.207699432410196 \tabularnewline
49 & 0.746119320249597 & 0.507761359500806 & 0.253880679750403 \tabularnewline
50 & 0.684234581079164 & 0.631530837841672 & 0.315765418920836 \tabularnewline
51 & 0.781163866207255 & 0.43767226758549 & 0.218836133792745 \tabularnewline
52 & 0.982531294436327 & 0.0349374111273456 & 0.0174687055636728 \tabularnewline
53 & 0.990419384277556 & 0.0191612314448882 & 0.0095806157224441 \tabularnewline
54 & 0.972584606461741 & 0.0548307870765179 & 0.0274153935382590 \tabularnewline
55 & 0.98399671564356 & 0.0320065687128818 & 0.0160032843564409 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58539&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]0.0274980816320827[/C][C]0.0549961632641654[/C][C]0.972501918367917[/C][/ROW]
[ROW][C]6[/C][C]0.0265279601236305[/C][C]0.0530559202472611[/C][C]0.97347203987637[/C][/ROW]
[ROW][C]7[/C][C]0.0152526478162500[/C][C]0.0305052956325001[/C][C]0.98474735218375[/C][/ROW]
[ROW][C]8[/C][C]0.00478475311789227[/C][C]0.00956950623578454[/C][C]0.995215246882108[/C][/ROW]
[ROW][C]9[/C][C]0.00161378932011810[/C][C]0.00322757864023619[/C][C]0.998386210679882[/C][/ROW]
[ROW][C]10[/C][C]0.00414849135594805[/C][C]0.0082969827118961[/C][C]0.995851508644052[/C][/ROW]
[ROW][C]11[/C][C]0.00150220832250578[/C][C]0.00300441664501156[/C][C]0.998497791677494[/C][/ROW]
[ROW][C]12[/C][C]0.00051212153298762[/C][C]0.00102424306597524[/C][C]0.999487878467012[/C][/ROW]
[ROW][C]13[/C][C]0.000165069933210901[/C][C]0.000330139866421801[/C][C]0.999834930066789[/C][/ROW]
[ROW][C]14[/C][C]5.22067028762867e-05[/C][C]0.000104413405752573[/C][C]0.999947793297124[/C][/ROW]
[ROW][C]15[/C][C]2.6221709576041e-05[/C][C]5.2443419152082e-05[/C][C]0.999973778290424[/C][/ROW]
[ROW][C]16[/C][C]1.21096151177987e-05[/C][C]2.42192302355973e-05[/C][C]0.999987890384882[/C][/ROW]
[ROW][C]17[/C][C]0.000204370546039924[/C][C]0.000408741092079849[/C][C]0.99979562945396[/C][/ROW]
[ROW][C]18[/C][C]0.00329605166570514[/C][C]0.00659210333141027[/C][C]0.996703948334295[/C][/ROW]
[ROW][C]19[/C][C]0.0256585484139815[/C][C]0.051317096827963[/C][C]0.974341451586018[/C][/ROW]
[ROW][C]20[/C][C]0.0942678977668756[/C][C]0.188535795533751[/C][C]0.905732102233124[/C][/ROW]
[ROW][C]21[/C][C]0.162926019432227[/C][C]0.325852038864455[/C][C]0.837073980567773[/C][/ROW]
[ROW][C]22[/C][C]0.155546742565706[/C][C]0.311093485131412[/C][C]0.844453257434294[/C][/ROW]
[ROW][C]23[/C][C]0.165352530691414[/C][C]0.330705061382829[/C][C]0.834647469308586[/C][/ROW]
[ROW][C]24[/C][C]0.307506000135365[/C][C]0.615012000270729[/C][C]0.692493999864635[/C][/ROW]
[ROW][C]25[/C][C]0.426320720573305[/C][C]0.85264144114661[/C][C]0.573679279426695[/C][/ROW]
[ROW][C]26[/C][C]0.458209578796707[/C][C]0.916419157593415[/C][C]0.541790421203293[/C][/ROW]
[ROW][C]27[/C][C]0.406262300800039[/C][C]0.812524601600077[/C][C]0.593737699199961[/C][/ROW]
[ROW][C]28[/C][C]0.346410221939443[/C][C]0.692820443878886[/C][C]0.653589778060557[/C][/ROW]
[ROW][C]29[/C][C]0.289374193247156[/C][C]0.578748386494313[/C][C]0.710625806752844[/C][/ROW]
[ROW][C]30[/C][C]0.233584100678006[/C][C]0.467168201356012[/C][C]0.766415899321994[/C][/ROW]
[ROW][C]31[/C][C]0.181716619756316[/C][C]0.363433239512633[/C][C]0.818283380243684[/C][/ROW]
[ROW][C]32[/C][C]0.137745753148958[/C][C]0.275491506297916[/C][C]0.862254246851042[/C][/ROW]
[ROW][C]33[/C][C]0.102489204013804[/C][C]0.204978408027608[/C][C]0.897510795986196[/C][/ROW]
[ROW][C]34[/C][C]0.0993922756983946[/C][C]0.198784551396789[/C][C]0.900607724301605[/C][/ROW]
[ROW][C]35[/C][C]0.0834254156259888[/C][C]0.166850831251978[/C][C]0.916574584374011[/C][/ROW]
[ROW][C]36[/C][C]0.0949671354706091[/C][C]0.189934270941218[/C][C]0.90503286452939[/C][/ROW]
[ROW][C]37[/C][C]0.171196124386296[/C][C]0.342392248772592[/C][C]0.828803875613704[/C][/ROW]
[ROW][C]38[/C][C]0.321566705291908[/C][C]0.643133410583815[/C][C]0.678433294708092[/C][/ROW]
[ROW][C]39[/C][C]0.581474842908859[/C][C]0.837050314182281[/C][C]0.418525157091141[/C][/ROW]
[ROW][C]40[/C][C]0.672202294987515[/C][C]0.65559541002497[/C][C]0.327797705012485[/C][/ROW]
[ROW][C]41[/C][C]0.648405104813611[/C][C]0.703189790372779[/C][C]0.351594895186389[/C][/ROW]
[ROW][C]42[/C][C]0.63118988088586[/C][C]0.73762023822828[/C][C]0.36881011911414[/C][/ROW]
[ROW][C]43[/C][C]0.656070136640316[/C][C]0.687859726719367[/C][C]0.343929863359684[/C][/ROW]
[ROW][C]44[/C][C]0.700070305428296[/C][C]0.599859389143407[/C][C]0.299929694571704[/C][/ROW]
[ROW][C]45[/C][C]0.787181528410556[/C][C]0.425636943178888[/C][C]0.212818471589444[/C][/ROW]
[ROW][C]46[/C][C]0.771453676349413[/C][C]0.457092647301173[/C][C]0.228546323650587[/C][/ROW]
[ROW][C]47[/C][C]0.754947221662361[/C][C]0.490105556675279[/C][C]0.245052778337639[/C][/ROW]
[ROW][C]48[/C][C]0.792300567589804[/C][C]0.415398864820391[/C][C]0.207699432410196[/C][/ROW]
[ROW][C]49[/C][C]0.746119320249597[/C][C]0.507761359500806[/C][C]0.253880679750403[/C][/ROW]
[ROW][C]50[/C][C]0.684234581079164[/C][C]0.631530837841672[/C][C]0.315765418920836[/C][/ROW]
[ROW][C]51[/C][C]0.781163866207255[/C][C]0.43767226758549[/C][C]0.218836133792745[/C][/ROW]
[ROW][C]52[/C][C]0.982531294436327[/C][C]0.0349374111273456[/C][C]0.0174687055636728[/C][/ROW]
[ROW][C]53[/C][C]0.990419384277556[/C][C]0.0191612314448882[/C][C]0.0095806157224441[/C][/ROW]
[ROW][C]54[/C][C]0.972584606461741[/C][C]0.0548307870765179[/C][C]0.0274153935382590[/C][/ROW]
[ROW][C]55[/C][C]0.98399671564356[/C][C]0.0320065687128818[/C][C]0.0160032843564409[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58539&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58539&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	0.0274980816320827	0.0549961632641654	0.972501918367917
6	0.0265279601236305	0.0530559202472611	0.97347203987637
7	0.0152526478162500	0.0305052956325001	0.98474735218375
8	0.00478475311789227	0.00956950623578454	0.995215246882108
9	0.00161378932011810	0.00322757864023619	0.998386210679882
10	0.00414849135594805	0.0082969827118961	0.995851508644052
11	0.00150220832250578	0.00300441664501156	0.998497791677494
12	0.00051212153298762	0.00102424306597524	0.999487878467012
13	0.000165069933210901	0.000330139866421801	0.999834930066789
14	5.22067028762867e-05	0.000104413405752573	0.999947793297124
15	2.6221709576041e-05	5.2443419152082e-05	0.999973778290424
16	1.21096151177987e-05	2.42192302355973e-05	0.999987890384882
17	0.000204370546039924	0.000408741092079849	0.99979562945396
18	0.00329605166570514	0.00659210333141027	0.996703948334295
19	0.0256585484139815	0.051317096827963	0.974341451586018
20	0.0942678977668756	0.188535795533751	0.905732102233124
21	0.162926019432227	0.325852038864455	0.837073980567773
22	0.155546742565706	0.311093485131412	0.844453257434294
23	0.165352530691414	0.330705061382829	0.834647469308586
24	0.307506000135365	0.615012000270729	0.692493999864635
25	0.426320720573305	0.85264144114661	0.573679279426695
26	0.458209578796707	0.916419157593415	0.541790421203293
27	0.406262300800039	0.812524601600077	0.593737699199961
28	0.346410221939443	0.692820443878886	0.653589778060557
29	0.289374193247156	0.578748386494313	0.710625806752844
30	0.233584100678006	0.467168201356012	0.766415899321994
31	0.181716619756316	0.363433239512633	0.818283380243684
32	0.137745753148958	0.275491506297916	0.862254246851042
33	0.102489204013804	0.204978408027608	0.897510795986196
34	0.0993922756983946	0.198784551396789	0.900607724301605
35	0.0834254156259888	0.166850831251978	0.916574584374011
36	0.0949671354706091	0.189934270941218	0.90503286452939
37	0.171196124386296	0.342392248772592	0.828803875613704
38	0.321566705291908	0.643133410583815	0.678433294708092
39	0.581474842908859	0.837050314182281	0.418525157091141
40	0.672202294987515	0.65559541002497	0.327797705012485
41	0.648405104813611	0.703189790372779	0.351594895186389
42	0.63118988088586	0.73762023822828	0.36881011911414
43	0.656070136640316	0.687859726719367	0.343929863359684
44	0.700070305428296	0.599859389143407	0.299929694571704
45	0.787181528410556	0.425636943178888	0.212818471589444
46	0.771453676349413	0.457092647301173	0.228546323650587
47	0.754947221662361	0.490105556675279	0.245052778337639
48	0.792300567589804	0.415398864820391	0.207699432410196
49	0.746119320249597	0.507761359500806	0.253880679750403
50	0.684234581079164	0.631530837841672	0.315765418920836
51	0.781163866207255	0.43767226758549	0.218836133792745
52	0.982531294436327	0.0349374111273456	0.0174687055636728
53	0.990419384277556	0.0191612314448882	0.0095806157224441
54	0.972584606461741	0.0548307870765179	0.0274153935382590
55	0.98399671564356	0.0320065687128818	0.0160032843564409

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	11	0.215686274509804	NOK
5% type I error level	15	0.294117647058824	NOK
10% type I error level	19	0.372549019607843	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 & 11 & 0.215686274509804 & NOK \tabularnewline
5% type I error level & 15 & 0.294117647058824 & NOK \tabularnewline
10% type I error level & 19 & 0.372549019607843 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58539&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]11[/C][C]0.215686274509804[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]15[/C][C]0.294117647058824[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]19[/C][C]0.372549019607843[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58539&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58539&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	11	0.215686274509804	NOK
5% type I error level	15	0.294117647058824	NOK
10% type I error level	19	0.372549019607843	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