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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Tue, 17 Nov 2009 12:35:42 -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/17/t1258486714vqwg3ml850bvok7.htm/, Retrieved Mon, 03 Nov 2025 03:51:20 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=57413, Retrieved Mon, 03 Nov 2025 03:51:20 +0000

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No (this computation is public)

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Estimated Impact

430

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]
-   PD      [Multiple Regression] [] [2009-11-17 19:35:42] [24029b2c7217429de6ff94b5379eb52c] [Current]

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103.8		122.5		80.2		19
103.5		122.4		74.8		18
104.1		121.9		77.8		19
101.9		122.2		73		19
102		123.7		72		22
100.7		122.6		75.8		23
99		115.7		72.6		20
96.5		116.1		71.9		14
101.8		120.5		74.8		14
100.5		122.6		72.9		14
103.3		119.9		72.9		15
102.3		120.7		79.9		11
100.4		120.2		74		17
103		122.1		76		16
99		119.3		69.6		20
104.8		121.7		77.3		24
104.5		113.5		75.2		23
104.8		123.7		75.8		20
103.8		123.4		77.6		21
106.3		126.4		76.7		19
105.2		124.1		77		23
108.2		125.6		77.9		23
106.2		124.8		76.7		23
103.9		123		71.9		23
104.9		126.9		73.4		27
106.2		127.3		72.5		26
107.9		129		73.7		17
106.9		126.2		69.5		24
110.3		125.4		74.7		26
109.8		126.3		72.5		24
108.3		126.3		72.1		27
110.9		128.4		70.7		27
109.8		127.2		71.4		26
109.3		128.5		69.5		24
109		129		73.5		23
107.9		128.9		72.4		23
108.4		128.3		74.5		24
107.2		124.6		72.2		17
109.5		126.2		73		21
109.9		129.1		73.3		19
108		127.3		71.3		22
114.7		129.2		73.6		22
115.6		130.4		71.3		18
107.6		125.9		71.2		16
115.9		135.8		81.4		14
111.8		126.4		76.1		12
110		129.5		71.1		14
109.2		128.4		75.7		16
108		125.6		70		8
105.6		127.7		68.5		3
103		126.4		56.7		0
99.6		124.2		57.9		5
97.9		126.4		58.8		1
97.6		123.7		59.3		1
96.2		121.8		61.3		3
97.9		124		62.9		6
94.5		122.7		61.4		7
95.4		122.9		64.5		8
94.4		121		63.8		14
96.3		122.8		61.6		14
95.1		122.9		64.7		13

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
R Framework error message	Warning: there are blank lines in the 'Data X' field. Please, use NA for missing data - blank lines are simply deleted and are NOT treated as missing values.

\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
R Framework error message & Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values. \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57413&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]
[ROW][C]R Framework error message[/C][C]Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57413&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57413&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
R Framework error message	Warning: there are blank lines in the 'Data X' field. Please, use NA for missing data - blank lines are simply deleted and are NOT treated as missing values.

Multiple Linear Regression - Estimated Regression Equation
dzcg[t] = + 77.4426660376957 + 0.756479459281622totid[t] -0.720234730937658ndzcg[t] + 0.27624207061515`indc `[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
dzcg[t] =  +  77.4426660376957 +  0.756479459281622totid[t] -0.720234730937658ndzcg[t] +  0.27624207061515`indc
`[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57413&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]dzcg[t] =  +  77.4426660376957 +  0.756479459281622totid[t] -0.720234730937658ndzcg[t] +  0.27624207061515`indc
`[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57413&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57413&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
dzcg[t] = + 77.4426660376957 + 0.756479459281622totid[t] -0.720234730937658ndzcg[t] + 0.27624207061515`indc `[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)	77.4426660376957	16.198668	4.7808	1.3e-05	6e-06
totid	0.756479459281622	0.161372	4.6878	1.8e-05	9e-06
ndzcg	-0.720234730937658	0.196732	-3.661	0.000551	0.000276
`indc `	0.27624207061515	0.085093	3.2463	0.00196	0.00098

\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) & 77.4426660376957 & 16.198668 & 4.7808 & 1.3e-05 & 6e-06 \tabularnewline
totid & 0.756479459281622 & 0.161372 & 4.6878 & 1.8e-05 & 9e-06 \tabularnewline
ndzcg & -0.720234730937658 & 0.196732 & -3.661 & 0.000551 & 0.000276 \tabularnewline
`indc
` & 0.27624207061515 & 0.085093 & 3.2463 & 0.00196 & 0.00098 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57413&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]77.4426660376957[/C][C]16.198668[/C][C]4.7808[/C][C]1.3e-05[/C][C]6e-06[/C][/ROW]
[ROW][C]totid[/C][C]0.756479459281622[/C][C]0.161372[/C][C]4.6878[/C][C]1.8e-05[/C][C]9e-06[/C][/ROW]
[ROW][C]ndzcg[/C][C]-0.720234730937658[/C][C]0.196732[/C][C]-3.661[/C][C]0.000551[/C][C]0.000276[/C][/ROW]
[ROW][C]`indc
`[/C][C]0.27624207061515[/C][C]0.085093[/C][C]3.2463[/C][C]0.00196[/C][C]0.00098[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57413&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57413&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)	77.4426660376957	16.198668	4.7808	1.3e-05	6e-06
totid	0.756479459281622	0.161372	4.6878	1.8e-05	9e-06
ndzcg	-0.720234730937658	0.196732	-3.661	0.000551	0.000276
`indc `	0.27624207061515	0.085093	3.2463	0.00196	0.00098

Multiple Linear Regression - Regression Statistics
Multiple R	0.76256897325449
R-squared	0.581511438970407
Adjusted R-squared	0.559485725232007
F-TEST (value)	26.4014799192000
F-TEST (DF numerator)	3
F-TEST (DF denominator)	57
p-value	7.78024311642866e-11
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	3.77517882779633
Sum Squared Residuals	812.362585364977

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.76256897325449 \tabularnewline
R-squared & 0.581511438970407 \tabularnewline
Adjusted R-squared & 0.559485725232007 \tabularnewline
F-TEST (value) & 26.4014799192000 \tabularnewline
F-TEST (DF numerator) & 3 \tabularnewline
F-TEST (DF denominator) & 57 \tabularnewline
p-value & 7.78024311642866e-11 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 3.77517882779633 \tabularnewline
Sum Squared Residuals & 812.362585364977 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57413&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.76256897325449[/C][/ROW]
[ROW][C]R-squared[/C][C]0.581511438970407[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.559485725232007[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]26.4014799192000[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]3[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]57[/C][/ROW]
[ROW][C]p-value[/C][C]7.78024311642866e-11[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]3.77517882779633[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]812.362585364977[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57413&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57413&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	0.76256897325449
R-squared	0.581511438970407
Adjusted R-squared	0.559485725232007
F-TEST (value)	26.4014799192000
F-TEST (DF numerator)	3
F-TEST (DF denominator)	57
p-value	7.78024311642866e-11
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	3.77517882779633
Sum Squared Residuals	812.362585364977

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	80.2	72.9850787129528	7.21492128704717
2	74.8	72.553916277647	2.24608372235302
3	77.8	73.6441633892999	4.15583661070009
4	73	71.763838159599	1.23616184040094
5	72	71.5878602209662	0.412139779033817
6	75.8	71.6729371985467	4.12706280145334
7	72.6	74.5278155493923	-1.92781554939228
8	71.9	70.6910705851223	1.20892941487774
9	74.8	71.5313789031892	3.26862109681083
10	72.9	69.035462671154	3.86453732884602
11	72.9	73.3744810012893	-0.47448100128933
12	79.9	70.936845474797	8.96315452520301
13	74	71.5171042913216	2.48289570867836
14	76	71.8392628260572	4.16073717394284
15	69.6	71.9349705180167	-2.33497051801672
16	77.3	75.6989563100603	1.60104368993966
17	75.2	81.1016951953495	-5.90169519534949
18	75.8	73.1535185657244	2.64648143427558
19	77.6	72.8893515963392	4.71064840366075
20	76.7	72.0673619105	4.63263808949998
21	77	73.9967426689075	3.00325733109253
22	77.9	75.1858289503459	2.71417104965416
23	76.7	74.2490578165327	2.45094218346728
24	71.9	73.8055775758728	-1.90557757587277
25	73.4	72.8581098669581	0.541890133041872
26	72.5	73.277197201034	-0.777197201034032
27	73.7	70.8526346036824	2.84736539631758
28	69.5	74.0465068853323	-4.54650688533229
29	74.7	77.7472089728702	-3.04720897287022
30	72.5	76.1682738441552	-3.66827384415522
31	72.1	75.8622808670782	-3.76228086707825
32	70.7	76.3166345262414	-5.61663452624137
33	71.4	76.0725467275416	-4.67254672754162
34	69.5	74.2055177064516	-4.70551770645156
35	73.5	73.3422144325831	0.157785567416899
36	72.4	72.5821105004671	-0.182110500467078
37	74.5	73.6687331392856	0.831266860714366
38	72.2	73.492131798311	-1.29213179831098
39	73	75.184627267619	-2.18462726761905
40	73.3	72.8460541903822	0.453945809617794
41	71.3	73.5338919452803	-2.23389194528035
42	73.6	77.2338583336857	-3.63385833368568
43	71.3	75.9454398874533	-4.64543988745333
44	71.2	72.5821763611895	-1.38217636118951
45	81.4	71.1781478957139	10.2218521042861
46	76.1	74.2943044422429	1.80569555775710
47	71.1	71.2523978908595	-0.152397890859551
48	75.7	71.991956668696	3.70804333130403
49	70	70.8909019992623	-0.890901999262271
50	68.5	66.1816480089415	2.31835199105846
51	56.7	64.3223803531828	-7.62238035318283
52	57.9	64.7160769527639	-6.81607695276391
53	58.8	60.7405771814617	-1.94057718146172
54	59.3	62.4582671172089	-3.1582671172089
55	61.3	63.3201260042265	-2.02012600422649
56	62.9	63.8503508887878	-0.950350888787848
57	61.4	62.4908679480644	-1.09086794806443
58	64.5	63.3038945858455	1.19610541415449
59	63.8	65.5733135390363	-1.77331353903634
60	61.6	65.7142019959836	-4.11420199598363
61	64.7	64.4581611011368	0.241838898863238

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 80.2 & 72.9850787129528 & 7.21492128704717 \tabularnewline
2 & 74.8 & 72.553916277647 & 2.24608372235302 \tabularnewline
3 & 77.8 & 73.6441633892999 & 4.15583661070009 \tabularnewline
4 & 73 & 71.763838159599 & 1.23616184040094 \tabularnewline
5 & 72 & 71.5878602209662 & 0.412139779033817 \tabularnewline
6 & 75.8 & 71.6729371985467 & 4.12706280145334 \tabularnewline
7 & 72.6 & 74.5278155493923 & -1.92781554939228 \tabularnewline
8 & 71.9 & 70.6910705851223 & 1.20892941487774 \tabularnewline
9 & 74.8 & 71.5313789031892 & 3.26862109681083 \tabularnewline
10 & 72.9 & 69.035462671154 & 3.86453732884602 \tabularnewline
11 & 72.9 & 73.3744810012893 & -0.47448100128933 \tabularnewline
12 & 79.9 & 70.936845474797 & 8.96315452520301 \tabularnewline
13 & 74 & 71.5171042913216 & 2.48289570867836 \tabularnewline
14 & 76 & 71.8392628260572 & 4.16073717394284 \tabularnewline
15 & 69.6 & 71.9349705180167 & -2.33497051801672 \tabularnewline
16 & 77.3 & 75.6989563100603 & 1.60104368993966 \tabularnewline
17 & 75.2 & 81.1016951953495 & -5.90169519534949 \tabularnewline
18 & 75.8 & 73.1535185657244 & 2.64648143427558 \tabularnewline
19 & 77.6 & 72.8893515963392 & 4.71064840366075 \tabularnewline
20 & 76.7 & 72.0673619105 & 4.63263808949998 \tabularnewline
21 & 77 & 73.9967426689075 & 3.00325733109253 \tabularnewline
22 & 77.9 & 75.1858289503459 & 2.71417104965416 \tabularnewline
23 & 76.7 & 74.2490578165327 & 2.45094218346728 \tabularnewline
24 & 71.9 & 73.8055775758728 & -1.90557757587277 \tabularnewline
25 & 73.4 & 72.8581098669581 & 0.541890133041872 \tabularnewline
26 & 72.5 & 73.277197201034 & -0.777197201034032 \tabularnewline
27 & 73.7 & 70.8526346036824 & 2.84736539631758 \tabularnewline
28 & 69.5 & 74.0465068853323 & -4.54650688533229 \tabularnewline
29 & 74.7 & 77.7472089728702 & -3.04720897287022 \tabularnewline
30 & 72.5 & 76.1682738441552 & -3.66827384415522 \tabularnewline
31 & 72.1 & 75.8622808670782 & -3.76228086707825 \tabularnewline
32 & 70.7 & 76.3166345262414 & -5.61663452624137 \tabularnewline
33 & 71.4 & 76.0725467275416 & -4.67254672754162 \tabularnewline
34 & 69.5 & 74.2055177064516 & -4.70551770645156 \tabularnewline
35 & 73.5 & 73.3422144325831 & 0.157785567416899 \tabularnewline
36 & 72.4 & 72.5821105004671 & -0.182110500467078 \tabularnewline
37 & 74.5 & 73.6687331392856 & 0.831266860714366 \tabularnewline
38 & 72.2 & 73.492131798311 & -1.29213179831098 \tabularnewline
39 & 73 & 75.184627267619 & -2.18462726761905 \tabularnewline
40 & 73.3 & 72.8460541903822 & 0.453945809617794 \tabularnewline
41 & 71.3 & 73.5338919452803 & -2.23389194528035 \tabularnewline
42 & 73.6 & 77.2338583336857 & -3.63385833368568 \tabularnewline
43 & 71.3 & 75.9454398874533 & -4.64543988745333 \tabularnewline
44 & 71.2 & 72.5821763611895 & -1.38217636118951 \tabularnewline
45 & 81.4 & 71.1781478957139 & 10.2218521042861 \tabularnewline
46 & 76.1 & 74.2943044422429 & 1.80569555775710 \tabularnewline
47 & 71.1 & 71.2523978908595 & -0.152397890859551 \tabularnewline
48 & 75.7 & 71.991956668696 & 3.70804333130403 \tabularnewline
49 & 70 & 70.8909019992623 & -0.890901999262271 \tabularnewline
50 & 68.5 & 66.1816480089415 & 2.31835199105846 \tabularnewline
51 & 56.7 & 64.3223803531828 & -7.62238035318283 \tabularnewline
52 & 57.9 & 64.7160769527639 & -6.81607695276391 \tabularnewline
53 & 58.8 & 60.7405771814617 & -1.94057718146172 \tabularnewline
54 & 59.3 & 62.4582671172089 & -3.1582671172089 \tabularnewline
55 & 61.3 & 63.3201260042265 & -2.02012600422649 \tabularnewline
56 & 62.9 & 63.8503508887878 & -0.950350888787848 \tabularnewline
57 & 61.4 & 62.4908679480644 & -1.09086794806443 \tabularnewline
58 & 64.5 & 63.3038945858455 & 1.19610541415449 \tabularnewline
59 & 63.8 & 65.5733135390363 & -1.77331353903634 \tabularnewline
60 & 61.6 & 65.7142019959836 & -4.11420199598363 \tabularnewline
61 & 64.7 & 64.4581611011368 & 0.241838898863238 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57413&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]80.2[/C][C]72.9850787129528[/C][C]7.21492128704717[/C][/ROW]
[ROW][C]2[/C][C]74.8[/C][C]72.553916277647[/C][C]2.24608372235302[/C][/ROW]
[ROW][C]3[/C][C]77.8[/C][C]73.6441633892999[/C][C]4.15583661070009[/C][/ROW]
[ROW][C]4[/C][C]73[/C][C]71.763838159599[/C][C]1.23616184040094[/C][/ROW]
[ROW][C]5[/C][C]72[/C][C]71.5878602209662[/C][C]0.412139779033817[/C][/ROW]
[ROW][C]6[/C][C]75.8[/C][C]71.6729371985467[/C][C]4.12706280145334[/C][/ROW]
[ROW][C]7[/C][C]72.6[/C][C]74.5278155493923[/C][C]-1.92781554939228[/C][/ROW]
[ROW][C]8[/C][C]71.9[/C][C]70.6910705851223[/C][C]1.20892941487774[/C][/ROW]
[ROW][C]9[/C][C]74.8[/C][C]71.5313789031892[/C][C]3.26862109681083[/C][/ROW]
[ROW][C]10[/C][C]72.9[/C][C]69.035462671154[/C][C]3.86453732884602[/C][/ROW]
[ROW][C]11[/C][C]72.9[/C][C]73.3744810012893[/C][C]-0.47448100128933[/C][/ROW]
[ROW][C]12[/C][C]79.9[/C][C]70.936845474797[/C][C]8.96315452520301[/C][/ROW]
[ROW][C]13[/C][C]74[/C][C]71.5171042913216[/C][C]2.48289570867836[/C][/ROW]
[ROW][C]14[/C][C]76[/C][C]71.8392628260572[/C][C]4.16073717394284[/C][/ROW]
[ROW][C]15[/C][C]69.6[/C][C]71.9349705180167[/C][C]-2.33497051801672[/C][/ROW]
[ROW][C]16[/C][C]77.3[/C][C]75.6989563100603[/C][C]1.60104368993966[/C][/ROW]
[ROW][C]17[/C][C]75.2[/C][C]81.1016951953495[/C][C]-5.90169519534949[/C][/ROW]
[ROW][C]18[/C][C]75.8[/C][C]73.1535185657244[/C][C]2.64648143427558[/C][/ROW]
[ROW][C]19[/C][C]77.6[/C][C]72.8893515963392[/C][C]4.71064840366075[/C][/ROW]
[ROW][C]20[/C][C]76.7[/C][C]72.0673619105[/C][C]4.63263808949998[/C][/ROW]
[ROW][C]21[/C][C]77[/C][C]73.9967426689075[/C][C]3.00325733109253[/C][/ROW]
[ROW][C]22[/C][C]77.9[/C][C]75.1858289503459[/C][C]2.71417104965416[/C][/ROW]
[ROW][C]23[/C][C]76.7[/C][C]74.2490578165327[/C][C]2.45094218346728[/C][/ROW]
[ROW][C]24[/C][C]71.9[/C][C]73.8055775758728[/C][C]-1.90557757587277[/C][/ROW]
[ROW][C]25[/C][C]73.4[/C][C]72.8581098669581[/C][C]0.541890133041872[/C][/ROW]
[ROW][C]26[/C][C]72.5[/C][C]73.277197201034[/C][C]-0.777197201034032[/C][/ROW]
[ROW][C]27[/C][C]73.7[/C][C]70.8526346036824[/C][C]2.84736539631758[/C][/ROW]
[ROW][C]28[/C][C]69.5[/C][C]74.0465068853323[/C][C]-4.54650688533229[/C][/ROW]
[ROW][C]29[/C][C]74.7[/C][C]77.7472089728702[/C][C]-3.04720897287022[/C][/ROW]
[ROW][C]30[/C][C]72.5[/C][C]76.1682738441552[/C][C]-3.66827384415522[/C][/ROW]
[ROW][C]31[/C][C]72.1[/C][C]75.8622808670782[/C][C]-3.76228086707825[/C][/ROW]
[ROW][C]32[/C][C]70.7[/C][C]76.3166345262414[/C][C]-5.61663452624137[/C][/ROW]
[ROW][C]33[/C][C]71.4[/C][C]76.0725467275416[/C][C]-4.67254672754162[/C][/ROW]
[ROW][C]34[/C][C]69.5[/C][C]74.2055177064516[/C][C]-4.70551770645156[/C][/ROW]
[ROW][C]35[/C][C]73.5[/C][C]73.3422144325831[/C][C]0.157785567416899[/C][/ROW]
[ROW][C]36[/C][C]72.4[/C][C]72.5821105004671[/C][C]-0.182110500467078[/C][/ROW]
[ROW][C]37[/C][C]74.5[/C][C]73.6687331392856[/C][C]0.831266860714366[/C][/ROW]
[ROW][C]38[/C][C]72.2[/C][C]73.492131798311[/C][C]-1.29213179831098[/C][/ROW]
[ROW][C]39[/C][C]73[/C][C]75.184627267619[/C][C]-2.18462726761905[/C][/ROW]
[ROW][C]40[/C][C]73.3[/C][C]72.8460541903822[/C][C]0.453945809617794[/C][/ROW]
[ROW][C]41[/C][C]71.3[/C][C]73.5338919452803[/C][C]-2.23389194528035[/C][/ROW]
[ROW][C]42[/C][C]73.6[/C][C]77.2338583336857[/C][C]-3.63385833368568[/C][/ROW]
[ROW][C]43[/C][C]71.3[/C][C]75.9454398874533[/C][C]-4.64543988745333[/C][/ROW]
[ROW][C]44[/C][C]71.2[/C][C]72.5821763611895[/C][C]-1.38217636118951[/C][/ROW]
[ROW][C]45[/C][C]81.4[/C][C]71.1781478957139[/C][C]10.2218521042861[/C][/ROW]
[ROW][C]46[/C][C]76.1[/C][C]74.2943044422429[/C][C]1.80569555775710[/C][/ROW]
[ROW][C]47[/C][C]71.1[/C][C]71.2523978908595[/C][C]-0.152397890859551[/C][/ROW]
[ROW][C]48[/C][C]75.7[/C][C]71.991956668696[/C][C]3.70804333130403[/C][/ROW]
[ROW][C]49[/C][C]70[/C][C]70.8909019992623[/C][C]-0.890901999262271[/C][/ROW]
[ROW][C]50[/C][C]68.5[/C][C]66.1816480089415[/C][C]2.31835199105846[/C][/ROW]
[ROW][C]51[/C][C]56.7[/C][C]64.3223803531828[/C][C]-7.62238035318283[/C][/ROW]
[ROW][C]52[/C][C]57.9[/C][C]64.7160769527639[/C][C]-6.81607695276391[/C][/ROW]
[ROW][C]53[/C][C]58.8[/C][C]60.7405771814617[/C][C]-1.94057718146172[/C][/ROW]
[ROW][C]54[/C][C]59.3[/C][C]62.4582671172089[/C][C]-3.1582671172089[/C][/ROW]
[ROW][C]55[/C][C]61.3[/C][C]63.3201260042265[/C][C]-2.02012600422649[/C][/ROW]
[ROW][C]56[/C][C]62.9[/C][C]63.8503508887878[/C][C]-0.950350888787848[/C][/ROW]
[ROW][C]57[/C][C]61.4[/C][C]62.4908679480644[/C][C]-1.09086794806443[/C][/ROW]
[ROW][C]58[/C][C]64.5[/C][C]63.3038945858455[/C][C]1.19610541415449[/C][/ROW]
[ROW][C]59[/C][C]63.8[/C][C]65.5733135390363[/C][C]-1.77331353903634[/C][/ROW]
[ROW][C]60[/C][C]61.6[/C][C]65.7142019959836[/C][C]-4.11420199598363[/C][/ROW]
[ROW][C]61[/C][C]64.7[/C][C]64.4581611011368[/C][C]0.241838898863238[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57413&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57413&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	80.2	72.9850787129528	7.21492128704717
2	74.8	72.553916277647	2.24608372235302
3	77.8	73.6441633892999	4.15583661070009
4	73	71.763838159599	1.23616184040094
5	72	71.5878602209662	0.412139779033817
6	75.8	71.6729371985467	4.12706280145334
7	72.6	74.5278155493923	-1.92781554939228
8	71.9	70.6910705851223	1.20892941487774
9	74.8	71.5313789031892	3.26862109681083
10	72.9	69.035462671154	3.86453732884602
11	72.9	73.3744810012893	-0.47448100128933
12	79.9	70.936845474797	8.96315452520301
13	74	71.5171042913216	2.48289570867836
14	76	71.8392628260572	4.16073717394284
15	69.6	71.9349705180167	-2.33497051801672
16	77.3	75.6989563100603	1.60104368993966
17	75.2	81.1016951953495	-5.90169519534949
18	75.8	73.1535185657244	2.64648143427558
19	77.6	72.8893515963392	4.71064840366075
20	76.7	72.0673619105	4.63263808949998
21	77	73.9967426689075	3.00325733109253
22	77.9	75.1858289503459	2.71417104965416
23	76.7	74.2490578165327	2.45094218346728
24	71.9	73.8055775758728	-1.90557757587277
25	73.4	72.8581098669581	0.541890133041872
26	72.5	73.277197201034	-0.777197201034032
27	73.7	70.8526346036824	2.84736539631758
28	69.5	74.0465068853323	-4.54650688533229
29	74.7	77.7472089728702	-3.04720897287022
30	72.5	76.1682738441552	-3.66827384415522
31	72.1	75.8622808670782	-3.76228086707825
32	70.7	76.3166345262414	-5.61663452624137
33	71.4	76.0725467275416	-4.67254672754162
34	69.5	74.2055177064516	-4.70551770645156
35	73.5	73.3422144325831	0.157785567416899
36	72.4	72.5821105004671	-0.182110500467078
37	74.5	73.6687331392856	0.831266860714366
38	72.2	73.492131798311	-1.29213179831098
39	73	75.184627267619	-2.18462726761905
40	73.3	72.8460541903822	0.453945809617794
41	71.3	73.5338919452803	-2.23389194528035
42	73.6	77.2338583336857	-3.63385833368568
43	71.3	75.9454398874533	-4.64543988745333
44	71.2	72.5821763611895	-1.38217636118951
45	81.4	71.1781478957139	10.2218521042861
46	76.1	74.2943044422429	1.80569555775710
47	71.1	71.2523978908595	-0.152397890859551
48	75.7	71.991956668696	3.70804333130403
49	70	70.8909019992623	-0.890901999262271
50	68.5	66.1816480089415	2.31835199105846
51	56.7	64.3223803531828	-7.62238035318283
52	57.9	64.7160769527639	-6.81607695276391
53	58.8	60.7405771814617	-1.94057718146172
54	59.3	62.4582671172089	-3.1582671172089
55	61.3	63.3201260042265	-2.02012600422649
56	62.9	63.8503508887878	-0.950350888787848
57	61.4	62.4908679480644	-1.09086794806443
58	64.5	63.3038945858455	1.19610541415449
59	63.8	65.5733135390363	-1.77331353903634
60	61.6	65.7142019959836	-4.11420199598363
61	64.7	64.4581611011368	0.241838898863238

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
7	0.330883140661123	0.661766281322245	0.669116859338877
8	0.306615251444900	0.613230502889801	0.6933847485551
9	0.190755980142293	0.381511960284586	0.809244019857707
10	0.112761385157002	0.225522770314003	0.887238614842998
11	0.119116497095482	0.238232994190964	0.880883502904518
12	0.260316456612487	0.520632913224975	0.739683543387513
13	0.191743532548235	0.38348706509647	0.808256467451765
14	0.152217388801154	0.304434777602309	0.847782611198846
15	0.130683180156204	0.261366360312409	0.869316819843796
16	0.094730088579176	0.189460177158352	0.905269911420824
17	0.0639868605975354	0.127973721195071	0.936013139402465
18	0.0500027331045407	0.100005466209081	0.94999726689546
19	0.055725222922247	0.111450445844494	0.944274777077753
20	0.057814424751571	0.115628849503142	0.94218557524843
21	0.0538605429090855	0.107721085818171	0.946139457090915
22	0.052298675062481	0.104597350124962	0.94770132493752
23	0.0536657673802234	0.107331534760447	0.946334232619777
24	0.0706802789068912	0.141360557813782	0.929319721093109
25	0.0538055097356303	0.107611019471261	0.94619449026437
26	0.0549360083310147	0.109872016662029	0.945063991668985
27	0.096829505424212	0.193659010848424	0.903170494575788
28	0.208467267827513	0.416934535655027	0.791532732172487
29	0.177205502030119	0.354411004060239	0.82279449796988
30	0.181063536626334	0.362127073252667	0.818936463373666
31	0.151957522829256	0.303915045658512	0.848042477170744
32	0.206769486197014	0.413538972394029	0.793230513802986
33	0.209759229475018	0.419518458950036	0.790240770524982
34	0.313684411808084	0.627368823616169	0.686315588191916
35	0.250888063543353	0.501776127086707	0.749111936456647
36	0.205549994485081	0.411099988970162	0.794450005514919
37	0.156034305655474	0.312068611310948	0.843965694344526
38	0.146530762240531	0.293061524481063	0.853469237759469
39	0.110320243250338	0.220640486500675	0.889679756749662
40	0.0789187386511197	0.157837477302239	0.92108126134888
41	0.0692874155226142	0.138574831045228	0.930712584477386
42	0.0807506559452565	0.161501311890513	0.919249344054744
43	0.284410319087084	0.568820638174168	0.715589680912916
44	0.278657642113115	0.55731528422623	0.721342357886885
45	0.425523603305933	0.851047206611865	0.574476396694067
46	0.359391623515959	0.718783247031919	0.64060837648404
47	0.354259117267709	0.708518234535418	0.645740882732291
48	0.281716115878698	0.563432231757395	0.718283884121302
49	0.282976424644732	0.565952849289464	0.717023575355268
50	0.952423803322037	0.0951523933559261	0.0475761966779631
51	0.96556566096102	0.0688686780779618	0.0344343390389809
52	0.960550815089572	0.0788983698208551	0.0394491849104276
53	0.929823459512642	0.140353080974717	0.0701765404873585
54	0.88613171060993	0.22773657878014	0.11386828939007

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
7 & 0.330883140661123 & 0.661766281322245 & 0.669116859338877 \tabularnewline
8 & 0.306615251444900 & 0.613230502889801 & 0.6933847485551 \tabularnewline
9 & 0.190755980142293 & 0.381511960284586 & 0.809244019857707 \tabularnewline
10 & 0.112761385157002 & 0.225522770314003 & 0.887238614842998 \tabularnewline
11 & 0.119116497095482 & 0.238232994190964 & 0.880883502904518 \tabularnewline
12 & 0.260316456612487 & 0.520632913224975 & 0.739683543387513 \tabularnewline
13 & 0.191743532548235 & 0.38348706509647 & 0.808256467451765 \tabularnewline
14 & 0.152217388801154 & 0.304434777602309 & 0.847782611198846 \tabularnewline
15 & 0.130683180156204 & 0.261366360312409 & 0.869316819843796 \tabularnewline
16 & 0.094730088579176 & 0.189460177158352 & 0.905269911420824 \tabularnewline
17 & 0.0639868605975354 & 0.127973721195071 & 0.936013139402465 \tabularnewline
18 & 0.0500027331045407 & 0.100005466209081 & 0.94999726689546 \tabularnewline
19 & 0.055725222922247 & 0.111450445844494 & 0.944274777077753 \tabularnewline
20 & 0.057814424751571 & 0.115628849503142 & 0.94218557524843 \tabularnewline
21 & 0.0538605429090855 & 0.107721085818171 & 0.946139457090915 \tabularnewline
22 & 0.052298675062481 & 0.104597350124962 & 0.94770132493752 \tabularnewline
23 & 0.0536657673802234 & 0.107331534760447 & 0.946334232619777 \tabularnewline
24 & 0.0706802789068912 & 0.141360557813782 & 0.929319721093109 \tabularnewline
25 & 0.0538055097356303 & 0.107611019471261 & 0.94619449026437 \tabularnewline
26 & 0.0549360083310147 & 0.109872016662029 & 0.945063991668985 \tabularnewline
27 & 0.096829505424212 & 0.193659010848424 & 0.903170494575788 \tabularnewline
28 & 0.208467267827513 & 0.416934535655027 & 0.791532732172487 \tabularnewline
29 & 0.177205502030119 & 0.354411004060239 & 0.82279449796988 \tabularnewline
30 & 0.181063536626334 & 0.362127073252667 & 0.818936463373666 \tabularnewline
31 & 0.151957522829256 & 0.303915045658512 & 0.848042477170744 \tabularnewline
32 & 0.206769486197014 & 0.413538972394029 & 0.793230513802986 \tabularnewline
33 & 0.209759229475018 & 0.419518458950036 & 0.790240770524982 \tabularnewline
34 & 0.313684411808084 & 0.627368823616169 & 0.686315588191916 \tabularnewline
35 & 0.250888063543353 & 0.501776127086707 & 0.749111936456647 \tabularnewline
36 & 0.205549994485081 & 0.411099988970162 & 0.794450005514919 \tabularnewline
37 & 0.156034305655474 & 0.312068611310948 & 0.843965694344526 \tabularnewline
38 & 0.146530762240531 & 0.293061524481063 & 0.853469237759469 \tabularnewline
39 & 0.110320243250338 & 0.220640486500675 & 0.889679756749662 \tabularnewline
40 & 0.0789187386511197 & 0.157837477302239 & 0.92108126134888 \tabularnewline
41 & 0.0692874155226142 & 0.138574831045228 & 0.930712584477386 \tabularnewline
42 & 0.0807506559452565 & 0.161501311890513 & 0.919249344054744 \tabularnewline
43 & 0.284410319087084 & 0.568820638174168 & 0.715589680912916 \tabularnewline
44 & 0.278657642113115 & 0.55731528422623 & 0.721342357886885 \tabularnewline
45 & 0.425523603305933 & 0.851047206611865 & 0.574476396694067 \tabularnewline
46 & 0.359391623515959 & 0.718783247031919 & 0.64060837648404 \tabularnewline
47 & 0.354259117267709 & 0.708518234535418 & 0.645740882732291 \tabularnewline
48 & 0.281716115878698 & 0.563432231757395 & 0.718283884121302 \tabularnewline
49 & 0.282976424644732 & 0.565952849289464 & 0.717023575355268 \tabularnewline
50 & 0.952423803322037 & 0.0951523933559261 & 0.0475761966779631 \tabularnewline
51 & 0.96556566096102 & 0.0688686780779618 & 0.0344343390389809 \tabularnewline
52 & 0.960550815089572 & 0.0788983698208551 & 0.0394491849104276 \tabularnewline
53 & 0.929823459512642 & 0.140353080974717 & 0.0701765404873585 \tabularnewline
54 & 0.88613171060993 & 0.22773657878014 & 0.11386828939007 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57413&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]7[/C][C]0.330883140661123[/C][C]0.661766281322245[/C][C]0.669116859338877[/C][/ROW]
[ROW][C]8[/C][C]0.306615251444900[/C][C]0.613230502889801[/C][C]0.6933847485551[/C][/ROW]
[ROW][C]9[/C][C]0.190755980142293[/C][C]0.381511960284586[/C][C]0.809244019857707[/C][/ROW]
[ROW][C]10[/C][C]0.112761385157002[/C][C]0.225522770314003[/C][C]0.887238614842998[/C][/ROW]
[ROW][C]11[/C][C]0.119116497095482[/C][C]0.238232994190964[/C][C]0.880883502904518[/C][/ROW]
[ROW][C]12[/C][C]0.260316456612487[/C][C]0.520632913224975[/C][C]0.739683543387513[/C][/ROW]
[ROW][C]13[/C][C]0.191743532548235[/C][C]0.38348706509647[/C][C]0.808256467451765[/C][/ROW]
[ROW][C]14[/C][C]0.152217388801154[/C][C]0.304434777602309[/C][C]0.847782611198846[/C][/ROW]
[ROW][C]15[/C][C]0.130683180156204[/C][C]0.261366360312409[/C][C]0.869316819843796[/C][/ROW]
[ROW][C]16[/C][C]0.094730088579176[/C][C]0.189460177158352[/C][C]0.905269911420824[/C][/ROW]
[ROW][C]17[/C][C]0.0639868605975354[/C][C]0.127973721195071[/C][C]0.936013139402465[/C][/ROW]
[ROW][C]18[/C][C]0.0500027331045407[/C][C]0.100005466209081[/C][C]0.94999726689546[/C][/ROW]
[ROW][C]19[/C][C]0.055725222922247[/C][C]0.111450445844494[/C][C]0.944274777077753[/C][/ROW]
[ROW][C]20[/C][C]0.057814424751571[/C][C]0.115628849503142[/C][C]0.94218557524843[/C][/ROW]
[ROW][C]21[/C][C]0.0538605429090855[/C][C]0.107721085818171[/C][C]0.946139457090915[/C][/ROW]
[ROW][C]22[/C][C]0.052298675062481[/C][C]0.104597350124962[/C][C]0.94770132493752[/C][/ROW]
[ROW][C]23[/C][C]0.0536657673802234[/C][C]0.107331534760447[/C][C]0.946334232619777[/C][/ROW]
[ROW][C]24[/C][C]0.0706802789068912[/C][C]0.141360557813782[/C][C]0.929319721093109[/C][/ROW]
[ROW][C]25[/C][C]0.0538055097356303[/C][C]0.107611019471261[/C][C]0.94619449026437[/C][/ROW]
[ROW][C]26[/C][C]0.0549360083310147[/C][C]0.109872016662029[/C][C]0.945063991668985[/C][/ROW]
[ROW][C]27[/C][C]0.096829505424212[/C][C]0.193659010848424[/C][C]0.903170494575788[/C][/ROW]
[ROW][C]28[/C][C]0.208467267827513[/C][C]0.416934535655027[/C][C]0.791532732172487[/C][/ROW]
[ROW][C]29[/C][C]0.177205502030119[/C][C]0.354411004060239[/C][C]0.82279449796988[/C][/ROW]
[ROW][C]30[/C][C]0.181063536626334[/C][C]0.362127073252667[/C][C]0.818936463373666[/C][/ROW]
[ROW][C]31[/C][C]0.151957522829256[/C][C]0.303915045658512[/C][C]0.848042477170744[/C][/ROW]
[ROW][C]32[/C][C]0.206769486197014[/C][C]0.413538972394029[/C][C]0.793230513802986[/C][/ROW]
[ROW][C]33[/C][C]0.209759229475018[/C][C]0.419518458950036[/C][C]0.790240770524982[/C][/ROW]
[ROW][C]34[/C][C]0.313684411808084[/C][C]0.627368823616169[/C][C]0.686315588191916[/C][/ROW]
[ROW][C]35[/C][C]0.250888063543353[/C][C]0.501776127086707[/C][C]0.749111936456647[/C][/ROW]
[ROW][C]36[/C][C]0.205549994485081[/C][C]0.411099988970162[/C][C]0.794450005514919[/C][/ROW]
[ROW][C]37[/C][C]0.156034305655474[/C][C]0.312068611310948[/C][C]0.843965694344526[/C][/ROW]
[ROW][C]38[/C][C]0.146530762240531[/C][C]0.293061524481063[/C][C]0.853469237759469[/C][/ROW]
[ROW][C]39[/C][C]0.110320243250338[/C][C]0.220640486500675[/C][C]0.889679756749662[/C][/ROW]
[ROW][C]40[/C][C]0.0789187386511197[/C][C]0.157837477302239[/C][C]0.92108126134888[/C][/ROW]
[ROW][C]41[/C][C]0.0692874155226142[/C][C]0.138574831045228[/C][C]0.930712584477386[/C][/ROW]
[ROW][C]42[/C][C]0.0807506559452565[/C][C]0.161501311890513[/C][C]0.919249344054744[/C][/ROW]
[ROW][C]43[/C][C]0.284410319087084[/C][C]0.568820638174168[/C][C]0.715589680912916[/C][/ROW]
[ROW][C]44[/C][C]0.278657642113115[/C][C]0.55731528422623[/C][C]0.721342357886885[/C][/ROW]
[ROW][C]45[/C][C]0.425523603305933[/C][C]0.851047206611865[/C][C]0.574476396694067[/C][/ROW]
[ROW][C]46[/C][C]0.359391623515959[/C][C]0.718783247031919[/C][C]0.64060837648404[/C][/ROW]
[ROW][C]47[/C][C]0.354259117267709[/C][C]0.708518234535418[/C][C]0.645740882732291[/C][/ROW]
[ROW][C]48[/C][C]0.281716115878698[/C][C]0.563432231757395[/C][C]0.718283884121302[/C][/ROW]
[ROW][C]49[/C][C]0.282976424644732[/C][C]0.565952849289464[/C][C]0.717023575355268[/C][/ROW]
[ROW][C]50[/C][C]0.952423803322037[/C][C]0.0951523933559261[/C][C]0.0475761966779631[/C][/ROW]
[ROW][C]51[/C][C]0.96556566096102[/C][C]0.0688686780779618[/C][C]0.0344343390389809[/C][/ROW]
[ROW][C]52[/C][C]0.960550815089572[/C][C]0.0788983698208551[/C][C]0.0394491849104276[/C][/ROW]
[ROW][C]53[/C][C]0.929823459512642[/C][C]0.140353080974717[/C][C]0.0701765404873585[/C][/ROW]
[ROW][C]54[/C][C]0.88613171060993[/C][C]0.22773657878014[/C][C]0.11386828939007[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57413&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57413&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
7	0.330883140661123	0.661766281322245	0.669116859338877
8	0.306615251444900	0.613230502889801	0.6933847485551
9	0.190755980142293	0.381511960284586	0.809244019857707
10	0.112761385157002	0.225522770314003	0.887238614842998
11	0.119116497095482	0.238232994190964	0.880883502904518
12	0.260316456612487	0.520632913224975	0.739683543387513
13	0.191743532548235	0.38348706509647	0.808256467451765
14	0.152217388801154	0.304434777602309	0.847782611198846
15	0.130683180156204	0.261366360312409	0.869316819843796
16	0.094730088579176	0.189460177158352	0.905269911420824
17	0.0639868605975354	0.127973721195071	0.936013139402465
18	0.0500027331045407	0.100005466209081	0.94999726689546
19	0.055725222922247	0.111450445844494	0.944274777077753
20	0.057814424751571	0.115628849503142	0.94218557524843
21	0.0538605429090855	0.107721085818171	0.946139457090915
22	0.052298675062481	0.104597350124962	0.94770132493752
23	0.0536657673802234	0.107331534760447	0.946334232619777
24	0.0706802789068912	0.141360557813782	0.929319721093109
25	0.0538055097356303	0.107611019471261	0.94619449026437
26	0.0549360083310147	0.109872016662029	0.945063991668985
27	0.096829505424212	0.193659010848424	0.903170494575788
28	0.208467267827513	0.416934535655027	0.791532732172487
29	0.177205502030119	0.354411004060239	0.82279449796988
30	0.181063536626334	0.362127073252667	0.818936463373666
31	0.151957522829256	0.303915045658512	0.848042477170744
32	0.206769486197014	0.413538972394029	0.793230513802986
33	0.209759229475018	0.419518458950036	0.790240770524982
34	0.313684411808084	0.627368823616169	0.686315588191916
35	0.250888063543353	0.501776127086707	0.749111936456647
36	0.205549994485081	0.411099988970162	0.794450005514919
37	0.156034305655474	0.312068611310948	0.843965694344526
38	0.146530762240531	0.293061524481063	0.853469237759469
39	0.110320243250338	0.220640486500675	0.889679756749662
40	0.0789187386511197	0.157837477302239	0.92108126134888
41	0.0692874155226142	0.138574831045228	0.930712584477386
42	0.0807506559452565	0.161501311890513	0.919249344054744
43	0.284410319087084	0.568820638174168	0.715589680912916
44	0.278657642113115	0.55731528422623	0.721342357886885
45	0.425523603305933	0.851047206611865	0.574476396694067
46	0.359391623515959	0.718783247031919	0.64060837648404
47	0.354259117267709	0.708518234535418	0.645740882732291
48	0.281716115878698	0.563432231757395	0.718283884121302
49	0.282976424644732	0.565952849289464	0.717023575355268
50	0.952423803322037	0.0951523933559261	0.0475761966779631
51	0.96556566096102	0.0688686780779618	0.0344343390389809
52	0.960550815089572	0.0788983698208551	0.0394491849104276
53	0.929823459512642	0.140353080974717	0.0701765404873585
54	0.88613171060993	0.22773657878014	0.11386828939007

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	0	0	OK
5% type I error level	0	0	OK
10% type I error level	3	0.0625	OK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 0 & 0 & OK \tabularnewline
10% type I error level & 3 & 0.0625 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57413&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]3[/C][C]0.0625[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57413&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57413&T=6

As an alternative you can also use a QR Code:

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	0	0	OK
5% type I error level	0	0	OK
10% type I error level	3	0.0625	OK

Figure 1

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Figure 2

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Figure 3

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Figure 4

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Figure 5

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Figure 6

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Figure 7

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Figure 8

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Figure 9

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Figure 10

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Parameters (Session):

par1 = 3 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 3 ; 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