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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Sat, 19 Dec 2009 08:44:35 -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/Dec/19/t1261237593dnq081ufioijep5.htm/, Retrieved Sat, 04 May 2024 00:17:46 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=69648, Retrieved Sat, 04 May 2024 00:17:46 +0000

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IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

107

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] [] [2009-12-19 15:44:35] [2b679e8ec54382eeb0ec0b6bb527570a] [Current]

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Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	11 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 & 11 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=69648&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]11 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=69648&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=69648&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	11 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

Multiple Linear Regression - Estimated Regression Equation
Y[t] = + 100.388333333333 -1.68064102564103X[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Y[t] =  +  100.388333333333 -1.68064102564103X[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=69648&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Y[t] =  +  100.388333333333 -1.68064102564103X[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=69648&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=69648&T=1

As an alternative you can also use a QR Code:

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

Multiple Linear Regression - Estimated Regression Equation
Y[t] = + 100.388333333333 -1.68064102564103X[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)	100.388333333333	0.200241	501.3368	0	0
X	-1.68064102564103	0.433757	-3.8746	0.00027	0.000135

\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) & 100.388333333333 & 0.200241 & 501.3368 & 0 & 0 \tabularnewline
X & -1.68064102564103 & 0.433757 & -3.8746 & 0.00027 & 0.000135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=69648&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]100.388333333333[/C][C]0.200241[/C][C]501.3368[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]X[/C][C]-1.68064102564103[/C][C]0.433757[/C][C]-3.8746[/C][C]0.00027[/C][C]0.000135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=69648&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=69648&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)	100.388333333333	0.200241	501.3368	0	0
X	-1.68064102564103	0.433757	-3.8746	0.00027	0.000135

Multiple Linear Regression - Regression Statistics
Multiple R	0.450375989886427
R-squared	0.202838532266179
Adjusted R-squared	0.189327320948657
F-TEST (value)	15.0126089733437
F-TEST (DF numerator)	1
F-TEST (DF denominator)	59
p-value	0.000270485574901058
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.38731234758470
Sum Squared Residuals	113.553497435898

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.450375989886427 \tabularnewline
R-squared & 0.202838532266179 \tabularnewline
Adjusted R-squared & 0.189327320948657 \tabularnewline
F-TEST (value) & 15.0126089733437 \tabularnewline
F-TEST (DF numerator) & 1 \tabularnewline
F-TEST (DF denominator) & 59 \tabularnewline
p-value & 0.000270485574901058 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 1.38731234758470 \tabularnewline
Sum Squared Residuals & 113.553497435898 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=69648&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.450375989886427[/C][/ROW]
[ROW][C]R-squared[/C][C]0.202838532266179[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.189327320948657[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]15.0126089733437[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]1[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]59[/C][/ROW]
[ROW][C]p-value[/C][C]0.000270485574901058[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]1.38731234758470[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]113.553497435898[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=69648&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=69648&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	0.450375989886427
R-squared	0.202838532266179
Adjusted R-squared	0.189327320948657
F-TEST (value)	15.0126089733437
F-TEST (DF numerator)	1
F-TEST (DF denominator)	59
p-value	0.000270485574901058
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.38731234758470
Sum Squared Residuals	113.553497435898

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	101.09	100.388333333333	0.701666666666981
2	102.71	100.388333333333	2.32166666666665
3	102.11	100.388333333333	1.72166666666666
4	101.68	100.388333333333	1.29166666666667
5	101.7	100.388333333333	1.31166666666666
6	101.53	100.388333333333	1.14166666666666
7	101.76	100.388333333333	1.37166666666667
8	101.15	100.388333333333	0.761666666666666
9	100.92	100.388333333333	0.531666666666662
10	100.73	100.388333333333	0.341666666666664
11	100.55	100.388333333333	0.161666666666657
12	102.15	100.388333333333	1.76166666666667
13	100.79	100.388333333333	0.401666666666667
14	99.93	100.388333333333	-0.458333333333333
15	100.03	100.388333333333	-0.358333333333338
16	100.25	100.388333333333	-0.138333333333340
17	99.6	100.388333333333	-0.788333333333345
18	100.16	100.388333333333	-0.228333333333343
19	100.49	100.388333333333	0.101666666666655
20	99.72	100.388333333333	-0.66833333333334
21	100.14	100.388333333333	-0.248333333333339
22	98.48	100.388333333333	-1.90833333333334
23	100.38	100.388333333333	-0.00833333333334421
24	101.45	100.388333333333	1.06166666666666
25	98.42	100.388333333333	-1.96833333333334
26	98.6	100.388333333333	-1.78833333333335
27	100.06	100.388333333333	-0.328333333333337
28	98.62	100.388333333333	-1.76833333333334
29	100.84	100.388333333333	0.451666666666664
30	100.02	100.388333333333	-0.368333333333344
31	97.95	100.388333333333	-2.43833333333334
32	98.32	100.388333333333	-2.06833333333335
33	98.27	100.388333333333	-2.11833333333334
34	97.22	100.388333333333	-3.16833333333334
35	99.28	100.388333333333	-1.10833333333334
36	100.38	100.388333333333	-0.00833333333334421
37	99.02	100.388333333333	-1.36833333333334
38	100.32	100.388333333333	-0.0683333333333465
39	99.81	100.388333333333	-0.578333333333337
40	100.6	100.388333333333	0.211666666666655
41	101.19	100.388333333333	0.801666666666658
42	100.47	100.388333333333	0.0816666666666592
43	101.77	100.388333333333	1.38166666666666
44	102.32	100.388333333333	1.93166666666665
45	102.39	100.388333333333	2.00166666666666
46	101.16	100.388333333333	0.771666666666657
47	100.63	100.388333333333	0.241666666666656
48	101.48	100.388333333333	1.09166666666666
49	101.44	98.7076923076923	2.73230769230769
50	100.09	98.7076923076923	1.38230769230770
51	100.7	98.7076923076923	1.99230769230769
52	100.78	98.7076923076923	2.07230769230769
53	99.81	98.7076923076923	1.10230769230769
54	98.45	98.7076923076923	-0.257692307692305
55	98.49	98.7076923076923	-0.217692307692313
56	97.48	98.7076923076923	-1.22769230769230
57	97.91	98.7076923076923	-0.797692307692311
58	96.94	98.7076923076923	-1.76769230769231
59	98.53	98.7076923076923	-0.177692307692307
60	96.82	98.7076923076923	-1.88769230769231
61	95.76	98.7076923076923	-2.9476923076923

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 101.09 & 100.388333333333 & 0.701666666666981 \tabularnewline
2 & 102.71 & 100.388333333333 & 2.32166666666665 \tabularnewline
3 & 102.11 & 100.388333333333 & 1.72166666666666 \tabularnewline
4 & 101.68 & 100.388333333333 & 1.29166666666667 \tabularnewline
5 & 101.7 & 100.388333333333 & 1.31166666666666 \tabularnewline
6 & 101.53 & 100.388333333333 & 1.14166666666666 \tabularnewline
7 & 101.76 & 100.388333333333 & 1.37166666666667 \tabularnewline
8 & 101.15 & 100.388333333333 & 0.761666666666666 \tabularnewline
9 & 100.92 & 100.388333333333 & 0.531666666666662 \tabularnewline
10 & 100.73 & 100.388333333333 & 0.341666666666664 \tabularnewline
11 & 100.55 & 100.388333333333 & 0.161666666666657 \tabularnewline
12 & 102.15 & 100.388333333333 & 1.76166666666667 \tabularnewline
13 & 100.79 & 100.388333333333 & 0.401666666666667 \tabularnewline
14 & 99.93 & 100.388333333333 & -0.458333333333333 \tabularnewline
15 & 100.03 & 100.388333333333 & -0.358333333333338 \tabularnewline
16 & 100.25 & 100.388333333333 & -0.138333333333340 \tabularnewline
17 & 99.6 & 100.388333333333 & -0.788333333333345 \tabularnewline
18 & 100.16 & 100.388333333333 & -0.228333333333343 \tabularnewline
19 & 100.49 & 100.388333333333 & 0.101666666666655 \tabularnewline
20 & 99.72 & 100.388333333333 & -0.66833333333334 \tabularnewline
21 & 100.14 & 100.388333333333 & -0.248333333333339 \tabularnewline
22 & 98.48 & 100.388333333333 & -1.90833333333334 \tabularnewline
23 & 100.38 & 100.388333333333 & -0.00833333333334421 \tabularnewline
24 & 101.45 & 100.388333333333 & 1.06166666666666 \tabularnewline
25 & 98.42 & 100.388333333333 & -1.96833333333334 \tabularnewline
26 & 98.6 & 100.388333333333 & -1.78833333333335 \tabularnewline
27 & 100.06 & 100.388333333333 & -0.328333333333337 \tabularnewline
28 & 98.62 & 100.388333333333 & -1.76833333333334 \tabularnewline
29 & 100.84 & 100.388333333333 & 0.451666666666664 \tabularnewline
30 & 100.02 & 100.388333333333 & -0.368333333333344 \tabularnewline
31 & 97.95 & 100.388333333333 & -2.43833333333334 \tabularnewline
32 & 98.32 & 100.388333333333 & -2.06833333333335 \tabularnewline
33 & 98.27 & 100.388333333333 & -2.11833333333334 \tabularnewline
34 & 97.22 & 100.388333333333 & -3.16833333333334 \tabularnewline
35 & 99.28 & 100.388333333333 & -1.10833333333334 \tabularnewline
36 & 100.38 & 100.388333333333 & -0.00833333333334421 \tabularnewline
37 & 99.02 & 100.388333333333 & -1.36833333333334 \tabularnewline
38 & 100.32 & 100.388333333333 & -0.0683333333333465 \tabularnewline
39 & 99.81 & 100.388333333333 & -0.578333333333337 \tabularnewline
40 & 100.6 & 100.388333333333 & 0.211666666666655 \tabularnewline
41 & 101.19 & 100.388333333333 & 0.801666666666658 \tabularnewline
42 & 100.47 & 100.388333333333 & 0.0816666666666592 \tabularnewline
43 & 101.77 & 100.388333333333 & 1.38166666666666 \tabularnewline
44 & 102.32 & 100.388333333333 & 1.93166666666665 \tabularnewline
45 & 102.39 & 100.388333333333 & 2.00166666666666 \tabularnewline
46 & 101.16 & 100.388333333333 & 0.771666666666657 \tabularnewline
47 & 100.63 & 100.388333333333 & 0.241666666666656 \tabularnewline
48 & 101.48 & 100.388333333333 & 1.09166666666666 \tabularnewline
49 & 101.44 & 98.7076923076923 & 2.73230769230769 \tabularnewline
50 & 100.09 & 98.7076923076923 & 1.38230769230770 \tabularnewline
51 & 100.7 & 98.7076923076923 & 1.99230769230769 \tabularnewline
52 & 100.78 & 98.7076923076923 & 2.07230769230769 \tabularnewline
53 & 99.81 & 98.7076923076923 & 1.10230769230769 \tabularnewline
54 & 98.45 & 98.7076923076923 & -0.257692307692305 \tabularnewline
55 & 98.49 & 98.7076923076923 & -0.217692307692313 \tabularnewline
56 & 97.48 & 98.7076923076923 & -1.22769230769230 \tabularnewline
57 & 97.91 & 98.7076923076923 & -0.797692307692311 \tabularnewline
58 & 96.94 & 98.7076923076923 & -1.76769230769231 \tabularnewline
59 & 98.53 & 98.7076923076923 & -0.177692307692307 \tabularnewline
60 & 96.82 & 98.7076923076923 & -1.88769230769231 \tabularnewline
61 & 95.76 & 98.7076923076923 & -2.9476923076923 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=69648&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]101.09[/C][C]100.388333333333[/C][C]0.701666666666981[/C][/ROW]
[ROW][C]2[/C][C]102.71[/C][C]100.388333333333[/C][C]2.32166666666665[/C][/ROW]
[ROW][C]3[/C][C]102.11[/C][C]100.388333333333[/C][C]1.72166666666666[/C][/ROW]
[ROW][C]4[/C][C]101.68[/C][C]100.388333333333[/C][C]1.29166666666667[/C][/ROW]
[ROW][C]5[/C][C]101.7[/C][C]100.388333333333[/C][C]1.31166666666666[/C][/ROW]
[ROW][C]6[/C][C]101.53[/C][C]100.388333333333[/C][C]1.14166666666666[/C][/ROW]
[ROW][C]7[/C][C]101.76[/C][C]100.388333333333[/C][C]1.37166666666667[/C][/ROW]
[ROW][C]8[/C][C]101.15[/C][C]100.388333333333[/C][C]0.761666666666666[/C][/ROW]
[ROW][C]9[/C][C]100.92[/C][C]100.388333333333[/C][C]0.531666666666662[/C][/ROW]
[ROW][C]10[/C][C]100.73[/C][C]100.388333333333[/C][C]0.341666666666664[/C][/ROW]
[ROW][C]11[/C][C]100.55[/C][C]100.388333333333[/C][C]0.161666666666657[/C][/ROW]
[ROW][C]12[/C][C]102.15[/C][C]100.388333333333[/C][C]1.76166666666667[/C][/ROW]
[ROW][C]13[/C][C]100.79[/C][C]100.388333333333[/C][C]0.401666666666667[/C][/ROW]
[ROW][C]14[/C][C]99.93[/C][C]100.388333333333[/C][C]-0.458333333333333[/C][/ROW]
[ROW][C]15[/C][C]100.03[/C][C]100.388333333333[/C][C]-0.358333333333338[/C][/ROW]
[ROW][C]16[/C][C]100.25[/C][C]100.388333333333[/C][C]-0.138333333333340[/C][/ROW]
[ROW][C]17[/C][C]99.6[/C][C]100.388333333333[/C][C]-0.788333333333345[/C][/ROW]
[ROW][C]18[/C][C]100.16[/C][C]100.388333333333[/C][C]-0.228333333333343[/C][/ROW]
[ROW][C]19[/C][C]100.49[/C][C]100.388333333333[/C][C]0.101666666666655[/C][/ROW]
[ROW][C]20[/C][C]99.72[/C][C]100.388333333333[/C][C]-0.66833333333334[/C][/ROW]
[ROW][C]21[/C][C]100.14[/C][C]100.388333333333[/C][C]-0.248333333333339[/C][/ROW]
[ROW][C]22[/C][C]98.48[/C][C]100.388333333333[/C][C]-1.90833333333334[/C][/ROW]
[ROW][C]23[/C][C]100.38[/C][C]100.388333333333[/C][C]-0.00833333333334421[/C][/ROW]
[ROW][C]24[/C][C]101.45[/C][C]100.388333333333[/C][C]1.06166666666666[/C][/ROW]
[ROW][C]25[/C][C]98.42[/C][C]100.388333333333[/C][C]-1.96833333333334[/C][/ROW]
[ROW][C]26[/C][C]98.6[/C][C]100.388333333333[/C][C]-1.78833333333335[/C][/ROW]
[ROW][C]27[/C][C]100.06[/C][C]100.388333333333[/C][C]-0.328333333333337[/C][/ROW]
[ROW][C]28[/C][C]98.62[/C][C]100.388333333333[/C][C]-1.76833333333334[/C][/ROW]
[ROW][C]29[/C][C]100.84[/C][C]100.388333333333[/C][C]0.451666666666664[/C][/ROW]
[ROW][C]30[/C][C]100.02[/C][C]100.388333333333[/C][C]-0.368333333333344[/C][/ROW]
[ROW][C]31[/C][C]97.95[/C][C]100.388333333333[/C][C]-2.43833333333334[/C][/ROW]
[ROW][C]32[/C][C]98.32[/C][C]100.388333333333[/C][C]-2.06833333333335[/C][/ROW]
[ROW][C]33[/C][C]98.27[/C][C]100.388333333333[/C][C]-2.11833333333334[/C][/ROW]
[ROW][C]34[/C][C]97.22[/C][C]100.388333333333[/C][C]-3.16833333333334[/C][/ROW]
[ROW][C]35[/C][C]99.28[/C][C]100.388333333333[/C][C]-1.10833333333334[/C][/ROW]
[ROW][C]36[/C][C]100.38[/C][C]100.388333333333[/C][C]-0.00833333333334421[/C][/ROW]
[ROW][C]37[/C][C]99.02[/C][C]100.388333333333[/C][C]-1.36833333333334[/C][/ROW]
[ROW][C]38[/C][C]100.32[/C][C]100.388333333333[/C][C]-0.0683333333333465[/C][/ROW]
[ROW][C]39[/C][C]99.81[/C][C]100.388333333333[/C][C]-0.578333333333337[/C][/ROW]
[ROW][C]40[/C][C]100.6[/C][C]100.388333333333[/C][C]0.211666666666655[/C][/ROW]
[ROW][C]41[/C][C]101.19[/C][C]100.388333333333[/C][C]0.801666666666658[/C][/ROW]
[ROW][C]42[/C][C]100.47[/C][C]100.388333333333[/C][C]0.0816666666666592[/C][/ROW]
[ROW][C]43[/C][C]101.77[/C][C]100.388333333333[/C][C]1.38166666666666[/C][/ROW]
[ROW][C]44[/C][C]102.32[/C][C]100.388333333333[/C][C]1.93166666666665[/C][/ROW]
[ROW][C]45[/C][C]102.39[/C][C]100.388333333333[/C][C]2.00166666666666[/C][/ROW]
[ROW][C]46[/C][C]101.16[/C][C]100.388333333333[/C][C]0.771666666666657[/C][/ROW]
[ROW][C]47[/C][C]100.63[/C][C]100.388333333333[/C][C]0.241666666666656[/C][/ROW]
[ROW][C]48[/C][C]101.48[/C][C]100.388333333333[/C][C]1.09166666666666[/C][/ROW]
[ROW][C]49[/C][C]101.44[/C][C]98.7076923076923[/C][C]2.73230769230769[/C][/ROW]
[ROW][C]50[/C][C]100.09[/C][C]98.7076923076923[/C][C]1.38230769230770[/C][/ROW]
[ROW][C]51[/C][C]100.7[/C][C]98.7076923076923[/C][C]1.99230769230769[/C][/ROW]
[ROW][C]52[/C][C]100.78[/C][C]98.7076923076923[/C][C]2.07230769230769[/C][/ROW]
[ROW][C]53[/C][C]99.81[/C][C]98.7076923076923[/C][C]1.10230769230769[/C][/ROW]
[ROW][C]54[/C][C]98.45[/C][C]98.7076923076923[/C][C]-0.257692307692305[/C][/ROW]
[ROW][C]55[/C][C]98.49[/C][C]98.7076923076923[/C][C]-0.217692307692313[/C][/ROW]
[ROW][C]56[/C][C]97.48[/C][C]98.7076923076923[/C][C]-1.22769230769230[/C][/ROW]
[ROW][C]57[/C][C]97.91[/C][C]98.7076923076923[/C][C]-0.797692307692311[/C][/ROW]
[ROW][C]58[/C][C]96.94[/C][C]98.7076923076923[/C][C]-1.76769230769231[/C][/ROW]
[ROW][C]59[/C][C]98.53[/C][C]98.7076923076923[/C][C]-0.177692307692307[/C][/ROW]
[ROW][C]60[/C][C]96.82[/C][C]98.7076923076923[/C][C]-1.88769230769231[/C][/ROW]
[ROW][C]61[/C][C]95.76[/C][C]98.7076923076923[/C][C]-2.9476923076923[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=69648&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=69648&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	101.09	100.388333333333	0.701666666666981
2	102.71	100.388333333333	2.32166666666665
3	102.11	100.388333333333	1.72166666666666
4	101.68	100.388333333333	1.29166666666667
5	101.7	100.388333333333	1.31166666666666
6	101.53	100.388333333333	1.14166666666666
7	101.76	100.388333333333	1.37166666666667
8	101.15	100.388333333333	0.761666666666666
9	100.92	100.388333333333	0.531666666666662
10	100.73	100.388333333333	0.341666666666664
11	100.55	100.388333333333	0.161666666666657
12	102.15	100.388333333333	1.76166666666667
13	100.79	100.388333333333	0.401666666666667
14	99.93	100.388333333333	-0.458333333333333
15	100.03	100.388333333333	-0.358333333333338
16	100.25	100.388333333333	-0.138333333333340
17	99.6	100.388333333333	-0.788333333333345
18	100.16	100.388333333333	-0.228333333333343
19	100.49	100.388333333333	0.101666666666655
20	99.72	100.388333333333	-0.66833333333334
21	100.14	100.388333333333	-0.248333333333339
22	98.48	100.388333333333	-1.90833333333334
23	100.38	100.388333333333	-0.00833333333334421
24	101.45	100.388333333333	1.06166666666666
25	98.42	100.388333333333	-1.96833333333334
26	98.6	100.388333333333	-1.78833333333335
27	100.06	100.388333333333	-0.328333333333337
28	98.62	100.388333333333	-1.76833333333334
29	100.84	100.388333333333	0.451666666666664
30	100.02	100.388333333333	-0.368333333333344
31	97.95	100.388333333333	-2.43833333333334
32	98.32	100.388333333333	-2.06833333333335
33	98.27	100.388333333333	-2.11833333333334
34	97.22	100.388333333333	-3.16833333333334
35	99.28	100.388333333333	-1.10833333333334
36	100.38	100.388333333333	-0.00833333333334421
37	99.02	100.388333333333	-1.36833333333334
38	100.32	100.388333333333	-0.0683333333333465
39	99.81	100.388333333333	-0.578333333333337
40	100.6	100.388333333333	0.211666666666655
41	101.19	100.388333333333	0.801666666666658
42	100.47	100.388333333333	0.0816666666666592
43	101.77	100.388333333333	1.38166666666666
44	102.32	100.388333333333	1.93166666666665
45	102.39	100.388333333333	2.00166666666666
46	101.16	100.388333333333	0.771666666666657
47	100.63	100.388333333333	0.241666666666656
48	101.48	100.388333333333	1.09166666666666
49	101.44	98.7076923076923	2.73230769230769
50	100.09	98.7076923076923	1.38230769230770
51	100.7	98.7076923076923	1.99230769230769
52	100.78	98.7076923076923	2.07230769230769
53	99.81	98.7076923076923	1.10230769230769
54	98.45	98.7076923076923	-0.257692307692305
55	98.49	98.7076923076923	-0.217692307692313
56	97.48	98.7076923076923	-1.22769230769230
57	97.91	98.7076923076923	-0.797692307692311
58	96.94	98.7076923076923	-1.76769230769231
59	98.53	98.7076923076923	-0.177692307692307
60	96.82	98.7076923076923	-1.88769230769231
61	95.76	98.7076923076923	-2.9476923076923

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
5	0.144908018266437	0.289816036532874	0.855091981733563
6	0.0658171720093947	0.131634344018789	0.934182827990605
7	0.0260041847002618	0.0520083694005237	0.973995815299738
8	0.0162893054500249	0.0325786109000497	0.983710694549975
9	0.0130187688182039	0.0260375376364079	0.986981231181796
10	0.0119235155610133	0.0238470311220266	0.988076484438987
11	0.0119491683088611	0.0238983366177222	0.98805083169114
12	0.00944853283178785	0.0188970656635757	0.990551467168212
13	0.0065652469568167	0.0131304939136334	0.993434753043183
14	0.0144088813457019	0.0288177626914038	0.985591118654298
15	0.0180086311972183	0.0360172623944366	0.981991368802782
16	0.0153857353438151	0.0307714706876302	0.984614264656185
17	0.0237643424563702	0.0475286849127404	0.97623565754363
18	0.0186236051376477	0.0372472102752953	0.981376394862352
19	0.0118927704968941	0.0237855409937883	0.988107229503106
20	0.0123024340698937	0.0246048681397874	0.987697565930106
21	0.00869087017273396	0.0173817403454679	0.991309129827266
22	0.0324028014960795	0.0648056029921591	0.96759719850392
23	0.0210157114250330	0.0420314228500660	0.978984288574967
24	0.0163512115302237	0.0327024230604475	0.983648788469776
25	0.0417335956758906	0.0834671913517812	0.95826640432411
26	0.0664435514754025	0.132887102950805	0.933556448524598
27	0.0470583281653682	0.0941166563307364	0.952941671834632
28	0.0658102453702674	0.131620490740535	0.934189754629733
29	0.0465410777818822	0.0930821555637643	0.953458922218118
30	0.0320091258244527	0.0640182516489054	0.967990874175547
31	0.0753134700693726	0.150626940138745	0.924686529930627
32	0.112132837169321	0.224265674338643	0.887867162830679
33	0.164875802480063	0.329751604960126	0.835124197519937
34	0.434420922630691	0.868841845261381	0.565579077369309
35	0.424575307790421	0.849150615580842	0.575424692209579
36	0.357103477735894	0.714206955471788	0.642896522264106
37	0.391972221407747	0.783944442815493	0.608027778592253
38	0.334263184407355	0.668526368814709	0.665736815592645
39	0.311876050865013	0.623752101730025	0.688123949134987
40	0.25995365820836	0.51990731641672	0.74004634179164
41	0.2117636275466	0.4235272550932	0.7882363724534
42	0.17695433910183	0.35390867820366	0.82304566089817
43	0.149218059312931	0.298436118625861	0.85078194068707
44	0.145809259746941	0.291618519493881	0.85419074025306
45	0.148915052100319	0.297830104200639	0.85108494789968
46	0.108113973732769	0.216227947465539	0.89188602626723
47	0.0761014289452382	0.152202857890476	0.923898571054762
48	0.0526491067244698	0.105298213448940	0.94735089327553
49	0.100649136082101	0.201298272164202	0.899350863917899
50	0.102894366627818	0.205788733255636	0.897105633372182
51	0.172007660030672	0.344015320061345	0.827992339969328
52	0.409841615586704	0.819683231173407	0.590158384413296
53	0.61579161527955	0.768416769440901	0.384208384720450
54	0.587543936541866	0.82491212691627	0.412456063458135
55	0.580779860587393	0.838440278825213	0.419220139412607
56	0.443159490020202	0.886318980040403	0.556840509979798

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
5 & 0.144908018266437 & 0.289816036532874 & 0.855091981733563 \tabularnewline
6 & 0.0658171720093947 & 0.131634344018789 & 0.934182827990605 \tabularnewline
7 & 0.0260041847002618 & 0.0520083694005237 & 0.973995815299738 \tabularnewline
8 & 0.0162893054500249 & 0.0325786109000497 & 0.983710694549975 \tabularnewline
9 & 0.0130187688182039 & 0.0260375376364079 & 0.986981231181796 \tabularnewline
10 & 0.0119235155610133 & 0.0238470311220266 & 0.988076484438987 \tabularnewline
11 & 0.0119491683088611 & 0.0238983366177222 & 0.98805083169114 \tabularnewline
12 & 0.00944853283178785 & 0.0188970656635757 & 0.990551467168212 \tabularnewline
13 & 0.0065652469568167 & 0.0131304939136334 & 0.993434753043183 \tabularnewline
14 & 0.0144088813457019 & 0.0288177626914038 & 0.985591118654298 \tabularnewline
15 & 0.0180086311972183 & 0.0360172623944366 & 0.981991368802782 \tabularnewline
16 & 0.0153857353438151 & 0.0307714706876302 & 0.984614264656185 \tabularnewline
17 & 0.0237643424563702 & 0.0475286849127404 & 0.97623565754363 \tabularnewline
18 & 0.0186236051376477 & 0.0372472102752953 & 0.981376394862352 \tabularnewline
19 & 0.0118927704968941 & 0.0237855409937883 & 0.988107229503106 \tabularnewline
20 & 0.0123024340698937 & 0.0246048681397874 & 0.987697565930106 \tabularnewline
21 & 0.00869087017273396 & 0.0173817403454679 & 0.991309129827266 \tabularnewline
22 & 0.0324028014960795 & 0.0648056029921591 & 0.96759719850392 \tabularnewline
23 & 0.0210157114250330 & 0.0420314228500660 & 0.978984288574967 \tabularnewline
24 & 0.0163512115302237 & 0.0327024230604475 & 0.983648788469776 \tabularnewline
25 & 0.0417335956758906 & 0.0834671913517812 & 0.95826640432411 \tabularnewline
26 & 0.0664435514754025 & 0.132887102950805 & 0.933556448524598 \tabularnewline
27 & 0.0470583281653682 & 0.0941166563307364 & 0.952941671834632 \tabularnewline
28 & 0.0658102453702674 & 0.131620490740535 & 0.934189754629733 \tabularnewline
29 & 0.0465410777818822 & 0.0930821555637643 & 0.953458922218118 \tabularnewline
30 & 0.0320091258244527 & 0.0640182516489054 & 0.967990874175547 \tabularnewline
31 & 0.0753134700693726 & 0.150626940138745 & 0.924686529930627 \tabularnewline
32 & 0.112132837169321 & 0.224265674338643 & 0.887867162830679 \tabularnewline
33 & 0.164875802480063 & 0.329751604960126 & 0.835124197519937 \tabularnewline
34 & 0.434420922630691 & 0.868841845261381 & 0.565579077369309 \tabularnewline
35 & 0.424575307790421 & 0.849150615580842 & 0.575424692209579 \tabularnewline
36 & 0.357103477735894 & 0.714206955471788 & 0.642896522264106 \tabularnewline
37 & 0.391972221407747 & 0.783944442815493 & 0.608027778592253 \tabularnewline
38 & 0.334263184407355 & 0.668526368814709 & 0.665736815592645 \tabularnewline
39 & 0.311876050865013 & 0.623752101730025 & 0.688123949134987 \tabularnewline
40 & 0.25995365820836 & 0.51990731641672 & 0.74004634179164 \tabularnewline
41 & 0.2117636275466 & 0.4235272550932 & 0.7882363724534 \tabularnewline
42 & 0.17695433910183 & 0.35390867820366 & 0.82304566089817 \tabularnewline
43 & 0.149218059312931 & 0.298436118625861 & 0.85078194068707 \tabularnewline
44 & 0.145809259746941 & 0.291618519493881 & 0.85419074025306 \tabularnewline
45 & 0.148915052100319 & 0.297830104200639 & 0.85108494789968 \tabularnewline
46 & 0.108113973732769 & 0.216227947465539 & 0.89188602626723 \tabularnewline
47 & 0.0761014289452382 & 0.152202857890476 & 0.923898571054762 \tabularnewline
48 & 0.0526491067244698 & 0.105298213448940 & 0.94735089327553 \tabularnewline
49 & 0.100649136082101 & 0.201298272164202 & 0.899350863917899 \tabularnewline
50 & 0.102894366627818 & 0.205788733255636 & 0.897105633372182 \tabularnewline
51 & 0.172007660030672 & 0.344015320061345 & 0.827992339969328 \tabularnewline
52 & 0.409841615586704 & 0.819683231173407 & 0.590158384413296 \tabularnewline
53 & 0.61579161527955 & 0.768416769440901 & 0.384208384720450 \tabularnewline
54 & 0.587543936541866 & 0.82491212691627 & 0.412456063458135 \tabularnewline
55 & 0.580779860587393 & 0.838440278825213 & 0.419220139412607 \tabularnewline
56 & 0.443159490020202 & 0.886318980040403 & 0.556840509979798 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=69648&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]0.144908018266437[/C][C]0.289816036532874[/C][C]0.855091981733563[/C][/ROW]
[ROW][C]6[/C][C]0.0658171720093947[/C][C]0.131634344018789[/C][C]0.934182827990605[/C][/ROW]
[ROW][C]7[/C][C]0.0260041847002618[/C][C]0.0520083694005237[/C][C]0.973995815299738[/C][/ROW]
[ROW][C]8[/C][C]0.0162893054500249[/C][C]0.0325786109000497[/C][C]0.983710694549975[/C][/ROW]
[ROW][C]9[/C][C]0.0130187688182039[/C][C]0.0260375376364079[/C][C]0.986981231181796[/C][/ROW]
[ROW][C]10[/C][C]0.0119235155610133[/C][C]0.0238470311220266[/C][C]0.988076484438987[/C][/ROW]
[ROW][C]11[/C][C]0.0119491683088611[/C][C]0.0238983366177222[/C][C]0.98805083169114[/C][/ROW]
[ROW][C]12[/C][C]0.00944853283178785[/C][C]0.0188970656635757[/C][C]0.990551467168212[/C][/ROW]
[ROW][C]13[/C][C]0.0065652469568167[/C][C]0.0131304939136334[/C][C]0.993434753043183[/C][/ROW]
[ROW][C]14[/C][C]0.0144088813457019[/C][C]0.0288177626914038[/C][C]0.985591118654298[/C][/ROW]
[ROW][C]15[/C][C]0.0180086311972183[/C][C]0.0360172623944366[/C][C]0.981991368802782[/C][/ROW]
[ROW][C]16[/C][C]0.0153857353438151[/C][C]0.0307714706876302[/C][C]0.984614264656185[/C][/ROW]
[ROW][C]17[/C][C]0.0237643424563702[/C][C]0.0475286849127404[/C][C]0.97623565754363[/C][/ROW]
[ROW][C]18[/C][C]0.0186236051376477[/C][C]0.0372472102752953[/C][C]0.981376394862352[/C][/ROW]
[ROW][C]19[/C][C]0.0118927704968941[/C][C]0.0237855409937883[/C][C]0.988107229503106[/C][/ROW]
[ROW][C]20[/C][C]0.0123024340698937[/C][C]0.0246048681397874[/C][C]0.987697565930106[/C][/ROW]
[ROW][C]21[/C][C]0.00869087017273396[/C][C]0.0173817403454679[/C][C]0.991309129827266[/C][/ROW]
[ROW][C]22[/C][C]0.0324028014960795[/C][C]0.0648056029921591[/C][C]0.96759719850392[/C][/ROW]
[ROW][C]23[/C][C]0.0210157114250330[/C][C]0.0420314228500660[/C][C]0.978984288574967[/C][/ROW]
[ROW][C]24[/C][C]0.0163512115302237[/C][C]0.0327024230604475[/C][C]0.983648788469776[/C][/ROW]
[ROW][C]25[/C][C]0.0417335956758906[/C][C]0.0834671913517812[/C][C]0.95826640432411[/C][/ROW]
[ROW][C]26[/C][C]0.0664435514754025[/C][C]0.132887102950805[/C][C]0.933556448524598[/C][/ROW]
[ROW][C]27[/C][C]0.0470583281653682[/C][C]0.0941166563307364[/C][C]0.952941671834632[/C][/ROW]
[ROW][C]28[/C][C]0.0658102453702674[/C][C]0.131620490740535[/C][C]0.934189754629733[/C][/ROW]
[ROW][C]29[/C][C]0.0465410777818822[/C][C]0.0930821555637643[/C][C]0.953458922218118[/C][/ROW]
[ROW][C]30[/C][C]0.0320091258244527[/C][C]0.0640182516489054[/C][C]0.967990874175547[/C][/ROW]
[ROW][C]31[/C][C]0.0753134700693726[/C][C]0.150626940138745[/C][C]0.924686529930627[/C][/ROW]
[ROW][C]32[/C][C]0.112132837169321[/C][C]0.224265674338643[/C][C]0.887867162830679[/C][/ROW]
[ROW][C]33[/C][C]0.164875802480063[/C][C]0.329751604960126[/C][C]0.835124197519937[/C][/ROW]
[ROW][C]34[/C][C]0.434420922630691[/C][C]0.868841845261381[/C][C]0.565579077369309[/C][/ROW]
[ROW][C]35[/C][C]0.424575307790421[/C][C]0.849150615580842[/C][C]0.575424692209579[/C][/ROW]
[ROW][C]36[/C][C]0.357103477735894[/C][C]0.714206955471788[/C][C]0.642896522264106[/C][/ROW]
[ROW][C]37[/C][C]0.391972221407747[/C][C]0.783944442815493[/C][C]0.608027778592253[/C][/ROW]
[ROW][C]38[/C][C]0.334263184407355[/C][C]0.668526368814709[/C][C]0.665736815592645[/C][/ROW]
[ROW][C]39[/C][C]0.311876050865013[/C][C]0.623752101730025[/C][C]0.688123949134987[/C][/ROW]
[ROW][C]40[/C][C]0.25995365820836[/C][C]0.51990731641672[/C][C]0.74004634179164[/C][/ROW]
[ROW][C]41[/C][C]0.2117636275466[/C][C]0.4235272550932[/C][C]0.7882363724534[/C][/ROW]
[ROW][C]42[/C][C]0.17695433910183[/C][C]0.35390867820366[/C][C]0.82304566089817[/C][/ROW]
[ROW][C]43[/C][C]0.149218059312931[/C][C]0.298436118625861[/C][C]0.85078194068707[/C][/ROW]
[ROW][C]44[/C][C]0.145809259746941[/C][C]0.291618519493881[/C][C]0.85419074025306[/C][/ROW]
[ROW][C]45[/C][C]0.148915052100319[/C][C]0.297830104200639[/C][C]0.85108494789968[/C][/ROW]
[ROW][C]46[/C][C]0.108113973732769[/C][C]0.216227947465539[/C][C]0.89188602626723[/C][/ROW]
[ROW][C]47[/C][C]0.0761014289452382[/C][C]0.152202857890476[/C][C]0.923898571054762[/C][/ROW]
[ROW][C]48[/C][C]0.0526491067244698[/C][C]0.105298213448940[/C][C]0.94735089327553[/C][/ROW]
[ROW][C]49[/C][C]0.100649136082101[/C][C]0.201298272164202[/C][C]0.899350863917899[/C][/ROW]
[ROW][C]50[/C][C]0.102894366627818[/C][C]0.205788733255636[/C][C]0.897105633372182[/C][/ROW]
[ROW][C]51[/C][C]0.172007660030672[/C][C]0.344015320061345[/C][C]0.827992339969328[/C][/ROW]
[ROW][C]52[/C][C]0.409841615586704[/C][C]0.819683231173407[/C][C]0.590158384413296[/C][/ROW]
[ROW][C]53[/C][C]0.61579161527955[/C][C]0.768416769440901[/C][C]0.384208384720450[/C][/ROW]
[ROW][C]54[/C][C]0.587543936541866[/C][C]0.82491212691627[/C][C]0.412456063458135[/C][/ROW]
[ROW][C]55[/C][C]0.580779860587393[/C][C]0.838440278825213[/C][C]0.419220139412607[/C][/ROW]
[ROW][C]56[/C][C]0.443159490020202[/C][C]0.886318980040403[/C][C]0.556840509979798[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=69648&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=69648&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	0.144908018266437	0.289816036532874	0.855091981733563
6	0.0658171720093947	0.131634344018789	0.934182827990605
7	0.0260041847002618	0.0520083694005237	0.973995815299738
8	0.0162893054500249	0.0325786109000497	0.983710694549975
9	0.0130187688182039	0.0260375376364079	0.986981231181796
10	0.0119235155610133	0.0238470311220266	0.988076484438987
11	0.0119491683088611	0.0238983366177222	0.98805083169114
12	0.00944853283178785	0.0188970656635757	0.990551467168212
13	0.0065652469568167	0.0131304939136334	0.993434753043183
14	0.0144088813457019	0.0288177626914038	0.985591118654298
15	0.0180086311972183	0.0360172623944366	0.981991368802782
16	0.0153857353438151	0.0307714706876302	0.984614264656185
17	0.0237643424563702	0.0475286849127404	0.97623565754363
18	0.0186236051376477	0.0372472102752953	0.981376394862352
19	0.0118927704968941	0.0237855409937883	0.988107229503106
20	0.0123024340698937	0.0246048681397874	0.987697565930106
21	0.00869087017273396	0.0173817403454679	0.991309129827266
22	0.0324028014960795	0.0648056029921591	0.96759719850392
23	0.0210157114250330	0.0420314228500660	0.978984288574967
24	0.0163512115302237	0.0327024230604475	0.983648788469776
25	0.0417335956758906	0.0834671913517812	0.95826640432411
26	0.0664435514754025	0.132887102950805	0.933556448524598
27	0.0470583281653682	0.0941166563307364	0.952941671834632
28	0.0658102453702674	0.131620490740535	0.934189754629733
29	0.0465410777818822	0.0930821555637643	0.953458922218118
30	0.0320091258244527	0.0640182516489054	0.967990874175547
31	0.0753134700693726	0.150626940138745	0.924686529930627
32	0.112132837169321	0.224265674338643	0.887867162830679
33	0.164875802480063	0.329751604960126	0.835124197519937
34	0.434420922630691	0.868841845261381	0.565579077369309
35	0.424575307790421	0.849150615580842	0.575424692209579
36	0.357103477735894	0.714206955471788	0.642896522264106
37	0.391972221407747	0.783944442815493	0.608027778592253
38	0.334263184407355	0.668526368814709	0.665736815592645
39	0.311876050865013	0.623752101730025	0.688123949134987
40	0.25995365820836	0.51990731641672	0.74004634179164
41	0.2117636275466	0.4235272550932	0.7882363724534
42	0.17695433910183	0.35390867820366	0.82304566089817
43	0.149218059312931	0.298436118625861	0.85078194068707
44	0.145809259746941	0.291618519493881	0.85419074025306
45	0.148915052100319	0.297830104200639	0.85108494789968
46	0.108113973732769	0.216227947465539	0.89188602626723
47	0.0761014289452382	0.152202857890476	0.923898571054762
48	0.0526491067244698	0.105298213448940	0.94735089327553
49	0.100649136082101	0.201298272164202	0.899350863917899
50	0.102894366627818	0.205788733255636	0.897105633372182
51	0.172007660030672	0.344015320061345	0.827992339969328
52	0.409841615586704	0.819683231173407	0.590158384413296
53	0.61579161527955	0.768416769440901	0.384208384720450
54	0.587543936541866	0.82491212691627	0.412456063458135
55	0.580779860587393	0.838440278825213	0.419220139412607
56	0.443159490020202	0.886318980040403	0.556840509979798

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	16	0.307692307692308	NOK
10% type I error level	22	0.423076923076923	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 & 0 & 0 & OK \tabularnewline
5% type I error level & 16 & 0.307692307692308 & NOK \tabularnewline
10% type I error level & 22 & 0.423076923076923 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=69648&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]16[/C][C]0.307692307692308[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]22[/C][C]0.423076923076923[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=69648&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=69648&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	16	0.307692307692308	NOK
10% type I error level	22	0.423076923076923	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